From fb9fb1bcbf3c7096a67e0923b6b9f08f9abe1926 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Fri, 14 Mar 2025 14:29:47 +0100
Subject: [PATCH 01/43] updating benchmark structure

---
 .gitignore                                    |   1 +
 deephyper_benchmark/__init__.py               |   3 -
 deephyper_benchmark/core.py                   | 152 ----
 deephyper_benchmark/integration/__init__.py   |   0
 deephyper_benchmark/integration/keras.py      |  25 -
 deephyper_benchmark/integration/optuna.py     |  52 --
 deephyper_benchmark/integration/torch.py      |  22 -
 deephyper_benchmark/search/__init__.py        |  38 -
 deephyper_benchmark/search/_cobyqa.py         |  89 --
 deephyper_benchmark/search/_de_automl.py      |  85 --
 deephyper_benchmark/search/_mpi_doptuna.py    | 387 ---------
 deephyper_benchmark/search/_pybobyqa.py       |  87 --
 deephyper_benchmark/search/_smac.py           | 100 ---
 deephyper_benchmark/stopper/__init__.py       |   0
 deephyper_benchmark/stopper/lcpfn.py          | 237 ------
 deephyper_benchmark/utils/__init__.py         |   0
 deephyper_benchmark/utils/json_utils.py       |  44 -
 lib/AutoTuning/Mochi/HEPnOS/Makefile          |  11 -
 lib/AutoTuning/Mochi/HEPnOS/__init__.py       |   0
 lib/AutoTuning/Mochi/HEPnOS/benchmark.py      |  19 -
 lib/AutoTuning/Mochi/HEPnOS/hpo.py            | 281 -------
 lib/AutoTuning/Mochi/HEPnOS/metrics.py        |  39 -
 lib/AutoTuning/Mochi/README.md                |  13 -
 lib/AutoTuning/README.md                      |   3 -
 lib/C-BBO/README.md                           |  20 -
 lib/C-BBO/ackley/__init__.py                  |   0
 lib/C-BBO/ackley/benchmark.py                 |  12 -
 lib/C-BBO/ackley/hpo.py                       |  61 --
 lib/C-BBO/ackley/metrics.py                   |  42 -
 lib/C-BBO/branin/__init__.py                  |   0
 lib/C-BBO/branin/benchmark.py                 |  12 -
 lib/C-BBO/branin/hpo.py                       |  48 --
 lib/C-BBO/branin/metrics.py                   |  35 -
 lib/C-BBO/cossin/__init__.py                  |   0
 lib/C-BBO/cossin/benchmark.py                 |  10 -
 lib/C-BBO/cossin/hpo.py                       |  65 --
 lib/C-BBO/cossin/metrics.py                   |  42 -
 lib/C-BBO/easom/__init__.py                   |   0
 lib/C-BBO/easom/benchmark.py                  |  12 -
 lib/C-BBO/easom/hpo.py                        |  52 --
 lib/C-BBO/easom/metrics.py                    |  36 -
 lib/C-BBO/griewank/__init__.py                |   0
 lib/C-BBO/griewank/benchmark.py               |  12 -
 lib/C-BBO/griewank/hpo.py                     |  43 -
 lib/C-BBO/griewank/metrics.py                 |  36 -
 lib/C-BBO/hartmann6D/__init__.py              |   0
 lib/C-BBO/hartmann6D/benchmark.py             |  12 -
 lib/C-BBO/hartmann6D/hpo.py                   |  60 --
 lib/C-BBO/hartmann6D/metrics.py               |  45 -
 lib/C-BBO/levy/__init__.py                    |   0
 lib/C-BBO/levy/benchmark.py                   |  12 -
 lib/C-BBO/levy/hpo.py                         |  44 -
 lib/C-BBO/levy/metrics.py                     |  36 -
 lib/C-BBO/michal/__init__.py                  |   0
 lib/C-BBO/michal/benchmark.py                 |  12 -
 lib/C-BBO/michal/hpo.py                       |  44 -
 lib/C-BBO/michal/metrics.py                   |  55 --
 lib/C-BBO/rosen/__init__.py                   |   0
 lib/C-BBO/rosen/benchmark.py                  |  12 -
 lib/C-BBO/rosen/hpo.py                        |  35 -
 lib/C-BBO/rosen/metrics.py                    |  36 -
 lib/C-BBO/schwefel/__init__.py                |   0
 lib/C-BBO/schwefel/benchmark.py               |  12 -
 lib/C-BBO/schwefel/hpo.py                     |  40 -
 lib/C-BBO/schwefel/metrics.py                 |  37 -
 lib/C-BBO/shekel/__init__.py                  |   0
 lib/C-BBO/shekel/benchmark.py                 |  12 -
 lib/C-BBO/shekel/hpo.py                       |  56 --
 lib/C-BBO/shekel/metrics.py                   |  37 -
 lib/DTLZ/README.md                            | 130 ---
 lib/DTLZ/__init__.py                          |   1 -
 lib/DTLZ/benchmark.py                         |   6 -
 lib/DTLZ/hpo.py                               |  61 --
 lib/DTLZ/metrics.py                           | 201 -----
 lib/DTLZ/model.py                             | 601 --------------
 lib/ECP-Candle/Pilot1/Combo/Makefile          |  11 -
 lib/ECP-Candle/Pilot1/Combo/README.md         |  54 --
 lib/ECP-Candle/Pilot1/Combo/__init__.py       |   1 -
 lib/ECP-Candle/Pilot1/Combo/benchmark.py      |  28 -
 .../Pilot1/Combo/combo_default_model.yaml     |  31 -
 lib/ECP-Candle/Pilot1/Combo/hpo.py            | 160 ----
 lib/ECP-Candle/Pilot1/Combo/model.py          | 782 ------------------
 lib/ECP-Candle/Pilot1/Combo/requirements.txt  |   5 -
 lib/FNOBench/README.md                        |  64 --
 lib/FNOBench/__init__.py                      |   0
 lib/FNOBench/benchmark.py                     |  17 -
 lib/FNOBench/data.py                          | 227 -----
 lib/FNOBench/data/darcy_test_16.pt            | Bin 64999 -> 0 bytes
 lib/FNOBench/data/darcy_test_32.pt            | Bin 256999 -> 0 bytes
 lib/FNOBench/data/darcy_train_16.pt           | Bin 1280999 -> 0 bytes
 lib/FNOBench/hpo.py                           | 104 ---
 lib/FNOBench/model.py                         | 560 -------------
 lib/FNOBench/requirements.txt                 |   5 -
 lib/HPOBench/README.md                        |  28 -
 lib/HPOBench/tabular/__init__.py              |   0
 lib/HPOBench/tabular/benchmark.py             |  17 -
 lib/HPOBench/tabular/constant_predictor.py    | 127 ---
 lib/HPOBench/tabular/hpo.py                   | 139 ----
 lib/HPOBench/tabular/install.sh               |  39 -
 lib/HPOBench/tabular/metrics.py               |  78 --
 lib/HPOBench/tabular/test.py                  |  19 -
 lib/JAHSBench/README.md                       | 176 ----
 lib/JAHSBench/__init__.py                     |   1 -
 lib/JAHSBench/benchmark.py                    |  22 -
 lib/JAHSBench/hpo.py                          |  57 --
 lib/JAHSBench/install.sh                      |   1 -
 lib/JAHSBench/metrics.py                      | 119 ---
 lib/JAHSBench/model.py                        |  80 --
 lib/JAHSBench/requirements.txt                |   2 -
 lib/LCBench/Makefile                          |  14 -
 lib/LCBench/README.md                         |  19 -
 lib/LCBench/__init__.py                       |   2 -
 lib/LCBench/benchmark.py                      |  18 -
 lib/LCBench/hpo.py                            |  78 --
 lib/LCDB/README.md                            |   4 -
 lib/LCDB/algorithm_selection/__init__.py      |   0
 .../hyperparameter_optimization/__init__.py   |   0
 .../hyperparameter_optimization/benchmark.py  |  12 -
 lib/LCDB/hyperparameter_optimization/hpo.py   | 216 -----
 lib/LCMBench/README.md                        |   4 -
 lib/LCMBench/lcdb/README.md                   |   3 -
 lib/LCMBench/lcdb/__init__.py                 |   0
 lib/LCMBench/lcdb/benchmark.py                |  11 -
 lib/LCMBench/lcdb/hpo.py                      | 128 ---
 lib/LCMBench/loglin2/README.md                |   5 -
 lib/LCMBench/loglin2/__init__.py              |   0
 lib/LCMBench/loglin2/benchmark.py             |  12 -
 lib/LCMBench/loglin2/hpo.py                   |  71 --
 lib/LCMBench/pow3/README.md                   |   5 -
 lib/LCMBench/pow3/__init__.py                 |   0
 lib/LCMBench/pow3/benchmark.py                |  12 -
 lib/LCMBench/pow3/hpo.py                      |  81 --
 lib/PINNBench/Burgers/__init__.py             |   0
 lib/PINNBench/Burgers/benchmark.py            |  17 -
 lib/PINNBench/Burgers/hpo.py                  | 111 ---
 lib/PINNBench/Burgers/model.py                | 411 ---------
 lib/PINNBench/Burgers/requirements.txt        |   5 -
 lib/PINNBench/DiffusionReaction/__init__.py   |   1 -
 lib/PINNBench/DiffusionReaction/benchmark.py  |  28 -
 lib/PINNBench/DiffusionReaction/hpo.py        | 206 -----
 lib/PINNBench/DiffusionReaction/model.py      | 154 ----
 .../DiffusionReaction/requirements.txt        |   6 -
 lib/PINNBench/README.md                       | 144 ----
 lib/PINNBench/install.sh                      |  13 -
 lib/YAHPO/README.md                           |  13 -
 lib/YAHPO/lcbench/__init__.py                 |   0
 lib/YAHPO/lcbench/benchmark.py                |  27 -
 lib/YAHPO/lcbench/hpo.py                      |  75 --
 lib/YAHPO/lcbench/metrics.py                  |  69 --
 pyproject.toml                                |  74 +-
 setup.cfg                                     |   2 -
 setup.py                                      | 198 -----
 src/deephyper_benchmark/hpo/_optuna.py        | 303 +++++++
 .../lib/c_bbo/ackley/__init__.py              |  18 +
 .../lib/c_bbo/ackley/hpo_benchmark.py         | 114 +++
 .../lib/c_bbo/cossin/__init__.py              |  18 +
 .../lib/c_bbo/cossin/hpo_benchmark.py         | 115 +++
 tests/test_c_bbo.py                           |  12 +
 158 files changed, 651 insertions(+), 8955 deletions(-)
 delete mode 100644 deephyper_benchmark/__init__.py
 delete mode 100644 deephyper_benchmark/core.py
 delete mode 100644 deephyper_benchmark/integration/__init__.py
 delete mode 100644 deephyper_benchmark/integration/keras.py
 delete mode 100644 deephyper_benchmark/integration/optuna.py
 delete mode 100644 deephyper_benchmark/integration/torch.py
 delete mode 100644 deephyper_benchmark/search/__init__.py
 delete mode 100644 deephyper_benchmark/search/_cobyqa.py
 delete mode 100644 deephyper_benchmark/search/_de_automl.py
 delete mode 100644 deephyper_benchmark/search/_mpi_doptuna.py
 delete mode 100644 deephyper_benchmark/search/_pybobyqa.py
 delete mode 100644 deephyper_benchmark/search/_smac.py
 delete mode 100644 deephyper_benchmark/stopper/__init__.py
 delete mode 100644 deephyper_benchmark/stopper/lcpfn.py
 delete mode 100644 deephyper_benchmark/utils/__init__.py
 delete mode 100644 deephyper_benchmark/utils/json_utils.py
 delete mode 100644 lib/AutoTuning/Mochi/HEPnOS/Makefile
 delete mode 100644 lib/AutoTuning/Mochi/HEPnOS/__init__.py
 delete mode 100644 lib/AutoTuning/Mochi/HEPnOS/benchmark.py
 delete mode 100644 lib/AutoTuning/Mochi/HEPnOS/hpo.py
 delete mode 100644 lib/AutoTuning/Mochi/HEPnOS/metrics.py
 delete mode 100644 lib/AutoTuning/Mochi/README.md
 delete mode 100644 lib/AutoTuning/README.md
 delete mode 100644 lib/C-BBO/README.md
 delete mode 100644 lib/C-BBO/ackley/__init__.py
 delete mode 100644 lib/C-BBO/ackley/benchmark.py
 delete mode 100644 lib/C-BBO/ackley/hpo.py
 delete mode 100644 lib/C-BBO/ackley/metrics.py
 delete mode 100644 lib/C-BBO/branin/__init__.py
 delete mode 100644 lib/C-BBO/branin/benchmark.py
 delete mode 100644 lib/C-BBO/branin/hpo.py
 delete mode 100644 lib/C-BBO/branin/metrics.py
 delete mode 100644 lib/C-BBO/cossin/__init__.py
 delete mode 100644 lib/C-BBO/cossin/benchmark.py
 delete mode 100644 lib/C-BBO/cossin/hpo.py
 delete mode 100644 lib/C-BBO/cossin/metrics.py
 delete mode 100644 lib/C-BBO/easom/__init__.py
 delete mode 100644 lib/C-BBO/easom/benchmark.py
 delete mode 100644 lib/C-BBO/easom/hpo.py
 delete mode 100644 lib/C-BBO/easom/metrics.py
 delete mode 100644 lib/C-BBO/griewank/__init__.py
 delete mode 100644 lib/C-BBO/griewank/benchmark.py
 delete mode 100644 lib/C-BBO/griewank/hpo.py
 delete mode 100644 lib/C-BBO/griewank/metrics.py
 delete mode 100644 lib/C-BBO/hartmann6D/__init__.py
 delete mode 100644 lib/C-BBO/hartmann6D/benchmark.py
 delete mode 100644 lib/C-BBO/hartmann6D/hpo.py
 delete mode 100644 lib/C-BBO/hartmann6D/metrics.py
 delete mode 100644 lib/C-BBO/levy/__init__.py
 delete mode 100644 lib/C-BBO/levy/benchmark.py
 delete mode 100644 lib/C-BBO/levy/hpo.py
 delete mode 100644 lib/C-BBO/levy/metrics.py
 delete mode 100644 lib/C-BBO/michal/__init__.py
 delete mode 100644 lib/C-BBO/michal/benchmark.py
 delete mode 100644 lib/C-BBO/michal/hpo.py
 delete mode 100644 lib/C-BBO/michal/metrics.py
 delete mode 100644 lib/C-BBO/rosen/__init__.py
 delete mode 100644 lib/C-BBO/rosen/benchmark.py
 delete mode 100644 lib/C-BBO/rosen/hpo.py
 delete mode 100644 lib/C-BBO/rosen/metrics.py
 delete mode 100644 lib/C-BBO/schwefel/__init__.py
 delete mode 100644 lib/C-BBO/schwefel/benchmark.py
 delete mode 100644 lib/C-BBO/schwefel/hpo.py
 delete mode 100644 lib/C-BBO/schwefel/metrics.py
 delete mode 100644 lib/C-BBO/shekel/__init__.py
 delete mode 100644 lib/C-BBO/shekel/benchmark.py
 delete mode 100644 lib/C-BBO/shekel/hpo.py
 delete mode 100644 lib/C-BBO/shekel/metrics.py
 delete mode 100644 lib/DTLZ/README.md
 delete mode 100644 lib/DTLZ/__init__.py
 delete mode 100644 lib/DTLZ/benchmark.py
 delete mode 100644 lib/DTLZ/hpo.py
 delete mode 100644 lib/DTLZ/metrics.py
 delete mode 100644 lib/DTLZ/model.py
 delete mode 100644 lib/ECP-Candle/Pilot1/Combo/Makefile
 delete mode 100644 lib/ECP-Candle/Pilot1/Combo/README.md
 delete mode 100644 lib/ECP-Candle/Pilot1/Combo/__init__.py
 delete mode 100644 lib/ECP-Candle/Pilot1/Combo/benchmark.py
 delete mode 100644 lib/ECP-Candle/Pilot1/Combo/combo_default_model.yaml
 delete mode 100644 lib/ECP-Candle/Pilot1/Combo/hpo.py
 delete mode 100644 lib/ECP-Candle/Pilot1/Combo/model.py
 delete mode 100644 lib/ECP-Candle/Pilot1/Combo/requirements.txt
 delete mode 100644 lib/FNOBench/README.md
 delete mode 100644 lib/FNOBench/__init__.py
 delete mode 100644 lib/FNOBench/benchmark.py
 delete mode 100644 lib/FNOBench/data.py
 delete mode 100644 lib/FNOBench/data/darcy_test_16.pt
 delete mode 100644 lib/FNOBench/data/darcy_test_32.pt
 delete mode 100644 lib/FNOBench/data/darcy_train_16.pt
 delete mode 100644 lib/FNOBench/hpo.py
 delete mode 100644 lib/FNOBench/model.py
 delete mode 100644 lib/FNOBench/requirements.txt
 delete mode 100644 lib/HPOBench/README.md
 delete mode 100644 lib/HPOBench/tabular/__init__.py
 delete mode 100644 lib/HPOBench/tabular/benchmark.py
 delete mode 100644 lib/HPOBench/tabular/constant_predictor.py
 delete mode 100644 lib/HPOBench/tabular/hpo.py
 delete mode 100755 lib/HPOBench/tabular/install.sh
 delete mode 100644 lib/HPOBench/tabular/metrics.py
 delete mode 100644 lib/HPOBench/tabular/test.py
 delete mode 100644 lib/JAHSBench/README.md
 delete mode 100644 lib/JAHSBench/__init__.py
 delete mode 100644 lib/JAHSBench/benchmark.py
 delete mode 100644 lib/JAHSBench/hpo.py
 delete mode 100755 lib/JAHSBench/install.sh
 delete mode 100644 lib/JAHSBench/metrics.py
 delete mode 100644 lib/JAHSBench/model.py
 delete mode 100644 lib/JAHSBench/requirements.txt
 delete mode 100644 lib/LCBench/Makefile
 delete mode 100644 lib/LCBench/README.md
 delete mode 100644 lib/LCBench/__init__.py
 delete mode 100644 lib/LCBench/benchmark.py
 delete mode 100644 lib/LCBench/hpo.py
 delete mode 100644 lib/LCDB/README.md
 delete mode 100644 lib/LCDB/algorithm_selection/__init__.py
 delete mode 100644 lib/LCDB/hyperparameter_optimization/__init__.py
 delete mode 100644 lib/LCDB/hyperparameter_optimization/benchmark.py
 delete mode 100644 lib/LCDB/hyperparameter_optimization/hpo.py
 delete mode 100644 lib/LCMBench/README.md
 delete mode 100644 lib/LCMBench/lcdb/README.md
 delete mode 100644 lib/LCMBench/lcdb/__init__.py
 delete mode 100644 lib/LCMBench/lcdb/benchmark.py
 delete mode 100644 lib/LCMBench/lcdb/hpo.py
 delete mode 100644 lib/LCMBench/loglin2/README.md
 delete mode 100644 lib/LCMBench/loglin2/__init__.py
 delete mode 100644 lib/LCMBench/loglin2/benchmark.py
 delete mode 100644 lib/LCMBench/loglin2/hpo.py
 delete mode 100644 lib/LCMBench/pow3/README.md
 delete mode 100644 lib/LCMBench/pow3/__init__.py
 delete mode 100644 lib/LCMBench/pow3/benchmark.py
 delete mode 100644 lib/LCMBench/pow3/hpo.py
 delete mode 100644 lib/PINNBench/Burgers/__init__.py
 delete mode 100644 lib/PINNBench/Burgers/benchmark.py
 delete mode 100644 lib/PINNBench/Burgers/hpo.py
 delete mode 100644 lib/PINNBench/Burgers/model.py
 delete mode 100644 lib/PINNBench/Burgers/requirements.txt
 delete mode 100644 lib/PINNBench/DiffusionReaction/__init__.py
 delete mode 100644 lib/PINNBench/DiffusionReaction/benchmark.py
 delete mode 100644 lib/PINNBench/DiffusionReaction/hpo.py
 delete mode 100644 lib/PINNBench/DiffusionReaction/model.py
 delete mode 100644 lib/PINNBench/DiffusionReaction/requirements.txt
 delete mode 100644 lib/PINNBench/README.md
 delete mode 100644 lib/PINNBench/install.sh
 delete mode 100644 lib/YAHPO/README.md
 delete mode 100644 lib/YAHPO/lcbench/__init__.py
 delete mode 100644 lib/YAHPO/lcbench/benchmark.py
 delete mode 100644 lib/YAHPO/lcbench/hpo.py
 delete mode 100644 lib/YAHPO/lcbench/metrics.py
 delete mode 100644 setup.cfg
 delete mode 100644 setup.py
 create mode 100644 src/deephyper_benchmark/hpo/_optuna.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/ackley/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/cossin/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/cossin/hpo_benchmark.py
 create mode 100644 tests/test_c_bbo.py

diff --git a/.gitignore b/.gitignore
index 02fb80c..a1e3801 100644
--- a/.gitignore
+++ b/.gitignore
@@ -150,3 +150,4 @@ context.yaml
 test.py
 plot.py
 *.pkl
+*.code-workspace
\ No newline at end of file
diff --git a/deephyper_benchmark/__init__.py b/deephyper_benchmark/__init__.py
deleted file mode 100644
index 379d77f..0000000
--- a/deephyper_benchmark/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-__version__ = "0.0.1"
-
-from .core import install, load, Benchmark
\ No newline at end of file
diff --git a/deephyper_benchmark/core.py b/deephyper_benchmark/core.py
deleted file mode 100644
index c9baed5..0000000
--- a/deephyper_benchmark/core.py
+++ /dev/null
@@ -1,152 +0,0 @@
-import importlib
-import importlib.util
-import logging
-import os
-import sys
-
-# Path of the root directory of the deephyper_benchmark package
-PKG_ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
-
-# Path of the root directory of the library of benchmarks
-BCH_ROOT_DIR = os.path.join(os.path.dirname(PKG_ROOT_DIR), "lib")
-
-
-def find_benchmark(benchmark_name: str):
-    """Load a benchmark class from its string path.
-
-    Args:
-        benchmark_name (str): string path of the benchmark to load.
-
-    Returns:
-        Benchmark: the instanciated benchmark class.
-    """
-
-    benchmark_path = os.path.join(BCH_ROOT_DIR, benchmark_name)
-    sys.path.insert(0, benchmark_path)
-    # looking for "benchmark.py" in the benchmark folder
-    benchmark_module = importlib.import_module("benchmark")
-
-    benchmark_class = None
-    for attr_name in benchmark_module.__dict__:
-        attr = getattr(benchmark_module, attr_name)
-        try:
-            if issubclass(attr, Benchmark) and not (attr is Benchmark):
-                benchmark_class = attr
-                break
-        except TypeError:
-            pass
-
-    if benchmark_class is None:
-        logging.error("Cannot find any benchmark")
-        return
-    else:
-        logging.info(f"Found {attr.__name__}")
-
-    benchmark = benchmark_class()
-
-    # Working directory of the benchmark
-    benchmark.cwd = benchmark_path
-
-    # Python module name of the benchmark
-    benchmark.name = benchmark_name.replace("-", "_").replace("/", ".").lower()
-
-    return benchmark
-
-
-def install(name: str):
-    """Installing benchmark from string path name. The installation is executed from the root directory of the
-    requested benchmark."""
-
-    logging.info(f"Installing {name}")
-
-    benchmark = find_benchmark(name)
-    benchmark.install()
-
-    logging.info(f"Installation done")
-
-
-def load(name: str):
-    """Loading benchmark from string path name."""
-    logging.info(f"Loading {name}")
-
-    benchmark = find_benchmark(name)
-    module = benchmark.load()
-
-    logging.info(f"Loading done")
-
-    return module
-
-
-class Benchmark:
-    """Class representing a benchmark."""
-
-    requires = {}
-
-    def __init__(self):
-        self.name = None
-        self.cwd = None
-
-    def install(self) -> int:
-        """Install the benchmark.
-
-        Returns:
-            int: 0 if the installation is successful, error code otherwise.
-        """
-
-        for rname, rcommand in self.requires.items():
-            res = 0
-
-            if "install" in rcommand["step"]:
-                res = self.process_requirement(rname, rcommand)
-
-            if res != 0:
-                logging.error(f"Installation failed for requirement: {rname}")
-                return res
-
-    def load(self) -> int:
-        """Loads the benchmark and returns the corresponding Python module."""
-
-        for rname, rcommand in self.requires.items():
-            res = 0
-
-            if "load" in rcommand["step"]:
-                res = self.process_requirement(rname, rcommand)
-
-            if res != 0:
-                logging.error("Loading failed for requirement: {rname}")
-                return res
-
-        module_name = f"deephyper_benchmark.lib.{self.name}"
-        spec = importlib.util.spec_from_file_location(
-            module_name, f"{self.cwd}/__init__.py"
-        )
-        module = importlib.util.module_from_spec(spec)
-        sys.modules[module_name] = module
-        spec.loader.exec_module(module)
-        self.module = module
-
-        return self.module
-
-    def process_requirement(self, rname: str, rcommand: dict) -> int:
-        logging.info(f"Processing requirement: {rname}")
-
-        res = 0
-
-        # Add path to PYTHONPATH
-        if rcommand["type"] == "pythonpath":
-            path = rcommand["path"]
-            logging.info(f"Adding {path} to PYTHONPATH")
-            sys.path.insert(0, path)
-
-        # Add/Update environment variable to environment.
-        elif rcommand["type"] == "env":
-            os.environ[rcommand["key"]] = rcommand["value"]
-
-        elif rcommand["type"] == "cmd":
-            cmd = rcommand["cmd"]
-            res = os.system(f"cd {self.cwd} && {cmd}")
-        elif rcommand["type"] == "pip":
-            args = rcommand["args"]
-            res = os.system(f"pip {args}")
-
-        return res
diff --git a/deephyper_benchmark/integration/__init__.py b/deephyper_benchmark/integration/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/deephyper_benchmark/integration/keras.py b/deephyper_benchmark/integration/keras.py
deleted file mode 100644
index a2be3d8..0000000
--- a/deephyper_benchmark/integration/keras.py
+++ /dev/null
@@ -1,25 +0,0 @@
-import tensorflow as tf
-import tensorflow.keras.backend as K
-import numpy as np
-
-
-def count_params(model: tf.keras.Model) -> dict:
-    """Evaluate the number of parameters of a Keras model.
-
-    Args:
-        model (tf.keras.Model): a Keras model.
-
-    Returns:
-        dict: a dictionary with the number of trainable ``"num_parameters_train"`` and 
-        non-trainable parameters ``"num_parameters"``.
-    """
-    num_parameters_train = int(
-        np.sum([K.count_params(p) for p in set(model.trainable_weights)])
-    )
-    num_parameters = int(
-        np.sum([K.count_params(p) for p in set(model.non_trainable_weights)])
-    )
-    return {
-        "num_parameters": num_parameters,
-        "num_parameters_train": num_parameters_train,
-    }
diff --git a/deephyper_benchmark/integration/optuna.py b/deephyper_benchmark/integration/optuna.py
deleted file mode 100644
index e7017fc..0000000
--- a/deephyper_benchmark/integration/optuna.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import warnings
-
-import optuna
-import tensorflow as tf
-
-
-class KerasPruningCallback(tf.keras.callbacks.Callback):
-    """Keras callback to prune unpromising trials.
-
-    See `the example <https://github.com/optuna/optuna-examples/blob/main/
-    keras/keras_integration.py>`__
-    if you want to add a pruning callback which observes validation accuracy.
-
-    Args:
-        trial:
-            A :class:`~optuna.trial.Trial` corresponding to the current evaluation of the
-            objective function.
-        monitor:
-            An evaluation metric for pruning, e.g., ``val_loss`` and
-            ``val_accuracy``. Please refer to `keras.Callback reference
-            <https://keras.io/callbacks/#callback>`_ for further details.
-        interval:
-            Check if trial should be pruned every n-th epoch. By default ``interval=1`` and
-            pruning is performed after every epoch. Increase ``interval`` to run several
-            epochs faster before applying pruning.
-    """
-
-    def __init__(self, trial: optuna.trial.Trial, monitor: str, interval: int = 1) -> None:
-        super().__init__()
-
-        self._trial = trial
-        self._monitor = monitor
-        self._interval = interval
-
-    def on_epoch_end(self, epoch: int, logs = None) -> None:
-        self.epoch_stopped = epoch
-        if (epoch + 1) % self._interval != 0:
-            return
-
-        logs = logs or {}
-        current_score = logs.get(self._monitor)
-        if current_score is None:
-            message = (
-                "The metric '{}' is not in the evaluation logs for pruning. "
-                "Please make sure you set the correct metric name.".format(self._monitor)
-            )
-            warnings.warn(message)
-            return
-        self._trial.report(float(current_score), step=epoch)
-        if self._trial.should_prune():
-            self.message = "Trial was pruned at epoch {}.".format(epoch)
-            self.model.stop_training = True
\ No newline at end of file
diff --git a/deephyper_benchmark/integration/torch.py b/deephyper_benchmark/integration/torch.py
deleted file mode 100644
index b7f8355..0000000
--- a/deephyper_benchmark/integration/torch.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import numpy as np
-import torch.nn as nn
-
-
-def count_params(module: nn.Module) -> dict:
-    """Evaluate the number of parameters of a Torch module.
-
-    Args:
-        module(nn.Module): a Torch module.
-
-    Returns:
-        dict: a dictionary with the number of trainable ``"num_parameters_train"`` and
-        non-trainable parameters ``"num_parameters"``.
-    """
-    num_parameters = int(np.sum(p.numel() for p in module.parameters()))
-    num_parameters_train = int(
-        np.sum(p.numel() for p in module.parameters() if p.requires_grad)
-    )
-    return {
-        "num_parameters": num_parameters,
-        "num_parameters_train": num_parameters_train,
-    }
diff --git a/deephyper_benchmark/search/__init__.py b/deephyper_benchmark/search/__init__.py
deleted file mode 100644
index 6a7a59a..0000000
--- a/deephyper_benchmark/search/__init__.py
+++ /dev/null
@@ -1,38 +0,0 @@
-__all__ = []
-
-try:
-    from deephyper_benchmark.search._mpi_doptuna import (
-        MPIDistributedOptuna,
-    )  # noqa: F401
-
-    __all__.append("MPIDistributedOptuna")
-except ImportError:
-    pass
-
-try:
-    from deephyper_benchmark.search._cobyqa import COBYQA  # noqa: F401
-
-    __all__.append("COBYQA")
-except ImportError:
-    pass
-
-try:
-    from deephyper_benchmark.search._pybobyqa import PyBOBYQA  # noqa: F401
-
-    __all__.append("PyBOBYQA")
-except ImportError:
-    pass
-
-try:
-    from deephyper_benchmark.search._smac import SMAC  # noqa: F401
-
-    __all__.append("SMAC")
-except ImportError:
-    pass
-
-try:
-    from deephyper_benchmark.search._de_automl import DEAutoML  # noqa: F401
-
-    __all__.append("DEAutoML")
-except ImportError:
-    pass
diff --git a/deephyper_benchmark/search/_cobyqa.py b/deephyper_benchmark/search/_cobyqa.py
deleted file mode 100644
index 264e436..0000000
--- a/deephyper_benchmark/search/_cobyqa.py
+++ /dev/null
@@ -1,89 +0,0 @@
-import functools
-import logging
-import os
-
-import ConfigSpace.hyperparameters as csh
-import pandas as pd
-
-
-from deephyper.core.exceptions import SearchTerminationError  # noqa: E402
-from deephyper.core.utils._timeout import terminate_on_timeout  # noqa: E402
-from deephyper.evaluator import RunningJob  # noqa: E402
-from deephyper.search import Search  # noqa: E402
-
-import cobyqa
-
-
-def cobyqa_bounds_from_hp(cs_hp):
-    if isinstance(cs_hp, csh.UniformIntegerHyperparameter):
-        lower, upper = float(cs_hp.lower), float(cs_hp.upper)
-    elif isinstance(cs_hp, csh.UniformFloatHyperparameter):
-        lower, upper = cs_hp.lower, cs_hp.upper
-    elif isinstance(cs_hp, csh.CategoricalHyperparameter):
-        lower, upper = 0.0, float(len(cs_hp.choices) - 1)
-    elif isinstance(cs_hp, csh.OrdinalHyperparameter):
-        lower, upper = 0.0, float(len(cs_hp.sequence) - 1)
-    else:
-        raise TypeError(f"Cannot convert hyperparameter of type {type(cs_hp)}")
-    return lower, upper
-
-
-def convert_problem_to_cobyqa(problem):
-    xl, xu = [], []
-    for cs_hp in problem.space.get_hyperparameters():
-        lower, upper = cobyqa_bounds_from_hp(cs_hp)
-        xl.append(lower)
-        xu.append(upper)
-    return xl, xu
-
-
-class COBYQA(Search):
-    """Wrapper for COBYQA derivative-free optimizater.
-
-    $ git clone git@github.com:cobyqa/cobyqa.git
-    $ cd cobyqa/
-    $ pip install -e.
-
-    Args:
-        problem (HpProblem): Hyperparameter problem describing the search space to explore.
-        evaluator (Evaluator): An ``Evaluator`` instance responsible of distributing the tasks.
-        random_state (int, optional): Random seed. Defaults to ``None``.
-        log_dir (str, optional): Log directory where search's results are saved. Defaults to ``"."``.
-        verbose (int, optional): Indicate the verbosity level of the search. Defaults to ``0``.
-    """
-
-    def __init__(
-        self,
-        problem,
-        evaluator,
-        random_state: int = None,
-        log_dir: str = ".",
-        verbose: int = 0,
-        **kwargs,
-    ):
-        super().__init__(problem, evaluator, random_state, log_dir, verbose)
-
-        self._xl, self._xu = convert_problem_to_cobyqa(problem)
-
-    def _search(self, max_evals, timeout):
-        def objective_wrapper(x):
-            config = {k: v for k, v in zip(self._problem.hyperparameter_names, x)}
-            self._evaluator.submit([config])
-            job = self._evaluator.gather("ALL")[0]
-            _, y = job
-            return -y
-
-        x0 = self._random_state.uniform(low=self._xl, high=self._xu)
-
-        cobyqa_results = cobyqa.minimize(
-            objective_wrapper,
-            x0=x0,
-            xl=self._xl,
-            xu=self._xu,
-            options={"max_eval": max_evals, "scale": True},
-        )
-
-        self._evaluator.dump_evals(log_dir=self._log_dir)
-        df_path = os.path.join(self._log_dir, "results.csv")
-        df_results = pd.read_csv(df_path)
-        return df_results
diff --git a/deephyper_benchmark/search/_de_automl.py b/deephyper_benchmark/search/_de_automl.py
deleted file mode 100644
index a136dae..0000000
--- a/deephyper_benchmark/search/_de_automl.py
+++ /dev/null
@@ -1,85 +0,0 @@
-import functools
-import logging
-import os
-
-import ConfigSpace.hyperparameters as csh
-import numpy as np
-import pandas as pd
-
-
-from deephyper.core.exceptions import SearchTerminationError  # noqa: E402
-from deephyper.core.utils._timeout import terminate_on_timeout  # noqa: E402
-from deephyper.evaluator import RunningJob  # noqa: E402
-from deephyper.search import Search  # noqa: E402
-
-import dehb.optimizers
-
-
-MAP_acq_func = {"UCB": "LCB"}
-
-
-class DEAutoML(Search):
-    """Wrapper for DE optimizer from: https://github.com/automl/DEHB
-
-    $ pip install dehb
-
-    Papers:
-        1. Awad, Noor, Neeratyoy Mallik, and Frank Hutter. "Differential evolution for neural architecture search." arXiv preprint arXiv:2012.06400 (2020). ICLR Workshop, 2020.
-        2. Awad, Noor, Neeratyoy Mallik, and Frank Hutter. "Dehb: Evolutionary hyperband for scalable, robust and efficient hyperparameter optimization." arXiv preprint arXiv:2105.09821 (2021).
-
-
-
-    Args:
-        problem (HpProblem): Hyperparameter problem describing the search space to explore.
-        evaluator (Evaluator): An ``Evaluator`` instance responsible of distributing the tasks.
-        random_state (int, optional): Random seed. Defaults to ``None``.
-        log_dir (str, optional): Log directory where search's results are saved. Defaults to ``"."``.
-        verbose (int, optional): Indicate the verbosity level of the search. Defaults to ``0``.
-    """
-
-    def __init__(
-        self,
-        problem,
-        evaluator,
-        random_state: int = None,
-        log_dir: str = ".",
-        verbose: int = 0,
-        **kwargs,
-    ):
-        super().__init__(problem, evaluator, random_state, log_dir, verbose)
-
-    def _search(self, max_evals, timeout):
-        def objective_wrapper(config, **kwargs):
-            self._evaluator.submit([dict(config)])
-            job = self._evaluator.gather("ALL")[0]
-            _, y = job
-            if len(self._evaluator.jobs_done) >= max_evals:
-                raise SearchTerminationError("Maximum number of evaluations reached!")
-            return {"fitness": y, "cost": 0}
-
-        optimizer = dehb.optimizers.DE(
-            f=objective_wrapper,
-            cs=self._problem.space,
-            # TODO: Here we followed the default parameters of DEHB available in `dehb==0.0.7`
-            # https://github.com/automl/DEHB/blob/2d3d178df65ce4402dd8998e483386ad309ea315/src/dehb/optimizers/dehb.py#L170
-            mutation_factor=0.5,
-            crossover_prob=0.5,
-            strategy="rand1_bin",
-            pop_size=20,
-            output_path=os.path.join(self._log_dir, "optimizer-output"),
-            n_workers=1,
-        )
-
-        try:
-            optimizer.run(
-                generations=(max_evals // 20 + 1),
-                verbose=False,
-                save_intermediate=False,
-            )
-        except SearchTerminationError:
-            pass
-
-        self._evaluator.dump_evals(log_dir=self._log_dir)
-        df_path = os.path.join(self._log_dir, "results.csv")
-        df_results = pd.read_csv(df_path)
-        return df_results
diff --git a/deephyper_benchmark/search/_mpi_doptuna.py b/deephyper_benchmark/search/_mpi_doptuna.py
deleted file mode 100644
index 625aaa3..0000000
--- a/deephyper_benchmark/search/_mpi_doptuna.py
+++ /dev/null
@@ -1,387 +0,0 @@
-import functools
-import logging
-import os
-import time
-from typing import Union
-
-import ConfigSpace.hyperparameters as csh
-import mpi4py
-import numpy as np
-import optuna
-import pandas as pd
-from optuna.study import MaxTrialsCallback
-from optuna.trial import FrozenTrial, TrialState
-
-# !To avoid initializing MPI when module is imported (MPI is optional)
-mpi4py.rc.initialize = False
-mpi4py.rc.finalize = True
-from mpi4py import MPI  # noqa: E402
-
-from deephyper.core.exceptions import SearchTerminationError  # noqa: E402
-from deephyper.core.utils._timeout import terminate_on_timeout  # noqa: E402
-from deephyper.evaluator import RunningJob  # noqa: E402
-from deephyper.search import Search  # noqa: E402
-from deephyper.skopt.moo import non_dominated_set
-
-
-def optuna_suggest_from_hp(trial, cs_hp):
-    name = cs_hp.name
-    if isinstance(cs_hp, csh.UniformIntegerHyperparameter):
-        value = trial.suggest_int(name, cs_hp.lower, cs_hp.upper, log=cs_hp.log)
-    elif isinstance(cs_hp, csh.UniformFloatHyperparameter):
-        value = trial.suggest_float(name, cs_hp.lower, cs_hp.upper, log=cs_hp.log)
-    elif isinstance(cs_hp, csh.CategoricalHyperparameter):
-        value = trial.suggest_categorical(name, cs_hp.choices)
-    elif isinstance(cs_hp, csh.OrdinalHyperparameter):
-        value = trial.suggest_categorical(name, cs_hp.sequence)
-    else:
-        raise TypeError(f"Cannot convert hyperparameter of type {type(cs_hp)}")
-
-    return name, value
-
-
-def optuna_suggest_from_configspace(trial, cs_space):
-    config = {}
-    for cs_hp in cs_space.get_hyperparameters():
-        name, value = optuna_suggest_from_hp(trial, cs_hp)
-        config[name] = value
-    return config
-
-
-class CheckpointSaverCallback:
-    def __init__(self, log_dir=".", states=(TrialState.COMPLETE,)) -> None:
-        self._log_dir = log_dir
-        self._states = states
-
-    def __call__(self, study: optuna.study.Study, trial: FrozenTrial) -> None:
-        all_trials = study.get_trials(deepcopy=False, states=self._states)
-        # n_complete = len(all_trials)
-
-        pd.DataFrame([t.user_attrs["results"] for t in all_trials]).to_csv(
-            os.path.join(self._log_dir, "results.csv")
-        )
-
-
-# Constraints
-def constraints(trial):
-    return trial.user_attrs["constraints"]
-
-
-# Supported samplers
-supported_samplers = ["TPE", "CMAES", "NSGAII", "DUMMY", "BOTORCH", "QMC"]
-supported_pruners = ["NOP", "SHA", "HB", "MED"]
-
-
-class MPIDistributedOptuna(Search):
-    """Wrapper for Optuna to run distributed optimization with MPI.
-
-    Args:
-        problem (HpProblem): Hyperparameter problem describing the search space to explore.
-        evaluator (Evaluator): An ``Evaluator`` instance responsible of distributing the tasks.
-        random_state (int, optional): Random seed. Defaults to ``None``.
-        log_dir (str, optional): Log directory where search's results are saved. Defaults to ``"."``.
-        verbose (int, optional): Indicate the verbosity level of the search. Defaults to ``0``.
-        sampler (Union[str, optuna.samplers.BaseSampler], optional): Optimization strategy to suggest new hyperparameter configurations. Defaults to ``"TPE"``.
-        pruner (Union[str, optuna.pruners.BasePruner], optional): Pruning strategy to perform early discarding of unpromizing configuraitons. Defaults to ``None``.
-        n_objectives (int, optional): Number of objectives to optimize. Defaults to ``1``.
-        study_name (str, optional): Name of the study in the database used by Optuna. Defaults to ``None``.
-        storage (Union[str, optuna.storages.BaseStorage], optional): Database used by Optuna. Defaults to ``None``.
-        checkpoint (bool, optional): If results should be checkpointed regularly to the ``log_dir``. Defaults to ``True``.
-        comm (MPI.Comm, optional): The MPI communicator. Defaults to ``None``.
-        moo_lower_bounds ([type], optional): [description]. Defaults to ``None``.
-
-    Raises:
-        ValueError: _description_
-        TypeError: _description_
-        ValueError: _description_
-        ValueError: _description_
-        TypeError: _description_
-    """
-
-    def __init__(
-        self,
-        problem,
-        evaluator,
-        random_state: int = None,
-        log_dir: str = ".",
-        verbose: int = 0,
-        sampler: Union[str, optuna.samplers.BaseSampler] = "TPE",
-        pruner: Union[str, optuna.pruners.BasePruner] = None,
-        n_objectives: int = 1,
-        study_name: str = None,
-        storage: Union[str, optuna.storages.BaseStorage] = None,
-        checkpoint: bool = True,
-        n_initial_points: int = None,
-        comm: MPI.Comm = None,
-        moo_lower_bounds=None,
-        **kwargs,
-    ):
-        super().__init__(problem, evaluator, random_state, log_dir, verbose)
-
-        optuna.logging.set_verbosity(
-            optuna.logging.DEBUG if self._verbose else optuna.logging.ERROR
-        )
-
-        self._evaluator = evaluator
-
-        # get the __init__ parameters
-        _init_params = locals()
-
-        if not MPI.Is_initialized():
-            MPI.Init_thread()
-
-        self.comm = comm if comm else MPI.COMM_WORLD
-        self.rank = self.comm.Get_rank() if self.comm else 0
-        self.size = self.comm.Get_size() if self.comm else 1
-
-        # Set different random state for given rank
-        self._random_state = np.random.RandomState(
-            self._random_state.randint(low=0, high=2**31, size=self.size)[self.rank]
-        )
-
-        self._n_initial_points = (
-            2 * len(self._problem) if n_initial_points is None else n_initial_points
-        )
-
-        # Constraints
-        self._moo_lower_bounds = moo_lower_bounds
-        self._constraints_func = None
-        if moo_lower_bounds is not None:
-            if len(moo_lower_bounds) == n_objectives:
-                self._constraints_func = constraints
-            else:
-                raise ValueError(
-                    f"moo_lower_bounds should be of length {n_objectives} but is of length {len(moo_lower_bounds)}"
-                )
-
-        # Setup the sampler
-        if isinstance(sampler, optuna.samplers.BaseSampler):
-            pass
-        elif isinstance(sampler, str):
-            sampler_seed = self._random_state.randint(2**31)
-            if sampler == "TPE":
-                sampler = optuna.samplers.TPESampler(
-                    n_startup_trials=self._n_initial_points,
-                    seed=sampler_seed,
-                    constraints_func=self._constraints_func,
-                )
-            elif sampler == "CMAES":
-                sampler = optuna.samplers.CmaEsSampler(
-                    n_startup_trials=self._n_initial_points, seed=sampler_seed
-                )
-            elif sampler == "NSGAII":
-                sampler = optuna.samplers.NSGAIISampler(
-                    seed=sampler_seed,
-                    constraints_func=self._constraints_func,
-                )
-            elif sampler == "DUMMY":
-                sampler = optuna.samplers.RandomSampler(seed=sampler_seed)
-            elif sampler == "BOTORCH":
-                from optuna.integration import BoTorchSampler
-
-                sampler = BoTorchSampler(
-                    n_startup_trials=self._n_initial_points,
-                    seed=sampler_seed,
-                    constraints_func=self._constraints_func,
-                )
-            elif sampler == "QMC":
-                sampler = optuna.samplers.QMCSampler(seed=sampler_seed)
-
-            else:
-                raise ValueError(
-                    f"Requested unknown sampler {sampler} should be one of {supported_samplers}"
-                )
-        else:
-            raise TypeError(
-                f"Sampler is of type {type(sampler)} but must be a str or optuna.samplers.BaseSampler!"
-            )
-
-        self._n_objectives = n_objectives
-
-        # Setup the pruner
-        if self._n_objectives > 1 or pruner is None:
-            pruner = None
-            if pruner is not None:
-                raise ValueError(
-                    "Multi-objective optimization does not support pruning!"
-                )
-        else:
-            if isinstance(pruner, optuna.pruners.BasePruner):
-                pass
-            elif isinstance(pruner, str):
-                if pruner == "NOP":
-                    pruner = optuna.pruners.NopPruner()
-                elif pruner == "SHA":
-                    pruner = optuna.pruners.SuccessiveHalvingPruner()
-                elif pruner == "HB":
-                    pruner = optuna.pruners.HyperbandPruner()
-                elif pruner == "MED":
-                    pruner = optuna.pruners.MedianPruner()
-                else:
-                    raise ValueError(
-                        f"Requested unknown pruner {pruner} should be one of {supported_pruners}"
-                    )
-            else:
-                raise TypeError(
-                    f"Pruner is of type {type(pruner)} but must be a str or optuna.pruners.BasePruner!"
-                )
-        self.pruner = pruner
-
-        self._checkpoint = checkpoint
-
-        study_params = dict(
-            study_name=study_name,
-            storage=storage,
-            sampler=sampler,
-            pruner=pruner,
-        )
-
-        if self.rank == 0:
-            if self._n_objectives > 1:
-                study_params["directions"] = [
-                    "maximize" for _ in range(self._n_objectives)
-                ]
-            else:
-                study_params["direction"] = "maximize"
-
-        self.timestamp = None
-
-        # Root rank creates study and initilize the timestamp
-        if self.rank == 0:
-            self.timestamp = time.time()
-            self.study = optuna.create_study(**study_params)
-
-        if self.size > 1:
-            self.timestamp = comm.bcast(self.timestamp)
-
-        # Other ranks load the previously created study
-        if self.rank > 0:
-            self.study = optuna.load_study(**study_params)
-
-        self._init_params = _init_params
-
-        logging.info(f"MPIDistributedOptuna has {self.size} rank(s)")
-        logging.info(f"MPIDistributedOptuna rank {self.rank} has 1 local worker(s)")
-
-    def search(self, max_evals=None, timeout=-1):
-        if timeout == -1:
-            timeout = None
-        return self._search(max_evals, timeout)
-
-    def _search(self, max_evals, timeout):
-        def objective_wrapper(trial):
-            config = optuna_suggest_from_configspace(trial, self._problem.space)
-            if self.pruner is None:
-                output = self._evaluator(RunningJob(id=trial.number, parameters=config))
-            else:
-                output = self._evaluator(
-                    RunningJob(id=trial.number, parameters=config), optuna_trial=trial
-                )
-
-            # TODO: optuna constraint
-            if self._moo_lower_bounds is not None:
-                # https://optuna.readthedocs.io/en/stable/faq.html#how-can-i-optimize-a-model-with-some-constraints
-                # Constraints which are considered feasible if less than or equal to zero.
-                constraints = []
-                for i, lbi in enumerate(self._moo_lower_bounds):
-                    if lbi is not None and type(output["objective"][i]) is not str:
-                        ci = -(output["objective"][i] - lbi)  # <= 0
-                        constraints.append(ci)
-                trial.set_user_attr("constraints", tuple(constraints))
-
-            data = {f"p:{k}": v for k, v in config.items()}
-            if isinstance(output["objective"], list) or isinstance(
-                output["objective"], tuple
-            ):
-                for i, obj in enumerate(output["objective"]):
-                    data[f"objective_{i}"] = obj
-            else:
-                data["objective"] = output["objective"]
-            data["job_id"] = trial.number
-            data.update({f"m:{k}": v for k, v in output["metadata"].items()})
-            trial.set_user_attr("results", data)
-
-            if data.get("m:stopped", False):
-                raise optuna.TrialPruned()
-
-            return output["objective"]
-
-        def optimize_wrapper(duration):
-            callbacks = []
-
-            if max_evals > 0:
-                callbacks.append(MaxTrialsCallback(max_evals))
-
-            if self.rank == 0:
-                if self._checkpoint:
-                    callbacks += [
-                        CheckpointSaverCallback(
-                            self._log_dir,
-                            states=(
-                                optuna.trial.TrialState.COMPLETE,
-                                optuna.trial.TrialState.PRUNED,
-                                optuna.trial.TrialState.FAIL,
-                            ),
-                        )
-                    ]
-
-            self.study.optimize(
-                objective_wrapper,
-                n_trials=max_evals if max_evals > 0 else None,
-                timeout=duration,
-                callbacks=callbacks,
-            )
-
-        if timeout is None:
-            logging.info(f"Running without timeout and max_evals={max_evals}")
-            optimize_wrapper(None)
-        else:
-            logging.info(f"Running with timeout={timeout} and max_evals={max_evals}")
-            optimize = functools.partial(
-                terminate_on_timeout, timeout, optimize_wrapper
-            )
-            try:
-                optimize(timeout)
-            except SearchTerminationError:
-                pass
-
-        if self.rank == 0:
-            all_trials = self.study.get_trials(
-                deepcopy=True,
-                states=[
-                    optuna.trial.TrialState.COMPLETE,
-                    optuna.trial.TrialState.PRUNED,
-                    optuna.trial.TrialState.FAIL,
-                ],
-            )
-
-            df_results = pd.DataFrame([t.user_attrs["results"] for t in all_trials])
-            df_path = os.path.join(self._log_dir, "results.csv")
-
-            # Check if Multi-Objective Optimization was performed to save the pareto front
-            objective_columns = [
-                col for col in df_results.columns if col.startswith("objective")
-            ]
-
-            if len(objective_columns) > 1:
-                if pd.api.types.is_string_dtype(df_results[objective_columns[0]]):
-                    mask_no_failures = ~df_results[objective_columns[0]].str.startswith(
-                        "F"
-                    )
-                else:
-                    mask_no_failures = np.ones(len(df_results), dtype=bool)
-                objectives = -df_results.loc[
-                    mask_no_failures, objective_columns
-                ].values.astype(float)
-                mask_pareto_front = non_dominated_set(objectives)
-                df_results["pareto_efficient"] = False
-                df_results.loc[mask_no_failures, "pareto_efficient"] = mask_pareto_front
-            
-            df_results.to_csv(df_path, index=False)
-
-        if self.comm:
-            self.comm.Barrier()
-        
-        df_results = pd.read_csv(df_path)
-
-        return df_results
diff --git a/deephyper_benchmark/search/_pybobyqa.py b/deephyper_benchmark/search/_pybobyqa.py
deleted file mode 100644
index 890cc74..0000000
--- a/deephyper_benchmark/search/_pybobyqa.py
+++ /dev/null
@@ -1,87 +0,0 @@
-import os
-
-import ConfigSpace.hyperparameters as csh
-import pandas as pd
-
-
-from deephyper.core.exceptions import SearchTerminationError  # noqa: E402
-from deephyper.core.utils._timeout import terminate_on_timeout  # noqa: E402
-from deephyper.evaluator import RunningJob  # noqa: E402
-from deephyper.search import Search  # noqa: E402
-
-import pybobyqa
-
-
-def pybobyqa_bounds_from_hp(cs_hp):
-    if isinstance(cs_hp, csh.UniformIntegerHyperparameter):
-        lower, upper = float(cs_hp.lower), float(cs_hp.upper)
-    elif isinstance(cs_hp, csh.UniformFloatHyperparameter):
-        lower, upper = cs_hp.lower, cs_hp.upper
-    elif isinstance(cs_hp, csh.CategoricalHyperparameter):
-        lower, upper = 0.0, float(len(cs_hp.choices) - 1)
-    elif isinstance(cs_hp, csh.OrdinalHyperparameter):
-        lower, upper = 0.0, float(len(cs_hp.sequence) - 1)
-    else:
-        raise TypeError(f"Cannot convert hyperparameter of type {type(cs_hp)}")
-    return lower, upper
-
-
-def convert_problem_to_pybobyqa(problem):
-    xl, xu = [], []
-    for cs_hp in problem.space.get_hyperparameters():
-        lower, upper = pybobyqa_bounds_from_hp(cs_hp)
-        xl.append(lower)
-        xu.append(upper)
-    return xl, xu
-
-
-class PyBOBYQA(Search):
-    """Wrapper for PyBOBYQA derivative-free optimizer.
-
-    $ pip install Py-BOBYQA
-
-    Args:
-        problem (HpProblem): Hyperparameter problem describing the search space to explore.
-        evaluator (Evaluator): An ``Evaluator`` instance responsible of distributing the tasks.
-        random_state (int, optional): Random seed. Defaults to ``None``.
-        log_dir (str, optional): Log directory where search's results are saved. Defaults to ``"."``.
-        verbose (int, optional): Indicate the verbosity level of the search. Defaults to ``0``.
-    """
-
-    def __init__(
-        self,
-        problem,
-        evaluator,
-        random_state: int = None,
-        log_dir: str = ".",
-        verbose: int = 0,
-        seek_global_minimum: bool = True,
-        **kwargs,
-    ):
-        super().__init__(problem, evaluator, random_state, log_dir, verbose)
-
-        self._xl, self._xu = convert_problem_to_pybobyqa(problem)
-        self._seek_global_minimum = seek_global_minimum
-
-    def _search(self, max_evals, timeout):
-        def objective_wrapper(x):
-            config = {k: v for k, v in zip(self._problem.hyperparameter_names, x)}
-            self._evaluator.submit([config])
-            job = self._evaluator.gather("ALL")[0]
-            _, y = job
-            return -y
-
-        x0 = self._random_state.uniform(low=self._xl, high=self._xu)
-
-        pyboobyqa_results = pybobyqa.solve(
-            objective_wrapper,
-            x0=x0,
-            bounds=(self._xl, self._xu),
-            maxfun=max_evals,
-            seek_global_minimum=self._seek_global_minimum,
-        )
-
-        self._evaluator.dump_evals(log_dir=self._log_dir)
-        df_path = os.path.join(self._log_dir, "results.csv")
-        df_results = pd.read_csv(df_path)
-        return df_results
diff --git a/deephyper_benchmark/search/_smac.py b/deephyper_benchmark/search/_smac.py
deleted file mode 100644
index 2f2c5a8..0000000
--- a/deephyper_benchmark/search/_smac.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import functools
-import logging
-import os
-
-import ConfigSpace.hyperparameters as csh
-import pandas as pd
-
-
-from deephyper.core.exceptions import SearchTerminationError  # noqa: E402
-from deephyper.core.utils._timeout import terminate_on_timeout  # noqa: E402
-from deephyper.evaluator import RunningJob  # noqa: E402
-from deephyper.search import Search  # noqa: E402
-
-import smac
-import smac.acquisition.function
-import smac.multi_objective.parego
-
-
-MAP_acq_func = {"UCB": "LCB"}
-
-
-class SMAC(Search):
-    """Wrapper for SMAC optimizer.
-
-    $ conda install gxx_linux-64 gcc_linux-64 swig
-    $ pip install smac
-
-    Args:
-        problem (HpProblem): Hyperparameter problem describing the search space to explore.
-        evaluator (Evaluator): An ``Evaluator`` instance responsible of distributing the tasks.
-        random_state (int, optional): Random seed. Defaults to ``None``.
-        log_dir (str, optional): Log directory where search's results are saved. Defaults to ``"."``.
-        verbose (int, optional): Indicate the verbosity level of the search. Defaults to ``0``.
-    """
-
-    def __init__(
-        self,
-        problem,
-        evaluator,
-        random_state: int = None,
-        log_dir: str = ".",
-        verbose: int = 0,
-        acq_func: str = "UCB",
-        acq_func_kwargs: dict = None,
-        n_objectives: int = 1,
-        n_initial_points: int = None,
-        **kwargs,
-    ):
-        super().__init__(problem, evaluator, random_state, log_dir, verbose)
-
-        self._acq_func = MAP_acq_func.get(acq_func, acq_func)
-        self._acq_func_kwargs = acq_func_kwargs if acq_func_kwargs is not None else {}
-        self.n_objectives = n_objectives
-        self.n_initial_points = n_initial_points if n_initial_points is not None else 10
-        self.seed = self._random_state.randint(low=0, high=2**31)
-
-    def _search(self, max_evals, timeout):
-        def objective_wrapper(config, seed=None):
-            self._evaluator.submit([dict(config)])
-            job = self._evaluator.gather("ALL")[0]
-            _, y = job
-            if self.n_objectives > 1:
-                y = {f"cost_{i}": -y[i] for i in range(self.n_objectives)}
-            else:
-                y = -y
-            return y
-
-        scenario = smac.Scenario(
-            self._problem.space,
-            objectives="cost"
-            if self.n_objectives == 1
-            else [f"cost_{i}" for i in range(self.n_objectives)],
-            n_trials=max_evals,
-            output_directory=os.path.join(self._log_dir, "smac_output"),
-            seed=self.seed,
-        )
-
-        initial_design = smac.HyperparameterOptimizationFacade.get_initial_design(
-            scenario, n_configs=self.n_initial_points
-        )
-
-        # Use SMAC to find the best configuration/hyperparameters
-        optimizer = smac.HyperparameterOptimizationFacade(
-            scenario,
-            target_function=objective_wrapper,
-            acquisition_function=getattr(smac.acquisition.function, self._acq_func)(
-                **self._acq_func_kwargs
-            ),
-            initial_design=initial_design,
-            multi_objective_algorithm=None
-            if self.n_objectives == 1
-            else smac.multi_objective.parego.ParEGO(scenario),
-            logging_level=False,
-        )
-        incumbent = optimizer.optimize()
-
-        self._evaluator.dump_evals(log_dir=self._log_dir)
-        df_path = os.path.join(self._log_dir, "results.csv")
-        df_results = pd.read_csv(df_path)
-        return df_results
diff --git a/deephyper_benchmark/stopper/__init__.py b/deephyper_benchmark/stopper/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/deephyper_benchmark/stopper/lcpfn.py b/deephyper_benchmark/stopper/lcpfn.py
deleted file mode 100644
index 4f8fded..0000000
--- a/deephyper_benchmark/stopper/lcpfn.py
+++ /dev/null
@@ -1,237 +0,0 @@
-# Source code: https://github.com/automl/lcpfn
-#! LCPFN is not directly compatible with torch>=2.0.0
-#! to make it work I commented out the `is_causal` in the transformer forward
-#! and I commented out the `approximate` in the GELU forward
-import sys
-from numbers import Number
-
-import lcpfn
-import numpy as np
-import torch
-from deephyper.stopper._stopper import Stopper
-
-
-def area_learning_curve(z, f, z_max) -> float:
-    assert len(z) == len(f)
-    assert z[-1] <= z_max
-    area = 0
-    for i in range(1, len(z)):
-        # z: is always monotinic increasing but not f!
-        area += (z[i] - z[i - 1]) * f[i - 1]
-    if z[-1] < z_max:
-        area += (z_max - z[-1]) * f[-1]
-    return area
-
-
-class LCPFNStopper(Stopper):
-    """Stopper based on learning curve extrapolation (LCE) to evaluate if the iterations of the learning algorithm
-    should be stopped.
-
-    .. list-table::
-        :widths: 25 25 25
-        :header-rows: 1
-
-        * - Single-Objective
-          - Multi-Objectives
-          - Failures
-        * - ✅
-          - ❌
-          - ❌
-
-    The LCE is based on a parametric learning curve model (LCM) which is modeling the score as a function of the number of training steps. Training steps can correspond to the number of training epochs, the number of training batches, the number of observed samples or any other quantity that is iterated through during the training process. The LCE is based on the following steps:
-
-    1. An early stopping condition is always checked first. If the early stopping condition is met, the LCE is not applied.
-    2. Then, some safeguard conditions are checked to ensure that the LCE can be applied (number of observed steps must be greater or equal to the number of parameters of the LCM).
-    3. If the LCM cannot be fitted (number of observed steps is less than number of parameters of the model), then the last observed step is compared to hitorical performance of others at the same step to check if it is a low-performing outlier (outlier in the direction of performing worse!) using the IQR criterion.
-    4. If the LCM can be fitted, a least square fit is performed to estimate the parameters of the LCM.
-    5. The probability of the current LC to perform worse than the best observed score at the maximum iteration is computed using Monte-Carlo Markov Chain (MCMC).
-
-    To use this stopper, you need to install the following dependencies:
-
-    .. code-block:: bash
-
-        $ jax>=0.3.25
-        $ numpyro
-
-    Args:
-        max_steps (int): The maximum number of training steps which can be performed.
-        min_steps (int, optional): The minimum number of training steps which can be performed. Defaults to ``4``. It is better to have at least as many steps as the number of parameters of the fitted learning curve model. For example, if ``lc_model="mmf4"`` then ``min_steps`` should be at least ``4``.
-        min_done_for_outlier_detection (int, optional): The minimum number of observed scores at the same step to check for if it is a lower-bound outlier. Defaults to ``10``.
-        iqr_factor_for_outlier_detection (float, optional): The IQR factor for outlier detection. The higher it is the more inclusive the condition will be (i.e. if set very large it is likely not going to detect any outliers). Defaults to ``1.5``.
-        prob_promotion (float, optional): The threshold probabily to stop the iterations. If the current learning curve has a probability greater than ``prob_promotion`` to be worse that the best observed score accross all evaluations then the current iterations are stopped. Defaults to ``0.9`` (i.e. probability of 0.9 of being worse).
-        early_stopping_patience (float, optional): The patience of the early stopping condition. If it is an ``int`` it is directly corresponding to a number of iterations. If it is a ``float`` then it is corresponding to a proportion between [0,1] w.r.t. ``max_steps``. Defaults to ``0.25`` (i.e. 25% of ``max_steps``).
-        objective_returned (str, optional): The returned objective. It can be a value in ``["last", "max", "alc"]`` where ``"last"`` corresponds to the last observed score, ``"max"`` corresponds to the maximum observed score and ``"alc"`` corresponds to the area under the learning curve. Defaults to "last".
-
-    Raises:
-        ValueError: parameters are not valid.
-    """
-
-    def __init__(
-        self,
-        max_steps: int,
-        min_steps: int = 1,
-        min_obs_to_fit_lc_model=1,
-        min_done_for_outlier_detection=10,
-        iqr_factor_for_outlier_detection=1.5,
-        prob_promotion=0.9,
-        early_stopping_patience=0.25,
-        reduction_factor=1,
-        objective_returned="last",
-        random_state=None,
-    ) -> None:
-        super().__init__(max_steps=max_steps)
-        self.min_steps = min_steps
-
-        self._min_obs_to_fit_lc_model = min_obs_to_fit_lc_model
-        self._reduction_factor = reduction_factor
-
-        self.min_done_for_outlier_detection = min_done_for_outlier_detection
-        self.iqr_factor_for_outlier_detection = iqr_factor_for_outlier_detection
-
-        self.prob_promotion = prob_promotion
-        if type(early_stopping_patience) is int:
-            self.early_stopping_patience = early_stopping_patience
-        elif type(early_stopping_patience) is float:
-            self.early_stopping_patience = int(early_stopping_patience * self.max_steps)
-        else:
-            raise ValueError("early_stopping_patience must be int or float")
-        self.objective_returned = objective_returned
-
-        self._rung = 0
-
-        self._random_state = random_state
-        self.lc_model = lcpfn.LCPFN()
-
-        self._lc_objectives = []
-
-    def _compute_halting_step(self):
-        return (self.min_steps - 1) * self._reduction_factor**self._rung
-
-    def _retrieve_best_objective(self) -> float:
-        search_id, _ = self.job.id.split(".")
-        objectives = []
-
-        for obj in self.job.storage.load_out_from_all_jobs(search_id):
-            if isinstance(obj, Number):
-                objectives.append(obj)
-
-        if len(objectives) > 0:
-            return np.max(objectives)
-        else:
-            return np.max(self.observations[1])
-
-    def _get_competiting_objectives(self, rung) -> list:
-        search_id, _ = self.job.id.split(".")
-        values = self.job.storage.load_metadata_from_all_jobs(
-            search_id, f"_completed_rung_{rung}"
-        )
-        # Filter out non numerical values (e.g., "F" for failed jobs)
-        values = [v for v in values if isinstance(v, Number)]
-        return values
-
-    def observe(self, budget: float, objective: float):
-        super().observe(budget, objective)
-        self._budget = self.observed_budgets[-1]
-        self._lc_objectives.append(self.objective)
-        self._objective = self._lc_objectives[-1]
-
-        # For Early-Stopping based on Patience
-        if (
-            not (hasattr(self, "_local_best_objective"))
-            or self._objective > self._local_best_objective
-        ):
-            self._local_best_objective = self._objective
-            self._local_best_step = self.step
-
-        halting_step = self._compute_halting_step()
-        if self._budget >= halting_step:
-            self.job.storage.store_job_metadata(
-                self.job.id, f"_completed_rung_{self._rung}", self._objective
-            )
-
-    def stop(self) -> bool:
-        # Enforce Pre-conditions Before Learning-Curve based Early Discarding
-        if super().stop():
-            self.infos_stopped = "max steps reached"
-            return True
-
-        if self.step - self._local_best_step >= self.early_stopping_patience:
-            self.infos_stopped = "early stopping"
-            return True
-
-        # This condition will enforce the stopper to stop the evaluation at the first step
-        # for the first evaluation (The FABOLAS method does the same, bias the first samples with
-        # small budgets)
-        self.best_objective = self._retrieve_best_objective()
-
-        halting_step = self._compute_halting_step()
-
-        if self.step < self.min_steps:
-            if self.step >= halting_step:
-                self._rung += 1
-            return False
-
-        if self.step < self._min_obs_to_fit_lc_model:
-            if self.step >= halting_step:
-                competing_objectives = self._get_competiting_objectives(self._rung)
-                if len(competing_objectives) > self.min_done_for_outlier_detection:
-                    q1 = np.quantile(
-                        competing_objectives,
-                        q=0.25,
-                    )
-                    q3 = np.quantile(
-                        competing_objectives,
-                        q=0.75,
-                    )
-                    iqr = q3 - q1
-                    # lower than the minimum of a box plot
-                    if (
-                        self._objective
-                        < q1 - self.iqr_factor_for_outlier_detection * iqr
-                    ):
-                        self.infos_stopped = "outlier"
-                        return True
-                self._rung += 1
-
-            return False
-
-        # Check if the halting budget condition is met
-        if self.step < halting_step:
-            return False
-
-        # Check if the evaluation should be stopped based on LC-Model
-
-        # Fit and predict the performance of the learning curve model
-        z_train = self.observed_budgets
-        y_train = self._lc_objectives
-        z_train, y_train = np.asarray(z_train), np.asarray(y_train)
-
-        z_train = torch.Tensor(z_train.reshape(-1, 1))
-        y_train = torch.Tensor(y_train.reshape(-1, 1))
-        y_pred = self.lc_model.predict_quantiles(
-            x_train=z_train,
-            y_train=y_train,
-            x_test=torch.Tensor([[self.max_steps]]),
-            qs=[self.prob_promotion],
-        )
-        y_pred = y_pred[0][0].numpy()
-
-        # Return whether the configuration should be stopped
-        if self.objective <= y_pred:
-            self._rung += 1
-        else:
-            self.infos_stopped = f"objective={y_pred:.3f}"
-
-            return True
-
-    @property
-    def objective(self):
-        if self.objective_returned == "last":
-            return self.observations[-1][-1]
-        elif self.objective_returned == "max":
-            return max(self.observations[-1])
-        elif self.objective_returned == "alc":
-            z, y = self.observations
-            return area_learning_curve(z, y, z_max=self.max_steps)
-        else:
-            raise ValueError("objective_returned must be one of 'last', 'best', 'alc'")
diff --git a/deephyper_benchmark/utils/__init__.py b/deephyper_benchmark/utils/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/deephyper_benchmark/utils/json_utils.py b/deephyper_benchmark/utils/json_utils.py
deleted file mode 100644
index 0eb954e..0000000
--- a/deephyper_benchmark/utils/json_utils.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import json
-import numpy as np
-
-
-class NumpyArrayEncoder(json.JSONEncoder):
-    def default(self, obj):
-        if isinstance(obj, np.ndarray):
-            return obj.tolist()
-        return json.JSONEncoder.default(self, obj)
-
-
-def array_to_json(x: np.ndarray) -> str:
-    """Converts a numpy array to a json string.
-
-    Args:
-        x (np.ndarray): a numpy array.
-
-    Returns:
-        str: a json string.
-    """
-    x_json = json.dumps(x, cls=NumpyArrayEncoder)
-    return x_json
-
-
-def json_to_array(x_json: str) -> np.ndarray:
-    """Converts an array saved as JSON to a numpy array.
-
-    Args:
-        x_json (str): the json encoded array.
-
-    Returns:
-        np.ndarray: the decoded numpy array.
-    """
-    x = np.asarray(json.loads(x_json))
-    return x
-
-
-if __name__ == "__main__":
-    """Example of usage of array_to_json function."""
-    x = np.array([1, 2, 3])
-    print(x)
-    print(type(x))
-    print(array_to_json(x))
-    print(type(array_to_json(x)))
diff --git a/lib/AutoTuning/Mochi/HEPnOS/Makefile b/lib/AutoTuning/Mochi/HEPnOS/Makefile
deleted file mode 100644
index beec148..0000000
--- a/lib/AutoTuning/Mochi/HEPnOS/Makefile
+++ /dev/null
@@ -1,11 +0,0 @@
-
-VERSION=0.0.1
-
-build:
-	mkdir -p build
-	wget https://github.com/hepnos/HEPnOS-Autotuning-analysis/archive/refs/heads/main.tar.gz -O build/hepnos-autotuning-analysis.tar.gz
-	tar -xzvf build/hepnos-autotuning-analysis.tar.gz -C build
-	rm -rf build/hepnos-autotuning-analysis.tar.gz
-
-clean:
-	rm -rf build/
\ No newline at end of file
diff --git a/lib/AutoTuning/Mochi/HEPnOS/__init__.py b/lib/AutoTuning/Mochi/HEPnOS/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/AutoTuning/Mochi/HEPnOS/benchmark.py b/lib/AutoTuning/Mochi/HEPnOS/benchmark.py
deleted file mode 100644
index 4727358..0000000
--- a/lib/AutoTuning/Mochi/HEPnOS/benchmark.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class HEPnOS(Benchmark):
-    """
-    - Experimental data from the HEPnOS paper: https://github.com/hepnos/HEPnOS-Autotuning-analysis
-    - Experimental code from the HEPnOS paper: https://github.com/hepnos/HEPnOS-Autotuning/tree/dev-new-hepnos
-    - Paper from M. Dorier and R. Egele et al. "HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization". https://arxiv.org/pdf/2210.00798
-    """
-
-    version = "0.0.1"
-
-    requires = {
-        "makefile": {"step": "install", "type": "cmd", "cmd": "make build"},
-    }
diff --git a/lib/AutoTuning/Mochi/HEPnOS/hpo.py b/lib/AutoTuning/Mochi/HEPnOS/hpo.py
deleted file mode 100644
index c5ed94d..0000000
--- a/lib/AutoTuning/Mochi/HEPnOS/hpo.py
+++ /dev/null
@@ -1,281 +0,0 @@
-import os
-import time
-
-import numpy as np
-import pandas as pd
-from deephyper.analysis.hpo import filter_failed_objectives
-from deephyper.evaluator import RunningJob, profile
-from deephyper.hpo import HpProblem
-from sklearn.compose import ColumnTransformer
-from sklearn.neighbors import KNeighborsRegressor
-from sklearn.preprocessing import OneHotEncoder, FunctionTransformer
-from sklearn.pipeline import Pipeline
-
-BUILD_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "build")
-DATA_DIR = os.path.join(
-    BUILD_DIR, "HEPnOS-Autotuning-analysis-main", "results", "theta"
-)
-
-DEEPHYPER_BENCHMARK_HEPNOS_MODEL = os.environ.get(
-    "DEEPHYPER_BENCHMARK_HEPNOS_MODEL", "RAND"
-)
-DEEPHYPER_BENCHMARK_HEPNOS_SCALE = int(
-    os.environ.get("DEEPHYPER_BENCHMARK_HEPNOS_SCALE", "4")
-)
-DEEPHYPER_BENCHMARK_HEPNOS_DISABLE_PEP = bool(
-    os.environ.get("DEEPHYPER_BENCHMARK_HEPNOS_DISABLE_PEP", "0")
-)
-DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS = bool(
-    os.environ.get("DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS", "0")
-)
-
-NUMERIC_VARIABLES = []
-CATEGORICAL_VARIABLES = []
-
-problem = HpProblem()
-
-
-def add_parameter(name, domain, value_type, default_value, description=""):
-    problem.add_hyperparameter(domain, name, default_value=default_value)
-    if value_type in [int, float, bool]:
-        NUMERIC_VARIABLES.append(name)
-    else:
-        CATEGORICAL_VARIABLES.append(name)
-
-
-def create_problem():
-    add_parameter(
-        "busy_spin",
-        [0, 1],
-        int,
-        0,
-        "Whether Mercury should busy-spin instead of block",
-    )
-    add_parameter(
-        "hepnos_progress_thread",
-        [0, 1],
-        int,
-        0,
-        "Whether to use a dedicated progress thread in HEPnOS",
-    )
-    add_parameter(
-        "hepnos_num_rpc_threads",
-        (0, 63),
-        int,
-        0,
-        "Number of threads used for serving RPC requests",
-    )
-    add_parameter(
-        "hepnos_num_event_databases",
-        (1, 16),
-        int,
-        1,
-        "Number of databases per process used to store events",
-    )
-    add_parameter(
-        "hepnos_num_product_databases",
-        (1, 16),
-        int,
-        1,
-        "Number of databases per process used to store products",
-    )
-    add_parameter(
-        "hepnos_num_providers",
-        (1, 32),
-        int,
-        1,
-        "Number of database providers per process",
-    )
-    add_parameter(
-        "hepnos_pool_type",
-        ["fifo", "fifo_wait"],
-        str,
-        "fifo_wait",
-        "Thread-scheduling policity used by Argobots pools",
-    )
-    add_parameter(
-        "hepnos_pes_per_node",
-        [1, 2, 4, 8, 16, 32],
-        int,
-        2,
-        "Number of HEPnOS processes per node",
-    )
-    add_parameter(
-        "loader_progress_thread",
-        [0, 1],
-        int,
-        0,
-        "Whether to use a dedicated progress thread in the Dataloader",
-    )
-    add_parameter(
-        "loader_batch_size",
-        (1, 2048, "log-uniform"),
-        int,
-        512,
-        "Size of the batches of events sent by the Dataloader to HEPnOS",
-    )
-    add_parameter(
-        "loader_pes_per_node",
-        [1, 2, 4, 8, 16],
-        int,
-        2,
-        "Number of processes per node for the Dataloader",
-    )
-    if DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS:
-        add_parameter(
-            "loader_async",
-            [0, 1],
-            int,
-            0,
-            "Whether to use the HEPnOS AsyncEngine in the Dataloader",
-        )
-        add_parameter(
-            "loader_async_threads",
-            (1, 63, "log-uniform"),
-            int,
-            1,
-            "Number of threads for the AsyncEngine to use",
-        )
-
-    if DEEPHYPER_BENCHMARK_HEPNOS_DISABLE_PEP:
-        return
-
-    add_parameter(
-        "pep_progress_thread",
-        [0, 1],
-        int,
-        0,
-        "Whether to use a dedicated progress thread in the PEP step",
-    )
-    add_parameter(
-        "pep_num_threads",
-        (1, 31),
-        int,
-        4,
-        "Number of threads used for processing in the PEP step",
-    )
-    add_parameter(
-        "pep_ibatch_size",
-        (8, 1024, "log-uniform"),
-        int,
-        128,
-        "Batch size used when PEP processes are loading events from HEPnOS",
-    )
-    add_parameter(
-        "pep_obatch_size",
-        (8, 1024, "log-uniform"),
-        int,
-        128,
-        "Batch size used when PEP processes are exchanging events among themselves",
-    )
-    add_parameter(
-        "pep_pes_per_node",
-        [1, 2, 4, 8, 16, 32],
-        int,
-        8,
-        "Number of processes per node for the PEP step",
-    )
-
-    if DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS:
-        add_parameter(
-            "pep_no_preloading",
-            [0, 1],
-            int,
-            False,
-            "Whether to disable product-preloading in PEP",
-        )
-        add_parameter(
-            "pep_no_rdma",
-            [0, 1],
-            int,
-            0,
-            "Whether to disable RDMA in PEP",
-        )
-
-
-create_problem()
-
-CSV_SUFFIX = f"{str(DEEPHYPER_BENCHMARK_HEPNOS_DISABLE_PEP).lower()}-{str(DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS).lower()}"
-CSV_SUFFIX = f"{DEEPHYPER_BENCHMARK_HEPNOS_MODEL}-{DEEPHYPER_BENCHMARK_HEPNOS_SCALE}-{CSV_SUFFIX}"
-
-
-def load_data():
-    dataframes, dataframes_with_failures = [], []
-    for i in range(1, 6):
-        csv_path = os.path.join(
-            DATA_DIR,
-            f"exp-{CSV_SUFFIX}-{i}.csv",
-        )
-        df = pd.read_csv(csv_path, index_col=None, header=0)
-        df, df_with_failures = filter_failed_objectives(df)
-        dataframes.append(df)
-        dataframes_with_failures.append(df_with_failures)
-
-    dataframes = pd.concat(dataframes, axis=0, ignore_index=True)
-    dataframes_with_failures = pd.concat(
-        dataframes_with_failures, axis=0, ignore_index=True
-    )
-
-    # The objective is -log(time) initialy so we convert it back to time
-    dataframes["objective"] = dataframes["objective"] = np.exp(
-        -dataframes["objective"].values.astype(np.float32)
-    )
-    dataframes_with_failures["objective"] = -1
-
-    dataframes = pd.concat(
-        [dataframes, dataframes_with_failures], axis=0, ignore_index=True
-    )
-
-    return dataframes
-
-
-def create_model():
-    df = load_data()
-    names = problem.hyperparameter_names
-
-    X = df.loc[:, names]
-    y = df["objective"].values
-
-    transformer = ColumnTransformer(
-        [
-            (
-                "onehot",
-                OneHotEncoder(handle_unknown="ignore"),
-                CATEGORICAL_VARIABLES,
-            ),
-            ("passthrough", FunctionTransformer(), NUMERIC_VARIABLES),
-        ]
-    )
-
-    model = KNeighborsRegressor(n_neighbors=1)
-
-    pipeline = Pipeline([("transformer", transformer), ("model", model)])
-    pipeline.fit(X, y)
-
-    return pipeline
-
-
-model = create_model()
-
-
-@profile
-def run(job: RunningJob) -> dict:
-    config = job.parameters.copy()
-
-    df = pd.DataFrame([config])
-    y_pred = -model.predict(df)[0]
-
-    if y_pred > 0:
-        y_pred = "F"
-
-    return {"objective": y_pred}
-
-
-if __name__ == "__main__":
-    print(problem)
-    df = load_data()
-    print(df)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/AutoTuning/Mochi/HEPnOS/metrics.py b/lib/AutoTuning/Mochi/HEPnOS/metrics.py
deleted file mode 100644
index bc09e5a..0000000
--- a/lib/AutoTuning/Mochi/HEPnOS/metrics.py
+++ /dev/null
@@ -1,39 +0,0 @@
-import os
-
-import numpy as np
-
-from .hpo import load_data, problem
-
-class PerformanceEvaluator:
-    """A class defining performance evaluators for the HEPnOS problem."""
-
-    def __init__(self):
-        """Read the current problem defn from environment vars."""
-
-        df = load_data()
-        df = df[df["objective"] > 0]
-        idx_max = df["objective"].argmin()
-        self.x_min = df[problem.hyperparameter_names].iloc[idx_max].to_dict()
-        self.y_min = df.iloc[idx_max]["objective"]
-
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y - self.y_min
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
diff --git a/lib/AutoTuning/Mochi/README.md b/lib/AutoTuning/Mochi/README.md
deleted file mode 100644
index 3f4bb65..0000000
--- a/lib/AutoTuning/Mochi/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# Mochi Benchmarks
-
-From the Mochi documentation:
-
-> The Mochi project is a collaboration between Argonne National Laboratory, Los Alamos National Laboratory, Carnegie Mellon University, and the HDF Group. The objective of this project is to explore a software defined storage approach for composing storage services that provides new levels of functionality, performance, and reliability for science applications at extreme scale.
-
-- Documentation: https://mochi.readthedocs.io/en/latest/
-
-## Installation
-
-```console
-python -c "import deephyper_benchmark as dhb; dhb.install('AutoTuning/Mochi/HEPnOS');"
-```
\ No newline at end of file
diff --git a/lib/AutoTuning/README.md b/lib/AutoTuning/README.md
deleted file mode 100644
index bd2e296..0000000
--- a/lib/AutoTuning/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# AutoTuning Benchmarks
-
-AutoTuning corresponds to the automated configurations of software to improve performance. For example, it can be the configuration of a database, a compilator, or parallelism.
\ No newline at end of file
diff --git a/lib/C-BBO/README.md b/lib/C-BBO/README.md
deleted file mode 100644
index 6ecf11d..0000000
--- a/lib/C-BBO/README.md
+++ /dev/null
@@ -1,20 +0,0 @@
-# C-BBO: Continuous Black-Box Optimization
-
-> **Warning**
-> Work in progress, this benchmark is not yet ready.
-
-Set of continuous function benchmarks. The `DEEPHYPER_BENCHMARK_NDIMS` environment variable defines the number of dimensions of the problem.
-
-| Function Name | Number of Dimensions  |                   Comment                   |
-| ------------- | --------------------- | ------------------------------------------- |
-| ackley        | $\infty$ (default 5)  | Many local minima and single global optimum |
-| branin        | 2                     | Three global optimum                        |
-| cossin        | 1                     | Many local minima, good for visualisation.  |
-| easom         | 2                     | Almost flat everywhere                      |
-| griewank      | $\infty$ (default 5)  |                                             |
-| hartmann6D    | 6                     |                                             |
-| levy          | $\infty$ (default 5)  |                                             |
-| michal        | $\infty$ (default 2)  |                                             |
-| rosen         | $\infty$ (default 5)  |                                             |
-| schwefel      | $\infty$ (default 5)  |                                             |
-| shekel        | 4                     | Many local minima with flat areas           |
\ No newline at end of file
diff --git a/lib/C-BBO/ackley/__init__.py b/lib/C-BBO/ackley/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/C-BBO/ackley/benchmark.py b/lib/C-BBO/ackley/benchmark.py
deleted file mode 100644
index 5537d7a..0000000
--- a/lib/C-BBO/ackley/benchmark.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class AckleyFunction(Benchmark):
-    """https://www.sfu.ca/~ssurjano/ackley.html
-    """
-
-    version = "0.0.1"
diff --git a/lib/C-BBO/ackley/hpo.py b/lib/C-BBO/ackley/hpo.py
deleted file mode 100644
index a9ee4bc..0000000
--- a/lib/C-BBO/ackley/hpo.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import os
-import time
-
-import numpy as np
-
-from deephyper.hpo import HpProblem
-from deephyper.evaluator import profile, RunningJob
-
-
-DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
-DEEPHYPER_BENCHMARK_OFFSET = float(os.environ.get("DEEPHYPER_BENCHMARK_OFFSET", 4.0))
-DEEPHYPER_BENCHMARK_NDIMS_SLACK = int(
-    os.environ.get("DEEPHYPER_BENCHMARK_NDIMS_SLACK", 0)
-)
-
-# The original range is simetric (-32.768, 32.768) but we make it less simetric to avoid
-# Grid sampling or QMC sampling to directly hit the optimum...
-domain = (-32.768 - DEEPHYPER_BENCHMARK_OFFSET, 32.768 - DEEPHYPER_BENCHMARK_OFFSET)
-problem = HpProblem()
-for i in range(DEEPHYPER_BENCHMARK_NDIMS - DEEPHYPER_BENCHMARK_NDIMS_SLACK):
-    problem.add_hyperparameter(domain, f"x{i}")
-
-# Add slack/dummy dimensions (useful to test predicors which are sensitive to unimportant features)
-for i in range(
-    DEEPHYPER_BENCHMARK_NDIMS - DEEPHYPER_BENCHMARK_NDIMS_SLACK,
-    DEEPHYPER_BENCHMARK_NDIMS,
-):
-    problem.add_hyperparameter(domain, f"z{i}")
-
-
-def ackley(x, a=20, b=0.2, c=2 * np.pi):
-    d = len(x)
-    s1 = np.sum(x**2)
-    s2 = np.sum(np.cos(c * x))
-    term1 = -a * np.exp(-b * np.sqrt(s1 / d))
-    term2 = -np.exp(s2 / d)
-    y = term1 + term2 + a + np.exp(1)
-    return y
-
-
-@profile
-def run(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -ackley(x)
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/C-BBO/ackley/metrics.py b/lib/C-BBO/ackley/metrics.py
deleted file mode 100644
index 8bfdc77..0000000
--- a/lib/C-BBO/ackley/metrics.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import os
-
-import numpy as np
-
-from .hpo import (
-    DEEPHYPER_BENCHMARK_NDIMS,
-    DEEPHYPER_BENCHMARK_NDIMS_SLACK,
-)
-
-
-class PerformanceEvaluator:
-    """A class defining performance evaluators for the Ackley problem."""
-
-    def __init__(self):
-        """Read the current problem defn from environment vars."""
-
-        self.p_num = DEEPHYPER_BENCHMARK_NDIMS
-        self.x_min = np.zeros(self.p_num)
-        self.x_min[DEEPHYPER_BENCHMARK_NDIMS-DEEPHYPER_BENCHMARK_NDIMS_SLACK:] = np.nan
-        self.y_min = 0.0
-
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y - self.y_min
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
diff --git a/lib/C-BBO/branin/__init__.py b/lib/C-BBO/branin/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/C-BBO/branin/benchmark.py b/lib/C-BBO/branin/benchmark.py
deleted file mode 100644
index 25019b5..0000000
--- a/lib/C-BBO/branin/benchmark.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class BraninFunction(Benchmark):
-    """https://www.sfu.ca/~ssurjano/branin.html
-    """
-
-    version = "0.0.1"
diff --git a/lib/C-BBO/branin/hpo.py b/lib/C-BBO/branin/hpo.py
deleted file mode 100644
index 19c1841..0000000
--- a/lib/C-BBO/branin/hpo.py
+++ /dev/null
@@ -1,48 +0,0 @@
-import os
-import time
-
-import numpy as np
-
-from deephyper.hpo import HpProblem
-from deephyper.evaluator import profile, RunningJob
-
-
-problem = HpProblem()
-problem.add_hyperparameter((-5.0, 10.0), f"x0")
-problem.add_hyperparameter((0.0, 15.0), f"x1")
-
-
-def branin(x):
-    assert len(x) == 2
-    a = 1.0
-    b = 5.1 / (4.0 * np.pi**2)
-    c = 5.0 / np.pi
-    r = 6.0
-    s = 10.0
-    t = 1.0 / (8.0 * np.pi)
-    y = a * (x[1] - b * x[0] ** 2 + c * x[0] - r) ** 2 + s * (1 - t) * np.cos(x[0]) + s
-    return y
-
-
-@profile
-def run(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -branin(x)
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    default_config = {"x0": -np.pi, "x1": 12.275}  # sol
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/C-BBO/branin/metrics.py b/lib/C-BBO/branin/metrics.py
deleted file mode 100644
index f63a4fd..0000000
--- a/lib/C-BBO/branin/metrics.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-
-import numpy as np
-
-
-class PerformanceEvaluator:
-    """A class defining performance evaluators for the Ackley problem."""
-
-    def __init__(self):
-
-        self.p_num = 2
-        self.x_min = np.array([[-np.pi, 12.275], [np.pi, 2.275], [9.42478, 2.475]])
-        self.y_min = 0.397887
-
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y - self.y_min
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
diff --git a/lib/C-BBO/cossin/__init__.py b/lib/C-BBO/cossin/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/C-BBO/cossin/benchmark.py b/lib/C-BBO/cossin/benchmark.py
deleted file mode 100644
index 239116b..0000000
--- a/lib/C-BBO/cossin/benchmark.py
+++ /dev/null
@@ -1,10 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class CosSinFunction(Benchmark):
-
-    version = "0.0.1"
diff --git a/lib/C-BBO/cossin/hpo.py b/lib/C-BBO/cossin/hpo.py
deleted file mode 100644
index c809e22..0000000
--- a/lib/C-BBO/cossin/hpo.py
+++ /dev/null
@@ -1,65 +0,0 @@
-import os
-import time
-
-import numpy as np
-
-from deephyper.hpo import HpProblem
-from deephyper.evaluator import profile, RunningJob
-
-
-DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 1))
-DEEPHYPER_BENCHMARK_NDIMS_SLACK = int(
-    os.environ.get("DEEPHYPER_BENCHMARK_NDIMS_SLACK", 0)
-)
-
-# The original range is simetric (-32.768, 32.768) but we make it less simetric to avoid
-# Grid sampling or QMC sampling to directly hit the optimum...
-domain = (-50.0, 50.0)
-problem = HpProblem()
-for i in range(DEEPHYPER_BENCHMARK_NDIMS - DEEPHYPER_BENCHMARK_NDIMS_SLACK):
-    problem.add_hyperparameter(domain, f"x{i}")
-
-# Add slack/dummy dimensions (useful to test predicors which are sensitive to unimportant features)
-for i in range(
-    DEEPHYPER_BENCHMARK_NDIMS - DEEPHYPER_BENCHMARK_NDIMS_SLACK,
-    DEEPHYPER_BENCHMARK_NDIMS,
-):
-    problem.add_hyperparameter(domain, f"z{i}")
-
-
-def cossin(x):
-    x = np.sum(x)
-    y = np.cos(5 * x / 10) + 2 * np.sin(x / 10) + x / 100
-    return y
-
-
-@profile
-def run(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20, noise=0.0) -> dict:
-    job_id = int(job.id.split(".")[-1])
-    config = job.parameters
-
-    if sleep:
-        rng = np.random.RandomState(job_id)
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    y = cossin(x)
-
-    if noise > 0.0:
-        rng = np.random.RandomState(job_id)
-        eps = rng.normal(loc=0.0, scale=noise)
-        y += eps
-
-    return -y
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/C-BBO/cossin/metrics.py b/lib/C-BBO/cossin/metrics.py
deleted file mode 100644
index 3119d3d..0000000
--- a/lib/C-BBO/cossin/metrics.py
+++ /dev/null
@@ -1,42 +0,0 @@
-import os
-
-import numpy as np
-
-from .hpo import (
-    DEEPHYPER_BENCHMARK_NDIMS,
-    DEEPHYPER_BENCHMARK_NDIMS_SLACK,
-)
-
-
-class PerformanceEvaluator:
-    """A class defining performance evaluators for the Ackley problem."""
-
-    def __init__(self):
-        """Read the current problem defn from environment vars."""
-
-        self.p_num = DEEPHYPER_BENCHMARK_NDIMS
-        self.x_min = np.full((self.p_num,), 18.6738)
-        self.x_min[DEEPHYPER_BENCHMARK_NDIMS-DEEPHYPER_BENCHMARK_NDIMS_SLACK:] = np.nan
-        self.y_min = -3.1
-
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y - self.y_min
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
diff --git a/lib/C-BBO/easom/__init__.py b/lib/C-BBO/easom/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/C-BBO/easom/benchmark.py b/lib/C-BBO/easom/benchmark.py
deleted file mode 100644
index a6f04e0..0000000
--- a/lib/C-BBO/easom/benchmark.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class EasomFunction(Benchmark):
-    """https://www.sfu.ca/~ssurjano/easom.html
-    """
-
-    version = "0.0.1"
diff --git a/lib/C-BBO/easom/hpo.py b/lib/C-BBO/easom/hpo.py
deleted file mode 100644
index 5f3eee8..0000000
--- a/lib/C-BBO/easom/hpo.py
+++ /dev/null
@@ -1,52 +0,0 @@
-import os
-import time
-
-import numpy as np
-
-from deephyper.hpo import HpProblem
-from deephyper.evaluator import profile, RunningJob
-
-
-DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 2))
-DEEPHYPER_BENCHMARK_OFFSET = float(os.environ.get("DEEPHYPER_BENCHMARK_OFFSET", 20.0))
-
-# The original range is simetric (-100.0, 100.0) but we make it less simetric to avoid
-# Grid sampling or QMC sampling to directly hit the optimum...
-domain = (-100.0 - DEEPHYPER_BENCHMARK_OFFSET, 100.0 - DEEPHYPER_BENCHMARK_OFFSET)
-problem = HpProblem()
-for i in range(DEEPHYPER_BENCHMARK_NDIMS):
-    problem.add_hyperparameter(domain, f"x{i}")
-
-
-def easom(x):
-    assert len(x) == 2
-    y = (
-        -np.cos(x[0])
-        * np.cos(x[1])
-        * np.exp(-((x[0] - np.pi) ** 2 + (x[1] - np.pi) ** 2))
-    )
-    return y
-
-
-@profile
-def run(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -easom(x)
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    default_config = {"x0": np.pi, "x1": np.pi}  # sol
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/C-BBO/easom/metrics.py b/lib/C-BBO/easom/metrics.py
deleted file mode 100644
index 67c4007..0000000
--- a/lib/C-BBO/easom/metrics.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import os
-
-import numpy as np
-
-
-class PerformanceEvaluator:
-    """A class defining performance evaluators for the Ackley problem."""
-
-    def __init__(self):
-        """Read the current problem defn from environment vars."""
-
-        self.p_num = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
-        self.x_min = np.array([np.pi, np.pi])
-        self.y_min = -1.0
-
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y - self.y_min
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
diff --git a/lib/C-BBO/griewank/__init__.py b/lib/C-BBO/griewank/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/C-BBO/griewank/benchmark.py b/lib/C-BBO/griewank/benchmark.py
deleted file mode 100644
index e249cab..0000000
--- a/lib/C-BBO/griewank/benchmark.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class GriewankFunction(Benchmark):
-    """https://www.sfu.ca/~ssurjano/griewank.html
-    """
-
-    version = "0.0.1"
diff --git a/lib/C-BBO/griewank/hpo.py b/lib/C-BBO/griewank/hpo.py
deleted file mode 100644
index 4be000f..0000000
--- a/lib/C-BBO/griewank/hpo.py
+++ /dev/null
@@ -1,43 +0,0 @@
-import os
-import time
-
-import numpy as np
-from deephyper.evaluator import RunningJob, profile
-from deephyper.hpo import HpProblem
-
-DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
-domain = (-600.0, 600.0)
-problem = HpProblem()
-for i in range(DEEPHYPER_BENCHMARK_NDIMS):
-    problem.add_hyperparameter(domain, f"x{i}")
-
-
-def griewank(x, fr=4000):
-    n = len(x)
-    j = np.arange(1.0, n + 1)
-    s = np.sum(x**2)
-    p = np.prod(np.cos(x / np.sqrt(j)))
-    return s / fr - p + 1
-
-
-@profile
-def run(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -griewank(x)
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/C-BBO/griewank/metrics.py b/lib/C-BBO/griewank/metrics.py
deleted file mode 100644
index 85355f4..0000000
--- a/lib/C-BBO/griewank/metrics.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import os
-
-import numpy as np
-
-
-class PerformanceEvaluator:
-    """A class defining performance evaluators for the Griewank problem."""
-
-    def __init__(self):
-        """Read the current problem defn from environment vars."""
-
-        self.p_num = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
-        self.x_min = np.zeros(self.p_num)
-        self.y_min = 0.0
-
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y - self.y_min
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
diff --git a/lib/C-BBO/hartmann6D/__init__.py b/lib/C-BBO/hartmann6D/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/C-BBO/hartmann6D/benchmark.py b/lib/C-BBO/hartmann6D/benchmark.py
deleted file mode 100644
index 5af6d08..0000000
--- a/lib/C-BBO/hartmann6D/benchmark.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class Hartmann6DFunction(Benchmark):
-    """https://www.sfu.ca/~ssurjano/hart6.html
-    """
-
-    version = "0.0.1"
diff --git a/lib/C-BBO/hartmann6D/hpo.py b/lib/C-BBO/hartmann6D/hpo.py
deleted file mode 100644
index 3d7082a..0000000
--- a/lib/C-BBO/hartmann6D/hpo.py
+++ /dev/null
@@ -1,60 +0,0 @@
-import os
-
-import time
-import numpy as np
-from deephyper.hpo import HpProblem
-from deephyper.evaluator import profile, RunningJob
-
-DEEPHYPER_BENCHMARK_NDIMS = 6
-domain = (0.0, 1.0)
-problem = HpProblem()
-for i in range(DEEPHYPER_BENCHMARK_NDIMS):
-    problem.add_hyperparameter(domain, f"x{i}")
-
-
-def hartmann6D(x):
-    alpha = np.array([1.0, 1.2, 3.0, 3.2])
-    A = np.array(
-        [
-            [10, 3, 17, 3.5, 1.7, 8],
-            [0.05, 10, 17, 0.1, 8, 14],
-            [3, 3.5, 1.7, 10, 17, 8],
-            [17, 8, 0.05, 10, 0.1, 14],
-        ]
-    )
-    P = 1e-4 * np.array(
-        [
-            [1312, 1696, 5569, 124, 8283, 5886],
-            [2329, 4135, 8307, 3736, 1004, 9991],
-            [2348, 1451, 3522, 2883, 3047, 6650],
-            [4047, 8828, 8732, 5743, 1091, 381],
-        ]
-    )
-    X = np.array([x for _ in range(4)])
-    inner = np.sum(np.multiply(A, np.square(X - P)), axis=1)
-    outer = np.sum(alpha * np.exp(-inner))
-    y = -(2.58 + outer) / 1.94
-    return y
-
-
-@profile
-def run(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -hartmann6D(x)
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/C-BBO/hartmann6D/metrics.py b/lib/C-BBO/hartmann6D/metrics.py
deleted file mode 100644
index fedb6e4..0000000
--- a/lib/C-BBO/hartmann6D/metrics.py
+++ /dev/null
@@ -1,45 +0,0 @@
-import os
-
-import numpy as np
-
-
-class PerformanceEvaluator:
-    """A class defining performance evaluators for the Hartmann6D problem."""
-
-    def __init__(self):
-        """Read the current problem defn from environment vars."""
-
-        self.p_num = 6
-        self.x_min = np.array(
-            [
-                0.20169,
-                0.150011,
-                0.476874,
-                0.275332,
-                0.311652,
-                0.6573,
-            ]
-        )
-        self.y_min = -3.32237
-
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y - self.y_min
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
diff --git a/lib/C-BBO/levy/__init__.py b/lib/C-BBO/levy/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/C-BBO/levy/benchmark.py b/lib/C-BBO/levy/benchmark.py
deleted file mode 100644
index 82bf6ae..0000000
--- a/lib/C-BBO/levy/benchmark.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class LevyFunction(Benchmark):
-    """https://www.sfu.ca/~ssurjano/levy.html
-    """
-
-    version = "0.0.1"
diff --git a/lib/C-BBO/levy/hpo.py b/lib/C-BBO/levy/hpo.py
deleted file mode 100644
index 415bcfe..0000000
--- a/lib/C-BBO/levy/hpo.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import os
-import time
-
-import numpy as np
-from deephyper.evaluator import RunningJob, profile
-from deephyper.hpo import HpProblem
-
-DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
-domain = (-10.0, 10.0)
-problem = HpProblem()
-for i in range(DEEPHYPER_BENCHMARK_NDIMS):
-    problem.add_hyperparameter(domain, f"x{i}")
-
-
-def levy(x):
-    z = 1 + (x - 1) / 4
-    return (
-        np.sin(np.pi * z[0]) ** 2
-        + np.sum((z[:-1] - 1) ** 2 * (1 + 10 * np.sin(np.pi * z[:-1] + 1) ** 2))
-        + (z[-1] - 1) ** 2 * (1 + np.sin(2 * np.pi * z[-1]) ** 2)
-    )
-
-
-@profile
-def run(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -levy(x)
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/C-BBO/levy/metrics.py b/lib/C-BBO/levy/metrics.py
deleted file mode 100644
index 4bf4f40..0000000
--- a/lib/C-BBO/levy/metrics.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import os
-
-import numpy as np
-
-
-class PerformanceEvaluator:
-    """A class defining performance evaluators for the Levy problem."""
-
-    def __init__(self):
-        """Read the current problem defn from environment vars."""
-
-        self.p_num = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
-        self.x_min = np.ones(self.p_num)
-        self.y_min = 0.0
-
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y - self.y_min
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
diff --git a/lib/C-BBO/michal/__init__.py b/lib/C-BBO/michal/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/C-BBO/michal/benchmark.py b/lib/C-BBO/michal/benchmark.py
deleted file mode 100644
index fe72b10..0000000
--- a/lib/C-BBO/michal/benchmark.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class MichalFunction(Benchmark):
-    """https://www.sfu.ca/~ssurjano/michal.html
-    """
-
-    version = "0.0.1"
diff --git a/lib/C-BBO/michal/hpo.py b/lib/C-BBO/michal/hpo.py
deleted file mode 100644
index bc200c4..0000000
--- a/lib/C-BBO/michal/hpo.py
+++ /dev/null
@@ -1,44 +0,0 @@
-import os
-import time
-
-import numpy as np
-
-from deephyper.hpo import HpProblem
-from deephyper.evaluator import profile, RunningJob
-
-
-DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 2))
-
-domain = (0, np.pi)
-problem = HpProblem()
-for i in range(DEEPHYPER_BENCHMARK_NDIMS):
-    problem.add_hyperparameter(domain, f"x{i}")
-
-
-def michal(x, m=10):
-    ix2 = np.arange(1, len(x) + 1) * x**2
-    y = -np.sum(np.sin(x) * np.power(np.sin(ix2 / np.pi), 2 * m))
-    return y
-
-
-@profile
-def run(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -michal(x)
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/C-BBO/michal/metrics.py b/lib/C-BBO/michal/metrics.py
deleted file mode 100644
index e69958b..0000000
--- a/lib/C-BBO/michal/metrics.py
+++ /dev/null
@@ -1,55 +0,0 @@
-import os
-import warnings
-
-import numpy as np
-
-
-class PerformanceEvaluator:
-    """A class defining performance evaluators for the Ackley problem."""
-
-    def __init__(self):
-        """Read the current problem defn from environment vars."""
-
-        self.p_num = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
-        self.x_min = None
-        self.y_min = None
-
-        if self.p_num == 2:
-            self.x_min = np.array([2.20, 1.57])
-            self.y_min = -1.8013
-        elif self.p_num == 5:
-            self.y_min = -4.687658
-        elif self.p_num == 10:
-            self.y_min = -9.66015
-
-        if self.x_min is None:
-            warnings.warn(
-                f"PerformanceEvaluator: unknown optimum location 'x' for problem dimension {self.p_num}"
-            )
-
-        if self.y_min is None:
-            warnings.warn(
-                f"PerformanceEvaluator: unknown optimum value 'y' for problem dimension {self.p_num}"
-            )
-
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y - self.y_min
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
diff --git a/lib/C-BBO/rosen/__init__.py b/lib/C-BBO/rosen/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/C-BBO/rosen/benchmark.py b/lib/C-BBO/rosen/benchmark.py
deleted file mode 100644
index c8535af..0000000
--- a/lib/C-BBO/rosen/benchmark.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class RosenbrockFunction(Benchmark):
-    """https://www.sfu.ca/~ssurjano/rosen.html
-    """
-
-    version = "0.0.1"
diff --git a/lib/C-BBO/rosen/hpo.py b/lib/C-BBO/rosen/hpo.py
deleted file mode 100644
index 1f6e4f2..0000000
--- a/lib/C-BBO/rosen/hpo.py
+++ /dev/null
@@ -1,35 +0,0 @@
-import os
-import time
-
-import numpy as np
-from deephyper.evaluator import RunningJob, profile
-from deephyper.hpo import HpProblem
-from scipy.optimize import rosen
-
-DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
-domain = (-5.0, 10.0)
-problem = HpProblem()
-for i in range(DEEPHYPER_BENCHMARK_NDIMS):
-    problem.add_hyperparameter(domain, f"x{i}")
-
-@profile
-def run(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -rosen(x)
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/C-BBO/rosen/metrics.py b/lib/C-BBO/rosen/metrics.py
deleted file mode 100644
index 136d189..0000000
--- a/lib/C-BBO/rosen/metrics.py
+++ /dev/null
@@ -1,36 +0,0 @@
-import os
-
-import numpy as np
-
-
-class PerformanceEvaluator:
-    """A class defining performance evaluators for the Rosenbrock function."""
-
-    def __init__(self):
-        """Read the current problem defn from environment vars."""
-
-        self.p_num = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
-        self.x_min = np.ones(self.p_num)
-        self.y_min = 0.0
-
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y - self.y_min
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
diff --git a/lib/C-BBO/schwefel/__init__.py b/lib/C-BBO/schwefel/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/C-BBO/schwefel/benchmark.py b/lib/C-BBO/schwefel/benchmark.py
deleted file mode 100644
index 8771241..0000000
--- a/lib/C-BBO/schwefel/benchmark.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class SchwefelFunction(Benchmark):
-    """https://www.sfu.ca/~ssurjano/schwef.html
-    """
-
-    version = "0.0.1"
diff --git a/lib/C-BBO/schwefel/hpo.py b/lib/C-BBO/schwefel/hpo.py
deleted file mode 100644
index b181074..0000000
--- a/lib/C-BBO/schwefel/hpo.py
+++ /dev/null
@@ -1,40 +0,0 @@
-import os
-import time
-
-import numpy as np
-from deephyper.evaluator import RunningJob, profile
-from deephyper.hpo import HpProblem
-
-DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
-domain = (-500.0, 500.0)
-problem = HpProblem()
-for i in range(DEEPHYPER_BENCHMARK_NDIMS):
-    problem.add_hyperparameter(domain, f"x{i}")
-
-
-def schwefel(x):  # schw.m
-    n = len(x)
-    return 418.9829 * n - sum(x * np.sin(np.sqrt(np.abs(x))))
-
-
-@profile
-def run(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -schwefel(x)
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/C-BBO/schwefel/metrics.py b/lib/C-BBO/schwefel/metrics.py
deleted file mode 100644
index df6d4ba..0000000
--- a/lib/C-BBO/schwefel/metrics.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import os
-
-import numpy as np
-
-
-class PerformanceEvaluator:
-    """A class defining performance evaluators for the Levy problem."""
-
-    def __init__(self):
-        """Read the current problem defn from environment vars."""
-
-        self.p_num = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
-        self.x_min = np.zeros(self.p_num)
-        self.x_min[:] = 420.9687
-        self.y_min = 0.0
-
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y - self.y_min
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
diff --git a/lib/C-BBO/shekel/__init__.py b/lib/C-BBO/shekel/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/C-BBO/shekel/benchmark.py b/lib/C-BBO/shekel/benchmark.py
deleted file mode 100644
index 4c6b370..0000000
--- a/lib/C-BBO/shekel/benchmark.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class ShekelFunction(Benchmark):
-    """https://www.sfu.ca/~ssurjano/shekel.html
-    """
-
-    version = "0.0.1"
diff --git a/lib/C-BBO/shekel/hpo.py b/lib/C-BBO/shekel/hpo.py
deleted file mode 100644
index e90717e..0000000
--- a/lib/C-BBO/shekel/hpo.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import os
-import time
-
-import numpy as np
-from deephyper.evaluator import RunningJob, profile
-from deephyper.hpo import HpProblem
-
-DEEPHYPER_BENCHMARK_NDIMS = 4
-domain = (0.0, 10.0)
-problem = HpProblem()
-for i in range(DEEPHYPER_BENCHMARK_NDIMS):
-    problem.add_hyperparameter(domain, f"x{i}")
-
-
-def shekel(x):
-    m = 10
-    beta = 0.1 * np.array([1, 2, 2, 4, 4, 6, 3, 7, 5, 5]).T
-    C = np.array(
-        [
-            [4, 1, 8, 6, 3, 2, 5, 8, 6, 7],
-            [4, 1, 8, 6, 7, 9, 3, 1, 2, 3.6],
-            [4, 1, 8, 6, 3, 2, 5, 8, 6, 7],
-            [4, 1, 8, 6, 7, 9, 3, 1, 2, 3.6],
-        ]
-    )
-    return -sum(
-        [
-            1 / (np.sum((x - C[:, i]) ** 2) + beta[i])
-            for i in range(m)
-        ]
-    )
-
-
-@profile
-def run(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -shekel(x)
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    config = {f"x{i}": 4.0 for i in range(DEEPHYPER_BENCHMARK_NDIMS)}
-    print(f"{config=}")
-    result = run(RunningJob(parameters=config))
-    print(f"{result=}")
diff --git a/lib/C-BBO/shekel/metrics.py b/lib/C-BBO/shekel/metrics.py
deleted file mode 100644
index 7f5c262..0000000
--- a/lib/C-BBO/shekel/metrics.py
+++ /dev/null
@@ -1,37 +0,0 @@
-import os
-
-import numpy as np
-
-
-class PerformanceEvaluator:
-    """A class defining performance evaluators for the Shekel problem."""
-
-    def __init__(self):
-        """Read the current problem defn from environment vars."""
-
-        self.p_num = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 10))
-        self.x_min = np.zeros(self.p_num)
-        self.x_min[:] = 4.0
-        self.y_min = -10.5363
-
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y - self.y_min
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
diff --git a/lib/DTLZ/README.md b/lib/DTLZ/README.md
deleted file mode 100644
index 5c09f18..0000000
--- a/lib/DTLZ/README.md
+++ /dev/null
@@ -1,130 +0,0 @@
-
-# Modified Multiobjective DTLZ Test Suite
-
-✅ **Benchmark ready to be used.**
-
-This module contains objective function implementations of the DTLZ test
-suite, derived from the implementations in
-[ParMOO](https://github.com/parmoo/parmoo).
-
-------------------------------------------------------------------------------
-
-For further reference, the DTLZ test suite was originally proposed in:
-
-    Deb, Thiele, Laumanns, and Zitzler. "Scalable test problems for
-    evolutionary multiobjective optimization" in Evolutionary Multiobjective
-    Optimization, Theoretical Advances and Applications, Ch. 6 (pp. 105--145).
-    Springer-Verlag, London, UK, 2005. Abraham, Jain, and Goldberg (Eds).
-
-The original implementation was appropriate for testing randomized algorithms,
-but for many deterministic algorithms, the global solutions represent either
-best- or worst-case scenarios, so an configurable offset was introduced in:
-
-    Chang. "Mathematical Software for Multiobjective Optimization Problems."
-    Ph.D. dissertation, Virginia Tech, Dept. of Computer Science, 2020.
-
-Note that the DTLZ problems are minimization problems. Since DeepHyper
-maximizes, the implementation herein returns the negative value for each of
-the DTLZ objectives.
-
-Our performance evaluator ``metrics`` scripts can evaluate either the
-positive or negative solutions to estimate how well we have solved the
-problem.
-
-------------------------------------------------------------------------------
-
-The full list of public classes in this module includes the 7 unconstrained
-DTLZ problems
- * ``dtlz1``,
- * ``dtlz2``,
- * ``dtlz3``,
- * ``dtlz4``,
- * ``dtlz5``,
- * ``dtlz6``, and
- * ``dtlz7``
-
-which are selected by setting the environment variable
-``DEEPHYPER_BENCHMARK_DTLZ_PROB``.
-
-## Installation 
-
-To use the benchmark follow this example set of instructions:
-
-```python
-
-# Set DTLZ problem environment variables before loading
-import os
-os.environ["DEEPHYPER_BENCHMARK_NDIMS"] = "5" # 5 vars
-os.environ["DEEPHYPER_BENCHMARK_NOBJS"] = "3" # 2 objs
-os.environ["DEEPHYPER_BENCHMARK_DTLZ_PROB"] = "2" # DTLZ2 problem
-os.environ["DEEPHYPER_BENCHMARK_DTLZ_OFFSET"] = "0.6" # soln [x_o, .., x_n]=0.6
-
-# Load DTLZ benchmark suite
-import deephyper_benchmark as dhb
-dhb.load("DTLZ")
-
-# Example of running one evaluation of DTLZ problem
-from deephyper.evaluator import RunningJob
-config = dtlz.hpo.problem.default_configuration # get a default config to test
-res = dtlz.hpo.run(RunningJob(parameters=config))
-
-```
-
-## Configuration
-
-To configure the problem, set the following:
-
-- Environment variable `DEEPHYPER_BENCHMARK_PROB` with a value of `1`, `2`, ... `7` to select the DTLZ problem to run. Defaults to `2`.
-- Environment variable `DEEPHYPER_BENCHMARK_NDIMS` with an integer value to set the number of input variables. Defaults to `5`.
-- Environment variable `DEEPHYPER_BENCHMARK_NOBJS` with an integer value to set the number of objectives. Defaults to `2`
-- Environment variable `DEEPHYPER_BENCHMARK_OFFSET` with a value between `0.0` and `1.0` to select the offset of the solution to the DTLZ problem. Defaults to `0.5` for DTLZ1, ..., DTLZ5 and `0.0` for DTLZ6 and DTLZ7. One may wish to adjust this value when comparing against deterministic blackbox solvers, which may sample the center and boundaries of the input space on specific schedules.
-- Environment variable `DEEPHYPER_BENCHMARK_FAILURES` with value `0` or `1` to activate or deactivate failures. Defaults to `0`.
-
-## Metadata
-
-Since these problems are analytic, there is no metadata for this problem
-beyond the standard DeepHyper metadata (timestamp information).
-
-## Evaluating Results
-
-Evaluating the performance of a multiobjective solver is nontrivial.
-Typically, one should evaluate on two orthogonal bases:
- 1. Quality of solutions -- What is the (average) error in the solutions
-    returned by the solver?
- 2. Diversity of solutions -- How much of the true Pareto front is covered
-    by these solutions?
-
-To evaluate these two metrics, we use:
- 1. Improved generational distance (GD+): Let $F_i$ be a point in the solution
-    set returned by a solver,
-    and let $Y_i$ be the nearest point to $F_i$ on the true Pareto front,
-    for $i=1,\ldots, n$.
-    Then the GD+ is $\sum_{i} D^+(F_i, Y_i) / n$.
-    Where $D^+$ denotes the improved distance function
-    $D^+(F, Y) = ||\max(F - Y, 0)||_2^2$, where the "max" is taken
-    componentwise.
-    **Note that this metric may not increase monotonically. Additionally,
-    it may be impossible to calculate for an arbitrary blackbox function,
-    and can only be calculated here since the solution known and easily
-    expressed algebraically for all of the DTLZ problems.**
- 2. Hypervolume dominated: Let $F_i$ be defined as above, and let $R$ be
-    a pre-determined reference point such that all $F_i$ dominate $R$.
-    Then the hypervolume is given by the volume of the union of all
-    hyperboxes $B_i$ whose largest vertex is $F_i$ and smallest vertex
-    is $R$. The value (and usefulness) of the hypervolume metric is extremely
-    sensitive to the choice of $R$. Therefore, for this problem, we choose
-    $R$ to be the Nadir point for the true Pareto front. **Note that in order
-    to use the Nadir point as the reference point, we must throw out every
-    solution returned by the solver that is worse than the Nadir point. For
-    extremely difficult problems, this can result in zero hypervolume if no
-    solutions better than the Nadir point were found. This is most common
-    for DTLZ1, DTLZ3, and DTLZ7.**
-
-For a general problem, the two metrics listed above could be very difficult
-to compute and many researchers will use the hypervolume with an overly
-pessimistic reference point as a proxy for both quality and diversity.
-However, in general, the hypervolume tends to promote diversity over quality.
-For the DTLZ problems, since the shape of the true Pareto front is known,
-we can calculate each of these metrics, and both the ``gdPlus(results)`` and
-``hypervolume(results)`` functions are implemented in the ``dtlz.metrics``
-module.
diff --git a/lib/DTLZ/__init__.py b/lib/DTLZ/__init__.py
deleted file mode 100644
index f102a9c..0000000
--- a/lib/DTLZ/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = "0.0.1"
diff --git a/lib/DTLZ/benchmark.py b/lib/DTLZ/benchmark.py
deleted file mode 100644
index 2c12251..0000000
--- a/lib/DTLZ/benchmark.py
+++ /dev/null
@@ -1,6 +0,0 @@
-from deephyper_benchmark import *
-
-
-class DTLZBenchmark(Benchmark):
-
-    version = "0.0.1"
diff --git a/lib/DTLZ/hpo.py b/lib/DTLZ/hpo.py
deleted file mode 100644
index 70cd6d3..0000000
--- a/lib/DTLZ/hpo.py
+++ /dev/null
@@ -1,61 +0,0 @@
-import os
-import time
-
-import numpy as np
-from deephyper.evaluator import RunningJob, profile
-from deephyper.hpo import HpProblem
-
-from . import model as dtlz
-
-# Read DTLZ problem name and acquire pointer
-dtlz_prob = os.environ.get("DEEPHYPER_BENCHMARK_DTLZ_PROB", 2)
-dtlz_prob_name = f"dtlz{dtlz_prob}"
-dtlz_class_ptr = getattr(dtlz, dtlz_prob_name)
-
-# Read problem dims and definition (or read from ENV)
-nb_dim = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
-nb_obj = int(os.environ.get("DEEPHYPER_BENCHMARK_NOBJS", 2))
-if dtlz_prob_name in ["dtlz1", "dtlz2", "dtlz3", "dtlz4", "dtlz5"]:
-    soln_offset = float(os.environ.get("DEEPHYPER_BENCHMARK_DTLZ_OFFSET", 0.5))
-elif dtlz_prob_name in ["dtlz6", "dtlz7"]:
-    soln_offset = float(os.environ.get("DEEPHYPER_BENCHMARK_DTLZ_OFFSET", 0.0))
-else:
-    raise ValueError(f"Invalid problem {dtlz_prob_name}")
-domain = (0.0, 1.0)
-
-# Failures
-DEEPHYPER_BENCHMARK_FAILURES = bool(int(os.environ.get("DEEPHYPER_BENCHMARK_FAILURES", 0)))
-
-# Create problem
-problem = HpProblem()
-dtlz_obj = dtlz_class_ptr(nb_dim, nb_obj, offset=soln_offset)
-for i in range(nb_dim):
-    problem.add_hyperparameter(domain, f"x{i}")
-
-
-@profile
-def run(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-    ff = [-fi for fi in dtlz_obj(x)]
-
-    if DEEPHYPER_BENCHMARK_FAILURES:
-        if any(xi < 0.25 for xi in x[nb_obj-1:]):
-            ff = ["F" for _ in ff]
-
-    return ff
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/DTLZ/metrics.py b/lib/DTLZ/metrics.py
deleted file mode 100644
index 3d4a305..0000000
--- a/lib/DTLZ/metrics.py
+++ /dev/null
@@ -1,201 +0,0 @@
-import os
-import numpy as np
-from deephyper.skopt.moo import pareto_front, hypervolume
-
-
-class PerformanceEvaluator:
-    """ A class defining performance evaluators for the DTLZ problems.
-
-    Contains the following public methods:
-
-     * `__init__()` constructs a new instance by reading the problem defn
-       from environment variables,
-     * `hypervolume(pts)` calculates the total hypervolume dominated by
-       the current solution, using the Nadir point as the reference point
-       and filtering out solutions that do not dominate the Nadir point,
-     * `nadirPt()` calculates the Nadir point for the current problem,
-     * `numPts(pts)` calculates the number of solution points that dominate
-       the Nadir point, and
-     * `gdPlus(pts)` calculates the RMSE where the error in each point is
-       approximated by the 2-norm distance to the nearest solution point.
-
-    """
-
-    def __init__(self):
-        """ Read the current DTLZ problem defn from environment vars. """
-
-        self.p_num = os.environ.get("DEEPHYPER_BENCHMARK_DTLZ_PROB", "2")
-        self.nobjs = int(os.environ.get("DEEPHYPER_BENCHMARK_NOBJS", 2))
-
-    def hypervolume(self, pts):
-        """ Calculate the hypervolume dominated by soln, wrt the Nadir point.
-
-        Args:
-            pts (numpy.ndarra): A 2d array of objective values.
-                Each row is an objective value in the solution set.
-
-        Returns:
-            float: The total hypervolume dominated by the current solution,
-            filtering out points worse than the Nadir point and using the
-            Nadir point as the reference.
-
-        """
-
-        if np.any(pts < 0):
-            filtered_pts = -pts.copy()
-        else:
-            filtered_pts = pts.copy()
-        nadir = self.nadirPt()
-        for i in range(pts.shape[0]):
-            if np.any(filtered_pts[i, :] > nadir):
-                filtered_pts[i, :] = nadir
-        return hypervolume(filtered_pts, nadir)
-
-    def nadirPt(self):
-        """ Calculate the Nadir point for the given problem definition. """
-
-        if self.p_num == "1":
-            return np.ones(self.nobjs) * 0.5
-        elif self.p_num in ["2", "3", "4", "5", "6"]:
-            return np.ones(self.nobjs)
-        elif self.p_num == "7":
-            nadir = np.ones(self.nobjs)
-            nadir[self.nobjs - 1] = self.nobjs * 2.0
-            return nadir
-        else:
-            raise ValueError(f"DTLZ{self.p_num} is not a valid problem")
-
-    def numPts(self, pts):
-        """ Calculate the number of solutions that dominate the Nadir point.
-
-        Args:
-            pts (numpy.ndarra): A 2d array of objective values.
-                Each row is an objective value in the solution set.
-
-        Returns:
-            int: The number of fi in pts such that all(fi < self.nadirPt).
-
-        """
-
-        if np.any(pts < 0):
-            pareto_pts = pareto_front(-pts)
-        else:
-            pareto_pts = pareto_front(pts)
-        return sum([all(fi <= self.nadirPt()) for fi in pareto_pts])
-
-    def gdPlus(self, pts):
-        """ Calculate the p=1 generational distance for a given solution set.
-
-        Args:
-            pts (numpy.ndarra): A 2d array of objective values.
-                Each row is an objective value in the solution set.
-
-        Returns:
-            float: The p=1 generational distance over all points in pts.
-
-        """
-
-        if np.any(pts < 0):
-            pareto_pts = pareto_front(-pts)
-        else:
-            pareto_pts = pareto_front(pts)
-        if self.p_num == "1":
-            dists = self._dtlz1Dist(pareto_pts)
-        elif self.p_num in ["2", "3", "4", "5", "6"]:
-            dists = self._dtlz2Dist(pareto_pts)
-        elif self.p_num == "7":
-            dists = self._dtlz7Dist(pareto_pts)
-        else:
-            raise ValueError(f"DTLZ{self.p_num} is not a valid problem")
-        return np.sum(dists) / len(dists)
-
-    def _dtlz1Dist(self, pts):
-        """ Calculate the d+ from each fi to the nearest solution in DTLZ1.
-
-        Args:
-            pts (numpy.ndarra): A 2d array of objective values.
-                Each row is an objective value in the solution set.
-
-        Returns:
-            numpy.ndarray: A 1d array of distances to the nearest solution
-            point for DTLZ1.
-            
-
-        """
-
-        return np.array([np.linalg.norm(
-                             np.maximum(fi - (0.5 * fi / np.sum(fi)), 0)
-                         ) for fi in pts])
-
-    def _dtlz2Dist(self, pts):
-        """ Calculate the d+ from each fi to the nearest point on unit sphere.
-
-        Note: Works for DTLZ2-6
-
-        Args:
-            pts (numpy.ndarra): A 2d array of objective values.
-                Each row is an objective value in the solution set.
-
-        Returns:
-            numpy.ndarray: A 1d array of distances to the surface of the
-            unit sphere.
-
-        """
-
-        return np.array([np.linalg.norm(
-                             np.maximum(fi - (fi / np.linalg.norm(fi)), 0)
-                         ) for fi in pts])
-
-    def _dtlz7Dist(self, pts):
-        """ Calculate the d+ from each fi to the nearest soln in DTLZ7.
-
-        Args:
-            pts (numpy.ndarra): A 2d array of objective values.
-                Each row is an objective value in the solution set.
-
-        Returns:
-            numpy.ndarray: A 1d array of distances to the nearest solution
-            point to DTLZ7.
-
-        """
-
-        # Project each point onto DTLZ7 solution and calculate difference
-        pts_proj = []
-        for fi in pts:
-            gx = 2.0
-            hx = (-np.sum(fi[:self.nobjs-1] *
-                  (1.0 + np.sin(3.0 * np.pi * fi[:self.nobjs-1])) / gx)
-                  + float(self.nobjs))
-            pts_proj.append(gx * hx)
-        return np.array([np.abs(
-                             np.maximum(fi[-1] - fj, 0)
-                         ) for fi, fj in zip(pts, pts_proj)])
-
-
-if __name__ == "__main__":
-    """ Driver code to test performance metrics. """
-
-    os.environ["DEEPHYPER_BENCHMARK_DTLZ_PROB"] = "1" # DTLZ1 problem
-    dtlz1_eval = PerformanceEvaluator()
-    s1 = np.array([[0.5, 0], [0, 0.5], [.25, .25], [0.2, 0.8]])
-    os.environ["DEEPHYPER_BENCHMARK_DTLZ_PROB"] = "2" # DTLZ2 problem
-    dtlz2_eval = PerformanceEvaluator()
-    s2 = np.array([[1, 0], [0, 1], [1/np.sqrt(2), 1/np.sqrt(2)], [0.25, 2]])
-    os.environ["DEEPHYPER_BENCHMARK_DTLZ_PROB"] = "7" # DTLZ7 problem
-    dtlz7_eval = PerformanceEvaluator()
-    s7 = np.array([[0, 4], [1, 3], [.5, 4], [0.5, 6]])
-
-    assert abs(dtlz1_eval.hypervolume(s1) - .0625) < 1.0e-8
-    assert np.all(np.abs(dtlz1_eval.nadirPt() - 0.5) < 1.0e-8)
-    assert dtlz1_eval.numPts(s1) == 3
-    assert abs(dtlz1_eval.gdPlus(s1)) < 1.0e-8
-
-    assert abs(dtlz2_eval.hypervolume(s2) - (1.5 - np.sqrt(2))) < 1.0e-8
-    assert np.all(np.abs(dtlz2_eval.nadirPt() - 1) < 1.0e-8)
-    assert dtlz2_eval.numPts(s2) == 3
-    assert abs(dtlz2_eval.gdPlus(s2)) < 1.0e-8
-
-    assert abs(dtlz7_eval.hypervolume(s7)) < 1.0e-8
-    assert np.all(np.abs(dtlz7_eval.nadirPt() - np.array([1, 4])) < 1.0e-8)
-    assert dtlz7_eval.numPts(s7) == 2
-    assert abs(dtlz7_eval.gdPlus(s7)) < 1.0e-8
diff --git a/lib/DTLZ/model.py b/lib/DTLZ/model.py
deleted file mode 100644
index f761fb5..0000000
--- a/lib/DTLZ/model.py
+++ /dev/null
@@ -1,601 +0,0 @@
-""" This module contains objective function implementations of the DTLZ test
-suite, derived from the implementations in ParMOO:
-
-Chang and Wild. "ParMOO: A Python library for parallel multiobjective
-simulation optimization." Journal of Open Source Software 8(82):4468, 2023.
-
-------------------------------------------------------------------------------
-
-For further references, the DTLZ test suite was originally proposed in:
-
-Deb, Thiele, Laumanns, and Zitzler. "Scalable test problems for
-evolutionary multiobjective optimization" in Evolutionary Multiobjective
-Optimization, Theoretical Advances and Applications, Ch. 6 (pp. 105--145).
-Springer-Verlag, London, UK, 2005. Abraham, Jain, and Goldberg (Eds).
-
-The original implementation was appropriate for testing randomized algorithms,
-but for many deterministic algorithms, the global solutions represent either
-best- or worst-case scenarios, so an configurable offset was introduced in:
-
-Chang. "Mathematical Software for Multiobjective Optimization Problems."
-Ph.D. dissertation, Virginia Tech, Dept. of Computer Science, 2020.
-
-------------------------------------------------------------------------------
-
-The full list of public classes in this module includes the 7 unconstrained
-DTLZ problems:
- * ``dtlz1``
- * ``dtlz2``
- * ``dtlz3``
- * ``dtlz4``
- * ``dtlz5``
- * ``dtlz6``
- * ``dtlz7``
-
-"""
-
-import numpy as np
-
-
-class __dtlz_base__():
-    """ Base class implements re-used constructor """
-
-    def __init__(self, num_des, num_obj=3, offset=0.5):
-        """ Constructor for all DTLZ classes.
-
-        Args:
-            num_des (int): The number of design variables.
-
-            num_obj (int, optional): The number of objectives.
-
-            offset (optional, float): The location of the global minimizers
-                is x_i = offset for i = num_objectives, ..., num_des.
-                The default offset is 0.5.
-
-        """
-
-        self.n = num_des
-        self.o = num_obj
-        self.offset = offset
-        return
-
-    def __call__(self, x):
-        raise NotImplementedError("The call method must be implemented...")
-
-
-class __g1__(__dtlz_base__):
-    """ Class defining 1 of 4 kernel functions used in the DTLZ problem suite.
-
-    g1 = 100 ( (n - o + 1) +
-               sum_{i=o}^n ((x_i - offset)^2 - cos(20pi(x_i - offset))) )
-
-    Contains 2 methods:
-     * ``__init__(num_des, num_obj)``
-     * ``__call__(x)``
-
-    The ``__init__`` method creates a new kernel.
-
-    The ``__call__`` method performs an evaluation of the g1 kernel.
-
-    """
-
-    def __call__(self, x):
-        """ Define objective evaluation.
-
-        Args:
-            x (numpy.array): A numpy.ndarray containing the design point
-                to evaluate.
-
-        Returns:
-            float: The output of this objective for the input x.
-
-        """
-
-        return (1 + self.n - self.o +
-                np.sum((x[self.o-1:self.n] - self.offset) ** 2 -
-                        np.cos(20.0 * np.pi *
-                               (x[self.o-1:self.n] - self.offset)))) * 100.0
-
-
-class __g2__(__dtlz_base__):
-    """ Class defining 2 of 4 kernel functions used in the DTLZ problem suite.
-
-    g2 = (x_o - offset)^2 + ... + (x_n - offset)^2
-
-    Contains 2 methods:
-     * ``__init__(num_des, num_obj)``
-     * ``__call__(x)``
-
-    The ``__init__`` method inherits from the __dtlz_base__ ABC.
-
-    The ``__call__`` method performs an evaluation of the g2 problem.
-
-    """
-
-    def __call__(self, x):
-        """ Define objective evaluation.
-
-        Args:
-            x (numpy.array): A numpy.ndarray containing the design point
-                to evaluate.
-
-        Returns:
-            float: The output of this objective for the input x.
-
-        """
-
-        return np.sum((x[self.o-1:self.n] - self.offset) ** 2)
-
-
-class __g3__(__dtlz_base__):
-    """ Class defining 3 of 4 kernel functions used in the DTLZ problem suite.
-
-    g3 = |x_o - offset|^.1 + ... + |x_n - offset|^.1
-
-    Contains 2 methods:
-     * ``__init__(num_des, num_obj)``
-     * ``__call__(x)``
-
-    The ``__init__`` method inherits from the __dtlz_base__ ABC.
-
-    The ``__call__`` method performs an evaluation of the g3 problem.
-
-    """
-
-    def __init__(self, num_des, num_obj=3, offset=0.0):
-        """ Constructor for g3, with modified default offset.
-
-        Args:
-            num_des (int): The number of design variables.
-
-            num_obj (int, optional): The number of objectives.
-
-            offset (optional, float): The location of the global minimizers
-                is x_i = offset for i = num_objectives, ..., num_des.
-                The default offset is 0.0.
-
-        """
-
-        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
-        return
-
-    def __call__(self, x):
-        """ Define objective evaluation.
-
-        Args:
-            x (numpy.array): A numpy.ndarray containing the design point
-                to evaluate.
-
-        Returns:
-            float: The output of this objective for the input x.
-
-        """
-
-        return np.sum(np.abs(x[self.o-1:self.n] - self.offset) ** 0.1)
-
-
-class __g4__(__dtlz_base__):
-    """ Class defining 4 of 4 kernel functions used in the DTLZ problem suite.
-
-    g4 = 1 + (9 * (|x_o - offset| + ... + |x_n - offset|) / (n + 1 - o))
-
-    Contains 2 methods:
-     * ``__init__(num_des, num_obj)``
-     * ``__call__(x)``
-
-    The ``__init__`` method inherits from the __dtlz_base__ ABC.
-
-    The ``__call__`` method performs an evaluation of the g4 problem.
-
-    """
-
-    def __init__(self, num_des, num_obj=3, offset=0.0):
-        """ Constructor for g4, with modified default offset.
-
-        Args:
-            num_des (int): The number of design variables.
-
-            num_obj (int, optional): The number of objectives.
-
-            offset (optional, float): The location of the global minimizers
-                is x_i = offset for i = num_objectives, ..., num_des.
-                The default offset is 0.0.
-
-        """
-
-        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
-        return
-
-    def __call__(self, x):
-        """ Define objective evaluation.
-
-        Args:
-            x (numpy.array): A numpy.ndarray containing the design point
-                to evaluate.
-
-        Returns:
-            float: The output of this objective for the input x.
-
-        """
-
-        return (9 * np.sum(np.abs(x[self.o-1:self.n] - self.offset))
-                / float(self.n + 1 - self.o)) + 1.0
-
-
-class dtlz1(__dtlz_base__):
-    """ Class defining the DTLZ1 problem with offset minimizer.
-
-    DTLZ1 has a linear Pareto front, with all nondominated points
-    on the hyperplane F_1 + F_2 + ... + F_o = 0.5.
-    DTLZ1 has 11^k - 1 "local" Pareto fronts where k = n - o + 1, and
-    1 "global" Pareto front.
-
-    Contains 2 methods:
-     * ``__init__(num_des, num_obj)``
-     * ``__call__(x)``
-
-    The ``__init__`` method inherits from the __dtlz_base__ ABC.
-
-    The ``__call__`` method performs an evaluation of the DTLZ1 problem.
-
-    """
-
-    def __call__(self, x):
-        """ Define objective evaluation.
-
-        Args:
-            x (numpy.ndarray): A numpy.ndarray containing the design point
-                to evaluate.
-
-        Returns:
-            numpy float array: The output of this objective for the input x.
-
-        """
-
-        # Initialize kernel function
-        ker = __g1__(self.n, self.o, self.offset)
-        # Initialize output array
-        fx = np.zeros(self.o)
-        fx[:] = (1.0 + ker(x)) / 2.0
-        # Calculate the output array
-        for i in range(self.o):
-            for j in range(self.o - 1 - i):
-                fx[i] *= x[j]
-            if i > 0:
-                fx[i] *= (1.0 - x[self.o - 1 - i])
-        return fx
-
-
-class dtlz2(__dtlz_base__):
-    """ Class defining the DTLZ2 problem with offset minimizer.
-
-    DTLZ2 has a concave Pareto front, given by the unit sphere in
-    objective space, restricted to the positive orthant.
-    DTLZ2 has no "local" Pareto fronts, besides the true Pareto front.
-
-    Contains 2 methods:
-     * ``__init__(num_des, num_obj)``
-     * ``__call__(x)``
-
-    The ``__init__`` method inherits from the __dtlz_base__ ABC.
-
-    The ``__call__`` method performs an evaluation of the DTLZ2 problem.
-
-    """
-
-    def __call__(self, x):
-        """ Define objective evaluation.
-
-        Args:
-            x (numpy.ndarray): A numpy.ndarray containing the design point
-                to evaluate.
-
-        Returns:
-            numpy float array: The output of this objective for the input x.
-
-        """
-
-        # Initialize kernel function
-        ker = __g2__(self.n, self.o, self.offset)
-        # Initialize output array
-        fx = np.zeros(self.o)
-        fx[:] = (1.0 + ker(x))
-        # Calculate the output array
-        for i in range(self.o):
-            for j in range(self.o - 1 - i):
-                fx[i] *= np.cos(np.pi * x[j] / 2)
-            if i > 0:
-                fx[i] *= np.sin(np.pi * x[self.o - 1 - i] / 2)
-        return fx
-
-
-class dtlz3(__dtlz_base__):
-    """ Class defining the DTLZ3 problem with offset minimizer.
-
-    DTLZ3 has a concave Pareto front, given by the unit sphere in
-    objective space, restricted to the positive orthant.
-    DTLZ3 has 3^k - 1 "local" Pareto fronts where k = n - o + 1, and
-    1 "global" Pareto front.
-
-    Contains 2 methods:
-     * ``__init__(num_des, num_obj)``
-     * ``__call__(x)``
-
-    The ``__init__`` method inherits from the __dtlz_base__ ABC.
-
-    The ``__call__`` method performs an evaluation of the DTLZ3 problem.
-
-    """
-
-    def __call__(self, x):
-        """ Define objective evaluation.
-
-        Args:
-            x (numpy.ndarray): A numpy.ndarray containing the design point
-                to evaluate.
-
-        Returns:
-            numpy float array: The output of this objective for the input x.
-
-        """
-
-
-        # Initialize kernel function
-        ker = __g1__(self.n, self.o, self.offset)
-        # Initialize output array
-        fx = np.zeros(self.o)
-        fx[:] = (1.0 + ker(x))
-        # Calculate the output array
-        for i in range(self.o):
-            for j in range(self.o - 1 - i):
-                fx[i] *= np.cos(np.pi * x[j] / 2)
-            if i > 0:
-                fx[i] *= np.sin(np.pi * x[self.o - 1 - i] / 2)
-        return fx
-
-
-class dtlz4(__dtlz_base__):
-    """ Class defining the DTLZ4 problem with offset minimizer.
-
-    DTLZ4 has a concave Pareto front, given by the unit sphere in
-    objective space, restricted to the positive orthant.
-    DTLZ4 has no "local" Pareto fronts, besides the true Pareto front,
-    but by tuning the optional parameter alpha, one can adjust the
-    solution density, making it harder for MOO algorithms to produce
-    a uniform distribution of solutions.
-
-    Contains 2 methods:
-     * ``__init__(num_des, num_obj)``
-     * ``__call__(x)``
-
-    The ``__init__`` method inherits from the __dtlz_base__ ABC.
-
-    The ``__call__`` method performs an evaluation of the DTLZ4 problem.
-
-    """
-
-    def __init__(self, num_des, num_obj=3, offset=0.5, alpha=100.0):
-        """ Constructor for DTLZ7, with modified default offset.
-
-        Args:
-            num_des (int): The number of design variables.
-
-            num_obj (int, optional): The number of objectives.
-
-            offset (optional, float): The location of the global minimizers
-                is x_i = offset for i = num_objectives, ..., num_des.
-                The default offset is 0.0.
-
-            alpha (optional, float or int): The uniformity parameter used for
-                controlling the uniformity of the distribution of solutions
-                across the Pareto front. Must be greater than or equal to 1.
-                A value of 1 results in DTLZ2. Default value is 100.0.
-
-        """
-
-        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
-        self.alpha = alpha
-        return
-
-    def __call__(self, x):
-        """ Define objective evaluation.
-
-        Args:
-            x (numpy.ndarray): A numpy.ndarray containing the design point
-                to evaluate.
-
-        Returns:
-            numpy float array: The output of this objective for the input x.
-
-        """
-
-        # Initialize kernel function
-        ker = __g2__(self.n, self.o, self.offset)
-        # Initialize output array
-        fx = np.zeros(self.o)
-        fx[:] = (1.0 + ker(x))
-        # Calculate the output array
-        for i in range(self.o):
-            for j in range(self.o - 1 - i):
-                fx[i] *= np.cos(np.pi * x[j] ** self.alpha / 2)
-            if i > 0:
-                fx[i] *= np.sin(np.pi * x[self.o - 1 - i] ** self.alpha / 2)
-        return fx
-
-
-class dtlz5(__dtlz_base__):
-    """ Class defining the DTLZ5 problem with offset minimizer.
-
-    DTLZ5 has a lower-dimensional Pareto front embedded in the objective
-    space, given by an arc of the unit sphere in the positive orthant.
-    DTLZ5 has no "local" Pareto fronts, besides the true Pareto front.
-
-    Contains 2 methods:
-     * ``__init__(num_des, num_obj)``
-     * ``__call__(x)``
-
-    The ``__init__`` method inherits from the __dtlz_base__ ABC.
-
-    The ``__call__`` method performs an evaluation of the DTLZ5 problem.
-
-    """
-
-    def __call__(self, x):
-        """ Define objective evaluation.
-
-        Args:
-            x (numpy.ndarray): A numpy.ndarray containing the design point
-                to evaluate.
-
-        Returns:
-            numpy float array: The output of this objective for the input x.
-
-        """
-
-        # Initialize kernel function
-        ker = __g2__(self.n, self.o, self.offset)
-        # Calculate theta values
-        theta = np.zeros(self.o)
-        g2x = ker(x)
-        theta[0] = x[0]
-        for i in range(1, self.o):
-            theta[i] = (1 + 2 * g2x * x[i]) / (2 * (1 + g2x))
-        # Initialize output array
-        fx = np.zeros(self.o)
-        fx[:] = (1.0 + g2x)
-        # Calculate the output array
-        for i in range(self.o):
-            for j in range(self.o - 1 - i):
-                fx[i] *= np.cos(np.pi * theta[j] / 2)
-            if i > 0:
-                fx[i] *= np.sin(np.pi * theta[self.o - 1 - i] / 2)
-        return fx
-
-
-class dtlz6(__dtlz_base__):
-    """ Class defining the DTLZ6 problem with offset minimizer.
-
-    DTLZ6 has a lower-dimensional Pareto front embedded in the objective
-    space, given by an arc of the unit sphere in the positive orthant.
-    DTLZ6 has no "local" Pareto fronts, but tends to show very little
-    improvement until the algorithm is very close to its solution set.
-
-    Contains 2 methods:
-     * ``__init__(num_des, num_obj)``
-     * ``__call__(x)``
-
-    The ``__init__`` method inherits from the __dtlz_base__ ABC.
-
-    The ``__call__`` method performs an evaluation of the DTLZ6 problem.
-
-    """
-
-    def __init__(self, num_des, num_obj=3, offset=0.0):
-        """ Constructor for DTLZ6, with modified default offset.
-
-        Args:
-            num_des (int): The number of design variables.
-
-            num_obj (int, optional): The number of objectives.
-
-            offset (optional, float): The location of the global minimizers
-                is x_i = offset for i = num_objectives, ..., num_des.
-                The default offset is 0.0.
-
-        """
-
-        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
-        return
-
-    def __call__(self, x):
-        """ Define objective evaluation.
-
-        Args:
-            x (numpy.ndarray): A numpy.ndarray containing the design point
-                to evaluate.
-
-        Returns:
-            numpy float array: The output of this objective for the input x.
-
-        """
-
-        # Initialize kernel function
-        ker = __g3__(self.n, self.o, self.offset)
-        # Calculate theta values
-        theta = np.zeros(self.o)
-        g3x = ker(x)
-        theta[0] = x[0]
-        for i in range(1, self.o):
-            theta[i] = (1 + 2 * g3x * x[i]) / (2 * (1 + g3x))
-        # Initialize output array
-        fx = np.zeros(self.o)
-        fx[:] = (1.0 + g3x)
-        # Calculate the output array
-        for i in range(self.o):
-            for j in range(self.o - 1 - i):
-                fx[i] *= np.cos(np.pi * theta[j] / 2)
-            if i > 0:
-                fx[i] *= np.sin(np.pi * theta[self.o - 1 - i] / 2)
-        return fx
-
-
-class dtlz7(__dtlz_base__):
-    """ Class defining the DTLZ7 problem with offset minimizer.
-
-    DTLZ7 has a discontinuous Pareto front, with solutions on the 
-    2^(o-1) discontinuous nondominated regions of the surface:
-
-    F_m = o - F_1 (1 + sin(3pi F_1)) - ... - F_{o-1} (1 + sin3pi F_{o-1}).
-
-    Contains 2 methods:
-     * ``__init__(num_des, num_obj)``
-     * ``__call__(x)``
-
-    The ``__init__`` method inherits from the __dtlz_base__ ABC.
-
-    The ``__call__`` method performs an evaluation of the DTLZ7 problem.
-
-    """
-
-    def __init__(self, num_des, num_obj=3, offset=0.0):
-        """ Constructor for DTLZ7, with modified default offset.
-
-        Args:
-            num_des (int): The number of design variables.
-
-            num_obj (int, optional): The number of objectives.
-
-            offset (optional, float): The location of the global minimizers
-                is x_i = offset for i = num_objectives, ..., num_des.
-                The default offset is 0.0.
-
-        """
-
-        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
-        return
-
-    def __call__(self, x):
-        """ Define objective evaluation.
-
-        Args:
-            x (numpy.ndarray): A numpy.ndarray containing the design point
-                to evaluate.
-
-        Returns:
-            numpy float array: The output of this objective for the input x.
-
-        """
-
-        # Initialize kernel function
-        ker = __g4__(self.n, self.o, self.offset)
-        # Initialize first o-1 entries in the output array
-        fx = np.zeros(self.o)
-        fx[:self.o-1] = x[:self.o-1]
-        # Calculate kernel functions
-        gx = 1.0 + ker(x)
-        hx = (-np.sum(x[:self.o-1] *
-                      (1.0 + np.sin(3.0 * np.pi * x[:self.o-1])) / gx)
-                      + float(self.o))
-        # Calculate the last entry in the output array
-        fx[self.o-1] = gx * hx
-        return fx
diff --git a/lib/ECP-Candle/Pilot1/Combo/Makefile b/lib/ECP-Candle/Pilot1/Combo/Makefile
deleted file mode 100644
index 29e6a6b..0000000
--- a/lib/ECP-Candle/Pilot1/Combo/Makefile
+++ /dev/null
@@ -1,11 +0,0 @@
-
-VERSION=0.5.1
-
-build:
-	mkdir -p build
-	wget https://github.com/ECP-CANDLE/Benchmarks/archive/refs/tags/v$(VERSION).tar.gz -O build/ecp-candle-benchmarks.tar.gz
-	tar -xzvf build/ecp-candle-benchmarks.tar.gz -C build
-	mv build/Benchmarks-$(VERSION) build/Benchmarks
-
-clean:
-	rm -rf build/
\ No newline at end of file
diff --git a/lib/ECP-Candle/Pilot1/Combo/README.md b/lib/ECP-Candle/Pilot1/Combo/README.md
deleted file mode 100644
index ba8f255..0000000
--- a/lib/ECP-Candle/Pilot1/Combo/README.md
+++ /dev/null
@@ -1,54 +0,0 @@
-
-# ECP-Candle Benchmark - Pilot1/Combo
-
-This benchmark is based on materials from the [ECP-Candle/Benchmarks](https://github.com/ECP-CANDLE/Benchmarks) repository. More precisely it is a simplified replicate of the [Pilot1/Combo](https://github.com/ECP-CANDLE/Benchmarks/tree/master/Pilot1/Combo) benchmark.
-
-
-## Installation
-
-To use the benchmark follow this example set of instructions:
-
-```python
-from deephyper_benchmark import *
-
-install("ECP-Candle/Pilot1/Combo")
-
-load("ECP-Candle/Pilot1/Combo")
-
-from deephyper_benchmark.lib.ecp_candle.pilot1.combo import hpo
-```
-
-## Configuration
-
-Different parameters can be set to configure this benchmark.
-
-- Environment variable `DEEPHYPER_BENCHMARK_MAX_EPOCHS` sets the maximum number of training epochs. Defaults to `50`.
-- Environment variable `DEEPHYPER_BENCHMARK_TIMEOUT` sets the maximum duration of training in secondes. Defaults to `1800` (i.e., 30 minutes).
-- Environment variable `DEEPHYPER_BENCHMARK_MOO` with value `0` or `1` to select if the task should be run with single or multiple objectives. **Defaults to `0` for single-objective**. The objectives are `valid_r2`, `-num_parameters_train`, `-duration_batch_inference`.
-
-## Metadata
-
-The current set of returned metadata is:
-
-- [x] `num_parameters`
-- [x] `num_parameters_train`
-- [x] `duration_train`: time in secondes taken to train the model (`model.fit(...)`).
-- [x] `duration_batch_inference`: time in secondes taken to predict one batch.
-- [x] `budget`
-- [x] `stopped`
-- [x] `train_mse`
-- [x] `train_mae`
-- [x] `train_r2`
-- [x] `train_corr`
-- [x] `valid_mse`
-- [x] `valid_mae`
-- [x] `valid_r2`
-- [x] `valid_corr`
-- [x] `test_mse`
-- [x] `test_mae`
-- [x] `test_r2`
-- [x] `test_corr`
-- [ ] `flops`
-- [ ] `latency`
-- [x] `lc_train_mse`
-- [x] `lc_valid_mse`
\ No newline at end of file
diff --git a/lib/ECP-Candle/Pilot1/Combo/__init__.py b/lib/ECP-Candle/Pilot1/Combo/__init__.py
deleted file mode 100644
index 7dd6fda..0000000
--- a/lib/ECP-Candle/Pilot1/Combo/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = "0.5.1"
\ No newline at end of file
diff --git a/lib/ECP-Candle/Pilot1/Combo/benchmark.py b/lib/ECP-Candle/Pilot1/Combo/benchmark.py
deleted file mode 100644
index 4f7fb4d..0000000
--- a/lib/ECP-Candle/Pilot1/Combo/benchmark.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class ECPCandlePilot1Combo(Benchmark):
-    version = "0.5.1"
-
-    requires = {
-        "makefile": {"step": "install", "type": "cmd", "cmd": "make build"},
-        "py-pip-requirements": {
-            "step": "install",
-            "type": "pip",
-            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
-        },
-        "pkg-candle": {
-            "step": "load",
-            "type": "pythonpath",
-            "path": f"{DIR}/build/Benchmarks/common",
-        },
-        "pkg-combo": {
-            "step": "load",
-            "type": "pythonpath",
-            "path": f"{DIR}/build/Benchmarks/Pilot1/Combo",
-        },
-    }
diff --git a/lib/ECP-Candle/Pilot1/Combo/combo_default_model.yaml b/lib/ECP-Candle/Pilot1/Combo/combo_default_model.yaml
deleted file mode 100644
index 47e4ce1..0000000
--- a/lib/ECP-Candle/Pilot1/Combo/combo_default_model.yaml
+++ /dev/null
@@ -1,31 +0,0 @@
-cell_features: ['expression']
-drug_features: ['descriptors']
-dense: [1000, 1000, 1000]
-dense_feature_layers: [1000, 1000, 1000]
-activation: 'relu'
-loss: 'mse'
-optimizer: 'adam'
-scaling: 'std'
-dropout: 0
-epochs: 1
-batch_size: 32
-valid_split: 0.2
-test_split: 0.2
-max_val_loss: 1.0
-learning_rate: 0.001
-base_lr: 0.001
-residual: False
-reduce_lr: False
-reduce_lr_factor: 0.5
-reduce_lr_patience: 5
-warmup_lr: False
-early_stopping: False
-early_stopping_patience: 5
-batch_normalization: False
-feature_subsample: 0
-rng_seed: 2017
-save_path: 'save/combo'
-gen: False
-use_combo_score: False
-verbose : True
-timeout: 1800
diff --git a/lib/ECP-Candle/Pilot1/Combo/hpo.py b/lib/ECP-Candle/Pilot1/Combo/hpo.py
deleted file mode 100644
index 429607d..0000000
--- a/lib/ECP-Candle/Pilot1/Combo/hpo.py
+++ /dev/null
@@ -1,160 +0,0 @@
-import copy
-import os
-import traceback
-
-from deephyper.evaluator import profile
-from deephyper.hpo import HpProblem
-from deephyper.stopper.integration.tensorflow import TFKerasStopperCallback
-
-from .model import run_pipeline
-
-
-DEEPHYPER_BENCHMARK_MAX_EPOCHS = int(
-    os.environ.get("DEEPHYPER_BENCHMARK_MAX_EPOCHS", 50)
-)
-DEEPHYPER_BENCHMARK_TIMEOUT = int(os.environ.get("DEEPHYPER_BENCHMARK_TIMEOUT", 1800))
-DEEPHYPER_BENCHMARK_MOO = bool(int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 0)))
-
-
-problem = HpProblem()
-
-# Model hyperparameters
-ACTIVATIONS = [
-    "elu",
-    "gelu",
-    "hard_sigmoid",
-    "linear",
-    "relu",
-    "selu",
-    "sigmoid",
-    "softplus",
-    "softsign",
-    "swish",
-    "tanh",
-]
-default_dense = [1000, 1000, 1000]
-default_dense_feature_layers = [1000, 1000, 1000]
-
-for i in range(len(default_dense)):
-    problem.add_hyperparameter(
-        (10, 1024, "log-uniform"),
-        f"dense_{i}",
-        default_value=default_dense[i],
-    )
-
-    problem.add_hyperparameter(
-        (10, 1024, "log-uniform"),
-        f"dense_feature_layers_{i}",
-        default_value=default_dense_feature_layers[i],
-    )
-
-problem.add_hyperparameter(ACTIVATIONS, "activation", default_value="relu")
-
-# Optimization hyperparameters
-problem.add_hyperparameter(
-    [
-        "sgd",
-        "rmsprop",
-        "adagrad",
-        "adadelta",
-        "adam",
-    ],
-    "optimizer",
-    default_value="sgd",
-)
-
-problem.add_hyperparameter((0, 0.5), "dropout", default_value=0.0)
-problem.add_hyperparameter((8, 512, "log-uniform"), "batch_size", default_value=32)
-
-problem.add_hyperparameter(
-    (1e-5, 1e-2, "log-uniform"), "learning_rate", default_value=0.001
-)
-problem.add_hyperparameter((1e-5, 1e-2, "log-uniform"), "base_lr", default_value=0.001)
-problem.add_hyperparameter([True, False], "residual", default_value=False)
-
-problem.add_hyperparameter([True, False], "early_stopping", default_value=False)
-problem.add_hyperparameter((5, 20), "early_stopping_patience", default_value=5)
-
-problem.add_hyperparameter([True, False], "reduce_lr", default_value=False)
-problem.add_hyperparameter((0.1, 1.0), "reduce_lr_factor", default_value=0.5)
-problem.add_hyperparameter((5, 20), "reduce_lr_patience", default_value=5)
-
-problem.add_hyperparameter([True, False], "warmup_lr", default_value=False)
-problem.add_hyperparameter([True, False], "batch_normalization", default_value=False)
-
-problem.add_hyperparameter(
-    ["mse", "mae", "logcosh", "mape", "msle", "huber"], "loss", default_value="mse"
-)
-
-problem.add_hyperparameter(["std", "minmax", "maxabs"], "scaling", default_value="std")
-
-
-def remap_hyperparameters(config: dict):
-    """Transform input configurations of hyperparameters to the format accepted by the candle benchmark."""
-    dense = []
-    dense_feature_layers = []
-    for i in range(len(default_dense)):
-        key = f"dense_{i}"
-        dense.append(config.pop(key))
-
-        key = f"dense_feature_layers_{i}"
-        dense_feature_layers.append(config.pop(key))
-
-    config["dense"] = dense
-    config["dense_feature_layers"] = dense_feature_layers
-
-
-@profile
-def run(job, optuna_trial=None):
-    config = copy.deepcopy(job.parameters)
-
-    params = {
-        "epochs": DEEPHYPER_BENCHMARK_MAX_EPOCHS,
-        "timeout": DEEPHYPER_BENCHMARK_TIMEOUT,
-        "verbose": False,
-    }
-    if len(config) > 0:
-        remap_hyperparameters(config)
-        params.update(config)
-
-    if optuna_trial is None:
-        stopper_callback = TFKerasStopperCallback(job, monitor="val_r2", mode="max")
-    else:
-        from deephyper_benchmark.integration.optuna import KerasPruningCallback
-
-        stopper_callback = KerasPruningCallback(optuna_trial, "val_r2")
-
-    try:
-        score = run_pipeline(params, mode="valid", stopper_callback=stopper_callback)
-    except Exception as e:
-        print(traceback.format_exc())
-        score = {"objective": "F"}
-        keys = "m:num_parameters,m:num_parameters_train,m:duration_train,m:duration_batch_inference,m:budget,m:stopped,m:train_mse,m:train_mae,m:train_r2,m:train_corr,m:valid_mse,m:valid_mae,m:valid_r2,m:valid_corr,m:test_mse,m:test_mae,m:test_r2,m:test_corr,m:lc_train_mse,m:lc_valid_mse,m:lc_train_mae,m:lc_valid_mae,m:lc_train_r2,m:lc_valid_r2"
-        metadata = {k.strip("m:"): None for k in keys.split(",")}
-        score["metadata"] = metadata
-
-    # Handle multi-objective optimization (all maximized)
-    if DEEPHYPER_BENCHMARK_MOO:
-        if score["objective"] == "F":
-            score["objective"] = ["F", "F", "F"]
-        else:
-            score["objective"] = [
-                score["objective"],
-                -score["metadata"]["num_parameters_train"],
-                -score["metadata"]["duration_batch_inference"],
-            ]
-
-    return score
-
-
-def evaluate(config):
-    """Evaluate an hyperparameter configuration on training/validation and testing data."""
-
-    params = {
-        "epochs": 100,
-        "timeout": 60 * 60,
-        "verbose": True,
-    }  # 60 minutes per model
-    remap_hyperparameters(config)
-    params.update(config)
-    run_pipeline(params, mode="test")
diff --git a/lib/ECP-Candle/Pilot1/Combo/model.py b/lib/ECP-Candle/Pilot1/Combo/model.py
deleted file mode 100644
index 5234495..0000000
--- a/lib/ECP-Candle/Pilot1/Combo/model.py
+++ /dev/null
@@ -1,782 +0,0 @@
-from __future__ import division, print_function
-
-import collections
-import logging
-import os
-import time
-import warnings
-
-import yaml
-
-try:
-    from yaml import CLoader as Loader
-except ImportError:
-    from yaml import Loader
-
-HERE = os.path.dirname(os.path.abspath(__file__))
-DEFAULT_CONFIG = os.path.join(HERE, "combo_default_model.yaml")
-
-#! must be placed before "import candle"
-from tensorflow import keras
-from tensorflow.keras import backend as K
-from tensorflow.keras import optimizers
-from tensorflow.keras.callbacks import (
-    Callback,
-    EarlyStopping,
-    LearningRateScheduler,
-    ReduceLROnPlateau,
-)
-from tensorflow.keras.layers import Dense, Dropout, Input
-from tensorflow.keras.models import Model
-from tensorflow.keras.utils import get_custom_objects
-
-import candle
-import combo
-import NCI60
-import numpy as np
-import pandas as pd
-import tensorflow as tf
-from scipy.stats.stats import pearsonr
-from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
-
-from deephyper_benchmark.integration.keras import count_params
-from deephyper_benchmark.utils.json_utils import array_to_json
-
-
-logger = logging.getLogger(__name__)
-
-np.set_printoptions(precision=4)
-tf.compat.v1.disable_eager_execution()
-
-
-def verify_path(path):
-    folder = os.path.dirname(path)
-    if folder and not os.path.exists(folder):
-        os.makedirs(folder)
-
-
-def extension_from_parameters(args):
-    """Construct string for saving model with annotation of parameters"""
-    ext = ""
-    ext += ".A={}".format(args.activation)
-    ext += ".B={}".format(args.batch_size)
-    ext += ".E={}".format(args.epochs)
-    ext += ".O={}".format(args.optimizer)
-    # ext += '.LEN={}'.format(args.maxlen)
-    ext += ".LR={}".format(args.learning_rate)
-    ext += ".CF={}".format("".join([x[0] for x in sorted(args.cell_features)]))
-    ext += ".DF={}".format("".join([x[0] for x in sorted(args.drug_features)]))
-    if args.feature_subsample > 0:
-        ext += ".FS={}".format(args.feature_subsample)
-    if args.dropout > 0:
-        ext += ".DR={}".format(args.dropout)
-    if args.warmup_lr:
-        ext += ".wu_lr"
-    if args.reduce_lr:
-        ext += ".re_lr"
-    if args.residual:
-        ext += ".res"
-    if args.use_landmark_genes:
-        ext += ".L1000"
-    if args.gen:
-        ext += ".gen"
-    if args.use_combo_score:
-        ext += ".scr"
-    if args.use_mean_growth:
-        ext += ".mg"
-    for i, n in enumerate(args.dense):
-        if n > 0:
-            ext += ".D{}={}".format(i + 1, n)
-    if args.dense_feature_layers != args.dense:
-        for i, n in enumerate(args.dense):
-            if n > 0:
-                ext += ".FD{}={}".format(i + 1, n)
-
-    return ext
-
-
-def discretize(y, bins=5):
-    percentiles = [100 / bins * (i + 1) for i in range(bins - 1)]
-    thresholds = [np.percentile(y, x) for x in percentiles]
-    classes = np.digitize(y, thresholds)
-    return classes
-
-
-class ComboDataLoader(object):
-    """Load merged drug response, drug descriptors and cell line essay data"""
-
-    def __init__(
-        self,
-        seed,
-        valid_split=0.2,
-        test_split=0.2,
-        shuffle=True,
-        cell_features=["expression"],
-        drug_features=["descriptors"],
-        response_url=None,
-        use_landmark_genes=False,
-        use_combo_score=False,
-        use_mean_growth=False,
-        preprocess_rnaseq=None,
-        exclude_cells=[],
-        exclude_drugs=[],
-        feature_subsample=None,
-        scaling="std",
-        scramble=False,
-    ):
-        """Initialize data merging drug response, drug descriptors and cell line essay.
-           Shuffle and split training and validation set
-
-        Parameters
-        ----------
-        seed: integer
-            seed for random generation
-        val_split : float, optional (default 0.2)
-            fraction of data to use in validation
-        cell_features: list of strings from 'expression', 'expression_5platform', 'mirna', 'proteome', 'all', 'categorical' (default ['expression'])
-            use one or more cell line feature sets: gene expression, microRNA, proteome
-            use 'all' for ['expression', 'mirna', 'proteome']
-            use 'categorical' for one-hot encoded cell lines
-        drug_features: list of strings from 'descriptors', 'latent', 'all', 'categorical', 'noise' (default ['descriptors'])
-            use dragon7 descriptors, latent representations from Aspuru-Guzik's SMILES autoencoder
-            trained on NSC drugs, or both; use random features if set to noise
-            use 'categorical' for one-hot encoded drugs
-        shuffle : True or False, optional (default True)
-            if True shuffles the merged data before splitting training and validation sets
-        scramble: True or False, optional (default False)
-            if True randomly shuffle dose response data as a control
-        feature_subsample: None or integer (default None)
-            number of feature columns to use from cellline expressions and drug descriptors
-        use_landmark_genes: True or False
-            only use LINCS1000 landmark genes
-        use_combo_score: bool (default False)
-            use combination score in place of percent growth (stored in 'GROWTH' column)
-        use_mean_growth: bool (default False)
-            use mean aggregation instead of min on percent growth
-        scaling: None, 'std', 'minmax' or 'maxabs' (default 'std')
-            type of feature scaling: 'maxabs' to [-1,1], 'maxabs' to [-1, 1], 'std' for standard normalization
-        """
-
-        self._random_state = np.random.RandomState(seed)
-
-        df = NCI60.load_combo_response(
-            response_url=response_url,
-            use_combo_score=use_combo_score,
-            use_mean_growth=use_mean_growth,
-            fraction=True,
-            exclude_cells=exclude_cells,
-            exclude_drugs=exclude_drugs,
-        )
-        logger.info("Loaded {} unique (CL, D1, D2) response sets.".format(df.shape[0]))
-
-        if "all" in cell_features:
-            self.cell_features = ["expression", "mirna", "proteome"]
-        else:
-            self.cell_features = cell_features
-
-        if "all" in drug_features:
-            self.drug_features = ["descriptors", "latent"]
-        else:
-            self.drug_features = drug_features
-
-        for fea in self.cell_features:
-            if fea == "expression" or fea == "rnaseq":
-                self.df_cell_expr = NCI60.load_cell_expression_rnaseq(
-                    ncols=feature_subsample,
-                    scaling=scaling,
-                    use_landmark_genes=use_landmark_genes,
-                    preprocess_rnaseq=preprocess_rnaseq,
-                )
-                df = df.merge(self.df_cell_expr[["CELLNAME"]], on="CELLNAME")
-            elif fea == "expression_u133p2":
-                self.df_cell_expr = NCI60.load_cell_expression_u133p2(
-                    ncols=feature_subsample,
-                    scaling=scaling,
-                    use_landmark_genes=use_landmark_genes,
-                )
-                df = df.merge(self.df_cell_expr[["CELLNAME"]], on="CELLNAME")
-            elif fea == "expression_5platform":
-                self.df_cell_expr = NCI60.load_cell_expression_5platform(
-                    ncols=feature_subsample,
-                    scaling=scaling,
-                    use_landmark_genes=use_landmark_genes,
-                )
-                df = df.merge(self.df_cell_expr[["CELLNAME"]], on="CELLNAME")
-            elif fea == "mirna":
-                self.df_cell_mirna = NCI60.load_cell_mirna(
-                    ncols=feature_subsample, scaling=scaling
-                )
-                df = df.merge(self.df_cell_mirna[["CELLNAME"]], on="CELLNAME")
-            elif fea == "proteome":
-                self.df_cell_prot = NCI60.load_cell_proteome(
-                    ncols=feature_subsample, scaling=scaling
-                )
-                df = df.merge(self.df_cell_prot[["CELLNAME"]], on="CELLNAME")
-            elif fea == "categorical":
-                df_cell_ids = df[["CELLNAME"]].drop_duplicates()
-                cell_ids = df_cell_ids["CELLNAME"].map(lambda x: x.replace(":", "."))
-                df_cell_cat = pd.get_dummies(cell_ids)
-                df_cell_cat.index = df_cell_ids["CELLNAME"]
-                self.df_cell_cat = df_cell_cat.reset_index()
-
-        for fea in self.drug_features:
-            if fea == "descriptors":
-                self.df_drug_desc = NCI60.load_drug_descriptors(
-                    ncols=feature_subsample, scaling=scaling
-                )
-                df = df[
-                    df["NSC1"].isin(self.df_drug_desc["NSC"])
-                    & df["NSC2"].isin(self.df_drug_desc["NSC"])
-                ]
-            elif fea == "latent":
-                self.df_drug_auen = NCI60.load_drug_autoencoded_AG(
-                    ncols=feature_subsample, scaling=scaling
-                )
-                df = df[
-                    df["NSC1"].isin(self.df_drug_auen["NSC"])
-                    & df["NSC2"].isin(self.df_drug_auen["NSC"])
-                ]
-            elif fea == "categorical":
-                df_drug_ids = df[["NSC1"]].drop_duplicates()
-                df_drug_ids.columns = ["NSC"]
-                drug_ids = df_drug_ids["NSC"]
-                df_drug_cat = pd.get_dummies(drug_ids)
-                df_drug_cat.index = df_drug_ids["NSC"]
-                self.df_drug_cat = df_drug_cat.reset_index()
-            elif fea == "noise":
-                ids1 = df[["NSC1"]].drop_duplicates().rename(columns={"NSC1": "NSC"})
-                ids2 = df[["NSC2"]].drop_duplicates().rename(columns={"NSC2": "NSC"})
-                df_drug_ids = pd.concat([ids1, ids2]).drop_duplicates()
-                noise = np.random.normal(size=(df_drug_ids.shape[0], 500))
-                df_rand = pd.DataFrame(
-                    noise,
-                    index=df_drug_ids["NSC"],
-                    columns=["RAND-{:03d}".format(x) for x in range(500)],
-                )
-                self.df_drug_rand = df_rand.reset_index()
-
-        logger.info(
-            "Filtered down to {} rows with matching information.".format(df.shape[0])
-        )
-
-        ids1 = df[["NSC1"]].drop_duplicates().rename(columns={"NSC1": "NSC"})
-        ids2 = df[["NSC2"]].drop_duplicates().rename(columns={"NSC2": "NSC"})
-        df_drug_ids = pd.concat([ids1, ids2]).drop_duplicates().reset_index(drop=True)
-
-        n_drugs = df_drug_ids.shape[0]
-        n_valid_drugs = int(n_drugs * valid_split)
-        n_test_drugs = int(n_drugs * test_split)
-        n_train_drugs = n_drugs - n_valid_drugs - n_test_drugs
-
-        logger.info("Unique cell lines: {}".format(df["CELLNAME"].nunique()))
-        logger.info("Unique drugs: {}".format(n_drugs))
-
-        if shuffle:
-            df = df.sample(frac=1.0, random_state=seed).reset_index(drop=True)
-            df_drug_ids = df_drug_ids.sample(frac=1.0, random_state=seed).reset_index(
-                drop=True
-            )
-
-        self.df_response = df
-        self.df_drug_ids = df_drug_ids
-
-        self.train_drug_ids = df_drug_ids["NSC"][:n_train_drugs]
-        self.valid_drug_ids = df_drug_ids["NSC"][
-            n_train_drugs : n_train_drugs + n_valid_drugs
-        ]
-        self.test_drug_ids = df_drug_ids["NSC"][
-            n_train_drugs + n_valid_drugs : n_train_drugs + n_valid_drugs + n_test_drugs
-        ]
-
-        if scramble:
-            growth = df[["GROWTH"]]
-            random_growth = growth.iloc[
-                self._random_state.permutation(np.arange(growth.shape[0]))
-            ].reset_index()
-            self.df_response[["GROWTH"]] = random_growth["GROWTH"]
-            logger.warn("Randomly shuffled dose response growth values.")
-
-        logger.info("Distribution of dose response:")
-        logger.info(self.df_response[["GROWTH"]].describe())
-
-        self.total = df.shape[0]
-        self.n_valid = int(self.total * valid_split)
-        self.n_test = int(self.total * test_split)
-        self.n_train = self.total - self.n_valid - self.n_test
-        logger.info(
-            "Rows in train: {}, valid: {}, test: {}".format(
-                self.n_train, self.n_valid, self.n_test
-            )
-        )
-
-        self.cell_df_dict = {
-            "expression": "df_cell_expr",
-            "expression_5platform": "df_cell_expr",
-            "expression_u133p2": "df_cell_expr",
-            "rnaseq": "df_cell_expr",
-            "mirna": "df_cell_mirna",
-            "proteome": "df_cell_prot",
-            "categorical": "df_cell_cat",
-        }
-
-        self.drug_df_dict = {
-            "descriptors": "df_drug_desc",
-            "latent": "df_drug_auen",
-            "categorical": "df_drug_cat",
-            "noise": "df_drug_rand",
-        }
-
-        self.input_features = collections.OrderedDict()
-        self.feature_shapes = {}
-        for fea in self.cell_features:
-            feature_type = "cell." + fea
-            feature_name = "cell." + fea
-            df_cell = getattr(self, self.cell_df_dict[fea])
-            self.input_features[feature_name] = feature_type
-            self.feature_shapes[feature_type] = (df_cell.shape[1] - 1,)
-
-        for drug in ["drug1", "drug2"]:
-            for fea in self.drug_features:
-                feature_type = "drug." + fea
-                feature_name = drug + "." + fea
-                df_drug = getattr(self, self.drug_df_dict[fea])
-                self.input_features[feature_name] = feature_type
-                self.feature_shapes[feature_type] = (df_drug.shape[1] - 1,)
-
-        logger.info("Input features shapes:")
-        for k, v in self.input_features.items():
-            logger.info("  {}: {}".format(k, self.feature_shapes[v]))
-
-        self.input_dim = sum(
-            [np.prod(self.feature_shapes[x]) for x in self.input_features.values()]
-        )
-        logger.info("Total input dimensions: {}".format(self.input_dim))
-
-    def load_data_all(self, switch_drugs=False):
-        df_all = self.df_response
-        y_all = df_all["GROWTH"].values
-        x_all_list = []
-
-        for fea in self.cell_features:
-            df_cell = getattr(self, self.cell_df_dict[fea])
-            df_x_all = pd.merge(
-                df_all[["CELLNAME"]], df_cell, on="CELLNAME", how="left"
-            )
-            x_all_list.append(df_x_all.drop(["CELLNAME"], axis=1).values)
-
-        drugs = ["NSC1", "NSC2"]
-        if switch_drugs:
-            drugs = ["NSC2", "NSC1"]
-
-        for drug in drugs:
-            for fea in self.drug_features:
-                df_drug = getattr(self, self.drug_df_dict[fea])
-                df_x_all = pd.merge(
-                    df_all[[drug]], df_drug, left_on=drug, right_on="NSC", how="left"
-                )
-                x_all_list.append(df_x_all.drop([drug, "NSC"], axis=1).values)
-
-        return x_all_list, y_all, df_all
-
-    def load_data_by_index(self, train_index, valid_index, test_index):
-        x_all_list, y_all, df_all = self.load_data_all()
-
-        x_train_list = [x[train_index] for x in x_all_list]
-        x_valid_list = [x[valid_index] for x in x_all_list]
-        x_test_list = [x[test_index] for x in x_all_list]
-
-        y_train = y_all[train_index]
-        y_valid = y_all[valid_index]
-        y_test = y_all[test_index]
-
-        df_train = df_all.iloc[train_index, :]
-        df_valid = df_all.iloc[valid_index, :]
-        df_test = df_all.iloc[test_index, :]
-
-        return (
-            x_train_list,
-            y_train,
-            x_valid_list,
-            y_valid,
-            x_test_list,
-            y_test,
-            df_train,
-            df_valid,
-            df_test,
-        )
-
-    def load_data(self):
-        train_index = range(self.n_train)
-        valid_index = range(self.n_train, self.n_train + self.n_valid)
-        test_index = range(
-            self.n_train + self.n_valid, self.n_train + self.n_valid + self.n_test
-        )
-        return self.load_data_by_index(train_index, valid_index, test_index)
-
-
-def test_loader(loader):
-    (
-        x_train_list,
-        y_train,
-        x_val_list,
-        y_val,
-        x_test_list,
-        y_test,
-        _,
-        _,
-        _,
-    ) = loader.load_data()
-    print("x_train shapes:")
-    for x in x_train_list:
-        print(x.shape)
-    print("y_train shape:", y_train.shape)
-
-    print("x_valid shapes:")
-    for x in x_val_list:
-        print(x.shape)
-    print("y_valid shape:", y_val.shape)
-
-    print("x_test shapes:")
-    for x in x_test_list:
-        print(x.shape)
-    print("y_test shape:", y_test.shape)
-
-
-def r2(y_true, y_pred):
-    SS_res = K.sum(K.square(y_true - y_pred))
-    SS_tot = K.sum(K.square(y_true - K.mean(y_true)))
-    return 1 - SS_res / (SS_tot + K.epsilon())
-
-
-def mae(y_true, y_pred):
-    return keras.metrics.mean_absolute_error(y_true, y_pred)
-
-
-def mse(y_true, y_pred):
-    return keras.metrics.mean_squared_error(y_true, y_pred)
-
-
-def evaluate_prediction(y_true, y_pred):
-    mse = mean_squared_error(y_true, y_pred)
-    mae = mean_absolute_error(y_true, y_pred)
-    r2 = r2_score(y_true, y_pred)
-    corr, _ = pearsonr(y_true, y_pred)
-    return {"mse": mse, "mae": mae, "r2": r2, "corr": corr}
-
-
-def log_evaluation(metric_outputs, description="Comparing y_true and y_pred:"):
-    logger.info(description)
-    for metric, value in metric_outputs.items():
-        logger.info("  {}: {:.4f}".format(metric, value))
-
-
-class LoggingCallback(Callback):
-    def __init__(self, print_fcn=print):
-        Callback.__init__(self)
-        self.print_fcn = print_fcn
-
-    def on_epoch_end(self, epoch, logs={}):
-        msg = "[Epoch: %i] %s" % (
-            epoch,
-            ", ".join("%s: %f" % (k, v) for k, v in sorted(logs.items())),
-        )
-        self.print_fcn(msg)
-
-
-class PermanentDropout(Dropout):
-    def __init__(self, rate, **kwargs):
-        super(PermanentDropout, self).__init__(rate, **kwargs)
-        self.uses_learning_phase = False
-
-    def call(self, x, mask=None):
-        if 0.0 < self.rate < 1.0:
-            noise_shape = self._get_noise_shape(x)
-            x = K.dropout(x, self.rate, noise_shape)
-        return x
-
-
-class ModelRecorder(Callback):
-    def __init__(self, save_all_models=False):
-        Callback.__init__(self)
-        self.save_all_models = save_all_models
-        get_custom_objects()["PermanentDropout"] = PermanentDropout
-
-    def on_train_begin(self, logs={}):
-        self.val_losses = []
-        self.best_val_loss = np.Inf
-        self.best_model = None
-
-    def on_epoch_end(self, epoch, logs={}):
-        val_loss = logs.get("val_loss")
-        self.val_losses.append(val_loss)
-        if val_loss < self.best_val_loss:
-            self.best_model = keras.models.clone_model(self.model)
-            self.best_val_loss = val_loss
-
-
-def build_feature_model(
-    input_shape,
-    name="",
-    dense_layers=[1000, 1000],
-    activation="relu",
-    residual=False,
-    dropout_rate=0,
-    permanent_dropout=True,
-):
-    x_input = Input(shape=input_shape)
-    h = x_input
-    for i, layer in enumerate(dense_layers):
-        x = h
-        h = Dense(layer, activation=activation)(h)
-        if dropout_rate > 0:
-            if permanent_dropout:
-                h = PermanentDropout(dropout_rate)(h)
-            else:
-                h = Dropout(dropout_rate)(h)
-        if residual:
-            try:
-                h = keras.layers.add([h, x])
-            except ValueError:
-                pass
-    model = Model(x_input, h, name=name)
-    return model
-
-
-def build_model(loader, args, verbose=False):
-    input_models = {}
-    dropout_rate = args.dropout
-    permanent_dropout = True
-    for fea_type, shape in loader.feature_shapes.items():
-        box = build_feature_model(
-            input_shape=shape,
-            name=fea_type,
-            dense_layers=args.dense_feature_layers,
-            dropout_rate=dropout_rate,
-            permanent_dropout=permanent_dropout,
-        )
-        if verbose:
-            box.summary()
-        input_models[fea_type] = box
-
-    inputs = []
-    encoded_inputs = []
-    for fea_name, fea_type in loader.input_features.items():
-        shape = loader.feature_shapes[fea_type]
-        fea_input = Input(shape, name="input." + fea_name)
-        inputs.append(fea_input)
-        input_model = input_models[fea_type]
-        encoded = input_model(fea_input)
-        encoded_inputs.append(encoded)
-
-    merged = keras.layers.concatenate(encoded_inputs)
-
-    h = merged
-    for i, layer in enumerate(args.dense):
-        x = h
-        h = Dense(layer, activation=args.activation)(h)
-        if dropout_rate > 0:
-            if permanent_dropout:
-                h = PermanentDropout(dropout_rate)(h)
-            else:
-                h = Dropout(dropout_rate)(h)
-        if args.residual:
-            try:
-                h = keras.layers.add([h, x])
-            except ValueError:
-                pass
-    output = Dense(1)(h)
-
-    return Model(inputs, output)
-
-
-def initialize_parameters(default_model="combo_default_model.txt"):
-    # Build benchmark object
-    comboBmk = combo.BenchmarkCombo(
-        combo.file_path,
-        default_model,
-        "keras",
-        prog="combo_baseline",
-        desc="Build neural network based models to predict tumor response to drug pairs.",
-    )
-
-    # Initialize parameters
-    gParameters = candle.finalize_parameters(comboBmk)
-
-    return gParameters
-
-
-def yaml_load(path):
-    with open(path, "r") as f:
-        yaml_data = yaml.load(f, Loader=Loader)
-    return yaml_data
-
-
-def run_pipeline(config: dict = None, mode="valid", stopper_callback=None):
-    # Default Config from original Benchmark
-    params = initialize_parameters()
-
-    # Default Config from our Benchmark
-    params.update(yaml_load(DEFAULT_CONFIG))
-    params.pop("val_split")
-
-    # Ingest input configuration
-    if config:
-        params.update(config)
-
-    args = candle.ArgumentStruct(**params)
-    seed = args.rng_seed
-    candle.set_seed(seed)
-
-    loader = ComboDataLoader(
-        seed=args.rng_seed,
-        valid_split=args.valid_split,
-        test_split=args.test_split,
-        cell_features=args.cell_features,
-        drug_features=args.drug_features,
-        use_mean_growth=args.use_mean_growth,
-        response_url=args.response_url,
-        use_landmark_genes=args.use_landmark_genes,
-        preprocess_rnaseq=args.preprocess_rnaseq,
-        exclude_cells=args.exclude_cells,
-        exclude_drugs=args.exclude_drugs,
-        use_combo_score=args.use_combo_score,
-        scaling=args.scaling,
-    )
-
-    # test_loader(loader)
-
-    model = build_model(loader, args, verbose=args.verbose)
-
-    def warmup_scheduler(epoch):
-        lr = args.learning_rate or base_lr * args.batch_size / 100
-        if epoch <= 5:
-            K.set_value(model.optimizer.lr, (base_lr * (5 - epoch) + lr * epoch) / 5)
-        logger.debug("Epoch {}: lr={}".format(epoch, K.get_value(model.optimizer.lr)))
-        return K.get_value(model.optimizer.lr)
-
-    model = build_model(loader, args)
-
-    optimizer = optimizers.deserialize({"class_name": args.optimizer, "config": {}})
-    base_lr = args.base_lr or K.get_value(optimizer.lr)
-    if args.learning_rate:
-        K.set_value(optimizer.lr, args.learning_rate)
-
-    model.compile(loss=args.loss, optimizer=optimizer, metrics=[mse, mae, r2])
-
-    # calculate trainable and non-trainable params
-    params.update(candle.compute_trainable_params(model))
-
-    early_stopping = EarlyStopping(
-        monitor="val_loss", patience=args.early_stopping_patience
-    )
-
-    timeout_monitor = candle.TerminateOnTimeOut(params["timeout"])
-
-    reduce_lr = ReduceLROnPlateau(
-        monitor="val_loss",
-        factor=args.reduce_lr_factor,
-        patience=args.reduce_lr_patience,
-        min_lr=0.00001,
-    )
-    warmup_lr = LearningRateScheduler(warmup_scheduler)
-
-    callbacks = [timeout_monitor]
-    if args.early_stopping:
-        callbacks.append(early_stopping)
-    if args.reduce_lr:
-        callbacks.append(reduce_lr)
-    if args.warmup_lr:
-        callbacks.append(warmup_lr)
-
-    if stopper_callback:
-        callbacks.append(stopper_callback)
-
-    (
-        x_train_list,
-        y_train,
-        x_valid_list,
-        y_valid,
-        x_test_list,
-        y_test,
-        _,
-        _,
-        _,
-    ) = loader.load_data()
-    training_data = (x_train_list, y_train)
-    validation_data = (x_valid_list, y_valid)
-
-    num_parameters_info = count_params(model)
-
-    # Training
-    timestamp_duration = time.time() 
-    history = model.fit(
-        *training_data,
-        batch_size=args.batch_size,
-        shuffle=args.shuffle,
-        epochs=args.epochs,
-        validation_data=validation_data,
-        callbacks=callbacks,
-        verbose=args.verbose,
-    ).history
-    duration_train = time.time() - timestamp_duration
-
-
-    timestamp_duration = time.time() 
-    y_train_pred = model.predict(x_train_list, batch_size=args.batch_size).flatten()
-    duration_batch_inference = (time.time() - timestamp_duration) / np.ceil(len(y_train_pred) / args.batch_size)
-
-    scores_train = evaluate_prediction(y_train, y_train_pred)
-    y_valid_pred = model.predict(x_valid_list, batch_size=args.batch_size).flatten()
-    scores_valid = evaluate_prediction(y_valid, y_valid_pred)
-    y_test_pred = model.predict(x_test_list, batch_size=args.batch_size).flatten()
-    scores_test = evaluate_prediction(y_test, y_test_pred)
-
-    all_scores = {}
-    all_scores.update({f"train_{k}": float(v) for k, v in scores_train.items()})
-    all_scores.update({f"valid_{k}": float(v) for k, v in scores_valid.items()})
-    all_scores.update({f"test_{k}": float(v) for k, v in scores_test.items()})
-
-    if K.backend() == "tensorflow":
-        K.clear_session()
-
-    objective = all_scores["valid_r2"]  # validation R2
-    objective = max(-1, objective)
-
-    # collect learning curves
-    lc_train_mse = array_to_json(
-        np.asarray([[i + 1, l] for i, l in enumerate(history["mse"])])
-    )
-    lc_valid_mse = array_to_json(
-        np.asarray([[i + 1, l] for i, l in enumerate(history["val_mse"])])
-    )
-
-    lc_train_mae = array_to_json(
-        np.asarray([[i + 1, l] for i, l in enumerate(history["mae"])])
-    )
-    lc_valid_mae = array_to_json(
-        np.asarray([[i + 1, l] for i, l in enumerate(history["val_mae"])])
-    )
-
-    lc_train_r2 = array_to_json(
-        np.asarray([[i + 1, l] for i, l in enumerate(history["r2"])])
-    )
-    lc_valid_r2 = array_to_json(
-        np.asarray([[i + 1, l] for i, l in enumerate(history["val_r2"])])
-    )
-
-    metadata = {
-        "num_parameters": num_parameters_info["num_parameters"],
-        "num_parameters_train": num_parameters_info["num_parameters_train"],
-        "duration_train": duration_train,
-        "duration_batch_inference": duration_batch_inference,
-        "budget": len(history["loss"]),
-        "stopped": len(history["loss"]) < args.epochs,
-        "lc_train_mse": lc_train_mse,
-        "lc_valid_mse": lc_valid_mse,
-        "lc_train_mae": lc_train_mae,
-        "lc_valid_mae": lc_valid_mae,
-        "lc_train_r2": lc_train_r2,
-        "lc_valid_r2": lc_valid_r2,
-    }
-    metadata.update(all_scores)
-    return {"objective": objective, "metadata": metadata}
diff --git a/lib/ECP-Candle/Pilot1/Combo/requirements.txt b/lib/ECP-Candle/Pilot1/Combo/requirements.txt
deleted file mode 100644
index 8c168b6..0000000
--- a/lib/ECP-Candle/Pilot1/Combo/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-astropy
-patsy
-statsmodels
-numpy<=1.22 # for compatibility with numba.cuda
-numba~=0.55.2
\ No newline at end of file
diff --git a/lib/FNOBench/README.md b/lib/FNOBench/README.md
deleted file mode 100644
index fd26692..0000000
--- a/lib/FNOBench/README.md
+++ /dev/null
@@ -1,64 +0,0 @@
-# FNOBench: Fourier Neural Operator Benchmark
-
-> **Warning**
-> Work in progress, this benchmark is not yet ready.
-
-This is a simplified way of using deephyper to conduct the hyperparameter tuning for the TFNO model to solve the darcy flow. The FNO and the TFNO are models for training neural operators and they can be designed using the neural operator package.
-
-The Deep Hyper is a package that automates the design of neural networks using Hyperparameter Tuning and Neural Architecture Search.
-
-## Installation
-
-To use the benchmark follow this example set of instructions:
-
-```console
-python -c "import deephyper_benchmark as dhb; dhb.install('FNOBench');"
-```
-
-Then load the benchmark from Python:
-
-```python
-import deephyper_benchmark as dhb
-dhb.load("FNOBench")
-from deephyper_benchmark.lib.fnobench import hpo
-```
-
-## Configuration
-
-...
-
-## Metadata
-
-The current set of returned metadata is:
-
-- [x] `num_parameters`
-- [ ] `num_parameters_train`
-- [x] `duration_train`
-- [ ] `duration_batch_inference`
-- [x] `budget`
-- [ ] `stopped`
-- [x] `train_loss`: the training loss which is the H1Loss
-- [x] `valid_loss`: the validation loss which is the LpLoss
-- [ ] `flops`
-- [ ] `latency`
-- [ ] `lc_train_loss`
-- [ ] `lc_valid_loss`
-
-## Other Details
-
-### Training Procedure
-
-We especially edit the Trainer class from the neural operator package to include and return the validation loss and to optimize it.
-
-We also specifically edit the function that loads the data to accommodate the validation data and the test data. Note that the training and testing data file has been included, and is available for download
-
-### Optimized Losses
-
-The losses employed by the neural operator package are the H1Loss and the LpLoss and we stick with those in this benchmark for the sake of consistency.
-
-### Data Splitting Policy
-
-
-The data we use for this test is the darcy flow test, and we have it in the data folder, it is generated by the load_darcy_flow_small function. The actual function that 
-loads and splits the data into train, validation and test data is the load_darcy_pt, and how it achieves this is to load the data from the file 
-darcy_train and split it in this form, the train data takes 80% of the data, while the validation data takes 20% and saves it into (x_train, y_train); which is loaded into the trainloader, and (x_valid, y_valid); which is loaded into the validation_loader. Note that we train our model in a particular resolution (16), but we can carry out the test in other resolutions, (for this project we test in 16, 32). To get the test data, we already have the test data in the file darcy_test, and we load it similar to how we load the train data, but here we do not split the test data at all, so we have (x_test, y_test) loaded into the testloaders; testloaderS because we load for resolution 16 and resolution 32.
\ No newline at end of file
diff --git a/lib/FNOBench/__init__.py b/lib/FNOBench/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/FNOBench/benchmark.py b/lib/FNOBench/benchmark.py
deleted file mode 100644
index f9e85a6..0000000
--- a/lib/FNOBench/benchmark.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class FNOBenchmark(Benchmark):
-    version = "0.0.1"
-
-    requires = {
-        "py-pip-requirements": {
-            "step": "install",
-            "type": "pip",
-            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
-        },
-    }
diff --git a/lib/FNOBench/data.py b/lib/FNOBench/data.py
deleted file mode 100644
index b294151..0000000
--- a/lib/FNOBench/data.py
+++ /dev/null
@@ -1,227 +0,0 @@
-from pathlib import Path
-
-import torch
-from neuralop.datasets.tensor_dataset import TensorDataset
-from neuralop.datasets.transforms import PositionalEmbedding
-from neuralop.utils import UnitGaussianNormalizer
-
-
-# this function returns the train_loader, validation_loader, test_loader and output encoder for the darcy_flow dataset
-# it was originally written in the neuralop codes but we slightly edit it to include the validation_loader
-# which is 0.2% of the whole training_data
-def load_darcy_flow_small(
-    n_train,
-    n_tests,
-    batch_size,
-    test_batch_sizes,
-    test_resolutions=[16, 32],
-    grid_boundaries=[[0, 1], [0, 1]],
-    positional_encoding=True,
-    encode_input=False,
-    encode_output=True,
-    encoding="channel-wise",
-    channel_dim=1,
-):
-    """Loads a small Darcy-Flow dataset
-
-    Training contains 1000 samples in resolution 16x16.
-    Testing contains 100 samples at resolution 16x16 and
-    50 samples at resolution 32x32.
-
-    Parameters
-    ----------
-    n_train : int
-    n_tests : int
-    batch_size : int
-    test_batch_sizes : int list
-    test_resolutions : int list, default is [16, 32],
-    grid_boundaries : int list, default is [[0,1],[0,1]],
-    positional_encoding : bool, default is True
-    encode_input : bool, default is False
-    encode_output : bool, default is True
-    encoding : 'channel-wise'
-    channel_dim : int, default is 1
-        where to put the channel dimension, defaults size is batch, channel, height, width
-
-    Returns
-    -------
-    training_dataloader, testing_dataloaders
-
-    training_dataloader : torch DataLoader
-    testing_dataloaders : dict (key: DataLoader)
-    """
-    for res in test_resolutions:
-        if res not in [16, 32]:
-            raise ValueError(
-                f"Only 32 and 64 are supported for test resolution, but got {test_resolutions=}"
-            )
-    path = Path(__file__).resolve().parent.joinpath("data")
-    return load_darcy_pt(
-        str(path),
-        n_train=n_train,
-        n_tests=n_tests,
-        batch_size=batch_size,
-        test_batch_sizes=test_batch_sizes,
-        test_resolutions=test_resolutions,
-        train_resolution=16,
-        grid_boundaries=grid_boundaries,
-        positional_encoding=positional_encoding,
-        encode_input=encode_input,
-        encode_output=encode_output,
-        encoding=encoding,
-        channel_dim=channel_dim,
-    )
-
-
-def load_darcy_pt(
-    data_path,
-    n_train,
-    n_tests,
-    batch_size,
-    test_batch_sizes,
-    test_resolutions=[32],
-    train_resolution=32,
-    grid_boundaries=[[0, 1], [0, 1]],
-    positional_encoding=True,
-    encode_input=False,
-    encode_output=True,
-    encoding="channel-wise",
-    channel_dim=1,
-):
-    """Load the Navier-Stokes dataset"""
-    n_val = int(0.2 * n_train)
-    data = torch.load(
-        Path(data_path).joinpath(f"darcy_train_{train_resolution}.pt").as_posix()
-    )
-    x_train = (
-        data["x"][0 : n_train - n_val, :, :]
-        .unsqueeze(channel_dim)
-        .type(torch.float32)
-        .clone()
-    )
-    y_train = data["y"][0 : n_train - n_val, :, :].unsqueeze(channel_dim).clone()
-    x_val = (
-        data["x"][n_train - n_val : n_train, :, :]
-        .unsqueeze(channel_dim)
-        .type(torch.float32)
-        .clone()
-    )
-    y_val = data["y"][n_train - n_val : n_train, :, :].unsqueeze(channel_dim).clone()
-    del data
-
-    idx = test_resolutions.index(train_resolution)
-    test_resolutions.pop(idx)
-    n_test = n_tests.pop(idx)
-    test_batch_size = test_batch_sizes.pop(idx)
-
-    data = torch.load(
-        Path(data_path).joinpath(f"darcy_test_{train_resolution}.pt").as_posix()
-    )
-    x_test = data["x"][:n_test, :, :].unsqueeze(channel_dim).type(torch.float32).clone()
-    y_test = data["y"][:n_test, :, :].unsqueeze(channel_dim).clone()
-    del data
-
-    if encode_input:
-        if encoding == "channel-wise":
-            reduce_dims = list(range(x_train.ndim))
-        elif encoding == "pixel-wise":
-            reduce_dims = [0]
-
-        input_encoder = UnitGaussianNormalizer(x_train, reduce_dim=reduce_dims)
-        x_train = input_encoder.encode(x_train)
-        x_test = input_encoder.encode(x_test.contiguous())
-    else:
-        input_encoder = None
-
-    if encode_output:
-        if encoding == "channel-wise":
-            reduce_dims = list(range(y_train.ndim))
-        # reduce_d = list(range(y_val.ndim))
-        elif encoding == "pixel-wise":
-            reduce_dims = [0]
-        # reduce_d=[0]
-
-        output_encoder = UnitGaussianNormalizer(y_train, reduce_dim=reduce_dims)
-        y_train = output_encoder.encode(y_train)
-    else:
-        output_encoder = None
-
-    train_db = TensorDataset(
-        x_train,
-        y_train,
-        transform_x=PositionalEmbedding(grid_boundaries, 0)
-        if positional_encoding
-        else None,
-    )
-    train_loader = torch.utils.data.DataLoader(
-        train_db,
-        batch_size=batch_size,
-        shuffle=True,
-        num_workers=0,
-        pin_memory=True,
-        persistent_workers=False,
-    )
-    val_db = TensorDataset(
-        x_val,
-        y_val,
-        transform_x=PositionalEmbedding(grid_boundaries, 0)
-        if positional_encoding
-        else None,
-    )
-    val_loader = torch.utils.data.DataLoader(
-        val_db,
-        batch_size=batch_size,
-        shuffle=False,
-        num_workers=0,
-        pin_memory=True,
-        persistent_workers=False,
-    )
-    test_db = TensorDataset(
-        x_test,
-        y_test,
-        transform_x=PositionalEmbedding(grid_boundaries, 0)
-        if positional_encoding
-        else None,
-    )
-    test_loader = torch.utils.data.DataLoader(
-        test_db,
-        batch_size=test_batch_size,
-        shuffle=False,
-        num_workers=0,
-        pin_memory=True,
-        persistent_workers=False,
-    )
-    test_loaders = {train_resolution: test_loader}
-    for res, n_test, test_batch_size in zip(
-        test_resolutions, n_tests, test_batch_sizes
-    ):
-        print(
-            f"Loading test db at resolution {res} with {n_test} samples and batch-size={test_batch_size}"
-        )
-        data = torch.load(Path(data_path).joinpath(f"darcy_test_{res}.pt").as_posix())
-        x_test = (
-            data["x"][:n_test, :, :].unsqueeze(channel_dim).type(torch.float32).clone()
-        )
-        y_test = data["y"][:n_test, :, :].unsqueeze(channel_dim).clone()
-        del data
-        if input_encoder is not None:
-            x_test = input_encoder.encode(x_test)
-
-        test_db = TensorDataset(
-            x_test,
-            y_test,
-            transform_x=PositionalEmbedding(grid_boundaries, 0)
-            if positional_encoding
-            else None,
-        )
-        test_loader = torch.utils.data.DataLoader(
-            test_db,
-            batch_size=test_batch_size,
-            shuffle=False,
-            num_workers=0,
-            pin_memory=True,
-            persistent_workers=False,
-        )
-        test_loaders[res] = test_loader
-
-    return train_loader, val_loader, test_loaders, output_encoder
diff --git a/lib/FNOBench/data/darcy_test_16.pt b/lib/FNOBench/data/darcy_test_16.pt
deleted file mode 100644
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diff --git a/lib/FNOBench/hpo.py b/lib/FNOBench/hpo.py
deleted file mode 100644
index 8d3f7a9..0000000
--- a/lib/FNOBench/hpo.py
+++ /dev/null
@@ -1,104 +0,0 @@
-import torch
-from deephyper.evaluator import profile
-from deephyper.hpo import HpProblem
-
-from .model import build_and_train_model
-
-# now we define the hyperparameters with which we aim to get the best configuration for solving the problem
-problem = HpProblem()
-# this is for dimension 2 darcy flow, below we list the hyperparameters and where they are being used
-# 1. n_modes_height and n_modes_width are the number of modes to keep in fourier layer along each dimension, if we are working with 3 dimensions, we have height, width, and length. It is called when you are defining the model e.g model =TFNO(n_modes=...),The dimensionality of the TFNO is inferred from ``len(n_modes)``
-problem.add_hyperparameter((2, 19), "n_modes_height", default_value=16)
-problem.add_hyperparameter((2, 19), "n_modes_width", default_value=16)
-# 2. hidden_channels: width of the FNO (i.e. number of channels). they go into the model definition
-problem.add_hyperparameter((1, 128), "hidden_channels", default_value=32)
-# 3. lifting_channels: number of hidden channels of the lifting block of the FNO, they go into the model definition
-problem.add_hyperparameter((1, 1024), "lifting_channels", default_value=256)
-# 4 projection_channels:number of hidden channels of the projection block of the FNO, they go into the model definition
-problem.add_hyperparameter((1, 1024), "projection_channels", default_value=64)
-# 5. use_mlp: Whether to use an MLP layer after each FNO block, they go into the model definition
-problem.add_hyperparameter([True, False], "use_mlp", default_value=False)
-# 6 mlp['dropout']: parameter of the MLP, they go into the model definition
-problem.add_hyperparameter((0.0, 1.0), "mlp['dropout']", default_value=0)
-# 7 mlp['expansion']: MLP parameter, they go into the model definition
-problem.add_hyperparameter((0.0, 3.0), "mlp['expansion']", default_value=0.5)
-# 8 rank Rank of the tensor factorization of the Fourier weights, they go into the model definition
-problem.add_hyperparameter((0.0, 1.0), "rank", default_value=1.0)
-# 9 factorization:Tensor factorization of the parameters weight to use, they go into the model definition
-problem.add_hyperparameter(
-    ["tucker", "cp", "tt", "dense"], "factorization", default_value="dense"
-)
-# 10 learning_rate: this is the learning_rate, goes into the optimizer definition
-problem.add_hyperparameter(
-    (1e-6, 1e-2, "log-uniform"), "opt_learning_rate", default_value=5e-3
-)
-# 11 batch_size: the batch size for the training, goes into the train_loader for loading the datasel
-problem.add_hyperparameter((2, 64), "data_batch_size", default_value=16)
-# 12 weight_decay: this is the weight_decay, goes into the optimizer definition
-problem.add_hyperparameter(
-    (1e-6, 1e-2, "log-uniform"), "opt_weight_decay", default_value=1e-4
-)
-# 13 n_layers:Number of Fourier Layers
-problem.add_hyperparameter((1, 8), "n_layers", default_value=4)
-# 14 epochs: number of epochs, goes into the trainer
-problem.add_hyperparameter((1, 1000), "opt_n_epochs", default_value=300)
-# 15 scheduler_T_max: this goes into the scheduler, it's the max number of iterations in the scheduler
-problem.add_hyperparameter([500], "opt_scheduler_T_max", default_value=500)
-# 16 n_train: no of data samples to train the model, goes into the train_loader for loading the dataset
-problem.add_hyperparameter([1000], "data_n_train", default_value=1000)
-# the next set of hyperparameters are data related and we're keeping them constant for now,and for the particular problem
-problem.add_hyperparameter([16], "data_train_resolution", default_value=16)
-problem.add_hyperparameter([True], "data_positional_encoding", default_value=True)
-problem.add_hyperparameter([True], "data_encode_input", default_value=True)
-problem.add_hyperparameter([False], "data_encode_output", default_value=False)
-problem.add_hyperparameter([666], "distributed_seed", default_value=666)
-problem.add_hyperparameter([3], "data_channels", default_value=3)
-# implementation is about how the factorization is done,
-problem.add_hyperparameter(
-    ["factorized", "reconstructed"], "implementation", default_value="factorized"
-)
-# By default, None, otherwise tanh is used before FFT in the FNO block
-problem.add_hyperparameter(["None", "tanh"], "stabilizer", default_value=None)
-# if 'full', the FNO Block runs in full precision,
-# if 'half', the FFT, contraction, and inverse FFT run in half precision
-# if 'mixed', the contraction and inverse FFT run in half precision
-problem.add_hyperparameter(
-    ["full", "half", "mixed"], "fno_block_precision", default_value="half"
-)
-# Type of skip connection to use,
-problem.add_hyperparameter(
-    ["soft-gating", "identity", "linear"], "skip", default_value="linear"
-)
-# the scheduler_patience, used for only  ReduceLROnPlateau, we're keeping it constant
-problem.add_hyperparameter([5], "opt_scheduler_patience", default_value=5)
-# type of scheduler to use
-problem.add_hyperparameter(
-    ["StepLR", "CosineAnnealingLR", "ReduceLROnPlateau"],
-    "opt_scheduler",
-    default_value="StepLR",
-)
-# step_size for the optimizer
-problem.add_hyperparameter((10, 100), "opt_step_size", default_value=60)
-# gamma for the constant, we're keeping it constant
-problem.add_hyperparameter([0.5], "opt_gamma", default_value=0.5)
-
-
-@profile
-def run(config):
-    # important to avoid memory explosion
-    torch.cuda.empty_cache()
-    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-
-    _, history = build_and_train_model(config, device, verbose=0)
-    score = {
-        "objective": -history["val_loss"][-1],
-        "metadata": {
-            "train_err": history["train_err"][-1],
-            "num_parameters": history["num_parameters"],
-            "budget": history["Epochs"][-1],
-            "val_loss": history["val_loss"][-1],
-            "duration_train": sum(history["train_time"]),
-        },
-    }
-
-    return score
diff --git a/lib/FNOBench/model.py b/lib/FNOBench/model.py
deleted file mode 100644
index 8c38e42..0000000
--- a/lib/FNOBench/model.py
+++ /dev/null
@@ -1,560 +0,0 @@
-import sys
-from timeit import default_timer
-
-import matplotlib.pyplot as plt
-import neuralop.mpu.comm as comm
-import numpy as np
-import torch
-import wandb
-from neuralop import H1Loss, LpLoss
-from neuralop.models import TFNO2d
-from neuralop.training.patching import MultigridPatching2D
-from neuralop.utils import count_params
-
-from .data import load_darcy_flow_small
-
-
-# this is the trainer class that trains the neural operators,
-# it is from the neuralop package, but we had to slightly
-# modify it in order to
-# 1. calculate the validation loss instead of the testing loss
-# 2. return the history of the training, including the time for each epoch, the training loss for each epoch
-# and the validation loss for each epoch
-class Trainer:
-    def __init__(
-        self,
-        model,
-        n_epochs,
-        wandb_log=True,
-        device=None,
-        mg_patching_levels=0,
-        mg_patching_padding=0,
-        mg_patching_stitching=True,
-        log_test_interval=1,
-        log_output=False,
-        use_distributed=False,
-        verbose=True,
-    ):
-        """
-        A general Trainer class to train neural-operators on given datasets
-
-        Parameters
-        ----------
-        model : nn.Module
-        n_epochs : int
-        wandb_log : bool, default is True
-        device : torch.device
-        mg_patching_levels : int, default is 0
-            if 0, no multi-grid domain decomposition is used
-            if > 0, indicates the number of levels to use
-        mg_patching_padding : float, default is 0
-            value between 0 and 1, indicates the fraction of size to use as padding on each side
-            e.g. for an image of size 64, padding=0.25 will use 16 pixels of padding on each side
-        mg_patching_stitching : bool, default is True
-            if False, the patches are not stitched back together and the loss is instead computed per patch
-        log_test_interval : int, default is 1
-            how frequently to print updates
-        log_output : bool, default is False
-            if True, and if wandb_log is also True, log output images to wandb
-        use_distributed : bool, default is False
-            whether to use DDP
-        verbose : bool, default is True
-        """
-        self.n_epochs = n_epochs
-        self.wandb_log = wandb_log
-        self.log_test_interval = log_test_interval
-        self.log_output = log_output
-        self.verbose = verbose
-        self.mg_patching_levels = mg_patching_levels
-        self.mg_patching_stitching = mg_patching_stitching
-        self.use_distributed = use_distributed
-        self.device = device
-
-        if mg_patching_levels > 0:
-            self.mg_n_patches = 2**mg_patching_levels
-            if verbose:
-                print(f"Training on {self.mg_n_patches ** 2} multi-grid patches.")
-                sys.stdout.flush()
-        else:
-            self.mg_n_patches = 1
-            mg_patching_padding = 0
-            if verbose:
-                print(f"Training on regular inputs (no multi-grid patching).")
-                sys.stdout.flush()
-
-        self.mg_patching_padding = mg_patching_padding
-        self.patcher = MultigridPatching2D(
-            model,
-            levels=mg_patching_levels,
-            padding_fraction=mg_patching_padding,
-            use_distributed=use_distributed,
-            stitching=mg_patching_stitching,
-        )
-
-    def train(
-        self,
-        train_loader,
-        validation_loader,
-        output_encoder,
-        model,
-        optimizer,
-        scheduler,
-        regularizer,
-        training_loss=None,
-        eval_losses=None,
-    ):
-        """Trains the given model on the given datasets"""
-        n_train = len(train_loader.dataset)
-        history = {"Epochs": [], "train_err": [], "train_time": [], "val_loss": []}
-        # if not isinstance(test_loaders, dict):
-        #   test_loaders = dict(test=test_loaders)
-
-        if self.verbose:
-            print(f"Training on {n_train} samples")
-            # print(f'Testing on {[len(loader.dataset) for loader in test_loaders.values()]} samples'
-            #     f'         on resolutions {[name for name in test_loaders]}.')
-            sys.stdout.flush()
-
-        if training_loss is None:
-            training_loss = LpLoss(d=2)
-
-        if eval_losses is None:  # By default just evaluate on the training loss
-            eval_losses = dict(l2=training_loss)
-
-        if output_encoder is not None:
-            output_encoder.to(self.device)
-
-        if self.use_distributed:
-            is_logger = comm.get_world_rank() == 0
-        else:
-            is_logger = True
-
-        for epoch in range(self.n_epochs):
-            avg_loss = 0
-            avg_lasso_loss = 0
-            model.train()
-            t1 = default_timer()
-            train_err = 0.0
-
-            for idx, sample in enumerate(train_loader):
-                x, y = sample["x"], sample["y"]
-
-                if epoch == 0 and idx == 0 and self.verbose and is_logger:
-                    print(f"Training on raw inputs of size {x.shape=}, {y.shape=}")
-
-                x, y = self.patcher.patch(x, y)
-
-                if epoch == 0 and idx == 0 and self.verbose and is_logger:
-                    print(f".. patched inputs of size {x.shape=}, {y.shape=}")
-
-                x = x.to(self.device)
-                y = y.to(self.device)
-
-                optimizer.zero_grad(set_to_none=True)
-                if regularizer:
-                    regularizer.reset()
-
-                out = model(x)
-                if epoch == 0 and idx == 0 and self.verbose and is_logger:
-                    print(f"Raw outputs of size {out.shape=}")
-
-                out, y = self.patcher.unpatch(out, y)
-                # Output encoding only works if output is stiched
-                if output_encoder is not None and self.mg_patching_stitching:
-                    out = output_encoder.decode(out)
-                    y = output_encoder.decode(y)
-                if epoch == 0 and idx == 0 and self.verbose and is_logger:
-                    print(f".. Processed (unpatched) outputs of size {out.shape=}")
-
-                loss = training_loss(out.float(), y)
-
-                if regularizer:
-                    loss += regularizer.loss
-
-                loss.backward()
-
-                optimizer.step()
-                train_err += loss.item()
-
-                with torch.no_grad():
-                    avg_loss += loss.item()
-                    if regularizer:
-                        avg_lasso_loss += regularizer.loss
-
-            if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
-                scheduler.step(train_err)
-            else:
-                scheduler.step()
-
-            epoch_train_time = default_timer() - t1
-            del x, y
-
-            train_err /= n_train
-            avg_loss /= self.n_epochs
-            history["Epochs"].append(epoch)
-            history["train_err"].append(train_err)
-            history["train_time"].append(epoch_train_time)
-            # if epoch % self.log_test_interval == 0:
-
-            msg = f"[{epoch}] time={epoch_train_time:.2f}, avg_loss={avg_loss:.4f}, train_err={train_err:.4f}"
-
-            values_to_log = dict(
-                train_err=train_err, time=epoch_train_time, avg_loss=avg_loss
-            )
-            # for loader_name, loader in test_loaders.items():
-            # if epoch == self.n_epochs - 1 and self.log_output:
-            #     to_log_output = True
-            # else:
-            #     to_log_output = False
-
-            loader = validation_loader
-            loader_name = ""
-            errors = self.evaluate(
-                model, eval_losses, loader, output_encoder, log_prefix=loader_name
-            )
-
-            for loss_name, loss_value in errors.items():
-                msg += f", {loss_name}={loss_value:.4f}"
-                values_to_log[loss_name] = loss_value
-            history["val_loss"].append(values_to_log[loss_name])
-            if regularizer:
-                avg_lasso_loss /= self.n_epochs
-                msg += f", avg_lasso={avg_lasso_loss:.5f}"
-
-            if self.verbose and is_logger:
-                print(msg)
-                sys.stdout.flush()
-
-                # Wandb loging
-            if self.wandb_log and is_logger:
-                for pg in optimizer.param_groups:
-                    lr = pg["lr"]
-                    values_to_log["lr"] = lr
-                wandb.log(values_to_log, step=epoch, commit=True)
-        return history
-
-    def evaluate(
-        self, model, loss_dict, data_loader, output_encoder=None, log_prefix=""
-    ):
-        """Evaluates the model on a dictionary of losses
-
-        Parameters
-        ----------
-        model : model to evaluate
-        loss_dict : dict of functions
-          each function takes as input a tuple (prediction, ground_truth)
-          and returns the corresponding loss
-        data_loader : data_loader to evaluate on
-        output_encoder : used to decode outputs if not None
-        log_prefix : str, default is ''
-            if not '', used as prefix in output dictionary
-
-        Returns
-        -------
-        errors : dict
-            dict[f'{log_prefix}_{loss_name}] = loss for loss in loss_dict
-        """
-        model.eval()
-
-        if self.use_distributed:
-            is_logger = comm.get_world_rank() == 0
-        else:
-            is_logger = True
-
-        errors = {f"{log_prefix}_{loss_name}": 0 for loss_name in loss_dict.keys()}
-
-        n_samples = 0
-        with torch.no_grad():
-            for it, sample in enumerate(data_loader):
-                x, y = sample["x"], sample["y"]
-
-                n_samples += x.size(0)
-
-                x, y = self.patcher.patch(x, y)
-                y = y.to(self.device)
-                x = x.to(self.device)
-
-                out = model(x)
-
-                out, y = self.patcher.unpatch(out, y, evaluation=True)
-
-                if output_encoder is not None:
-                    out = output_encoder.decode(out)
-
-                if (it == 0) and self.log_output and self.wandb_log and is_logger:
-                    if out.ndim == 2:
-                        img = out
-                    else:
-                        img = out.squeeze()[0]
-                    wandb.log(
-                        {
-                            f"image_{log_prefix}": wandb.Image(
-                                img.unsqueeze(-1).cpu().numpy()
-                            )
-                        },
-                        commit=False,
-                    )
-
-                for loss_name, loss in loss_dict.items():
-                    errors[f"{log_prefix}_{loss_name}"] += loss(out, y).item()
-
-        del x, y, out
-
-        for key in errors.keys():
-            errors[key] /= n_samples
-
-        return errors
-
-
-# this function takes an input configuration and uses this to build a TFNO model in a format accepted by
-# the TFNO benchmark and then trains the model
-def build_and_train_model(config: dict, device="cpu", verbose: bool = 0):
-    """
-    this function takes an input configuration and uses this to build a TFNO model in
-    a format accepted by the TFNO benchmark and then trains the model
-    """
-    default_config = {
-
-        # General
-        # For computing compression
-        # 'n_params_baseline': None,
-
-        # Distributed computing
-        'distributed_seed': 666,
-
-        # FNO related
-        "lifting_channels": 256,
-        'data_channels': 3,
-        'n_modes_height': 16,
-        'n_modes_width': 16,
-        'hidden_channels': 32,
-        'projection_channels': 64,
-        'n_layers': 4,
-        'skip': 'linear',
-        'implementation': 'factorized',
-
-        "use_mlp": False,
-        "mlp": {"dropout": 0, "expansion": 0.5},
-
-        'factorization': None,
-        'rank': 1.0,
-
-        'stabilizer': None,
-        'fno_block_precision': 'half',
-
-        # Optimizer
-
-        'opt_n_epochs': 5,
-        'opt_learning_rate': 5e-3,
-        # 'opt_training_loss': 'h1',
-        'opt_weight_decay': 1e-4,
-
-        'opt_scheduler_T_max': 500,  # For cosine only, typically take n_epochs
-        'opt_scheduler_patience': 5,  # For ReduceLROnPlateau only
-        'opt_scheduler': 'StepLR',  # Or 'CosineAnnealingLR' OR 'ReduceLROnPlateau'
-        'opt_step_size': 60,
-        'opt_gamma': 0.5,
-
-        # Dataset related
-
-        'data_batch_size': 16,
-        'data_n_train': 1000,
-        'data_train_resolution': 16,
-        'data_n_tests': [100, 50],
-        'data_test_resolutions': [16, 32],
-        'data_test_batch_sizes': [16, 16],
-        'data_positional_encoding': True,
-
-        'data_encode_input': True,
-        'data_encode_output': False,
-
-        # Patching
-        'patching': {'levels': 0},
-        'patching_levels': 0,
-        'patching_padding': 0,
-        'patching_stitching': False,
-    }
-
-    default_config.update(config)
-    config_name = 'default'
-
-   
-
-    # Loading the Darcy flow dataset
-
-    (train_loader, validation_loader, test_loaders, output_encoder) = load_darcy_flow_small(
-        n_train=default_config['data_n_train'], batch_size=default_config['data_batch_size'],
-        positional_encoding=default_config['data_positional_encoding'],
-        test_resolutions=default_config['data_test_resolutions'], n_tests=default_config['data_n_tests'],
-        test_batch_sizes=default_config['data_test_batch_sizes'],
-        encode_input=default_config['data_encode_input'], encode_output=default_config['data_encode_output'],
-        )
-
-    model = TFNO2d(data_channels=default_config["data_channels"], n_modes_height=default_config['n_modes_height'],
-                   n_modes_width=default_config["n_modes_width"],
-                   n_layers=default_config["n_layers"], lifting_channels=default_config["lifting_channels"],
-                   hidden_channels=default_config["hidden_channels"],
-                   projection_channels=default_config["projection_channels"],
-                   factorization=default_config["factorization"], rank=default_config["rank"],
-                   use_mlp=default_config["use_mlp"], mlp=default_config["mlp"], skip=default_config["skip"],
-                   implementation=default_config["implementation"],
-                   stabilizer=default_config["stabilizer"], fno_block_precision=default_config["fno_block_precision"])
-    model = model.to(device)
-
-    # Log parameter count
-    n_params = count_params(model)
-
-
-    print(f'\nn_params: {n_params}')
-    sys.stdout.flush()
-
-
-    # Create the optimizer
-    optimizer = torch.optim.Adam(model.parameters(),
-                                 lr=default_config['opt_learning_rate'],
-                                 weight_decay=default_config['opt_weight_decay'])
-
-    if default_config['opt_scheduler'] == 'ReduceLROnPlateau':
-        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=default_config['opt_gamma'],
-                                                               patience=default_config['opt_scheduler_patience'],
-                                                               mode='min')
-    elif default_config['opt_scheduler'] == 'CosineAnnealingLR':
-        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=default_config['opt_scheduler_T_max'])
-    elif default_config['opt_scheduler'] == 'StepLR':
-        scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=default_config['opt_step_size'],
-                                                    gamma=default_config['opt_gamma'])
-    else:
-        raise ValueError(f"Got { default_config['opt_scheduler']= }")
-
-    # Creating the losses
-    l2loss = LpLoss(d=2, p=2)
-    h1loss = H1Loss(d=2)
-    train_loss = h1loss
-    eval_losses = dict(h1=train_loss)
-    # if default_config['opt_training_loss'] == 'l2':
-    #     train_loss = l2loss
-    #     eval_losses = dict(l2=train_loss)
-    # elif default_config['opt_training_loss'] == 'h1':
-    #     train_loss = h1loss
-    #     eval_losses = dict(h1=train_loss)
-    # else:
-    #     raise ValueError(f"Got training_loss={default_config['opt_training_loss']} but expected one of ['l2', 'h1']")
-    # # eval_losses = {'h1': h1loss, 'l2': l2loss}
-
-
-    print('\n### MODEL ###\n', model)
-    print('\n### OPTIMIZER ###\n', optimizer)
-    print('\n### SCHEDULER ###\n', scheduler)
-    print('\n### LOSSES ###')
-    print(f'\n * Train: {train_loss}')
-    print(f'\n * Test: {eval_losses}')
-    print(f'\n### Beginning Training...\n')
-    sys.stdout.flush()
-
-    trainer = Trainer(model, n_epochs=default_config['opt_n_epochs'], device=device,
-                      mg_patching_levels=default_config['patching_levels'],
-                      mg_patching_padding=default_config['patching_padding'],
-                      mg_patching_stitching=default_config['patching_stitching'], wandb_log=False, log_test_interval=1,
-                      log_output=True, use_distributed=False, verbose=True)
-
-    history = trainer.train(train_loader, validation_loader, output_encoder, model, optimizer, scheduler,
-                            regularizer=False, training_loss=train_loss, eval_losses=eval_losses, )
-
-    history["num_parameters"] = n_params
-
-    return model, history
-
-
-
-def bestevaluate(best_config):
-    """we design the model based on the best hyperparameter configuration,
-    we then evaluate this based on the training/validation and testing data"""
-
-    (
-        train_loader,
-        validation_loader,
-        test_loaders,
-        output_encoder,
-    ) = load_darcy_flow_small(
-        n_train=best_config["data_n_train"],
-        batch_size=best_config["data_batch_size"],
-        positional_encoding=best_config["data_positional_encoding"],
-        test_resolutions=[16, 32],
-        n_tests=[100, 50],
-        test_batch_sizes=[32, 32],
-        encode_input=best_config["data_encode_input"],
-        encode_output=best_config["data_encode_output"],
-    )
-    # the build_and_train_model function
-    # gives the training and validation losses
-    best_model, best_history = build_and_train_model(best_config, verbose=1)
-    # next we generate the testing losses
-    trainer = Trainer(
-        best_model,
-        n_epochs=best_config["opt_n_epochs"],
-        device=device,
-        mg_patching_levels=best_config["patching_levels"],
-        mg_patching_padding=best_config["patching_padding"],
-        mg_patching_stitching=best_config["patching_stitching"],
-        wandb_log=best_config["wandb_log"],
-        log_test_interval=best_config["wandb_log_test_interval"],
-        log_output=best_config["wandb_log_output"],
-        use_distributed=best_config["distributed_use_distributed"],
-        verbose=True,
-    )
-    for loader_name, loader in test_loaders.items():
-        l2loss = LpLoss(d=2, p=2)
-        h1loss = H1Loss(d=2)
-        eval_losses = {"h1": h1loss, "l2": l2loss}
-        errors = trainer.evaluate(
-            best_model, eval_losses, loader, output_encoder, log_prefix=loader_name
-        )
-        for loss_name, loss_value in errors.items():
-            best_history[loss_name] = loss_value
-
-    test_samples = test_loaders[32].dataset
-
-    fig = plt.figure(figsize=(7, 7))
-    for index in range(3):
-        data = test_samples[index]
-        # Input x
-        x = data["x"]
-        # Ground-truth
-        y = data["y"]
-        # Model prediction
-        out = best_model(x.unsqueeze(0))
-
-        ax = fig.add_subplot(4, 4, index * 4 + 1)
-        ax.imshow(x[0], cmap="gray")
-        if index == 0:
-            ax.set_title("Input x")
-        plt.xticks([], [])
-        plt.yticks([], [])
-
-        ax = fig.add_subplot(4, 4, index * 4 + 2)
-        ax.imshow(y.squeeze())
-        if index == 0:
-            ax.set_title("Ground-truth y")
-        plt.xticks([], [])
-        plt.yticks([], [])
-
-        ax = fig.add_subplot(4, 4, index * 4 + 3)
-        ax.imshow(out.squeeze().detach().numpy())
-        if index == 0:
-            ax.set_title("Model prediction")
-        plt.xticks([], [])
-        plt.yticks([], [])
-
-        ax = fig.add_subplot(4, 4, index * 4 + 4)
-        err = (np.abs(y.squeeze() - out.detach().numpy())) ** 2
-        im = ax.imshow(err.squeeze(), cmap="plasma", aspect="auto")
-        plt.colorbar(im, label="$|y - \hat{y}|^2$")
-        if index == 0:
-            ax.set_title("Squared Error")
-        plt.xticks([], [])
-        plt.yticks([], [])
-
-    fig.suptitle("Inputs, ground-truth output, prediction and absolute error.", y=0.98)
-    plt.tight_layout()
-    fig.show()
-    return best_history
diff --git a/lib/FNOBench/requirements.txt b/lib/FNOBench/requirements.txt
deleted file mode 100644
index 77a79ef..0000000
--- a/lib/FNOBench/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-neuraloperator
-tensorly
-tensorly-torch
-wandb
-zarr
\ No newline at end of file
diff --git a/lib/HPOBench/README.md b/lib/HPOBench/README.md
deleted file mode 100644
index 0ff9825..0000000
--- a/lib/HPOBench/README.md
+++ /dev/null
@@ -1,28 +0,0 @@
-# HPOBench
-
-✅ **Benchmark ready to be used.**
-
-This set of benchmarks provides precomputed evaluations of neural networks for hyperparameter optimization. This is particularly useful to compare optimization schemes without training neural networks for real.
-
-## Installation
-
-To install this benchmark the following command can be run:
-
-```console
-python -c "import deephyper_benchmark as dhb; dhb.install('HPOBench/tabular');"
-```
-
-For detailed information see the [NeurIPS paper describing the HPOBench benchmark](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/93db85ed909c13838ff95ccfa94cebd9-Abstract-round2.html).
-
-## Configuration
-
-Different parameters can be set to configure this benchmark.
-
-- Environment variable `DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME` with boolean value in `[0, 1]` which set if the "recorded training time" of each training epoch should be simulated or not. **Defaults to `0`.**
-- Environment variable `DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME` with real value $> 0$. It represents the proportion of the "recorded training time" which should be used for simulation. For example, `1.0` (default) corresponds to 100% of the training time while `0.1` corresponds to 10% of the training time. **Defaults to `1.0`.**
-- Environment variable `DEEPHYPER_BENCHMARK_TASK` to select the task on which to run the benchmark with values in:
-    - **`navalpropulsion` (Default)**
-    - `parkinsonstelemonitoring`
-    - `proteinstructure`
-    - `slicelocalization`
-- Environment variable `DEEPHYPER_BENCHMARK_MOO` with value `0` or `1` to select if the task should be run with single or multiple objectives. **Defaults to `0` for single-objective**.
\ No newline at end of file
diff --git a/lib/HPOBench/tabular/__init__.py b/lib/HPOBench/tabular/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/HPOBench/tabular/benchmark.py b/lib/HPOBench/tabular/benchmark.py
deleted file mode 100644
index 6906ef8..0000000
--- a/lib/HPOBench/tabular/benchmark.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class HPOBenchTabularNavalPropulsion(Benchmark):
-    version = "0.0.1"
-
-    requires = {
-        "bash-install": {
-            "step": "install",
-            "type": "cmd",
-            "cmd": os.path.join(DIR, "install.sh"),
-        },
-    }
diff --git a/lib/HPOBench/tabular/constant_predictor.py b/lib/HPOBench/tabular/constant_predictor.py
deleted file mode 100644
index 4a42b3b..0000000
--- a/lib/HPOBench/tabular/constant_predictor.py
+++ /dev/null
@@ -1,127 +0,0 @@
-from typing import Tuple
-
-import numpy as np
-import openml
-from sklearn.dummy import DummyRegressor
-from sklearn.model_selection import train_test_split
-from sklearn.metrics import mean_squared_error
-
-# Reference of HPOBench Paper: https://arxiv.org/pdf/2109.06716.pdf
-# Reference of original paper introducign these benchmarks: https://arxiv.org/pdf/1905.04970.pdf
-# Trying to reproduce as much as possible results/setup from the original paper
-
-map_task_to_openmlid = {
-    # https://archive.ics.uci.edu/dataset/316/condition+based+maintenance+of+naval+propulsion+plants
-    "navalpropulsion": "44969",
-    # https://archive.ics.uci.edu/dataset/189/parkinsons+telemonitoring
-    "parkinsonstelemonitoring": "4531",
-    # https://archive.ics.uci.edu/dataset/265/physicochemical+properties+of+protein+tertiary+structure
-    "proteinstructure": "44963",
-    # https://archive.ics.uci.edu/dataset/206/relative+location+of+ct+slices+on+axial+axis
-    "slicelocalization": "42973",
-}
-
-map_openmlid_to_task = {v: k for k, v in map_task_to_openmlid.items()}
-
-map_task_to_default_target_attribute = {
-    "navalpropulsion": ["gt_compressor_decay_state_coefficient"],
-    "parkinsonstelemonitoring": ["motor_UPDRS", "total_UPDRS"],
-    "proteinstructure": ["RMSD"],
-    "slicelocalization": ["reference"],
-}
-
-
-def load_from_openml(dataset_id: str) -> Tuple[np.ndarray, np.ndarray, dict]:
-    """Load dataset from openml library.
-
-    Args:
-        dataset_id (str): the identifier of the dataset to be loaded from the OpenML database.
-
-    Raises:
-        ValueError: if the dataset is not found.
-
-    Returns:
-        (np.ndarray, np.ndarray, dict): X, y, metadata where X is the input array, y is the target array, and metadata is a dictionary containing additional information about the data.
-    """
-    task_name = map_openmlid_to_task[dataset_id]
-    default_target_attribute = map_task_to_default_target_attribute[task_name]
-
-    dataset = openml.datasets.get_dataset(
-        dataset_id,
-        download_data=True,
-        download_qualities=True,
-        download_features_meta_data=True,
-    )
-
-    X, y, categorical_indicator, _ = dataset.get_data()
-    y = X[default_target_attribute]
-    X = X.drop(default_target_attribute, axis=1)
-    y = y.values
-
-    # Drop constant features
-    constant_features = []
-    continuous_features = []
-    for i in range(X.shape[1]):
-        values, _ = np.unique(X.values[:, i], return_counts=True)
-        if len(values) == 1:
-            constant_features.append(list(X.columns)[i])
-        elif len(values) >= 10:
-            continuous_features.append(list(X.columns)[i])
-
-    X = X.drop(constant_features, axis=1)
-
-    # Split into train and test
-    test_size = int(0.2 * len(X))
-    X_train, X_test, y_train, y_test = train_test_split(
-        X, y, test_size=test_size, random_state=42
-    )
-    X_train, X_valid, y_train, y_valid = train_test_split(
-        X_train, y_train, test_size=test_size, random_state=42
-    )
-
-    # Standard Normalization on features
-    m = np.mean(X_train[continuous_features], axis=0)
-    s = np.std(X_train[continuous_features], axis=0)
-    X_train[continuous_features] = (X_train[continuous_features] - m) / s
-    X_valid[continuous_features] = (X_valid[continuous_features] - m) / s
-    X_test[continuous_features] = (X_test[continuous_features] - m) / s
-
-    # Standard Normalization on targets
-    m = np.mean(y_train, axis=0)
-    s = np.std(y_train, axis=0)
-    y_train = (y_train - m) / s
-    y_valid = (y_valid - m) / s
-    y_test = (y_test - m) / s
-
-    return X_train.values, X_valid.values, X_test.values, y_train, y_valid, y_test
-
-
-if __name__ == "__main__":
-    # Some tests
-    task = "navalpropulsion"
-    # task = "parkinsonstelemonitoring"
-    # task = "proteinstructure"
-    # task = "slicelocalization"
-    print(f"Task: {task}")
-    openmlid = map_task_to_openmlid[task]
-    X_train, X_valid, X_test, y_train, y_valid, y_test = load_from_openml(openmlid)
-
-    print("Num samples:", X_train.shape[0] + X_valid.shape[0] + X_test.shape[0])
-    print("X_train.shape", X_train.shape)
-    print("X_valid.shape", X_valid.shape)
-    print("X_test.shape", X_test.shape)
-    print("y_train.shape", y_train.shape)
-    print("y_valid.shape", y_valid.shape)
-    print("y_test.shape", y_test.shape)
-
-    model = DummyRegressor(strategy="mean")
-    model.fit(X_train, y_train)
-
-    # As the data are normalized these values are expected to be close to 1.0
-    fold = ["train", "valid", "test"]
-    for i, (X, y) in enumerate(
-        zip([X_train, X_valid, X_test], [y_train, y_valid, y_test])
-    ):
-        print(f"Fold: {fold[i]}")
-        y_pred = model.predict(X)
-        print(f"  MSE: {mean_squared_error(y, y_pred)}")
diff --git a/lib/HPOBench/tabular/hpo.py b/lib/HPOBench/tabular/hpo.py
deleted file mode 100644
index 0c662ed..0000000
--- a/lib/HPOBench/tabular/hpo.py
+++ /dev/null
@@ -1,139 +0,0 @@
-import os
-import time
-
-import numpy as np
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-import hpobench.benchmarks.nas.tabular_benchmarks
-
-from deephyper.evaluator import profile, RunningJob
-from deephyper.hpo import HpProblem
-
-DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME = bool(
-    int(os.environ.get("DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME", 0))
-)
-DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME = float(
-    os.environ.get("DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME", 1.0)
-)
-DEEPHYPER_BENCHMARK_TASK = os.environ.get("DEEPHYPER_BENCHMARK_TASK", "navalpropulsion")
-DEEPHYPER_BENCHMARK_MOO = bool(int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 0)))
-
-
-map_task_to_benchmark = {
-    "navalpropulsion": "NavalPropulsionBenchmark",
-    "parkinsonstelemonitoring": "ParkinsonsTelemonitoringBenchmark",
-    "proteinstructure": "ProteinStructureBenchmark",
-    "slicelocalization": "SliceLocalizationBenchmark",
-}
-data_path = os.path.join(DIR, "build/HPOBench/data/fcnet_tabular_benchmarks/")
-
-
-benchmark_class = getattr(
-    hpobench.benchmarks.nas.tabular_benchmarks,
-    map_task_to_benchmark[DEEPHYPER_BENCHMARK_TASK],
-)
-b = benchmark_class(data_path=data_path)
-config_space = b.get_configuration_space()
-
-problem = HpProblem(config_space=config_space)
-
-
-@profile
-def run(job: RunningJob, optuna_trial=None) -> dict:
-    # otherwise failure
-    config = job.parameters
-
-    seed = int(job.id.split(".")[-1])
-    rng = np.random.RandomState(seed)
-    run_index = int(rng.choice((0, 1, 2, 3)))
-
-    benchmark_class = getattr(
-        hpobench.benchmarks.nas.tabular_benchmarks,
-        map_task_to_benchmark[DEEPHYPER_BENCHMARK_TASK],
-    )
-    b = benchmark_class(data_path=data_path)
-    budget_space = b.get_fidelity_space().get("budget")
-    min_b, max_b = budget_space.lower, budget_space.upper
-
-    # The objective is the coefficient of determination (R^2)
-    # As the target were standardized, the R^2 = 1 - MSE / MSE_baseline with MSE_baseline = 1.0
-    # The objective is maximized in deephyper
-    eval_test = b.objective_function_test(config, fidelity={"budget": max_b})
-    objective_test = 1 - eval_test["function_value"]
-    cost_eval = eval_test["cost"]
-    cost_step = cost_eval / (max_b - min_b + 1)
-
-    eval_val = b.objective_function(
-        config, fidelity={"budget": max_b}, run_index=run_index
-    )
-    objective_val = 1 - eval_val["function_value"]
-
-    other_metadata = {}
-
-    consumed_time = 0  # the cost here corresponds to time
-
-    if optuna_trial:
-        for budget_i in range(min_b, max_b + 1):
-            eval = b.objective_function(
-                config, fidelity={"budget": budget_i}, run_index=run_index
-            )
-
-            consumed_time += cost_step
-            if DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME:
-                time.sleep(cost_step * DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME)
-
-            objective_i = 1 - eval["function_value"]  # maximizing in deephyper
-
-            # Trial report is not support for MOO in Optuna
-            if DEEPHYPER_BENCHMARK_MOO:
-                continue
-
-            optuna_trial.report(objective_i, step=budget_i)
-            if optuna_trial.should_prune():
-                break
-
-        objective = objective_i
-
-    else:
-        for budget_i in range(min_b, max_b + 1):
-            eval = b.objective_function(
-                config, fidelity={"budget": budget_i}, run_index=run_index
-            )
-
-            consumed_time += cost_step
-            if DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME:
-                time.sleep(cost_step * DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME)
-
-            objective_i = 1 - eval["function_value"]  # maximizing in deephyper
-            job.record(budget_i, objective_i)
-            if job.stopped():
-                break
-
-        objective = job.objective
-
-        if hasattr(job, "stopper") and hasattr(job.stopper, "infos_stopped"):
-            other_metadata["infos_stopped"] = job.stopper.infos_stopped
-
-    metadata = {
-        "budget": budget_i,
-        "stopped": budget_i < max_b,
-        "objective_test": objective_test,
-        "objective_val": objective_val,
-    }
-    metadata.update(other_metadata)
-
-    return {
-        "objective": (objective, -consumed_time)
-        if DEEPHYPER_BENCHMARK_MOO
-        else objective,
-        "metadata": metadata,
-    }
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/HPOBench/tabular/install.sh b/lib/HPOBench/tabular/install.sh
deleted file mode 100755
index 1eb5d47..0000000
--- a/lib/HPOBench/tabular/install.sh
+++ /dev/null
@@ -1,39 +0,0 @@
-#!/bin/bash
-
-set -x
-
-mkdir build/ && cd build
-
-# Clone HPOBench repository
-git clone https://github.com/automl/HPOBench.git
-cd HPOBench/
-
-# Freeze commit used for the benchmark
-git checkout d8b45b1eca9a61c63fe79cdfbe509f77d3f5c779
-
-# Relax the constraint on the Python version required
-sed -i '' 's/>=3.6, <=3.10/>=3.6, <3.12/g' setup.py
-
-# Download data
-mkdir data && cd data/
-
-# For tabular benchmarks
-wget http://ml4aad.org/wp-content/uploads/2019/01/fcnet_tabular_benchmarks.tar.gz
-tar xf fcnet_tabular_benchmarks.tar.gz
-
-# Install other dependencies
-cd ..
-pip install "."
-pip install git+https://github.com/google-research/nasbench.git@master
-
-git clone https://github.com/automl/nas_benchmarks.git
-cd nas_benchmarks
-
-# Freeze commit used for the benchmark
-git checkout 1b09906ba3f522f15766b75643423acccd9db3a5
-
-# Comment out the last line of the file
-sed -i '' '5 s/./#&/' tabular_benchmarks/__init__.py
-
-python setup.py install
-
diff --git a/lib/HPOBench/tabular/metrics.py b/lib/HPOBench/tabular/metrics.py
deleted file mode 100644
index d135e38..0000000
--- a/lib/HPOBench/tabular/metrics.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import json
-import os
-
-import numpy as np
-
-from .hpo import b as BENCHMARK
-
-
-class PerformanceEvaluator:
-    """A class defining performance evaluators for the DTLZ problems."""
-
-    def __init__(self):
-        """Read the current problem defn from environment vars."""
-
-        # Retrieve the Best Configuration
-        configs, te, ve = [], [], []
-        for k in BENCHMARK.benchmark.data.keys():
-            configs.append(json.loads(k))
-            te.append(np.min(BENCHMARK.benchmark.data[k]["final_test_error"]))
-            ve.append(np.min(np.mean(BENCHMARK.benchmark.data[k]["valid_mse"], axis=0)))
-
-        idx_opt_ve = np.argmin(ve)
-        idx_opt_te = np.argmin(te)
-
-        # Configuration, Validation Error, Test Error based on the best validation error
-        self.x_min_on_valid = dict(configs[idx_opt_ve])
-        self.y_min_valid_on_valid = ve[idx_opt_ve]
-        self.y_min_test_on_valid = te[idx_opt_ve]
-
-        # Configuration, Validation Error, Test Error based on the best final test error
-        # only available for this benchmark
-        self.x_min_on_test = dict(configs[idx_opt_te])
-        self.y_min_valid_on_test = ve[idx_opt_te]
-        self.y_min_test_on_test = te[idx_opt_te]
-
-    def simple_regret_valid(self, y_valid: np.ndarray) -> np.ndarray:
-        """Compute the regret of the objective (validation RMSE) of a list of ordered solutions.
-
-        Args:
-            y_valid (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y_valid - self.y_min_valid_on_valid
-
-    def cumul_regret_valid(self, y_valid: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of the objective (validation RMSE) aon n array of ordered solutions.
-
-        Args:
-            y_valid (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret_valid(y_valid))
-
-    def simple_regret_test(self, y_test: np.ndarray) -> np.ndarray:
-        """Compute the regret of the test RMSE of a list of ordered solutions.
-
-        Args:
-            y_test (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y_test - self.y_min_test_on_test
-
-    def cumul_regret_test(self, y_test: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of the test RMSE aon n array of ordered solutions.
-
-        Args:
-            y_test (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret_test(y_test))
diff --git a/lib/HPOBench/tabular/test.py b/lib/HPOBench/tabular/test.py
deleted file mode 100644
index f4c197e..0000000
--- a/lib/HPOBench/tabular/test.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import os
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-from hpobench.benchmarks.nas.tabular_benchmarks import NavalPropulsionBenchmark
-
-b = NavalPropulsionBenchmark(
-    data_path=os.path.join(DIR, "build/HPOBench/data/fcnet_tabular_benchmarks/")
-)
-print(b.fidelity_space)
-
-config = b.get_configuration_space(seed=1).sample_configuration()
-
-result_dict = b.objective_function(configuration=config, fidelity={"budget": 50}, rng=1)
-
-# returns results on the highest budget
-result_dict = b.objective_function(configuration=config, rng=1)
-
-print(result_dict)
diff --git a/lib/JAHSBench/README.md b/lib/JAHSBench/README.md
deleted file mode 100644
index 26ac239..0000000
--- a/lib/JAHSBench/README.md
+++ /dev/null
@@ -1,176 +0,0 @@
-# JAHS Benchmark Suite
-
-> **Warning**
-> Work in progress, this benchmark is not yet ready.
-
-This module contains a DeepHyper wrapper for
- [JAHS-Bench-201](https://github.com/automl/jahs_bench_201).
-
-JAHSBench implements a random forest surrogate model, trained on real-world
-performance data for neural networks trained on three standard benchmark
-problems:
- - ``fashion_mnist`` (**default**)
- - ``cifar10`` 
- - ``colorectal_histology``
-
-Using these models as surrogates for the true performance, we can use this
-benchmark problem to study the performance of AutoML techniques on joint
-architecture-hyperparameter search tasks at minimal expense.
-
-The models allow us to tune 2 continuous training hyperparameters
- - ``LearningRate`` and
- - ``WeightDecay``,
-
-2 categorical training hyperparameters
- - ``Activation`` and
- - ``TrivialAugment``,
-
-and 5 categorical architecture parameters
- - ``Op{i}`` for ``i=0, ..., 4``.
-
-For DeepHyper's implementation, we have added 9th integer-valued parameter,
-which is the number of epochs trained
- - ``nepochs``.
-
-When run with the option ``wait=True``, ``JAHSBench`` will wait for an
-amount of time proportional to the ``runtime`` field returned by
-JAHS-Bench-201's surrogates. By default, this is 1% of the true runtime.
-
-The benchmark can be run to tune a single objective (``valid-acc``) or
-three objectives (``valid-acc``, ``latency``, and ``size_MB``).
-*Note that in the original JAHS-Bench-201 benchmark, there are only 2
-objectives and ``size_MB`` is not included as an objective.*
-
-For further information, see:
-
-```
-    @inproceedings{NEURIPS2022_fd78f2f6,
-        author = {Bansal, Archit and Stoll, Danny and Janowski, Maciej and Zela, Arber and Hutter, Frank},
-        booktitle = {Advances in Neural Information Processing Systems},
-        editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
-        pages = {38788--38802},
-        publisher = {Curran Associates, Inc.},
-        title = {JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search},
-        url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/fd78f2f65881c1c7ce47e26b040cf48f-Paper-Datasets_and_Benchmarks.pdf},
-        volume = {35},
-        year = {2022}
-    }
-```
-
-## Installation
-
-To install this benchmark the following command can be run:
-```
-python -c "import deephyper_benchmark as dhb; dhb.install('JAHSBench');"
-```
-
-## Configuration
-
-Prior to initialize the problem, set the following environment variables
-to configure the JAHS-Bench problem:
-- ``DEEPHYPER_BENCHMARK_MOO`` to `0` for single objective runs or `1` for
-  multiobjective runs. Defaults to `1`.
-- ``DEEPHYPER_BENCHMARK_JAHS_PROB`` to one of the following:
-  `fashion_mnist` (default), `cifar10`, or `colorectal_histology`. 
-
-## Metadata
-
-In addition to DeepHyper's standard metadata (timestamps), the following metadata
-is produced by JAHS-Bench-201, and recorded by DeepHyper:
-- ``m:size_MB``,
-- ``m:runtime``,
-- ``m:latency``,
-- ``m:FLOPS``,
-- ``m:valid-acc``,
-- ``m:train-acc``, and
-- ``m:test-acc``.
-
-The ``valid-acc`` is used to compute the objective in the single objective
-case.
-Additionally, the negative values of the ``latency`` and ``size_MB`` are
-used in the multiobjective case.
-All metadata is recorded regardless of the case.
-
-## Usage
-
-To use the benchmark follow this example set of instructions:
-
-```python
-
-import deephyper_benchmark as dhb
-
-
-# Load JAHS-bench-201
-dhb.load("JAHSBench")
-
-from deephyper_benchmark.lib.jahsbench import hpo
-
-# Example of running one evaluation of JAHSBench
-from deephyper.evaluator import RunningJob
-config = hpo.problem.jahs_obj.__sample__() # get a default config to test
-res = hpo.run(RunningJob(parameters=config))
-
-```
-
-Note that JAHS-Bench-201 uses XGBoost, which may not be compatible with older
-versions of MacOS.
-Additionally, the surrogate data has been pickled with an older version
-of scikit-learn and newer versions will fail to correctly load the surrogate
-models.
-
-For more information, see the following GitHub issues:
- - https://github.com/automl/jahs_bench_201/issues/6
- - https://github.com/automl/jahs_bench_201/issues/18
-
-## Evaluating Results
-
-To evaluate the results, the AutoML team recommends using the validation
-error for single-objective runs or the hypervolume metric over both
-validation error and evaluation latency for multiobjective-runs.
-See their
-[Evaluation Protocol](https://automl.github.io/jahs_bench_201/evaluation_protocol)
-for more details.
-
-**For multiobjective problems:**
-In their original benchmark, no recommended reference point is given,
-as discussed in
-[this GitHub issue](https://github.com/automl/jahs_bench_201/issues/19).
-Since we have already modified the original problem, we have taken the
-liberty of assigning a reference point.
-This reference point was chosen to include all true solutions for the
-``latency`` and ``size_MB`` objectives.
-These values were chosen by observing the true Pareto front for the raw data.
-However, we have purposefully created a lower bound on the "interesting range"
-for the ``valid-acc`` objective, which is higher than it was before.
-In particular, no accuracies less than 95% will be considered when
-computing hypervolume scores for the Fashion MNIST dataset,
-no accuracies less than 90% are considered for the CIFAR-10 dataset,
-and no accuracies less than 93% are considered for the colorectal histology
-dataset.
-When solving any of these problems via DeepHyper, the ``moo_lower_bounds``
-argument should be set accordingly.
-To evaluate hypervolume with these reference points, use our metrics as
-shown below
-
-```python
-
-from deephyper_benchmark.lib.jahsbench import metrics
-evaluator = metrics.PerformanceEvaluator()
-hv = evaluator.hypervolume(res)
-
-```
-
-## Additional Problem Details
-
-Each problem in JAHS-Bench-201 is given by a XGBoost **surrogate** for a
-joint neural network architecture/hyperparameter search problem.
-Surrogates are given for networks trained to solve image classification tasks
-for three different datasets.
-These datasets are listed below and have the following properties:
-
-- ``fashion_mnist`` is based on the Fashion MNIST dataset (https://github.com/zalandoresearch/fashion-mnist).
-  This problem has lower bound of 95% on the "interesting range" of validation accuracies, and the Kendall's tau for the XGBoost model is over 92% for all 3 objectives (92.2% for the ``valid-acc`` objective).
-- ``cifar10`` is based on the Cifar-10 dataset (https://www.cs.toronto.edu/~kriz/cifar.html)
-  This problem has lower bound of 90% on the "interesting range" of validation accuracies, and the Kendall's tau for the XGBoost model is over 89% for all 3 objectives (89% for the ``valid-acc`` objective).
-- ``colorecal-histology`` is based on the colorectal histology dataset (https://zenodo.org/record/53169#.XGZemKwzbmG).
-  This problem has lower bound of 93% on the "interesting range" of validation accuracies, and the Kendall's tau for the XGBoost model is considerably lower than with the other datasets (just 68.2% for the ``valid-acc`` objective), so it may not be an accurate representation of the true problem.
diff --git a/lib/JAHSBench/__init__.py b/lib/JAHSBench/__init__.py
deleted file mode 100644
index f102a9c..0000000
--- a/lib/JAHSBench/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-__version__ = "0.0.1"
diff --git a/lib/JAHSBench/benchmark.py b/lib/JAHSBench/benchmark.py
deleted file mode 100644
index 2b99dc3..0000000
--- a/lib/JAHSBench/benchmark.py
+++ /dev/null
@@ -1,22 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class JAHS201Benchmark(Benchmark):
-    version = "0.0.1"
-    
-    requires = {
-        "py-pip-requirements": {
-            "step": "install",
-            "type": "pip",
-            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
-        },
-        "bash-install": {
-            "step": "install",
-            "type": "cmd",
-            "cmd": "cd . && bash " + os.path.join(DIR, "./install.sh"),
-        },
-    }
diff --git a/lib/JAHSBench/hpo.py b/lib/JAHSBench/hpo.py
deleted file mode 100644
index 4e8738a..0000000
--- a/lib/JAHSBench/hpo.py
+++ /dev/null
@@ -1,57 +0,0 @@
-import os
-import numpy as np
-import time
-
-from deephyper.evaluator import profile, RunningJob
-from deephyper.hpo import HpProblem
-from . import model
-
-
-# Read in whether to do single- or multi-objectives
-multiobj = int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 1))
-prob_name = os.environ.get("DEEPHYPER_BENCHMARK_JAHS_PROB", "fashion_mnist")
-
-# Create problem
-problem = HpProblem()
-jahs_obj = model.jahs_bench(dataset=prob_name)
-# 2 continuous hyperparameters
-problem.add_hyperparameter((1e-3, 1.0, "log-uniform"), "LearningRate")
-problem.add_hyperparameter((1e-5, 1e-3, "log-uniform"), "WeightDecay")
-# 2 categorical hyperparameters
-problem.add_hyperparameter(["ReLU", "Hardswish", "Mish"], "Activation")
-problem.add_hyperparameter(["on", "off"], "TrivialAugment")
-# 6 categorical architecture design variables
-for i in range(1, 7):
-    problem.add_hyperparameter([0, 1, 2, 3, 4], f"Op{i}")
-# 1 integer hyperparameter number of training epochs (1 to 200)
-problem.add_hyperparameter((1, 200), "nepochs")
-
-
-@profile
-def run(job: RunningJob, sleep=False, sleep_scale=0.01) -> dict:
-    config = job.parameters
-    result = jahs_obj(config)
-
-    if sleep:
-        t_sleep = result["runtime"] * sleep_scale
-        time.sleep(t_sleep)
-
-    dh_data = {}
-    dh_data["metadata"] = result
-    if multiobj:
-        dh_data["objective"] = [
-            result["valid-acc"],
-            -result["latency"],
-            -result["size_MB"],
-        ]
-    else:
-        dh_data["objective"] = result["valid-acc"]
-    return dh_data
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/JAHSBench/install.sh b/lib/JAHSBench/install.sh
deleted file mode 100755
index 76a97b8..0000000
--- a/lib/JAHSBench/install.sh
+++ /dev/null
@@ -1 +0,0 @@
-python -m jahs_bench.download --target surrogates
diff --git a/lib/JAHSBench/metrics.py b/lib/JAHSBench/metrics.py
deleted file mode 100644
index f6040f7..0000000
--- a/lib/JAHSBench/metrics.py
+++ /dev/null
@@ -1,119 +0,0 @@
-import numpy as np
-from deephyper.skopt.moo import pareto_front, hypervolume
-
-
-class PerformanceEvaluator:
-    """ A class defining performance evaluators for JAHS Bench 201 problems.
-
-    Contains the following public methods:
-
-     * `__init__()` constructs a new instance by reading the problem
-       definition from environment variables,
-     * `hypervolume(pts)` calculates the total hypervolume dominated by
-       the current point set, w.r.t. the reference point and filtering out
-       solutions that do not dominate the reference point,
-     * `refPt()` calculates a reference point for the current problem, and
-     * `numPts(pts)` calculates the number of solution points that dominate
-       the reference point.
-
-    """
-
-    def __init__(self):
-        """ Read the current JAHS-Bench-201 problem definition and initialize. """
-
-        import os
-
-        multiobj = int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 1))
-        prob_name = os.environ.get("DEEPHYPER_BENCHMARK_JAHS_PROB", "fashion_mnist")
-        self.p_name = prob_name
-        if multiobj:
-            self.nobjs = 3
-        else:
-            self.nobjs = 1
-
-    def hypervolume(self, pts):
-        """ Calculate the hypervolume dominated, w.r.t. the reference point.
-
-        Args:
-            pts (numpy.ndarray): A 2d array of objective values.
-                Each row is an objective value in the solution set.
-
-        Returns:
-            float: The total hypervolume dominated by the current solution
-            w.r.t. the reference point, after filtering out points worse than
-            the reference point.
-
-        """
-
-        if self.nobjs < 2:
-            raise ValueError("Cannot calculate hypervolume for 1 objective")
-        if pts.size > 0 and pts[0, 0] > 0:
-            filtered_pts = -pts.copy()
-        else:
-            filtered_pts = pts.copy()
-        rp = self.refPt()
-        for i in range(pts.shape[0]):
-            if np.any(filtered_pts[i, :] > rp):
-                filtered_pts[i, :] = rp
-        return hypervolume(filtered_pts, rp)
-
-    def refPt(self):
-        """ Calculate the reference point for the given problem definition. """
-
-        if self.p_name in ["fashion_mnist"]:
-            rp = np.ones(self.nobjs)
-            rp[0] = -95
-            if self.nobjs > 1:
-                rp[1] = 1.75
-                rp[2] = 0.6
-            return rp
-        elif self.p_name in ["cifar10"]:
-            rp = np.ones(self.nobjs)
-            rp[0] = -90
-            if self.nobjs > 1:
-                rp[1] = 4.0
-                rp[2] = 0.9
-            return rp
-        elif self.p_name in ["colorectal_histology"]:
-            rp = np.ones(self.nobjs)
-            rp[0] = -93
-            if self.nobjs > 1:
-                rp[1] = 4.0
-                rp[2] = 0.4
-            return rp
-        else:
-            raise ValueError(f"{self.p_name} is not a valid problem")
-
-    def numPts(self, pts):
-        """ Calculate the number of solutions that dominate the reference point.
-
-        Args:
-            pts (numpy.ndarra): A 2d array of objective values.
-                Each row is an objective value in the solution set.
-
-        Returns:
-            int: The number of fi in pts such that all(fi < self.refPt).
-
-        """
-
-        if np.any(pts < 0):
-            pareto_pts = pareto_front(-pts)
-        else:
-            pareto_pts = pareto_front(pts)
-        return sum([all(fi <= self.refPt()) for fi in pareto_pts])
-
-
-if __name__ == "__main__":
-    """ Driver code to test performance metrics. """
-
-    result = np.array([[90, -1, -2],
-                       [94, -0.1, -0.2],
-                       [96, -0.75, -0.1],
-                       [99.0, -0.5, -200.0]])
-
-    evaluator = PerformanceEvaluator()
-
-    assert abs(evaluator.hypervolume(result) - 0.5) < 1.0e-8
-    assert evaluator.numPts(result) == 1
-    assert np.all(np.abs(evaluator.refPt() - np.array([-95.0, 1.75, 0.6]))
-                  < 1.0e-8)
diff --git a/lib/JAHSBench/model.py b/lib/JAHSBench/model.py
deleted file mode 100644
index 6f0eef4..0000000
--- a/lib/JAHSBench/model.py
+++ /dev/null
@@ -1,80 +0,0 @@
-""" This module contains objective function implementations of the JAHS 201
-benchmark suite, implemented as DeepHyper compatible models.
-
-"""
-
-import numpy as np
-
-class jahs_bench:
-    """ A callable class implementing the JAHS benchmark problems. """
-
-    def __init__(self, dataset="fashion_mnist"):
-        """ Import and configure the jahs-bench module.
-
-        Args:
-            dataset (str): One of "cifar10", "colorectal_histology",
-                or "fashion_mnist" (default)
-
-        """
-
-        from jahs_bench.api import Benchmark
-        import os
-
-        ### JAHS bench settings ###
-        MODEL_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)),
-                                  "jahs_bench_data")
-        # Define the benchmark
-        self.benchmark = Benchmark(
-                task=dataset,
-                save_dir=MODEL_PATH,
-                kind="surrogate",
-                download=False
-            )
-
-    def __sample__(self):
-        """ Randomly sample a JAHS-Bench-201 configuration.
-
-        Returns:
-            dict: A configuration dictionary.
-
-        """
-
-        config = self.benchmark.sample_config()
-        config['nepochs'] = np.random.randint(1, 200)
-        return config
-
-    def __call__(self, x):
-        """ DeepHyper compatible objective function calling jahs-bench.
-
-        Args:
-            x (dict): Configuration dictionary with same keys as jahs-bench.
-
-        Returns:
-            tuple of floats: In order: accuracy (to be maximized), latency
-            (to be minimized).
-
-        """
-
-        # Default config
-        config = {
-            'Optimizer': 'SGD',
-            'N': 5,
-            'W': 16,
-            'Resolution': 1.0,
-        }
-        # Update config using x
-        for key in x.keys():
-            config[key] = x[key]
-        # Special rule for setting "TrivialAugment"
-        if x['TrivialAugment'] == "on":
-            config['TrivialAugment'] = True
-        else:
-            config['TrivialAugment'] = False
-        # Check for nepochs
-        nepochs = 200
-        if 'nepochs' in x.keys():
-            nepochs = x['nepochs']
-        # Evaluate and return
-        fx = np.zeros(2)
-        result = self.benchmark(config, nepochs=nepochs)
-        return result[nepochs]
diff --git a/lib/JAHSBench/requirements.txt b/lib/JAHSBench/requirements.txt
deleted file mode 100644
index 702f41c..0000000
--- a/lib/JAHSBench/requirements.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-jahs-bench
-xgboost
diff --git a/lib/LCBench/Makefile b/lib/LCBench/Makefile
deleted file mode 100644
index cb13f98..0000000
--- a/lib/LCBench/Makefile
+++ /dev/null
@@ -1,14 +0,0 @@
-
-COMMIT=8de69dc0e7f7b4baee7129d599e74cc3e09b6d06
-
-build:
-	mkdir -p build
-	wget https://github.com/automl/LCBench/archive/$(COMMIT).zip -O build/LCBench.zip
-	unzip build/LCBench.zip -d build/
-	mv build/LCBench-$(COMMIT) build/LCBench
-	mkdir -p build/LCBench/data
-	wget https://figshare.com/ndownloader/files/21188607 -O build/LCBench/data/data_2k.zip
-	unzip build/LCBench/data/data_2k.zip -d build/LCBench/data/
-
-clean:
-	rm -rf build/
\ No newline at end of file
diff --git a/lib/LCBench/README.md b/lib/LCBench/README.md
deleted file mode 100644
index 14ff7ae..0000000
--- a/lib/LCBench/README.md
+++ /dev/null
@@ -1,19 +0,0 @@
-# LCBench
-
-✅ **Benchmark ready to be used.**
-
-This benchmark is only compatible with random search and only interesting to benchmark multi-fidelity algorithms that do not consider hyperparameter optimization. Candidate learners are represented by an id.
-
-## Installation
-
-To install the `LCBench` benchmark, run:
-```console
-python -c "import deephyper_benchmark as dhb; dhb.install('LCBench');"
-```
-
-Run the hyperparameter search
-```
-import deephyper_benchmark as dhb
-dhb.load("LCBench")
-from deephyper_benchmark.lib.lcbench import hpo
-```
\ No newline at end of file
diff --git a/lib/LCBench/__init__.py b/lib/LCBench/__init__.py
deleted file mode 100644
index 68a63b1..0000000
--- a/lib/LCBench/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-
-datasets = ['APSFailure', 'Amazon_employee_access', 'Australian', 'Fashion-MNIST', 'KDDCup09_appetency', 'MiniBooNE', 'adult', 'airlines', 'albert', 'bank-marketing', 'blood-transfusion-service-center', 'car', 'christine', 'cnae-9', 'connect-4', 'covertype', 'credit-g', 'dionis', 'fabert', 'helena', 'higgs', 'jannis', 'jasmine', 'jungle_chess_2pcs_raw_endgame_complete', 'kc1', 'kr-vs-kp', 'mfeat-factors', 'nomao', 'numerai28.6', 'phoneme', 'segment', 'shuttle', 'sylvine', 'vehicle', 'volkert']
\ No newline at end of file
diff --git a/lib/LCBench/benchmark.py b/lib/LCBench/benchmark.py
deleted file mode 100644
index 18ddea6..0000000
--- a/lib/LCBench/benchmark.py
+++ /dev/null
@@ -1,18 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class LCBench(Benchmark):
-    version = "0.0.1"
-
-    requires = {
-        "makefile": {"step": "install", "type": "cmd", "cmd": "make build"},
-        "lcdbench-api": {
-            "step": "load",
-            "type": "pythonpath",
-            "path": f"{DIR}/../build/LCBench/",
-        },
-    }
diff --git a/lib/LCBench/hpo.py b/lib/LCBench/hpo.py
deleted file mode 100644
index 9902dc0..0000000
--- a/lib/LCBench/hpo.py
+++ /dev/null
@@ -1,78 +0,0 @@
-import os
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-from deephyper.evaluator import profile, RunningJob
-from deephyper.hpo import HpProblem
-
-dataset = os.environ.get("DEEPHYPER_BENCHMARK_LCBENCH_DATASET", "APSFailure")
-
-data_path = os.path.join(DIR, "../build/LCBench/data/data_2k.json")
-
-from api import Benchmark as LCBenchBenchmark
-bench = LCBenchBenchmark(data_path, cache=True, cache_dir=os.path.join(DIR, "../build/LCBench/data/"))
-
-num_elements = 2000
-
-problem = HpProblem()
-problem.add_hyperparameter((0, num_elements - 1), "index")
-
-
-@profile
-def run(job: RunningJob, optuna_trial=None) -> dict:
-
-    
-    # otherwise failure
-    idx = job.parameters["index"]
-    y_valid = bench.query(dataset, "Train/val_cross_entropy", idx)[1:51]
-    y_test  = bench.query(dataset, "Train/test_cross_entropy", idx)[1:51]
-
-    min_b, max_b = 1, 50 
-
-    objective_val = -y_valid[-1]
-    objective_test = -y_test[-1]
-
-    other_metadata = {}
-
-    if optuna_trial:
-
-        for budget_i in range(min_b, max_b + 1):
-            objective_i = -y_valid[budget_i-1] # maximizing in deephyper
-            optuna_trial.report(objective_i, step=budget_i)
-            if optuna_trial.should_prune():
-                break
-
-        objective = objective_i
-
-    else:
-
-        for budget_i in range(min_b, max_b + 1):
-            objective_i = -y_valid[budget_i-1]  # maximizing in deephyper
-            job.record(budget_i, objective_i)
-            if job.stopped():
-                break
-
-        objective = job.objective
-
-        if hasattr(job, "stopper") and hasattr(job.stopper, "infos_stopped"):
-            other_metadata["infos_stopped"] = job.stopper.infos_stopped
-
-    metadata = {
-        "budget": budget_i,
-        "stopped": budget_i < max_b,
-        "objective_test": objective_test,
-        "objective_val": objective_val,
-    }
-    metadata.update(other_metadata)
-    return {
-        "objective": objective,
-        "metadata": metadata,
-    }
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/LCDB/README.md b/lib/LCDB/README.md
deleted file mode 100644
index a9d798d..0000000
--- a/lib/LCDB/README.md
+++ /dev/null
@@ -1,4 +0,0 @@
-# Learning Curve Databased Benchmark
-
-> **Warning**
-> Work in progress, this benchmark is not yet ready.
diff --git a/lib/LCDB/algorithm_selection/__init__.py b/lib/LCDB/algorithm_selection/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/LCDB/hyperparameter_optimization/__init__.py b/lib/LCDB/hyperparameter_optimization/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/LCDB/hyperparameter_optimization/benchmark.py b/lib/LCDB/hyperparameter_optimization/benchmark.py
deleted file mode 100644
index 377c4c8..0000000
--- a/lib/LCDB/hyperparameter_optimization/benchmark.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class LCDBHyperparameterOptimization(Benchmark):
-    """LCDB 2.0
-    """
-
-    version = "0.0.1"
diff --git a/lib/LCDB/hyperparameter_optimization/hpo.py b/lib/LCDB/hyperparameter_optimization/hpo.py
deleted file mode 100644
index 45100ae..0000000
--- a/lib/LCDB/hyperparameter_optimization/hpo.py
+++ /dev/null
@@ -1,216 +0,0 @@
-import gzip
-import os
-import time
-
-import numpy as np
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-from deephyper.evaluator import RunningJob, profile
-from deephyper.hpo import HpProblem
-from lcdb.analysis import read_csv_results
-from lcdb.analysis.json import (
-    QueryAnchorValues,
-    QueryMetricValuesFromAnchors,
-    QueryEpochValues,
-    QueryMetricValuesFromEpochs,
-)
-from lcdb.analysis.score import balanced_accuracy_from_confusion_matrix
-from lcdb.utils import import_attr_from_module
-
-# DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME = bool(
-#     int(os.environ.get("DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME", 0))
-# )
-# DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME = float(
-#     os.environ.get("DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME", 1.0)
-# )
-
-# DEEPHYPER_BENCHMARK_FIDELITY = os.environ.get("DEEPHYPER_BENCHMARK_FIDELITY", "anchor")
-# DEEPHYPER_BENCHMARK_WORKFLOW = "lcdb.workflow.sklearn.KNNWorkflow"
-# DEEPHYPER_BENCHMARK_CSV = os.environ.get(
-#     "DEEPHYPER_BENCHMARK_CSV",
-#     "/Users/romainegele/Documents/Research/LCDB/lcdb/publications/2023-neurips/experiments/alcf/polaris/knn/output/lcdb.workflow.sklearn.KNNWorkflow/3/42-42-42/results.csv.gz",
-# )
-DEEPHYPER_BENCHMARK_MAX_FIDELITY = int(
-    os.environ.get("DEEPHYPER_BENCHMARK_MAX_FIDELITY", 100)
-)
-DEEPHYPER_BENCHMARK_FIDELITY = os.environ.get("DEEPHYPER_BENCHMARK_FIDELITY", "epoch")
-DEEPHYPER_BENCHMARK_CSV = os.environ.get(
-    "DEEPHYPER_BENCHMARK_CSV",
-    "/Users/romainegele/Documents/Research/LCDB/lcdb/publications/2023-neurips/experiments/alcf/polaris/densenn/output/lcdb.workflow.keras.DenseNNWorkflow/3/42-42-42/results.csv.gz",
-)
-
-
-# WorkflowClass = import_attr_from_module(DEEPHYPER_BENCHMARK_WORKFLOW)
-# config_space = WorkflowClass.config_space()
-# problem = HpProblem(config_space=config_space)
-problem = HpProblem()
-
-with gzip.GzipFile(DEEPHYPER_BENCHMARK_CSV, "rb") as f:
-    r_df, r_df_failed = read_csv_results(f)
-DEEPHYPER_BENCHMARK_DATA = r_df
-
-if DEEPHYPER_BENCHMARK_FIDELITY == "anchor":
-    query_anchor_values = QueryAnchorValues()
-    query_confusion_matrix_values_on_val = QueryMetricValuesFromAnchors(
-        "confusion_matrix", split_name="val"
-    )
-    query_confusion_matrix_values_on_test = QueryMetricValuesFromAnchors(
-        "confusion_matrix", split_name="test"
-    )
-elif DEEPHYPER_BENCHMARK_FIDELITY == "epoch":
-    # Load the learning "epoch"-curve at last epoch
-    query_anchor_values_ = QueryEpochValues()
-    query_confusion_matrix_values_on_val_ = QueryMetricValuesFromEpochs(
-        "confusion_matrix", split_name="val"
-    )
-    query_confusion_matrix_values_on_test_ = QueryMetricValuesFromEpochs(
-        "confusion_matrix", split_name="test"
-    )
-    query_anchor_values = lambda x: query_anchor_values_(x)[-1]
-    query_confusion_matrix_values_on_val = (
-        lambda x: query_confusion_matrix_values_on_val_(x)[-1]
-    )
-    query_confusion_matrix_values_on_test = (
-        lambda x: query_confusion_matrix_values_on_test_(x)[-1]
-    )
-
-    source = DEEPHYPER_BENCHMARK_DATA["m:json"]
-    values = source.apply(lambda x: query_confusion_matrix_values_on_val(x)).to_list()
-    mask = [len(x) > 0 for x in values]
-    DEEPHYPER_BENCHMARK_DATA = DEEPHYPER_BENCHMARK_DATA[mask]
-
-    source = DEEPHYPER_BENCHMARK_DATA["m:json"]
-    values = source.apply(lambda x: query_confusion_matrix_values_on_test(x)).to_list()
-    mask = [len(x) > 0 for x in values]
-    DEEPHYPER_BENCHMARK_DATA = DEEPHYPER_BENCHMARK_DATA[mask]
-else:
-    raise ValueError(f"Unknown fidelity: {DEEPHYPER_BENCHMARK_FIDELITY}")
-
-problem.add_hyperparameter((0, len(DEEPHYPER_BENCHMARK_DATA) - 1), "eval_id")
-
-
-# TODO: To test LCPFN with accuracy metric
-def query_balanced_accuracy_values_on_val(x):
-    cm = query_confusion_matrix_values_on_val(x)
-    return list(map(balanced_accuracy_from_confusion_matrix, cm))
-
-
-def query_coefficient_of_determiniation_values_on_val(x):
-    cm = query_confusion_matrix_values_on_val(x)
-    num_classes = len(cm[0])
-    balanced_error_rate_baseline = 1 - 1 / num_classes
-    return list(
-        map(
-            lambda x: 1
-            - (1 - balanced_accuracy_from_confusion_matrix(x))
-            / balanced_error_rate_baseline,
-            cm,
-        )
-    )
-
-
-def query_coefficient_of_determiniation_values_on_test(x):
-    cm = query_confusion_matrix_values_on_test(x)
-    num_classes = len(cm[0])
-    balanced_error_rate_baseline = 1 - 1 / num_classes
-    return list(
-        map(
-            lambda x: 1
-            - (1 - balanced_accuracy_from_confusion_matrix(x))
-            / balanced_error_rate_baseline,
-            cm,
-        )
-    )
-
-
-@profile
-def run(job: RunningJob, optuna_trial=None) -> dict:
-    # otherwise failure
-    eval_id = job.parameters["eval_id"]
-
-    source = DEEPHYPER_BENCHMARK_DATA.iloc[eval_id]["m:json"]
-    anchor_values = query_anchor_values(source)
-
-    # TODO: to test with LCPFN
-    objective_values = query_balanced_accuracy_values_on_val(source)
-
-    objective_val_values = query_coefficient_of_determiniation_values_on_val(source)
-    objective_test_values = query_coefficient_of_determiniation_values_on_test(source)
-    length = min(len(objective_val_values), len(objective_test_values))
-    anchor_values = anchor_values[:length]
-    objective_values = objective_values[:length]
-    objective_val_values = objective_val_values[:length]
-    objective_test_values = objective_test_values[:length]
-
-    objective_values = np.array(objective_values)
-    objective_val_values = np.array(objective_val_values)
-    objective_test_values = np.array(objective_test_values)
-    anchor_values = np.array(anchor_values)
-
-    mask = anchor_values <= DEEPHYPER_BENCHMARK_MAX_FIDELITY
-    anchor_values = anchor_values[mask].tolist()
-    objective_values = objective_values[mask].tolist()
-    objective_val_values = objective_val_values[mask].tolist()
-    objective_test_values = objective_test_values[mask].tolist()
-
-    if anchor_values[-1] <= DEEPHYPER_BENCHMARK_MAX_FIDELITY:
-        anchor_values.append(DEEPHYPER_BENCHMARK_MAX_FIDELITY)
-        objective_values.append(objective_values[-1])
-        objective_val_values.append(objective_val_values[-1])
-        objective_test_values.append(objective_test_values[-1])
-
-    min_b, max_b = anchor_values[0], anchor_values[-1]
-
-    other_metadata = {}
-
-    consumed_time = 0  # the cost here corresponds to time
-
-    if optuna_trial:
-        for i, budget_i in enumerate(anchor_values):
-            objective_i = objective_val_values[i]
-
-            optuna_trial.report(objective_i, step=budget_i)
-            if optuna_trial.should_prune():
-                break
-
-        objective = objective_i
-
-    else:
-        for i, budget_i in enumerate(anchor_values):
-            # objective_i = objective_val_values[i]
-            # TODO: to test with LCPFN
-            objective_i = objective_values[i]
-
-            job.record(budget_i, objective_i)
-            if job.stopped():
-                break
-
-        objective = job.objective
-
-        if hasattr(job, "stopper") and hasattr(job.stopper, "infos_stopped"):
-            other_metadata["infos_stopped"] = job.stopper.infos_stopped
-
-    metadata = {
-        "budget": budget_i,
-        "stopped": budget_i < max_b,
-        "objective_test": objective_test_values[-1],
-        "objective_val": objective_val_values[-1],
-    }
-    metadata.update(other_metadata)
-
-    return {
-        "objective": objective,
-        "metadata": metadata,
-    }
-
-
-if __name__ == "__main__":
-    print(problem)
-    # default_config = problem.default_configuration
-    for eval_id in range(10):
-        default_config = dict(eval_id=eval_id)
-        print(f"{default_config=}")
-        result = run(RunningJob(parameters=default_config))
-        print(f"{result=}")
-        print()
diff --git a/lib/LCMBench/README.md b/lib/LCMBench/README.md
deleted file mode 100644
index f65fb4a..0000000
--- a/lib/LCMBench/README.md
+++ /dev/null
@@ -1,4 +0,0 @@
-# LCMBench: Learning Curve Models Benchmark
-
-> **Warning**
-> Work in progress, this benchmark is not yet ready.
diff --git a/lib/LCMBench/lcdb/README.md b/lib/LCMBench/lcdb/README.md
deleted file mode 100644
index 046e4b5..0000000
--- a/lib/LCMBench/lcdb/README.md
+++ /dev/null
@@ -1,3 +0,0 @@
-# LCDB: Learning Curve DataBase
-
-Benchmark based on the LCDB project: https://github.com/fmohr/lcdb
\ No newline at end of file
diff --git a/lib/LCMBench/lcdb/__init__.py b/lib/LCMBench/lcdb/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/LCMBench/lcdb/benchmark.py b/lib/LCMBench/lcdb/benchmark.py
deleted file mode 100644
index 8067a67..0000000
--- a/lib/LCMBench/lcdb/benchmark.py
+++ /dev/null
@@ -1,11 +0,0 @@
-from deephyper_benchmark import *
-
-
-class LCDBBenchmark(Benchmark):
-
-    version = "0.0.1"
-
-    requires = {
-        "py-lcdb": {"type": "pip", "name": "lcdb"},
-        "py-func-timeout": {"type": "pip", "name": "func-timeout"},
-    }
diff --git a/lib/LCMBench/lcdb/hpo.py b/lib/LCMBench/lcdb/hpo.py
deleted file mode 100644
index 280ce6e..0000000
--- a/lib/LCMBench/lcdb/hpo.py
+++ /dev/null
@@ -1,128 +0,0 @@
-import numpy as np
-
-import lcdb
-
-from deephyper.evaluator import profile, RunningJob
-from deephyper.hpo import HpProblem
-
-
-df_meta = lcdb.get_meta_features()
-
-all_openmlid = df_meta["openmlid"].tolist()
-all_name = df_meta["Name"].tolist()
-all_algorithms = [
-    "SVC_linear",
-    "SVC_poly",
-    "SVC_rbf",
-    "SVC_sigmoid",
-    "sklearn.tree.DecisionTreeClassifier",
-    "sklearn.tree.ExtraTreeClassifier",
-    "sklearn.linear_model.LogisticRegression",
-    "sklearn.linear_model.PassiveAggressiveClassifier",
-    "sklearn.linear_model.Perceptron",
-    "sklearn.linear_model.RidgeClassifier",
-    "sklearn.linear_model.SGDClassifier",
-    "sklearn.neural_network.MLPClassifier",
-    # "sklearn.discriminant_analysis.LinearDiscriminantAnalysis",
-    # "sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis",
-    "sklearn.naive_bayes.BernoulliNB",
-    # "sklearn.naive_bayes.MultinomialNB", # often missing for large datasets
-    "sklearn.neighbors.KNeighborsClassifier",
-    "sklearn.ensemble.ExtraTreesClassifier",
-    "sklearn.ensemble.RandomForestClassifier",
-    "sklearn.ensemble.GradientBoostingClassifier",
-]
-
-problem = HpProblem()
-problem.add_hyperparameter(all_algorithms, "model")
-
-
-@profile
-def run(job: RunningJob, optuna_trial=None, task_id=3) -> dict:
-
-    model = job.parameters["model"]
-
-    try:
-        curve = lcdb.get_curve(task_id, model, metric="accuracy")
-    except Exception:
-        if optuna_trial:
-            objective = 0  # accuracy 0
-        else:
-            objective = [[1], [0]]  # accuracy 0 at step 1
-        return {
-            "objective": objective,
-            "metadata": {
-                "budget": budget_i,
-                "stopped": budget_i < anchors[-1],
-                "duration": cum_time,
-            },
-        }
-
-    anchors, _, scores_valid, _ = curve
-    _, times = lcdb.get_train_times(task_id, model)
-
-    # scores_valid = [list(v) for v in scores_valid]
-    # times = [list(t) for t in times]
-
-    anchors = np.array(anchors).tolist()
-    # print(model, type(scores_valid), type(scores_valid[0]), type(scores_valid[0][0]))
-    # scores_valid = np.array(np.array(scores_valid).tolist())
-    # times = np.array(np.array(times).tolist())
-
-    # print("->", np.ndim(scores_valid))
-    # print("->", scores_valid)
-    # print("->", np.shape(scores_valid))
-
-    # if np.ndim(scores_valid) == 2:
-    # scores_valid = np.mean(scores_valid, axis=1)
-    scores_valid = np.array([v[0] for v in scores_valid])
-    times = np.array([t[0] for t in times])
-
-    scores_valid = scores_valid.tolist()
-    times = times.tolist()
-
-    cum_time = 0
-
-    if optuna_trial:
-
-        for i, budget_i in enumerate(anchors):
-            objective_i = scores_valid[i]
-            cum_time += times[i]
-            optuna_trial.report(objective_i, step=budget_i)
-            if optuna_trial.should_prune():
-                break
-
-        return {
-            "objective": objective_i,
-            "metadata": {
-                "budget": budget_i,
-                "stopped": budget_i < anchors[-1],
-                "duration": cum_time,
-            },
-        }
-
-    else:
-
-        for i, budget_i in enumerate(anchors):
-            objective_i = scores_valid[i]
-            cum_time += times[i]
-            job.record(budget_i, objective_i)
-            if job.stopped():
-                break
-
-        return {
-            "objective": job.observations,
-            "metadata": {
-                "budget": budget_i,
-                "stopped": budget_i < anchors[-1],
-                "duration": cum_time,
-            },
-        }
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/LCMBench/loglin2/README.md b/lib/LCMBench/loglin2/README.md
deleted file mode 100644
index e9bd949..0000000
--- a/lib/LCMBench/loglin2/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# `LogLin2`
-
-Benchmark for Multi-Fidelity Black-Box Optimization.
-
-$$f_\text{loglin2}(z;\rho) = b(z)^{\rho_1} \cdot \exp{\rho_0}$$
\ No newline at end of file
diff --git a/lib/LCMBench/loglin2/__init__.py b/lib/LCMBench/loglin2/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/LCMBench/loglin2/benchmark.py b/lib/LCMBench/loglin2/benchmark.py
deleted file mode 100644
index ca8cd5b..0000000
--- a/lib/LCMBench/loglin2/benchmark.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class LogLin2Benchmark(Benchmark):
-
-    version = "0.0.1"
-
-    requires = {}
\ No newline at end of file
diff --git a/lib/LCMBench/loglin2/hpo.py b/lib/LCMBench/loglin2/hpo.py
deleted file mode 100644
index 2382caa..0000000
--- a/lib/LCMBench/loglin2/hpo.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import os
-
-import numpy as np
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-from deephyper.evaluator import profile, RunningJob
-from deephyper.hpo import HpProblem
-
-
-problem = HpProblem()
-problem.add_hyperparameter((0.0, 1.0), "rho_0")
-problem.add_hyperparameter((0.0, 1.0/25.0), "rho_1")
-
-
-def f_loglin2(b, rho):
-    return np.power(b, rho[1]) * np.exp(rho[0])
-
-
-@profile
-def run(job: RunningJob, optuna_trial=None) -> dict:
-
-    # otherwise failure
-    min_b, max_b = 1, 100
-
-    rho = [job.parameters["rho_0"], job.parameters["rho_1"]]
-    f = lambda b: f_loglin2(b, rho)
-
-    objective_test = f(max_b)
-
-    if optuna_trial:
-
-        for budget_i in range(min_b, max_b + 1):
-            objective_i = f(budget_i)
-            optuna_trial.report(objective_i, step=budget_i)
-            if optuna_trial.should_prune():
-                break
-
-        return {
-            "objective": objective_i,
-            "metadata": {
-                "budget": budget_i,
-                "stopped": budget_i < max_b,
-                "objective_test": objective_test,
-            },
-        }
-
-    else:
-
-        for budget_i in range(min_b, max_b + 1):
-            objective_i = f(budget_i)
-            job.record(budget_i, objective_i)
-            if job.stopped():
-                break
-
-        return {
-            "objective": job.observations,
-            "metadata": {
-                "budget": budget_i,
-                "stopped": budget_i < max_b,
-                "objective_test": objective_test,
-            },
-        }
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/LCMBench/pow3/README.md b/lib/LCMBench/pow3/README.md
deleted file mode 100644
index 01c296a..0000000
--- a/lib/LCMBench/pow3/README.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# `Pow3`: Inverse Power Law with 3 Parameters
-
-Inverse-Power Law benchmark for Multi-Fidelity Black-Box Optimization.
-
-$$f_\text{pow3}(b;\rho) = \rho_0 + \rho_1 \cdot b^{-\rho_2}$$
\ No newline at end of file
diff --git a/lib/LCMBench/pow3/__init__.py b/lib/LCMBench/pow3/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/LCMBench/pow3/benchmark.py b/lib/LCMBench/pow3/benchmark.py
deleted file mode 100644
index e740b42..0000000
--- a/lib/LCMBench/pow3/benchmark.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class Pow3Benchmark(Benchmark):
-
-    version = "0.0.1"
-
-    requires = {}
\ No newline at end of file
diff --git a/lib/LCMBench/pow3/hpo.py b/lib/LCMBench/pow3/hpo.py
deleted file mode 100644
index 2f994a3..0000000
--- a/lib/LCMBench/pow3/hpo.py
+++ /dev/null
@@ -1,81 +0,0 @@
-import os
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-from deephyper.evaluator import profile, RunningJob
-from deephyper.hpo import HpProblem
-
-
-problem = HpProblem()
-problem.add_hyperparameter((1e-6, 10, "log-uniform"), "alpha")
-problem.add_hyperparameter((1.0, 10.0), "beta")
-problem.add_hyperparameter((0.1, 0.5), "gamma")
-
-# optimum is at
-# C(s=100, alpha=1e-6, beta=1.0, gamma=0.5) -> 0.100001
-
-
-def ipl_curve(s, alpha, beta, gamma):
-    """Inverse Power-Law Model"""
-    return alpha + beta * s**-gamma
-
-
-@profile
-def run(job: RunningJob, optuna_trial=None) -> dict:
-
-    print(f"{job.id=}")
-
-    # otherwise failure
-    min_b, max_b = 1, 1000
-
-    alpha = job.parameters["alpha"]
-    beta = job.parameters["beta"]
-    gamma = job.parameters["gamma"]
-
-    objective_test = -ipl_curve(max_b, alpha, beta, gamma)
-
-    if optuna_trial:
-
-        for budget_i in range(min_b, max_b + 1):
-            objective_i = -ipl_curve(
-                budget_i, alpha, beta, gamma
-            )  # maximisation in deephyper
-            optuna_trial.report(objective_i, step=budget_i)
-            if optuna_trial.should_prune():
-                break
-
-        return {
-            "objective": objective_i,
-            "metadata": {
-                "budget": budget_i,
-                "stopped": budget_i < max_b,
-                "objective_test": objective_test,
-            },
-        }
-
-    else:
-
-        for budget_i in range(min_b, max_b + 1):
-            objective_i = -ipl_curve(
-                budget_i, alpha, beta, gamma
-            )  # maximisation in deephyper
-            job.record(budget_i, objective_i)
-            if job.stopped():
-                break
-
-        return {
-            "objective": job.observations,
-            "metadata": {
-                "budget": budget_i,
-                "stopped": budget_i < max_b,
-                "objective_test": objective_test,
-            },
-        }
-
-
-if __name__ == "__main__":
-    print(problem)
-    default_config = problem.default_configuration
-    print(f"{default_config=}")
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/PINNBench/Burgers/__init__.py b/lib/PINNBench/Burgers/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/PINNBench/Burgers/benchmark.py b/lib/PINNBench/Burgers/benchmark.py
deleted file mode 100644
index cfc92cf..0000000
--- a/lib/PINNBench/Burgers/benchmark.py
+++ /dev/null
@@ -1,17 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class PINNDiffusionReactionBenchmark(Benchmark):
-    version = "0.0.1"
-
-    requires = {
-        "py-pip-requirements": {
-            "step": "install",
-            "type": "pip",
-            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
-        },
-    }
diff --git a/lib/PINNBench/Burgers/hpo.py b/lib/PINNBench/Burgers/hpo.py
deleted file mode 100644
index 43ab995..0000000
--- a/lib/PINNBench/Burgers/hpo.py
+++ /dev/null
@@ -1,111 +0,0 @@
-import numpy as np
-
-from deephyper.hpo import HpProblem
-from deephyper.search.hps import CBO
-from .model import get_data, PINN, BurgerSupervisor, plotter
-from deephyper.stopper import LCModelStopper
-from deephyper.evaluator import profile, RunningJob
-
-
-@profile
-def run(job: RunningJob) -> dict:
-    # load data
-    nu = 0.01 / np.pi
-    train, val, test = get_data(NT=200, NX=128, X_U=1, X_L=-1, T_max=1, Nu=nu)
-
-    config = job.parameters
-    min_b = 1
-    max_b = 1000
-
-    num_layers = config["num_layers"]
-    hidden_dim = config["hidden_dim"]
-    output_dim = 1
-    epochs = 100
-    lr = config["lr"]
-    alpha = config["alpha"]
-    activation = config["activation"]
-    net = PINN(
-        num_layers=num_layers,
-        hidden_dim=hidden_dim,
-        output_dim=output_dim,
-        act_fn=activation,
-    )
-    sup = BurgerSupervisor(nu, net, epochs, lr, alpha)
-
-    for budget_i in range(min_b, max_b + 1):
-        train_loss, eval = sup.step(train, val)
-        objective_i = -eval  # maximizing in deephyper
-        job.record(budget_i, objective_i)
-        if job.stopped():
-            break
-    objective = job.objective
-
-    # calculate test scores
-    pred_test = sup.net(test["x"][:, 0], test["x"][:, 1])
-    test_loss = np.mean((test["y"] - pred_test) ** 2)
-
-    metadata = {
-        "num_parameters": sup.net.count_params(),
-        "train_loss": train_loss,
-        "val_loss": eval,
-        "test_loss": test_loss,
-        "budget": budget_i,
-        "stopped": budget_i < max_b,
-        "infos_stopped": job.stopper.infos_stopped
-        if hasattr(job, "stopper") and hasattr(job.stopper, "infos_stopped")
-        else None,
-    }
-    return {"objective": objective, "metadata": metadata}
-
-
-# define the search space
-problem = HpProblem()
-problem.add_hyperparameter((5, 20), "num_layers", default_value=5)
-problem.add_hyperparameter((1e-5, 1e-2), "lr", default_value=0.01)
-problem.add_hyperparameter((5, 50), "hidden_dim", default_value=5)
-problem.add_hyperparameter((0.0, 5.0), "alpha", default_value=0.5)
-problem.add_hyperparameter(
-    ["relu", "leaky_relu", "tanh", "elu", "gelu", "sigmoid"],
-    "activation",
-    default_value="tanh",
-)
-
-
-def evaluate(config):
-    """
-    Evaluate an hyperparameter configuration
-    on training/validation and testing data.
-    """
-    nu = 0.01 / np.pi
-    train, val, test = get_data(NT=200, NX=100, X_U=1.0, X_L=-1.0, T_max=1, Nu=nu)
-    num_layers = config["num_layers"]
-    hidden_dim = config["hidden_dim"]
-    output_dim = 1
-    epochs = config["epochs"]
-    lr = config["lr"]
-    alpha = config["alpha"]
-    activation = config["activation"]
-    net = PINN(
-        num_layers=num_layers,
-        hidden_dim=hidden_dim,
-        output_dim=output_dim,
-        act_fn=activation,
-    )
-    sup = BurgerSupervisor(nu=nu, net=net, epochs=epochs, lr=lr, alpha=alpha)
-    val_f_loss = sup.train(train, test)
-
-
-if __name__ == "__main__":
-    import time
-
-    start_time = time.time()
-    stopper = LCModelStopper(max_steps=1000, lc_model="mmf4")
-    scheduler = {
-        "type": "periodic-exp-decay",
-        "periode": 25,
-        "rate": 0.1,
-    }
-
-    default_config = problem.default_configuration
-    result = run(RunningJob(parameters=default_config))
-    print(f"{result=}")
diff --git a/lib/PINNBench/Burgers/model.py b/lib/PINNBench/Burgers/model.py
deleted file mode 100644
index fb2a476..0000000
--- a/lib/PINNBench/Burgers/model.py
+++ /dev/null
@@ -1,411 +0,0 @@
-import numpy as np
-import tensorflow as tf
-import phi.flow as pl
-from scipy.stats import qmc
-
-
-def BurgersSolver(
-    NT: int, NX: int, X_U: float, X_L: float, T_max: float, Nu: float, IN_cond
-):
-    """
-    Burgers equation solver with Phiflow,
-    partially adopted from
-    https://physicsbaseddeeplearning.org/overview-burgers-forw.html
-
-    args:
-        NT: number of points in the t-direction.
-        NX: number of points in the x-direction.
-        X_U: the upper bound of x range.
-        X_L: the lower bound of x range.
-        T_max: the maximum time lenght.
-        Nu: viscosity.
-        IN_cond: the initial condition function.
-    """
-    DX = (X_U - X_L) / NX
-    DT = T_max / NT
-    x_ = np.linspace(X_L, X_U, NX)
-    t_ = np.linspace(0, T_max, NT)
-    xx, tt = np.meshgrid(x_, t_)
-    input_domain = np.stack((xx.flatten(), tt.flatten()), axis=1)
-
-    x_range = np.linspace(X_L + DX / 2.0, X_U - DX / 2.0, NX)
-    INITIAL = IN_cond(x_range)
-    INITIAL = pl.math.tensor(INITIAL, pl.spatial("x"))  # convert to phiflow tensor
-    velocity = pl.CenteredGrid(
-        INITIAL, pl.extrapolation.PERIODIC, x=NX, bounds=pl.Box(x=(X_L, X_U))
-    )
-    vt = pl.advect.semi_lagrangian(velocity, velocity, DT)
-
-    velocities = [velocity]
-    age = 0.0
-    for i in range(NT - 1):
-        v1 = pl.diffuse.explicit(velocities[-1], Nu, DT)
-        v2 = pl.advect.semi_lagrangian(v1, v1, DT)
-        age += DT
-        velocities.append(v2)
-    vels = np.array([v.values.numpy("x,vector") for v in velocities])
-    u = np.squeeze(vels)
-    return x_, t_, u
-
-
-def sample_latin_hypercube(self):
-    bc_engine = qmc.LatinHypercube(d=1)
-    t_d = bc_engine.random(n=50)
-    t_i = np.zeros((50, 1))
-    t_d = np.append(t_i, t_d, axis=0)
-    return
-
-
-def split_data(x_, t_, u, show_points=False):
-    """
-    Split data into train, val, and test.
-        train: input coordinates on the boundaries and its solution (boundary conditions)
-                and the input coordinates of collocation points
-        val: the collocation points (in training) with solution
-        test: the entire domain and its solution.
-    """
-    rd = np.random.RandomState(0)  # set local random seed
-    lhc = qmc.LatinHypercube(d=1, seed=0)
-
-    NUM_IC_PT = 50
-    NUM_BC_PT = 50
-    NUM_COL_PT = 10000
-
-    # x_ic_idx = np.array(rd.uniform(size=NUM_IC_PT) * len(x_), dtype=int)
-    x_ic_idx = np.array(lhc.random(n=NUM_IC_PT) * len(x_), dtype=int).flatten()
-    # print("sampled IC indices are:", x_ic_idx)
-    x_ic = x_[x_ic_idx]
-    input_ic = np.stack((x_ic, np.zeros(len(x_ic))), axis=1)
-    u_ic = u[0, x_ic_idx].reshape(-1, 1)
-
-    # t_bc_idx = np.array(rd.uniform(size=NUM_BC_PT//2) * len(t_), dtype=int)
-    t_bc_idx = np.array(lhc.random(n=NUM_BC_PT) * len(t_), dtype=int).flatten()
-    t_bc = t_[t_bc_idx]
-
-    input_bc_upper = np.stack(
-        [np.repeat(x_[-1], len(t_bc) // 2), t_bc[: NUM_BC_PT // 2]], axis=1
-    )
-    input_bc_lower = np.stack(
-        [np.repeat(x_[0], len(t_bc) // 2), t_bc[NUM_BC_PT // 2 :]], axis=1
-    )
-    input_bc = np.vstack([input_bc_lower, input_bc_upper])
-
-    u_bc_upper = u[t_bc_idx, -1]
-    u_bc_lower = u[t_bc_idx, 0]
-    u_bc = np.hstack([u_bc_lower, u_bc_upper]).reshape(-1, 1)
-
-    t_col_idx = np.array(rd.uniform(size=NUM_COL_PT) * len(t_), dtype=int)
-    x_col_idx = np.array(rd.uniform(size=NUM_COL_PT) * len(x_), dtype=int)
-    input_col = np.stack((x_[x_col_idx], t_[t_col_idx]), axis=1)
-    u_col = u[t_col_idx, x_col_idx].reshape(-1, 1)
-
-    x_u_train = np.vstack([input_ic, input_bc])
-    y_u_train = np.vstack([u_ic, u_bc])
-    idx = np.arange(0, NUM_IC_PT + NUM_BC_PT)
-    rd.shuffle(idx)
-    x_u_train = x_u_train[idx]
-    y_u_train = y_u_train[idx]
-
-    x_f_train = input_col
-
-    # prepare val data
-    rd2 = np.random.RandomState(42)
-    v_t_col_idx = np.array(rd2.uniform(size=NUM_COL_PT) * len(t_), dtype=int)
-    v_x_col_idx = np.array(rd2.uniform(size=NUM_COL_PT) * len(x_), dtype=int)
-    x_val = np.stack((x_[v_x_col_idx], t_[v_t_col_idx]), axis=1)
-    y_val = u[v_t_col_idx, v_x_col_idx].reshape(-1, 1)
-
-    # prepare testing data
-    xx, tt = np.meshgrid(x_, t_)
-    input_domain = np.stack([xx.flatten(), tt.flatten()], axis=1)
-    y_test = u.flatten().reshape(-1, 1)
-
-    train_data = {"x_u": x_u_train, "y_u": y_u_train, "x_f": x_f_train}
-    val_data = {"x": x_f_train, "y": u_col}
-    test_data = {"x": input_domain, "y": y_test}
-
-    if show_points == True:
-        import matplotlib.pyplot as plt
-
-        # plt.scatter(input_ic[:, 1], input_ic[:,0])
-        # plt.scatter(input_bc[:, 1], input_bc[:,0])
-        # plt.scatter(x_f_train[:,1], x_f_train[:,0])
-        plt.scatter(x_val[:, 1], x_val[:, 0])
-        plt.show()
-
-    return train_data, val_data, test_data
-
-
-def get_data(NT, NX, X_U, X_L, T_max, Nu):
-    def init_cond(x):
-        return -np.sin(np.pi * x)
-
-    x_, t_, u = BurgersSolver(
-        NT=NT, NX=NX, X_U=X_U, X_L=X_L, T_max=T_max, Nu=Nu, IN_cond=init_cond
-    )
-    train, val, test = split_data(x_, t_, u)
-    return train, val, test
-
-
-class PINN(tf.keras.Model):
-    def __init__(self, num_layers, hidden_dim, output_dim, act_fn):
-        super().__init__()
-        # tf.random.set_seed(0)
-        tf.keras.utils.set_random_seed(0)
-        if act_fn == "relu":
-            self.act_fn = tf.nn.relu
-        elif act_fn == "leaky_relu":
-            self.act_fn = tf.nn.leaky_relu
-        elif act_fn == "elu":
-            self.act_fn = tf.nn.elu
-        elif act_fn == "gelu":
-            self.act_fn = tf.nn.gelu
-        elif act_fn == "tanh":
-            self.act_fn = tf.nn.tanh
-        elif act_fn == "sigmoid":
-            self.act_fn = tf.nn.sigmoid
-        else:
-            raise ValueError("%s is not in the activation function list" % act_fn)
-
-        self.lys = [
-            tf.keras.layers.Dense(
-                units=hidden_dim,
-                activation=self.act_fn,
-                kernel_initializer="glorot_uniform",
-                bias_initializer="zeros",
-            )
-        ]
-        for n in range(num_layers):
-            self.lys.append(
-                tf.keras.layers.Dense(
-                    units=hidden_dim,
-                    activation=self.act_fn,
-                    kernel_initializer="glorot_uniform",
-                    bias_initializer="zeros",
-                )
-            )
-        self.lys.append(
-            tf.keras.layers.Dense(
-                units=output_dim,
-                activation=None,
-                kernel_initializer="glorot_uniform",
-                bias_initializer="zeros",
-            )
-        )
-
-    def call(self, x, t):
-        x = tf.cast(x, tf.float32)
-        t = tf.cast(t, tf.float32)
-
-        x_concat = tf.stack((x, t), axis=1)
-        for layer in self.lys:
-            x_concat = layer(x_concat)
-        return x_concat
-
-
-class BurgerSupervisor:
-    def __init__(self, nu, net, epochs, lr, alpha):
-        self.nu = nu
-        self.net = net  # the net predicting u from x, t pairs.
-        self.epochs = epochs
-
-        self.alpha = alpha
-        self.optimizer = tf.keras.optimizers.Adam(learning_rate=lr)
-
-        self.loss = tf.losses.MeanSquaredError()
-
-    @tf.function
-    def f_net(self, x, t):
-        x = tf.cast(x, tf.float32)
-        t = tf.cast(t, tf.float32)
-        u0 = tf.squeeze(self.net(x, t))
-        u_t = tf.gradients(u0, t)[0]
-        u_x = tf.gradients(u0, x)[0]
-        u_xx = tf.gradients(u_x, x)[0]
-        F = u_t + u0 * u_x - (0.01 / np.pi) * u_xx
-        return F
-
-    def step(self, train, val):
-        x_u_train = tf.cast(train["x_u"], tf.float32)
-        x_f_train = tf.cast(train["x_f"], tf.float32)
-        y_train = tf.cast(train["y_u"], tf.float32)
-
-        x_val = tf.cast(val["x"], tf.float32)
-        y_val = tf.cast(val["y"], tf.float32)
-        x_u = x_u_train[:, 0]
-        t_u = x_u_train[:, 1]
-
-        x_f = x_f_train[:, 0]
-        t_f = x_f_train[:, 1]
-        with tf.GradientTape() as t:
-            u_out = self.net(x_u, t_u)
-            f_out = self.f_net(x_f, t_f)
-            loss_u = self.loss(y_train, u_out)
-            loss_f = self.loss(0, f_out)
-            tot_loss = loss_u + self.alpha * loss_f
-
-        grads = t.gradient(tot_loss, self.net.trainable_weights)
-        self.optimizer.apply_gradients(zip(grads, self.net.trainable_weights))
-        train_loss = loss_u.numpy() + loss_f.numpy()
-        val_pred = self.net(x_val[:, 0], x_val[:, 1])
-        val_u_loss = self.loss(y_val, val_pred).numpy()
-        return train_loss, val_u_loss
-
-    def train(self, train, val):
-        x_u_train = tf.cast(train["x_u"], tf.float32)
-        x_f_train = tf.cast(train["x_f"], tf.float32)
-        y_train = tf.cast(train["y_u"], tf.float32)
-
-        x_val = tf.cast(val["x"], tf.float32)
-        y_val = tf.cast(val["y"], tf.float32)
-        x_u = x_u_train[:, 0]
-        t_u = x_u_train[:, 1]
-
-        x_f = x_f_train[:, 0]
-        t_f = x_f_train[:, 1]
-        for i in range(self.epochs):
-            train_ls = []
-            with tf.GradientTape() as t:
-                u_out = self.net(x_u, t_u)
-                f_out = self.f_net(x_f, t_f)
-                loss_u = self.loss(y_train, u_out)
-                loss_f = self.loss(0, f_out)
-                tot_loss = loss_u + self.alpha * loss_f
-                train_ls.append(tot_loss)
-            grads = t.gradient(tot_loss, self.net.trainable_weights)
-            self.optimizer.apply_gradients(zip(grads, self.net.trainable_weights))
-            obj = loss_u.numpy() + loss_f.numpy()
-            # print('epoch {}, obj is {}'.format(i, obj))
-            # validation loss
-            val_r2 = self.r2(y_val, self.net(x_val[:, 0], x_val[:, 1]))
-            val_f_loss = self.loss(0, self.f_net(x_val[:, 0], x_val[:, 1])).numpy()
-            val_pred = self.net(x_val[:, 0], x_val[:, 1])
-            val_u_loss = self.loss(y_val, val_pred).numpy()
-            # val_f_loss = self.loss(0, self.f_net(x_val[:, 0], x_val[:, 1])).numpy()
-            val_loss = val_u_loss
-            print(
-                "Epoch %d, training loss: (%.4f, %.4f), obj loss %.4f"
-                % (i + 1, loss_u.numpy(), loss_f.numpy(), val_loss)
-            )
-        return val_loss  # return the objective.
-
-    def r2(self, true, pred):
-        true = tf.cast(true, tf.float32)
-        rss = tf.reduce_sum((true - pred) ** 2)
-        tss = tf.reduce_sum((true - tf.reduce_mean(true)) ** 2)
-        return 1 - rss / tss
-
-    def test(self, x_test, y_test):
-        out = self.net(x_test)
-        r2 = self.r2(y_test, out)
-        f_loss = self.f_loss(x_test)
-        return r2, f_loss
-
-
-def plotter(x_, u_, name, NUMX, NUMT, **kwargs):
-    """
-    x_: shape=[num_points, 2]. First dim being x and second t.
-    u_: shape=[num_points,]
-    NUMX: number of points the in x-direction.
-    NUMT: number of points the in t-direction.
-    """
-    import matplotlib.animation as animation
-
-    u_ = u_.reshape(NUMT, NUMX).T
-    xv = x_[:, 0].reshape(NUMT, NUMX)
-    tv = x_[:, 1].reshape(NUMT, NUMX)
-
-    import matplotlib.pyplot as plt
-
-    fig, ax = plt.subplots(2, 2, figsize=(16, 9))
-    ax[0, 0].imshow(
-        u_,
-        interpolation="nearest",
-        cmap="rainbow",
-        extent=[tv.min(), tv.max(), xv.min(), xv.max()],
-        origin="lower",
-        aspect="auto",
-    )
-    ax[0, 0].set_xlabel("t")
-    ax[0, 0].set_ylabel("x")
-
-    (line,) = ax[1, 0].plot(xv[0], u_[:, 0])
-    text = ax[1, 0].text(0.8, 0.9, " ", transform=ax[1, 0].transAxes)
-    ax[1, 0].set_xlabel("x")
-    ax[1, 0].set_ylabel("u")
-
-    u_true = kwargs["u_true"].reshape(NUMT, NUMX).T
-    ax[0, 1].set_title("True", fontsize=24)
-    ax[0, 0].set_title("Predicted", fontsize=24)
-    ax[0, 1].imshow(
-        u_true,
-        interpolation="nearest",
-        cmap="rainbow",
-        extent=[tv.min(), tv.max(), xv.min(), xv.max()],
-        origin="lower",
-        aspect="auto",
-    )
-    ax[0, 1].set_xlabel("t")
-    ax[0, 1].set_ylabel("x")
-
-    (line_true,) = ax[1, 1].plot(xv[0], u_true[:, 0])
-    text_true = ax[1, 1].text(0.8, 0.9, " ", transform=ax[1, 1].transAxes)
-    ax[1, 0].set_xlabel("x")
-    ax[1, 0].set_ylabel("u")
-
-    T = np.linspace(x_[:, 1].min(), x_[:, 1].max(), NUMT)
-
-    def animate(t):
-        line.set_ydata(u_[:, t])
-        line_true.set_ydata(u_true[:, t])
-        text.set_text("t= %.3f" % T[t])
-        text_true.set_text("t= %.3f" % T[t])
-        return line, text, line_true, text_true
-
-    ani = animation.FuncAnimation(fig, animate, frames=u_.shape[1], interval=50)
-    plt.tight_layout()
-    writergif = animation.PillowWriter(fps=60)
-    ani.save("./Burgers_" + name + ".gif", writer=writergif)
-
-
-if __name__ == "__main__":
-    import time
-    import pickle
-
-    start_time = time.time()
-    config = {
-        "num_layers": 17,
-        "hidden_dim": 9,
-        "epochs": 1000,
-        "lr": 0.00580381,
-        "alpha": 0.050742893,
-        "activation": "tanh",
-    }
-
-    nu = 0.01 / np.pi
-    train, val, test = get_data(NT=200, NX=128, X_U=1, X_L=-1, T_max=1, Nu=nu)
-    with open("./data/data.pkl", "rb") as f:
-        data = pickle.load(f)
-
-    train = data["train"]
-    val = data["val"]
-
-    num_layers = config["num_layers"]
-    hidden_dim = config["hidden_dim"]
-    output_dim = 1
-    epochs = config["epochs"]
-    lr = config["lr"]
-    alpha = config["alpha"]
-    activation = config["activation"]
-    net = PINN(
-        num_layers=num_layers,
-        hidden_dim=hidden_dim,
-        output_dim=output_dim,
-        act_fn=activation,
-    )
-    sup = BurgerSupervisor(nu, net, epochs, lr, alpha)
-    val_f_loss = sup.train(train, val)
-    print("--- %s seconds ---" % (time.time() - start_time))
-    print("objective is ", val_f_loss)
-    # pred_test = net(test['x'][:,0], test['x'][:,1]).numpy()
-    # plotter(test['x'], pred_test, 'true_pred_hps-adbo', 128, 200, u_true=test['y'])
diff --git a/lib/PINNBench/Burgers/requirements.txt b/lib/PINNBench/Burgers/requirements.txt
deleted file mode 100644
index ff2206e..0000000
--- a/lib/PINNBench/Burgers/requirements.txt
+++ /dev/null
@@ -1,5 +0,0 @@
-deephyper>=0.5.0
-matplotlib==3.4.2
-numpy==1.23.4
-phiflow==2.2.7
-tensorflow==2.10.0
\ No newline at end of file
diff --git a/lib/PINNBench/DiffusionReaction/__init__.py b/lib/PINNBench/DiffusionReaction/__init__.py
deleted file mode 100644
index dc5340c..0000000
--- a/lib/PINNBench/DiffusionReaction/__init__.py
+++ /dev/null
@@ -1 +0,0 @@
-from . import hpo
diff --git a/lib/PINNBench/DiffusionReaction/benchmark.py b/lib/PINNBench/DiffusionReaction/benchmark.py
deleted file mode 100644
index bacef6c..0000000
--- a/lib/PINNBench/DiffusionReaction/benchmark.py
+++ /dev/null
@@ -1,28 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class PINNDiffusionReactionBenchmark(Benchmark):
-    version = "0.0.1"
-
-    requires = {
-        "py-pip-requirements": {
-            "step": "install",
-            "type": "pip",
-            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
-        },
-        "bash-install": {
-            "step": "install",
-            "type": "cmd",
-            "cmd": "cd .. && bash " + os.path.join(DIR, "./install.sh"),
-        },
-        "deepxde-backend": {
-            "step": "load",
-            "type": "env",
-            "key": "DDE_BACKEND",
-            "value": "pytorch",
-        },
-    }
diff --git a/lib/PINNBench/DiffusionReaction/hpo.py b/lib/PINNBench/DiffusionReaction/hpo.py
deleted file mode 100644
index 7df1633..0000000
--- a/lib/PINNBench/DiffusionReaction/hpo.py
+++ /dev/null
@@ -1,206 +0,0 @@
-import os
-
-import numpy as np
-import torch
-from deephyper.evaluator import RunningJob, profile
-from deephyper.hpo import HpProblem
-from deephyper.stopper.integration.deepxde import DeepXDEStopperCallback
-from deepxde.callbacks import EarlyStopping
-from fvcore.nn import FlopCountAnalysis
-from pdebench.models.pinn.train import run_training
-
-from deephyper_benchmark.integration.torch import count_params
-from deephyper_benchmark.utils.json_utils import array_to_json
-
-from .model import FNN, ACTIVATIONS
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-DEEPHYPER_BENCHMARK_MOO = bool(int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 0)))
-
-# define the search space
-problem = HpProblem()
-problem.add_hyperparameter((5, 20), "num_layers", default_value=10)
-problem.add_hyperparameter((5, 100), "num_neurons", default_value=10)
-problem.add_hyperparameter((100, 1000), "epochs", default_value=100)
-problem.add_hyperparameter(
-    list(ACTIVATIONS.keys()),
-    "activation",
-    default_value="relu",
-)
-problem.add_hyperparameter([True, False], "skip_co", default_value=False)
-problem.add_hyperparameter((0.0, 0.5), "dropout", default_value=0)
-
-# Regularization hyperparameters
-problem.add_hyperparameter([True, False], "batch_norm", default_value=False)
-problem.add_hyperparameter(["None", "l2"], "regularization", default_value="None")
-problem.add_hyperparameter(
-    (1e-5, 1.0, "log-uniform"), "weight_decay", default_value=0.01
-)
-problem.add_hyperparameter(
-    ["Glorot normal", "Glorot uniform", "He normal", "He uniform"],
-    "kernel_initializer",
-    default_value="Glorot normal",
-)
-
-# Optimization hyperparameters
-problem.add_hyperparameter(["None", "step"], "decay", default_value="None")
-problem.add_hyperparameter((1, 100), "decay_step_size", default_value=5)
-problem.add_hyperparameter((1e-5, 1.0, "log-uniform"), "decay_gamma", default_value=0.1)
-
-problem.add_hyperparameter(
-    ["adam", "sgd", "rmsprop", "adamw"], "optimizer", default_value="adam"
-)
-problem.add_hyperparameter(
-    (1e-5, 1e-1, "log-uniform"), "learning_rate", default_value=0.01
-)
-
-# Loss weights is tuned only if MOO is activated.
-if DEEPHYPER_BENCHMARK_MOO:
-    problem.add_hyperparameter((0.1, 0.9), "loss_weights", default_value=0.5)
-
-
-@profile
-def run(job: RunningJob) -> dict:
-    config = job.parameters.copy()
-    dataset = "2D_diff-react_NA_NA"
-
-    for k, v in config.items():
-        if v == "None":
-            config[k] = None
-
-    if "loss_weights" not in config:
-        lw = 0.5
-    else:
-        lw = config["loss_weights"]
-    config["loss_weights"] = np.array([lw, lw, 1 - lw, 1 - lw, 1 - lw, 1 - lw])
-
-    # https://github.com/lululxvi/deepxde/blob/master/deepxde/optimizers/pytorch/optimizers.py
-    if config["decay"] == "step":
-        config["decay"] = ("step", config["decay_step_size"], config["decay_gamma"])
-
-    # To avoid error: https://github.com/lululxvi/deepxde/blob/master/deepxde/optimizers/pytorch/optimizers.py#L45C4-L45C4
-    if config["optimizer"] == "adamw":
-        config["regularization"] = "l2"
-
-    stopper_callback = DeepXDEStopperCallback(job)
-
-    error_type = None
-    try:
-        (
-            val_loss,
-            test_loss,
-            losshistory,
-            model,
-            duration_batch_inference,
-        ) = run_training(
-            net_class=FNN,
-            scenario="diff-react",
-            epochs=config["epochs"],
-            learning_rate=config["learning_rate"],
-            model_update=500,
-            root_path=os.path.join(
-                DIR, "../build/PDEBench-DH/pdebench/data/" + dataset
-            ),
-            flnm=dataset + ".h5",
-            config=config,
-            seed="0000",
-            callbacks=stopper_callback,
-        )
-    except torch.cuda.OutOfMemoryError:
-        error_type = "F_OOM"
-
-    # Managing failures to learn memory constraints
-    if error_type is not None:
-        metadata = {
-            "num_parameters": None,
-            "num_parameters_train": None,
-            "val_loss": None,
-            "test_rmse": None,
-            "budget": None,
-            "stopped": None,
-            "lc_train_loss": None,
-            "lc_val_loss": None,
-            "flops": None,
-            "duration_batch_inference": None,
-        }
-
-        if DEEPHYPER_BENCHMARK_MOO:
-            objective = [error_type for _ in range(3)]
-        else:
-            objective = error_type
-
-        return {"objective": objective, "metadata": metadata}
-
-    param_count = count_params(model)
-    flops = FlopCountAnalysis(model, inputs=(torch.randn(1, 3))).total()
-
-    train_ls = np.array(losshistory.loss_train).sum(axis=1)
-    val_ls = np.array(losshistory.loss_test).sum(axis=1)
-    steps = np.array(losshistory.steps)
-    lc_train_X = np.stack([steps, train_ls], axis=1)
-    lc_val_X = np.stack([steps, val_ls], axis=1)
-    lc_train_X_json = array_to_json(lc_train_X)
-    lc_val_X_json = array_to_json(lc_val_X)
-
-    if DEEPHYPER_BENCHMARK_MOO:
-        print("Optimizing multiple objectives...")
-
-        if np.isnan(val_loss[:2]).any() or np.isinf(val_loss[:2]).any():
-            objective_0 = "F"
-        else:
-            objective_0 = -(val_loss[:2] / config["loss_weights"][:2]).sum()
-
-        if np.isnan(val_loss[2:]).any() or np.isinf(val_loss[2:]).any():
-            objective_1 = "F"
-        else:
-            objective_1 = -(val_loss[2:] / config["loss_weights"][2:]).sum()
-
-        objective = [
-            objective_0,
-            objective_1,
-            -flops,
-        ]
-    else:
-        objective = (
-            "F"
-            if np.isnan(val_loss).any() or np.isinf(val_loss).any()
-            else -(val_loss / config["loss_weights"]).sum()
-        )
-    metadata = {
-        "num_parameters": param_count["num_parameters"],
-        "num_parameters_train": param_count["num_parameters_train"],
-        "val_loss": array_to_json(val_loss),  # array of 4 elements
-        "test_rmse": float(test_loss[0]),
-        "budget": stopper_callback.budget,
-        "stopped": stopper_callback.stopped,
-        "lc_train_loss": lc_train_X_json,
-        "lc_val_loss": lc_val_X_json,
-        "flops": flops,
-        "duration_batch_inference": duration_batch_inference,  # add the inference time in seconds
-    }
-
-    return {"objective": objective, "metadata": metadata}
-
-
-def evaluate(config):
-    """
-    Evaluate an hyperparameter configuration
-    on training/validation and testing data.
-    """
-    callbacks = EarlyStopping(patience=100_000)
-    DIR = os.path.dirname(os.path.abspath(__file__))
-    dataset = os.environ.get("DEEPHYPER_BENCHMARK_DATASET")
-
-    val_loss, test_loss, losshistory, model = run_training(
-        net_class=FNN,
-        scenario="diff-react",
-        epochs=config["epochs"],
-        learning_rate=config["lr"],
-        model_update=1,
-        root_path=os.path.join(DIR, "../build/PDEBench-DH/pdebench/data/" + dataset),
-        flnm="2D_diff-react_NA_NA.h5",
-        config=config,
-        seed="0000",
-        callbacks=callbacks,
-    )
-    return val_loss, test_loss, losshistory
diff --git a/lib/PINNBench/DiffusionReaction/model.py b/lib/PINNBench/DiffusionReaction/model.py
deleted file mode 100644
index ff9e3d0..0000000
--- a/lib/PINNBench/DiffusionReaction/model.py
+++ /dev/null
@@ -1,154 +0,0 @@
-import torch
-import torch.nn as nn
-
-from deepxde.nn import NN
-from deepxde.nn import initializers
-from deepxde import config
-
-INITIALIZERS = initializers
-
-class Sin(nn.Module):
-    def __init__(self):
-        super(Sin, self).__init__()
-
-    def forward(self, x):
-        return torch.sin(x)
-
-ACTIVATIONS = {
-        # "id": nn.Identity,
-        "elu": nn.ELU,
-        "relu": nn.ReLU,
-        "selu": nn.SELU,
-        "sigmoid": nn.Sigmoid,
-        "silu": nn.SiLU, # same as swish
-        "sin": Sin, 
-        "tanh": nn.Tanh,
-        "hardswish": nn.Hardswish,
-        "leakyrelu": nn.LeakyReLU,
-        "mish": nn.Mish,
-        "softplus": nn.Softplus,
-}
-
-
-class FNN(NN):
-    """Fully-connected neural network."""
-
-    def __init__(
-        self,
-        input_dim: int,
-        output_dim: int,
-        num_layers: int = 5,
-        num_neurons: int = 30,
-        activation: str = "elu",
-        kernel_initializer: str = "Glorot normal",
-        batch_norm: bool = False,
-        skip_co: bool = False,
-        dropout_rate: float = 0.0,
-        regularization: str = None,
-        weight_decay: float = 0.01,
-        **kwargs,
-    ):
-        super(FNN, self).__init__()
-
-        if regularization is None:
-            self.regularizer = None
-        else:
-            self.regularizer = [regularization, weight_decay]
-
-        layer_sizes = [input_dim] + [num_neurons for _ in range(num_layers)]
-
-        initializer = INITIALIZERS.get(kernel_initializer)
-        initializer_zero = INITIALIZERS.get("zeros")
-
-        self.linears = nn.Sequential()
-        for i in range(1, len(layer_sizes)):
-            if skip_co:
-                self.linears.append(
-                    SkipConnection(
-                        in_dim=layer_sizes[i - 1],
-                        out_dim=layer_sizes[i],
-                        batch_norm=batch_norm,
-                        kernel_initializer=kernel_initializer,
-                        activation=activation,
-                    )
-                )
-
-            else:
-                linear_module = nn.Linear(
-                    layer_sizes[i - 1], layer_sizes[i], dtype=config.real(torch)
-                )
-                initializer(linear_module.weight)
-                initializer_zero(linear_module.bias)
-                self.linears.append(linear_module)
-                if batch_norm:
-                    self.linears.append(
-                        nn.BatchNorm1d(layer_sizes[i], dtype=config.real(torch))
-                    )
-                
-                if activation != "id":
-                    self.linears.append(ACTIVATIONS.get(activation)())
-
-            self.linears.append(nn.Dropout(p=dropout_rate))
-
-        self.linears.append(
-            nn.Linear(layer_sizes[-1], output_dim, dtype=config.real(torch))
-        )
-
-    def forward(self, inputs):
-        x = inputs
-        if self._input_transform is not None:
-            x = self._input_transform(x)
-        x = self.linears(x)
-        if self._output_transform is not None:
-            x = self._output_transform(inputs, x)
-        return x
-
-
-class SkipConnection(nn.Module):
-    def __init__(
-        self,
-        in_dim,
-        out_dim,
-        kernel_initializer="Glorot normal",
-        batch_norm=False,
-        activation="elu",
-    ) -> None:
-        super(SkipConnection, self).__init__()
-
-        self.in_dim = in_dim
-        self.out_dim = out_dim
-
-        initializer = INITIALIZERS.get(kernel_initializer)
-        initializer_zero = INITIALIZERS.get("zeros")
-
-        if self.in_dim != self.out_dim:
-            self.map = nn.Linear(in_dim, out_dim)
-            initializer(self.map.weight)
-            initializer_zero(self.map.bias)
-
-        self.block = nn.Sequential()
-        
-        linear_module = nn.Linear(in_dim, out_dim, dtype=config.real(torch))
-        initializer(linear_module.weight)
-        initializer_zero(linear_module.bias)
-        self.block.append(linear_module)
-
-        if batch_norm:
-            self.block.append(nn.BatchNorm1d(out_dim, dtype=config.real(torch)))
-
-        if activation != "id":
-            self.block.append(ACTIVATIONS.get(activation)())
-        
-        if activation != "id":
-            self.act = ACTIVATIONS.get(activation)()
-        else:
-            self.act = None
-
-    def forward(self, x):
-        residual = x
-        if self.in_dim != self.out_dim:
-            residual = self.map(residual)
-        out = self.block(x) + residual
-        if self.act is not None:
-            out = self.act(out)
-        return out
diff --git a/lib/PINNBench/DiffusionReaction/requirements.txt b/lib/PINNBench/DiffusionReaction/requirements.txt
deleted file mode 100644
index 3ad0199..0000000
--- a/lib/PINNBench/DiffusionReaction/requirements.txt
+++ /dev/null
@@ -1,6 +0,0 @@
-DeepXDE>=1.8.2
-numpy>=1.24.2
-omegaconf>=2.3.0
-packaging>=23.0
-torch>=2.0.0
-fvcore>=0.1.5
\ No newline at end of file
diff --git a/lib/PINNBench/README.md b/lib/PINNBench/README.md
deleted file mode 100644
index 520a485..0000000
--- a/lib/PINNBench/README.md
+++ /dev/null
@@ -1,144 +0,0 @@
-# Physics-informed Neural Networks Benchmark
-
-> **Warning**
-> Work in progress, this benchmark is not yet ready.
-
-Physics-Informed Neural Networks (PINNs) are a class of machine learning models that combine the strengths of neural networks and physics-based modeling. PINNs are used to solve partial differential equations (PDEs) and other physical problems by learning a solution directly from data.
-
-The basic idea behind PINNs is to use a neural network to approximate the solution to a PDE, while also enforcing the underlying physical laws that govern the problem. This is achieved by incorporating the PDE as a constraint in the neural network training process. More details can be found in the [original work](https://arxiv.org/abs/1711.10561).
-
-This set of benchmarks seeks to incorporate AutoML workflow into the development of PINNs with DeepHyper. The PINN benchmark problems support Hyperparameter Optimization (HPO), Neural Architecture Search (NAS), and Multi-fidelity evaluations. 
-
-The currently available problems are 
-
-<!-- [Burgers Equation](#burgers-equation) -->
-
-- [Diffusion-reaction Equation](#diffusion-reaction-equation) (Dataset Size: 13 GB)
-
-<!-- 
-## Burgers Equation
-### Installation
-To install
-
-```
-python -c "import deephyper_benchmark as dhb; dhb.install('PINNBench/Burgers');"
-```
--->
-
-
-<!-- PINNs are neural networks are a type of machine learning method that combines deep neural networks with physical equations to solve complex physical problems. In the case of 1-D Burgers Equation, training a PINN, $\hat{u}$, is to minimize a compound loss function. The 1-D Burgers equation has the following form:
-
-$$\frac{\partial u }{\partial t} + u\frac{\partial u}{\partial x} = \nu \frac{\partial^2 u}{\partial x^2}$$
-
-The loss functions has two component,
-$$L_{tot} = L_u + \alpha L_f$$
-
-the solution loss for specified initial and boundary conditions,
-$$L_u = \vert \vert \hat{u} - u \vert \vert^2_2$$
-
-and the loss according to the governing equation (PDE loss) for every other points in the domain,
-$$L_f  = \vert \vert \frac{\partial \hat{u}}{\partial t} + \hat{u}\frac{\partial \hat{u}}{\partial x} - \nu \frac{\partial^2 \hat{u}}{\partial x^2})\vert \vert^2_2.$$ -->
-
-
-<!-- ### Hyperparameter Search
-<!-- ### Hyperparameter Search
-The objective of this benchmark is to optimize a set of hyperparameters for a feedforward neural network, including `num_layers`, `lr` (the learning rate of the optimizer), `hidden_dim` (the number of neurons in the hidden layers), `alpha` (the PDE loss coefficient), and `activation` (the activation function). The optimization is performed by minimizing the negative total loss value from the validation dataset.
-
-To improve the efficiency of the search, the benchmark uses MCModelStopper to predict the training dynamics and perform early stopping when the predicted subsequent iterations are unlikely to lead to a further improvement in model performance. The maximum budget, `max_b`, for this search is 1000 epochs.
-
-```python
-for budget_i in range(min_b, max_b + 1):
-        train_loss, eval = sup.step(train, val)
-        objective_i = -eval  # maximizing in deephyper
-        job.record(budget_i, objective_i)
-        if job.stopped():
-            break
-```
-### Defualt configuration and results
-The default configuration of the PINN.
-
-| | |
-|:--|:--|
-|`num_layers`| 5 |
-|`lr` | 0.01|
-| `hidden_dim`| 5|
-|`alpha`| 5 |
-|`activation`| `tanh`|
-
-```
-result={'objective': -0.06480624, 'metadata': {'timestamp_start': 1680036315.47377, 'timestamp_end': 1680036380.2708638, 'num_parameters': 171, 'train_loss': 0.06834503, 'val_loss': 0.06480624, 'test_loss': 0.066540696, 'budget': 1000, 'stopped': False, 'infos_stopped': None}}
-``` --> 
-
-
-## Diffusion-reaction Equation
-
-This benchmark is based on **modified** [`PDEBench`](https://github.com/pdebench/PDEBench) and [`DeepXDE`](https://github.com/lululxvi/deepxde). 
-
-### Installation
-
-To install the **modified** `PDEBench` and this benchmark, run:
-```
-python -c "import deephyper_benchmark as dhb; dhb.install('PINNBench/DiffusionReaction');"
-```
-
-Run the hyperparameter search
-```
-import os
-os.environ['DEEPHYPER_BENCHMARK_MOO'] = '1' # enable multi-objective optimization
-
-import deephyper_benchmark as dhb
-diff_react = dhb.load("PINNBench/DiffusionReaction")
-
-from deephyper.evaluator import RunningJob
-config = diff_react.hpo.problem.default_configuration # get a default config to test
-res = diff_react.hpo.run(RunningJob(parameters=config))
-```
-
-
-### Configuration
-
-It is necessary to configure `DeepXDE` to use `PyTorch` backend. The instructions can be found [here](https://deepxde.readthedocs.io/en/latest/user/installation.html#working-with-different-backends).
-
-### Metadata
-
-- [x] `num_parameters`: integer value of the number of parameters in the neural network.
-- [x] `num_parameters_train`: integer value of the number of **trainable** parameters of the neural network.
-- [x] `budget`: scalar value (float/int) of the budget consumed by the neural network. Therefore the budget should be defined for each benchmark (e.g., number of epochs in general).
-- [x] `stopped`: boolean value indicating if the evaluation was stopped before consuming the maximum budget.
-- [x] `train_loss`:  scalar value of the training metrics (replace `X` by the metric name, 1 key per metric).
-- [x] `valid_loss`: scalar value of the validation metrics (replace `X` by the metric name, 1 key per metric).
-- [x] `test_rmse`: scalar value of the testing metrics (replace `X` by the metric name, 1 key per metric).
-- [x] `flops`: number of flops of the model such as computed in `fvcore.nn.FlopCountAnalysis(...).total()` (See [documentation](https://detectron2.readthedocs.io/en/latest/modules/fvcore.html#module-fvcore.nn)).
-- [ ] `latency`: TO BE CLARIFIED
-- [x] `lc_train_loss`: recorded learning curves of the trained model, the `bi` variables are the budget value (e.g., epochs/batches), and the `yi` values are the recorded metric. `X` in `train_X` is replaced by the name of the metric such as `train_loss` or `train_accuracy`. The format is `[[b0, y0], [b1, y1], ...]`.
-- [x] `lc_valid_loss`: Same as `lc_train_X` but for validation data.
-- [x] `duration_batch_inference`: average inference time for a single data point.
-
-### Other details
-
-#### Supported datasets
-
-The currently available dataset is `2D_diff-react_NA_NA`. The rest datasets from PDEBench (see [list](https://github.com/iamyixuan/PDEBench-DH/tree/main/pdebench/data_download) )will be supported in the future.
-
-#### Supported hyperparameters
-
-- [x] `num_layers`: number of layers (or other building blocks) in the network.
-- [x] `lr`: learning rate for the optimizer.
-- [x] `num_neurons`: number of neurons per layer.
-- [x] `epochs`: number of maximum epochs for training.
-- [x] `activation`: activation functions.
-- [x] `skip_co`: if using skip connection (residual block).
-- [x] `dropout_rate`: dropout rate.
-- [x] `optimizer`: choices of the optimizer.
-- [x] `weight_decay`: magnitude of L2 regularization.
-- [x] `initialization`: initialization strategy for network weights.
-- [x] `loss_weights`: weights assigned to the PDE loss.
-  
-#### Multi-objective Optimization (MOO)
-
-MOO is supported for the `DiffusionReaction` benchmark. To enable MOO, set environment variable `DEEPHYPER_BENCHMARK_MOO=1`. There are five minimized objectives in this case:
-
-- `objective_0`: Validation PDE loss.
-- `objective_1`: Validation boundary and initial condition solution loss.
-- `objective_2`: Batch inference duration (in seconds).
-- `objective_3`: FLOPS from [FVCORE Package](https://github.com/facebookresearch/fvcore/blob/main/docs/flop_count.md).
diff --git a/lib/PINNBench/install.sh b/lib/PINNBench/install.sh
deleted file mode 100644
index d28fea3..0000000
--- a/lib/PINNBench/install.sh
+++ /dev/null
@@ -1,13 +0,0 @@
-#!/bin/bash
-
-
-mkdir build/ && cd build/
-# install modified PDEBench
-git clone https://github.com/iamyixuan/PDEBench-DH.git
-cd PDEBench-DH/
-python -m pip install -e . 
-
-# generate data
-mkdir ./pdebench/data/
-cd ./pdebench/data_gen/
-python gen_diff_react.py
diff --git a/lib/YAHPO/README.md b/lib/YAHPO/README.md
deleted file mode 100644
index 6fbb7c4..0000000
--- a/lib/YAHPO/README.md
+++ /dev/null
@@ -1,13 +0,0 @@
-# YAHPO Gym - Surrogate Benchmark for Hyperparameter Optimization
-
-> **Warning**
-> Work in progress, this benchmark is not yet ready.
-
-* [YAHPO Gym - Github](https://github.com/slds-lmu/yahpo_gym)
-
-
-## Installation
-
-```console
-python -c "import deephyper_benchmark as dhb; dhb.install('YAHPO/lcbench');"
-```
diff --git a/lib/YAHPO/lcbench/__init__.py b/lib/YAHPO/lcbench/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/lib/YAHPO/lcbench/benchmark.py b/lib/YAHPO/lcbench/benchmark.py
deleted file mode 100644
index 18ec8d7..0000000
--- a/lib/YAHPO/lcbench/benchmark.py
+++ /dev/null
@@ -1,27 +0,0 @@
-import os
-
-from deephyper_benchmark import *
-
-DIR = os.path.dirname(os.path.abspath(__file__))
-
-
-class YAHPOLCBench(Benchmark):
-    version = "0.0.1"
-
-    data_dir = os.path.join(DIR, "..", "build", "data")
-    requires = {
-        "download-data": {
-            "step": "install",
-            "type": "cmd",
-            "cmd": f"git clone https://github.com/slds-lmu/yahpo_data.git {data_dir}",
-        },
-        "pip-yahpo-gym": {"step": "install", "type": "pip", "args": "install yahpo-gym"},
-    }
-
-    def install(self):
-        super().install()
-
-        from yahpo_gym import local_config
-
-        local_config.init_config()
-        local_config.set_data_path(self.data_dir)
diff --git a/lib/YAHPO/lcbench/hpo.py b/lib/YAHPO/lcbench/hpo.py
deleted file mode 100644
index 539ba7a..0000000
--- a/lib/YAHPO/lcbench/hpo.py
+++ /dev/null
@@ -1,75 +0,0 @@
-import os
-
-from ConfigSpace import ConfigurationSpace
-from deephyper.evaluator import profile, RunningJob
-from deephyper.hpo import HpProblem
-
-from yahpo_gym import benchmark_set
-import yahpo_gym.benchmarks.lcbench
-
-DEEPHYPER_BENCHMARK_INSTANCE = os.environ.get("DEEPHYPER_BENCHMARK_INSTANCE", "3945")
-
-
-bench = benchmark_set.BenchmarkSet("lcbench")
-bench.set_instance(DEEPHYPER_BENCHMARK_INSTANCE)
-
-config_space = ConfigurationSpace()
-config_space.add_hyperparameters(
-    [
-        hp
-        for hp in bench.config_space.get_hyperparameters()
-        if not (hp.name in ["OpenML_task_id", "epoch"])
-    ]
-)
-problem = HpProblem(config_space=config_space)
-
-
-@profile()
-def run(job: RunningJob) -> dict:
-    config = job.parameters.copy()
-    config["OpenML_task_id"] = DEEPHYPER_BENCHMARK_INSTANCE
-
-    # Min/Max budget (here epochs)
-    min_b, max_b = 2, 51
-
-    # Run the benchmark (batch for better performance)
-    def update_config(config, budget):
-        config = config.copy()
-        config["epoch"] = budget
-        return config
-
-    outputs = [
-        bench.objective_function(update_config(config, budget=b))
-        for b in range(min_b, max_b + 1)
-    ]
-
-    for i, out_i in enumerate(outputs):
-        out_i = out_i[0]
-        budget_i = i + 1
-        objective_i = -out_i["val_cross_entropy"]
-
-        job.record(budget_i, objective_i)
-        if job.stopped():
-            break
-
-    return {
-        "objective": objective_i,
-        "metadata": {
-            "budget": budget_i,
-            "stopped": budget_i < len(outputs),
-            "test_balanced_accuracy": outputs[-1][0]["test_balanced_accuracy"],
-            "val_balanced_accuracy": outputs[-1][0]["val_balanced_accuracy"],
-            "test_cross_entropy": outputs[-1][0]["test_cross_entropy"],
-            "val_cross_entropy": outputs[-1][0]["val_cross_entropy"],
-            "time": outputs[-1][0]["time"],
-        },
-    }
-
-
-if __name__ == "__main__":
-    print(problem)
-    config = bench.config_space.sample_configuration(1).get_dictionary()
-    config.pop("OpenML_task_id")
-    print(config)
-    result = run(RunningJob(id=0, parameters=config))
-    print(result)
diff --git a/lib/YAHPO/lcbench/metrics.py b/lib/YAHPO/lcbench/metrics.py
deleted file mode 100644
index 92d0359..0000000
--- a/lib/YAHPO/lcbench/metrics.py
+++ /dev/null
@@ -1,69 +0,0 @@
-import json
-import os
-
-import numpy as np
-
-from .hpo import bench as BENCHMARK
-
-
-class PerformanceEvaluator:
-    """A class defining performance evaluators for the YAHPO/lcbench problems."""
-
-    def __init__(self):
-        """Read the current problem defn from environment vars."""
-
-        df = BENCHMARK.target_stats
-
-        self.x_min = None
-
-        self.y_metric = "cross_entropy"
-
-        cond = (df["metric"] == "val_cross_entropy") & (df["statistic"] == "min")
-        self.y_min_valid = df[cond].iloc[0]["value"]
-
-        cond = (df["metric"] == "test_cross_entropy") & (df["statistic"] == "min")
-        self.y_min_test = df[cond].iloc[0]["value"]
-
-    def simple_regret_valid(self, y_valid: np.ndarray) -> np.ndarray:
-        """Compute the regret of the objective (validation RMSE) of a list of ordered solutions.
-
-        Args:
-            y_valid (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y_valid - self.y_min_valid
-
-    def cumul_regret_valid(self, y_valid: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of the objective (validation RMSE) aon n array of ordered solutions.
-
-        Args:
-            y_valid (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret_valid(y_valid))
-
-    def simple_regret_test(self, y_test: np.ndarray) -> np.ndarray:
-        """Compute the regret of the test RMSE of a list of ordered solutions.
-
-        Args:
-            y_test (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return y_test - self.y_min_test
-
-    def cumul_regret_test(self, y_test: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of the test RMSE aon n array of ordered solutions.
-
-        Args:
-            y_test (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret_test(y_test))
diff --git a/pyproject.toml b/pyproject.toml
index b5a3c46..f692c9b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,72 @@
 [build-system]
-requires = [
-    "setuptools>=42",
-    "wheel"
+requires = ["hatchling"]
+build-backend = "hatchling.build"
+
+[project]
+name = "deephyper-benchmark"
+version = "0.0.2"
+dependencies = [
+    # "deephyper",
+    "deephyper>=0.9.3",
 ]
-build-backend = "setuptools.build_meta"
\ No newline at end of file
+requires-python = ">=3.10"
+authors = [
+    { name = "Romain Egele", email = "regele@ornl.gov" },
+]
+maintainers = [
+    { name = "Romain Egele", email = "regele@ornl.gov" },
+]
+description = "Some description..."
+readme = "README.md"
+license = { file = "LICENSE" }
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Programming Language :: Python",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+]
+
+[project.optional-dependencies]
+
+
+[project.scripts]
+
+
+[project.urls]
+Documentation = "http://deephyper.readthedocs.io"
+Changes = "https://github.com/deephyper/deephyper/releases"
+Forum = "https://github.com/deephyper/deephyper/discussions"
+GitHub = "https://github.com/deephyper/deephyper"
+Issues = "https://github.com/deephyper/deephyper/issues"
+
+[tool.pytest.ini_options]
+norecursedirs = ".git"
+
+
+[tool.coverage.paths]
+source = ["src/", "*/site-packages"]
+
+[tool.coverage.run]
+branch = true
+source = ["deephyper"]
+
+[tool.coverage.report]
+exclude_lines = ["if __name__ == '__main__':"]
+
+[tool.ruff]
+exclude = [
+]
+line-length = 100
+
+[tool.ruff.lint]
+extend-select = ["D", "E501"]
+ignore = ["D107"]
+
+[tool.ruff.lint.pydocstyle]
+convention = "google"
+
+[tool.ruff.lint.per-file-ignores]
+"tests/*" = ["D"]
diff --git a/setup.cfg b/setup.cfg
deleted file mode 100644
index 8183238..0000000
--- a/setup.cfg
+++ /dev/null
@@ -1,2 +0,0 @@
-[metadata]
-license_files = LICENSE
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 4b6e845..0000000
--- a/setup.py
+++ /dev/null
@@ -1,198 +0,0 @@
-"""A setuptools based setup module.
-See:
-https://packaging.python.org/guides/distributing-packages-using-setuptools/
-https://github.com/pypa/sampleproject
-"""
-
-# Always prefer setuptools over distutils
-from setuptools import setup, find_packages
-import pathlib
-
-here = pathlib.Path(__file__).parent.resolve()
-
-# Get the long description from the README file
-long_description = (here / 'README.md').read_text(encoding='utf-8')
-
-# Arguments marked as "Required" below must be included for upload to PyPI.
-# Fields marked as "Optional" may be commented out.
-
-setup(
-    # This is the name of your project. The first time you publish this
-    # package, this name will be registered for you. It will determine how
-    # users can install this project, e.g.:
-    #
-    # $ pip install sampleproject
-    #
-    # And where it will live on PyPI: https://pypi.org/project/sampleproject/
-    #
-    # There are some restrictions on what makes a valid project name
-    # specification here:
-    # https://packaging.python.org/specifications/core-metadata/#name
-    name='deephyper-benchmark',  # Required
-
-    # Versions should comply with PEP 440:
-    # https://www.python.org/dev/peps/pep-0440/
-    #
-    # For a discussion on single-sourcing the version across setup.py and the
-    # project code, see
-    # https://packaging.python.org/en/latest/single_source_version.html
-    version='0.0.1',  # Required
-
-    # This is an optional longer description of your project that represents
-    # the body of text which users will see when they visit PyPI.
-    #
-    # Often, this is the same as your README, so you can just read it in from
-    # that file directly (as we have already done above)
-    #
-    # This field corresponds to the "Description" metadata field:
-    # https://packaging.python.org/specifications/core-metadata/#description-optional
-    long_description=long_description,  # Optional
-
-    # Denotes that our long_description is in Markdown; valid values are
-    # text/plain, text/x-rst, and text/markdown
-    #
-    # Optional if long_description is written in reStructuredText (rst) but
-    # required for plain-text or Markdown; if unspecified, "applications should
-    # attempt to render [the long_description] as text/x-rst; charset=UTF-8 and
-    # fall back to text/plain if it is not valid rst" (see link below)
-    #
-    # This field corresponds to the "Description-Content-Type" metadata field:
-    # https://packaging.python.org/specifications/core-metadata/#description-content-type-optional
-    long_description_content_type='text/markdown',  # Optional (see note above)
-
-    # This should be a valid link to your project's main homepage.
-    #
-    # This field corresponds to the "Home-Page" metadata field:
-    # https://packaging.python.org/specifications/core-metadata/#home-page-optional
-    url='https://github.com/deephyper/benchmark',  # Optional
-
-    # This should be your name or the name of the organization which owns the
-    # project.
-    author='Romain Egele',  # Optional
-
-    # This should be a valid email address corresponding to the author listed
-    # above.
-    author_email='romainegele@gmail.com',  # Optional
-
-    # Classifiers help users find your project by categorizing it.
-    #
-    # For a list of valid classifiers, see https://pypi.org/classifiers/
-    classifiers=[  # Optional
-        # How mature is this project? Common values are
-        #   3 - Alpha
-        #   4 - Beta
-        #   5 - Production/Stable
-        'Development Status :: 3 - Alpha',
-
-        # Indicate who your project is intended for
-        'Intended Audience :: Developers',
-
-        # Pick your license as you wish
-        'License :: OSI Approved :: BSD License',
-
-        # Specify the Python versions you support here. In particular, ensure
-        # that you indicate you support Python 3. These classifiers are *not*
-        # checked by 'pip install'. See instead 'python_requires' below.
-        'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.8',
-        'Programming Language :: Python :: 3.9',
-        'Programming Language :: Python :: 3.10',
-        'Programming Language :: Python :: 3.11',
-        'Programming Language :: Python :: 3 :: Only',
-    ],
-
-    # This field adds keywords for your project which will appear on the
-    # project page. What does your project relate to?
-    #
-    # Note that this is a list of additional keywords, separated
-    # by commas, to be used to assist searching for the distribution in a
-    # larger catalog.
-    keywords='benchmark, deephyper, performance, hpc',  # Optional
-
-    # When your source code is in a subdirectory under the project root, e.g.
-    # `src/`, it is necessary to specify the `package_dir` argument.
-    # package_dir={'': 'src'},  # Optional
-
-    # You can just specify package directories manually here if your project is
-    # simple. Or you can use find_packages().
-    #
-    # Alternatively, if you just want to distribute a single Python file, use
-    # the `py_modules` argument instead as follows, which will expect a file
-    # called `my_module.py` to exist:
-    #
-    #   py_modules=["my_module"],
-    #
-    packages=find_packages(where="."),  # Required
-
-    # Specify which Python versions you support. In contrast to the
-    # 'Programming Language' classifiers above, 'pip install' will check this
-    # and refuse to install the project if the version does not match. See
-    # https://packaging.python.org/guides/distributing-packages-using-setuptools/#python-requires
-    python_requires='>=3.6',
-
-    # This field lists other packages that your project depends on to run.
-    # Any package you put here will be installed by pip when your project is
-    # installed, so they must be valid existing projects.
-    #
-    # For an analysis of "install_requires" vs pip's requirements files see:
-    # https://packaging.python.org/en/latest/requirements.html
-    install_requires=[
-        # "deephyper",
-        # "tinydb",
-        # "streamlit"
-        ],  # Optional
-
-    # List additional groups of dependencies here (e.g. development
-    # dependencies). Users will be able to install these using the "extras"
-    # syntax, for example:
-    #
-    #   $ pip install sampleproject[dev]
-    #
-    # Similar to `install_requires` above, these must be valid existing
-    # projects.
-    extras_require={  # Optional
-        'dev': ['check-manifest'],
-        'test': ['coverage'],
-    },
-
-    # If there are data files included in your packages that need to be
-    # installed, specify them here.
-    package_data={  # Optional
-        # 'sample': ['package_data.dat'],
-    },
-
-    # Although 'package_data' is the preferred approach, in some case you may
-    # need to place data files outside of your packages. See:
-    # http://docs.python.org/distutils/setupscript.html#installing-additional-files
-    #
-    # In this case, 'data_file' will be installed into '<sys.prefix>/my_data'
-    data_files=[
-        # ('my_data', ['data/data_file'])
-        ],  # Optional
-
-    # To provide executable scripts, use entry points in preference to the
-    # "scripts" keyword. Entry points provide cross-platform support and allow
-    # `pip` to create the appropriate form of executable for the target
-    # platform.
-    #
-    # For example, the following would provide a command called `sample` which
-    # executes the function `main` from this package when invoked:
-    entry_points={  # Optional
-        # 'console_scripts': [
-        #     'deephyper-benchmark=deephyper_benchmark.cli:main',
-        # ],
-    },
-
-    # List additional URLs that are relevant to your project as a dict.
-    #
-    # This field corresponds to the "Project-URL" metadata fields:
-    # https://packaging.python.org/specifications/core-metadata/#project-url-multiple-use
-    #
-    # Examples listed include a pattern for specifying where the package tracks
-    # issues, where the source is hosted, where to say thanks to the package
-    # maintainers, and where to support the project financially. The key is
-    # what's used to render the link text on PyPI.
-    project_urls={  # Optional
-        'Bug Reports': 'https://github.com/deephyper/benchmark/issues',
-    },
-)
\ No newline at end of file
diff --git a/src/deephyper_benchmark/hpo/_optuna.py b/src/deephyper_benchmark/hpo/_optuna.py
new file mode 100644
index 0000000..b47c240
--- /dev/null
+++ b/src/deephyper_benchmark/hpo/_optuna.py
@@ -0,0 +1,303 @@
+import os
+from typing import Union, List, Dict
+
+import ConfigSpace.hyperparameters as csh
+import optuna
+import pandas as pd
+from optuna.trial import FrozenTrial, TrialState, Trial
+from ConfigSpace import ConfigurationSpace
+from ConfigSpace.hyperparameters import Hyperparameter
+from ConfigSpace.util import deactivate_inactive_hyperparameters
+from deephyper.evaluator import HPOJob
+from deephyper.hpo import Search
+from deephyper.hpo.utils import get_inactive_value_of_hyperparameter
+
+
+def optuna_suggest_from_hp(trial: Trial, hp: Hyperparameter):
+    name = hp.name
+    if isinstance(hp, csh.UniformIntegerHyperparameter):
+        value = trial.suggest_int(name, hp.lower, hp.upper, log=hp.log)
+    elif isinstance(hp, csh.UniformFloatHyperparameter):
+        value = trial.suggest_float(name, hp.lower, hp.upper, log=hp.log)
+    elif isinstance(hp, csh.CategoricalHyperparameter):
+        value = trial.suggest_categorical(name, hp.choices)
+    elif isinstance(hp, csh.OrdinalHyperparameter):
+        value = trial.suggest_categorical(name, hp.sequence)
+    else:
+        raise TypeError(f"Cannot convert hyperparameter of type {type(hp)}")
+
+    return value
+
+
+def optuna_suggest_from_configspace(trial: Trial, config_space: ConfigurationSpace) -> dict:
+    config = {}
+    for name in config_space:
+        value = optuna_suggest_from_hp(trial, config_space[name])
+        config[name] = value
+
+    config = dict(deactivate_inactive_hyperparameters(config, config_space))
+
+    for name in config_space:
+        # If the parameter is inactive due to some conditions then we attribute the
+        # lower bound value to break symmetries and enforce the same representation.
+        if name not in config:
+            config[name] = get_inactive_value_of_hyperparameter(config_space[name])
+
+    return config
+
+
+class CheckpointSaverCallback:
+    def __init__(self, log_dir=".", states=(TrialState.COMPLETE,)) -> None:
+        self._log_dir = log_dir
+        self._states = states
+
+    def __call__(self, study: optuna.study.Study, trial: FrozenTrial) -> None:
+        all_trials = study.get_trials(deepcopy=False, states=self._states)
+        # n_complete = len(all_trials)
+
+        pd.DataFrame([t.user_attrs["results"] for t in all_trials]).to_csv(
+            os.path.join(self._log_dir, "results.csv")
+        )
+
+
+# Constraints
+def constraints(trial):
+    return trial.user_attrs["constraints"]
+
+
+# Supported samplers
+supported_samplers = ["TPE", "CMAES", "NSGAII", "DUMMY", "BOTORCH", "QMC"]
+supported_pruners = ["NOP", "SHA", "HB", "MED"]
+
+
+# TODO: CMAES requires: $ pip install cmaes
+# TODO: GP requires: $ pip install torch
+
+class OptunaSearch(Search):
+    """Wrapper for Optuna to run distributed optimization with MPI.
+
+    Args:
+        problem (HpProblem):
+            Hyperparameter problem describing the search space to explore.
+
+        evaluator (Evaluator):
+            An ``Evaluator`` instance responsible of distributing the tasks.
+
+        random_state (int, optional):
+            Random seed. Defaults to ``None``.
+
+        log_dir (str, optional):
+            Log directory where search's results are saved. Defaults to ``"."``.
+
+        verbose (int, optional):
+            Indicate the verbosity level of the search. Defaults to ``0``.
+
+        sampler (Union[str, optuna.samplers.BaseSampler], optional):
+            Optimization strategy to suggest new hyperparameter configurations. Defaults
+            to ``"TPE"``.
+
+        pruner (Union[str, optuna.pruners.BasePruner], optional):
+            Pruning strategy to perform early discarding of unpromizing configuraitons.
+            Defaults to ``None``.
+
+        n_objectives (int, optional): Number of objectives to optimize. Defaults to ``1``.
+
+        study_name (str, optional):
+            Name of the study in the database used by Optuna. Defaults to ``None``.
+
+        storage (Union[str, optuna.storages.BaseStorage], optional):
+            Database used by Optuna. Defaults to ``None``.
+
+        checkpoint (bool, optional):
+            If results should be checkpointed regularly to the ``log_dir``. Defaults to ``True``.
+
+        moo_lower_bounds ([type], optional): [description]. Defaults to ``None``.
+
+    Raises:
+        ValueError: _description_
+        TypeError: _description_
+        ValueError: _description_
+        ValueError: _description_
+        TypeError: _description_
+    """
+
+    def __init__(
+        self,
+        problem,
+        evaluator,
+        random_state: int = None,
+        log_dir: str = ".",
+        verbose: int = 0,
+        sampler: Union[str, optuna.samplers.BaseSampler] = "TPE",
+        pruner: Union[str, optuna.pruners.BasePruner] = None,
+        n_objectives: int = 1,
+        study_name: str = None,
+        storage: Union[str, optuna.storages.BaseStorage] = None,
+        checkpoint: bool = True,
+        n_initial_points: int = None,
+        moo_lower_bounds=None,
+        **kwargs,
+    ):
+        super().__init__(problem, evaluator, random_state, log_dir, verbose)
+
+        optuna.logging.set_verbosity(
+            optuna.logging.DEBUG if self._verbose else optuna.logging.ERROR
+        )
+
+        self._evaluator = evaluator
+
+        # get the __init__ parameters
+        _init_params = locals()
+
+        self._n_initial_points = (
+            2 * len(self._problem) if n_initial_points is None else n_initial_points
+        )
+
+        # Constraints
+        self._moo_lower_bounds = moo_lower_bounds
+        self._constraints_func = None
+        if moo_lower_bounds is not None:
+            if len(moo_lower_bounds) == n_objectives:
+                self._constraints_func = constraints
+            else:
+                raise ValueError(
+                    f"moo_lower_bounds should be of length {n_objectives} but is of length "
+                    f"{len(moo_lower_bounds)}"
+                )
+
+        # Setup the sampler
+        if isinstance(sampler, optuna.samplers.BaseSampler):
+            pass
+        elif isinstance(sampler, str):
+            sampler_seed = self._random_state.randint(2**31)
+            if sampler == "TPE":
+                sampler = optuna.samplers.TPESampler(
+                    n_startup_trials=self._n_initial_points,
+                    seed=sampler_seed,
+                    constraints_func=self._constraints_func,
+                )
+            elif sampler == "GP":
+                sampler = optuna.samplers.GPSampler(
+                    n_startup_trials=self._n_initial_points,
+                    seed=sampler_seed,
+                    constraints_func=self._constraints_func,
+                )
+            elif sampler == "CMAES":
+                sampler = optuna.samplers.CmaEsSampler(
+                    n_startup_trials=self._n_initial_points, seed=sampler_seed
+                )
+            elif sampler == "NSGAII":
+                sampler = optuna.samplers.NSGAIISampler(
+                    seed=sampler_seed,
+                    constraints_func=self._constraints_func,
+                )
+            elif sampler == "DUMMY":
+                sampler = optuna.samplers.RandomSampler(seed=sampler_seed)
+            elif sampler == "BOTORCH":
+                from optuna.integration import BoTorchSampler
+
+                sampler = BoTorchSampler(
+                    n_startup_trials=self._n_initial_points,
+                    seed=sampler_seed,
+                    constraints_func=self._constraints_func,
+                )
+            elif sampler == "QMC":
+                sampler = optuna.samplers.QMCSampler(seed=sampler_seed)
+
+            else:
+                raise ValueError(
+                    f"Requested unknown sampler {sampler} should be one of {supported_samplers}"
+                )
+        else:
+            raise TypeError(
+                f"Sampler is of type {type(sampler)} but must be a str or "
+                "optuna.samplers.BaseSampler!"
+            )
+
+        self._n_objectives = n_objectives
+
+        # Setup the pruner
+        if self._n_objectives > 1 or pruner is None:
+            pruner = None
+            if pruner is not None:
+                raise ValueError("Multi-objective optimization does not support pruning!")
+        else:
+            if isinstance(pruner, optuna.pruners.BasePruner):
+                pass
+            elif isinstance(pruner, str):
+                if pruner == "NOP":
+                    pruner = optuna.pruners.NopPruner()
+                elif pruner == "SHA":
+                    pruner = optuna.pruners.SuccessiveHalvingPruner()
+                elif pruner == "HB":
+                    pruner = optuna.pruners.HyperbandPruner()
+                elif pruner == "MED":
+                    pruner = optuna.pruners.MedianPruner()
+                else:
+                    raise ValueError(
+                        f"Requested unknown pruner {pruner} should be one of {supported_pruners}"
+                    )
+            else:
+                raise TypeError(
+                    f"Pruner is of type {type(pruner)} but must be a str or "
+                    "optuna.pruners.BasePruner!"
+                )
+        self.pruner = pruner
+
+        self._checkpoint = checkpoint
+
+        study_params = dict(
+            study_name=study_name,
+            storage=storage,
+            sampler=sampler,
+            pruner=pruner,
+        )
+
+        if self._n_objectives > 1:
+            study_params["directions"] = ["maximize" for _ in range(self._n_objectives)]
+        else:
+            study_params["direction"] = "maximize"
+
+        self.timestamp = None
+
+        self.study = optuna.create_study(**study_params)
+
+        self._init_params = _init_params
+
+        self._trials = {}
+        self._trials_count = 0
+
+    def _ask(self, n: int = 1) -> List[Dict]:
+        """Ask the search for new configurations to evaluate.
+
+        Args:
+            n (int, optional): The number of configurations to ask. Defaults to 1.
+
+        Returns:
+            List[Dict]: a list of hyperparameter configurations to evaluate.
+        """
+        new_samples = []
+        for i in range(n):
+            trial = self.study.ask()
+            new_sample = optuna_suggest_from_configspace(trial, self._problem.space)
+            new_samples.append(new_sample)
+            self._trials[self._trials_count] = trial
+            self._trials_count += 1
+
+        return new_samples
+
+    def _tell(self, results: List[HPOJob]):
+        """Tell the search the results of the evaluations.
+
+        Args:
+            results (List[HPOJob]): a dictionary containing the results of the evaluations.
+        """
+        for job in results:
+            i = int(job.id.split(".")[-1])
+            obj = job.objective
+            # Do not add failures to population
+            if isinstance(obj, str):
+                obj = TrialState.FAIL
+
+            trial = self._trials.pop(i)
+            self.study.tell(trial, obj)
diff --git a/src/deephyper_benchmark/lib/c_bbo/ackley/__init__.py b/src/deephyper_benchmark/lib/c_bbo/ackley/__init__.py
new file mode 100644
index 0000000..f7c7dbe
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/ackley/__init__.py
@@ -0,0 +1,18 @@
+"""Ackley function benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/ackley.html
+"""
+
+from . import hpo_benchmark
+
+__all__ = ["install", "init", "hpo"]
+
+hpo = hpo_benchmark.benchmark
+
+
+def install(config: dict = None):
+    """Install the benchmark."""
+
+
+def init(config: dict = None):
+    """Initialize the benchmark."""
diff --git a/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
new file mode 100644
index 0000000..f686619
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
@@ -0,0 +1,114 @@
+"""Module defining the problem and run-function of the benchmark."""
+
+import os
+import time
+
+import numpy as np
+
+from deephyper.hpo import HpProblem
+from deephyper.evaluator import profile, RunningJob
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+__all__ = ["benchmark"]
+
+
+def ackley(x, a=20, b=0.2, c=2 * np.pi):
+    d = len(x)
+    s1 = np.sum(x**2)
+    s2 = np.sum(np.cos(c * x))
+    term1 = -a * np.exp(-b * np.sqrt(s1 / d))
+    term2 = -np.exp(s2 / d)
+    y = term1 + term2 + a + np.exp(1)
+    return y
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -ackley(x)
+
+
+class AckleyHPOScorer(HPOScorer):
+    """A class defining performance evaluators for the Ackley problem."""
+
+    def __init__(
+        self,
+        p_num,
+        p_num_slack,
+    ):
+        self.p_num = p_num
+        self.x_min = np.zeros(self.p_num)
+        self.x_min[p_num - p_num_slack :] = np.nan
+        self.y_min = 0.0
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_min - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class AckleyHPOBenchmark(HPOBenchmark):
+    def refresh_settings(self):
+        self.DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
+        self.DEEPHYPER_BENCHMARK_OFFSET = float(os.environ.get("DEEPHYPER_BENCHMARK_OFFSET", 4.0))
+        self.DEEPHYPER_BENCHMARK_NDIMS_SLACK = int(
+            os.environ.get("DEEPHYPER_BENCHMARK_NDIMS_SLACK", 0)
+        )
+
+    @property
+    def problem(self):
+        # The original range is simetric (-32.768, 32.768) but we make it less simetric to avoid
+        # Grid sampling or QMC sampling to directly hit the optimum...
+        domain = (
+            -32.768 - self.DEEPHYPER_BENCHMARK_OFFSET,
+            32.768 - self.DEEPHYPER_BENCHMARK_OFFSET,
+        )
+        problem = HpProblem()
+        for i in range(self.DEEPHYPER_BENCHMARK_NDIMS - self.DEEPHYPER_BENCHMARK_NDIMS_SLACK):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+        # Add slack/dummy dimensions (useful to test predicors which are sensitive
+        # to unimportant features)
+        for i in range(
+            self.DEEPHYPER_BENCHMARK_NDIMS - self.DEEPHYPER_BENCHMARK_NDIMS_SLACK,
+            self.DEEPHYPER_BENCHMARK_NDIMS,
+        ):
+            problem.add_hyperparameter(domain, f"z{i}")
+        return problem
+
+    @property
+    def run_function(self):
+        return run_function
+
+    @property
+    def scorer(self):
+        return AckleyHPOScorer(self.DEEPHYPER_BENCHMARK_NDIMS, self.DEEPHYPER_BENCHMARK_NDIMS_SLACK)
+
+
+benchmark = AckleyHPOBenchmark()
diff --git a/src/deephyper_benchmark/lib/c_bbo/cossin/__init__.py b/src/deephyper_benchmark/lib/c_bbo/cossin/__init__.py
new file mode 100644
index 0000000..a02e0ff
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/cossin/__init__.py
@@ -0,0 +1,18 @@
+"""Cos-Sin function benchmark.
+
+Description of the function: ...
+"""
+
+from . import hpo_benchmark
+
+__all__ = ["install", "init", "hpo"]
+
+hpo = hpo_benchmark.benchmark
+
+
+def install(config: dict = None):
+    """Install the benchmark."""
+
+
+def init(config: dict = None):
+    """Initialize the benchmark."""
diff --git a/src/deephyper_benchmark/lib/c_bbo/cossin/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/cossin/hpo_benchmark.py
new file mode 100644
index 0000000..b4d0487
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/cossin/hpo_benchmark.py
@@ -0,0 +1,115 @@
+"""Module defining the problem and run-function of the benchmark."""
+
+import os
+import time
+
+import numpy as np
+
+from deephyper.hpo import HpProblem
+from deephyper.evaluator import profile, RunningJob
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+__all__ = ["benchmark"]
+
+
+def cossin(x):
+    x = np.sum(x)
+    y = np.cos(5 * x / 10) + 2 * np.sin(x / 10) + x / 100
+    return y
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20, y_noise=0.0) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    y = cossin(x)
+
+    if y_noise > 0.0:
+        rng = np.random.RandomState(int(job.id.split(".")[-1]))
+        eps = rng.normal(loc=0.0, scale=y_noise)
+        y += eps
+
+    return y
+
+
+class CosSinHPOScorer(HPOScorer):
+    """A class defining performance evaluators for the CosSin problem."""
+
+    def __init__(
+        self,
+        p_num,
+        p_num_slack,
+    ):
+        self.p_num = p_num
+        self.x_min = np.full((self.p_num,), 18.6738)
+        self.x_min[self.p_num - p_num_slack :] = np.nan
+        self.y_min = 3.1
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_min - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class CosSinHPOBenchmark(HPOBenchmark):
+    def refresh_settings(self):
+        self.DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 1))
+        self.DEEPHYPER_BENCHMARK_OFFSET = float(os.environ.get("DEEPHYPER_BENCHMARK_OFFSET", 0.0))
+        self.DEEPHYPER_BENCHMARK_NDIMS_SLACK = int(
+            os.environ.get("DEEPHYPER_BENCHMARK_NDIMS_SLACK", 0)
+        )
+
+    @property
+    def problem(self):
+        domain = (
+            -50.0 - self.DEEPHYPER_BENCHMARK_OFFSET,
+            50.0 - self.DEEPHYPER_BENCHMARK_OFFSET,
+        )
+        problem = HpProblem()
+        for i in range(self.DEEPHYPER_BENCHMARK_NDIMS - self.DEEPHYPER_BENCHMARK_NDIMS_SLACK):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+        # Add slack/dummy dimensions (useful to test predicors which are sensitive
+        # to unimportant features)
+        for i in range(
+            self.DEEPHYPER_BENCHMARK_NDIMS - self.DEEPHYPER_BENCHMARK_NDIMS_SLACK,
+            self.DEEPHYPER_BENCHMARK_NDIMS,
+        ):
+            problem.add_hyperparameter(domain, f"z{i}")
+        return problem
+
+    @property
+    def run_function(self):
+        return run_function
+
+    @property
+    def scorer(self):
+        return CosSinHPOScorer(self.DEEPHYPER_BENCHMARK_NDIMS, self.DEEPHYPER_BENCHMARK_NDIMS_SLACK)
+
+
+benchmark = CosSinHPOBenchmark()
diff --git a/tests/test_c_bbo.py b/tests/test_c_bbo.py
new file mode 100644
index 0000000..543f8f8
--- /dev/null
+++ b/tests/test_c_bbo.py
@@ -0,0 +1,12 @@
+import deephyper_benchmark.lib.c_bbo.ackley as bench
+
+
+def test_benchmark():
+
+    bench.hpo.problem
+    bench.hpo.run_function
+    bench.hpo.scorer
+
+
+if __name__ == "__main__":
+    test_benchmark()

From 4912d0a7ecc122056503dbe0782768141d4d83b3 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Fri, 14 Mar 2025 14:31:20 +0100
Subject: [PATCH 02/43] update

---
 src/deephyper_benchmark/__init__.py           |  6 ++++
 src/deephyper_benchmark/_benchmark.py         | 27 +++++++++++++++++
 src/deephyper_benchmark/_scorer.py            | 30 +++++++++++++++++++
 src/deephyper_benchmark/lib/c_bbo/README.md   | 20 +++++++++++++
 src/deephyper_benchmark/lib/c_bbo/__init__.py |  0
 5 files changed, 83 insertions(+)
 create mode 100644 src/deephyper_benchmark/__init__.py
 create mode 100644 src/deephyper_benchmark/_benchmark.py
 create mode 100644 src/deephyper_benchmark/_scorer.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/README.md
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/__init__.py

diff --git a/src/deephyper_benchmark/__init__.py b/src/deephyper_benchmark/__init__.py
new file mode 100644
index 0000000..7672c1f
--- /dev/null
+++ b/src/deephyper_benchmark/__init__.py
@@ -0,0 +1,6 @@
+"""DeepHyper-Benchmark package."""
+
+from ._benchmark import Benchmark, HPOBenchmark
+from ._scorer import Scorer, HPOScorer
+
+__all__ = ["Benchmark", "HPOBenchmark", "Scorer", "HPOScorer"]
diff --git a/src/deephyper_benchmark/_benchmark.py b/src/deephyper_benchmark/_benchmark.py
new file mode 100644
index 0000000..3850d7f
--- /dev/null
+++ b/src/deephyper_benchmark/_benchmark.py
@@ -0,0 +1,27 @@
+import abc
+
+
+class Benchmark(abc.ABC):
+    def __init__(self):
+        super().__init__()
+        self.refresh_settings()
+
+    def refresh_settings(self):
+        """Refresh benchmark settings."""
+
+
+class HPOBenchmark(Benchmark):
+    @property
+    @abc.abstractmethod
+    def problem(self):
+        """The benchmark hyperparameter problem."""
+
+    @property
+    @abc.abstractmethod
+    def run_function(self):
+        """The benchmark hyperparameter run_function."""
+
+    @property
+    @abc.abstractmethod
+    def scorer(self):
+        """The benchmark hyperparameter scorer."""
diff --git a/src/deephyper_benchmark/_scorer.py b/src/deephyper_benchmark/_scorer.py
new file mode 100644
index 0000000..cd0e695
--- /dev/null
+++ b/src/deephyper_benchmark/_scorer.py
@@ -0,0 +1,30 @@
+import abc
+import numpy as np
+
+
+class Scorer(abc.ABC):
+    pass
+
+
+class HPOScorer(Scorer):
+    @abc.abstractmethod
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+
+    @abc.abstractmethod
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
diff --git a/src/deephyper_benchmark/lib/c_bbo/README.md b/src/deephyper_benchmark/lib/c_bbo/README.md
new file mode 100644
index 0000000..6ecf11d
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/README.md
@@ -0,0 +1,20 @@
+# C-BBO: Continuous Black-Box Optimization
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
+
+Set of continuous function benchmarks. The `DEEPHYPER_BENCHMARK_NDIMS` environment variable defines the number of dimensions of the problem.
+
+| Function Name | Number of Dimensions  |                   Comment                   |
+| ------------- | --------------------- | ------------------------------------------- |
+| ackley        | $\infty$ (default 5)  | Many local minima and single global optimum |
+| branin        | 2                     | Three global optimum                        |
+| cossin        | 1                     | Many local minima, good for visualisation.  |
+| easom         | 2                     | Almost flat everywhere                      |
+| griewank      | $\infty$ (default 5)  |                                             |
+| hartmann6D    | 6                     |                                             |
+| levy          | $\infty$ (default 5)  |                                             |
+| michal        | $\infty$ (default 2)  |                                             |
+| rosen         | $\infty$ (default 5)  |                                             |
+| schwefel      | $\infty$ (default 5)  |                                             |
+| shekel        | 4                     | Many local minima with flat areas           |
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/c_bbo/__init__.py b/src/deephyper_benchmark/lib/c_bbo/__init__.py
new file mode 100644
index 0000000..e69de29

From 08c3d64b67914ae8cba33f3089a98694aeea383d Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Fri, 14 Mar 2025 15:20:18 +0100
Subject: [PATCH 03/43] adding branin

---
 .../lib/c_bbo/branin/__init__.py              | 18 ++++
 .../lib/c_bbo/branin/hpo_benchmark.py         | 89 +++++++++++++++++++
 2 files changed, 107 insertions(+)
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/branin/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/branin/hpo_benchmark.py

diff --git a/src/deephyper_benchmark/lib/c_bbo/branin/__init__.py b/src/deephyper_benchmark/lib/c_bbo/branin/__init__.py
new file mode 100644
index 0000000..3ff56ce
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/branin/__init__.py
@@ -0,0 +1,18 @@
+"""Branin function benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/branin.html
+"""
+
+from . import hpo_benchmark
+
+__all__ = ["install", "init", "hpo"]
+
+hpo = hpo_benchmark.benchmark
+
+
+def install(config: dict = None):
+    """Install the benchmark."""
+
+
+def init(config: dict = None):
+    """Initialize the benchmark."""
diff --git a/src/deephyper_benchmark/lib/c_bbo/branin/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/branin/hpo_benchmark.py
new file mode 100644
index 0000000..e935472
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/branin/hpo_benchmark.py
@@ -0,0 +1,89 @@
+"""Module defining the problem and run-function of the benchmark."""
+
+import time
+
+import numpy as np
+
+from deephyper.hpo import HpProblem
+from deephyper.evaluator import profile, RunningJob
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+__all__ = ["benchmark"]
+
+
+def branin(x):
+    assert len(x) == 2
+    a = 1.0
+    b = 5.1 / (4.0 * np.pi**2)
+    c = 5.0 / np.pi
+    r = 6.0
+    s = 10.0
+    t = 1.0 / (8.0 * np.pi)
+    y = a * (x[1] - b * x[0] ** 2 + c * x[0] - r) ** 2 + s * (1 - t) * np.cos(x[0]) + s
+    return y
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -branin(x)
+
+
+class BraninHPOScorer(HPOScorer):
+    """A class defining performance evaluators for the Ackley problem."""
+
+    def __init__(self):
+        self.p_num = 2
+        self.x_min = np.array([[-np.pi, 12.275], [np.pi, 2.275], [9.42478, 2.475]])
+        self.y_min = 0.397887
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_min - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class BraninHPOBenchmark(HPOBenchmark):
+    @property
+    def problem(self):
+        problem = HpProblem()
+        problem.add_hyperparameter((-5.0, 10.0), "x0")
+        problem.add_hyperparameter((0.0, 15.0), "x1")
+        return problem
+
+    @property
+    def run_function(self):
+        return run_function
+
+    @property
+    def scorer(self):
+        return BraninHPOScorer()
+
+
+benchmark = BraninHPOBenchmark()

From 9f6b545d5cbf69580d12df6165f92a7955a6cba9 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Fri, 14 Mar 2025 15:35:50 +0100
Subject: [PATCH 04/43] update

---
 .../lib/c_bbo/ackley/hpo_benchmark.py         |  9 +-
 .../lib/c_bbo/easom/__init__.py               | 18 ++++
 .../lib/c_bbo/easom/hpo_benchmark.py          | 92 +++++++++++++++++++
 3 files changed, 117 insertions(+), 2 deletions(-)
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/easom/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py

diff --git a/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
index f686619..ffaab68 100644
--- a/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
+++ b/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
@@ -44,9 +44,10 @@ def __init__(
         self,
         p_num,
         p_num_slack,
+        offset=0,
     ):
         self.p_num = p_num
-        self.x_min = np.zeros(self.p_num)
+        self.x_min = np.full(self.p_num, fill_value=-offset)
         self.x_min[p_num - p_num_slack :] = np.nan
         self.y_min = 0.0
 
@@ -108,7 +109,11 @@ def run_function(self):
 
     @property
     def scorer(self):
-        return AckleyHPOScorer(self.DEEPHYPER_BENCHMARK_NDIMS, self.DEEPHYPER_BENCHMARK_NDIMS_SLACK)
+        return AckleyHPOScorer(
+            self.DEEPHYPER_BENCHMARK_NDIMS,
+            self.DEEPHYPER_BENCHMARK_NDIMS_SLACK,
+            self.DEEPHYPER_BENCHMARK_OFFSET,
+        )
 
 
 benchmark = AckleyHPOBenchmark()
diff --git a/src/deephyper_benchmark/lib/c_bbo/easom/__init__.py b/src/deephyper_benchmark/lib/c_bbo/easom/__init__.py
new file mode 100644
index 0000000..0edb90b
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/easom/__init__.py
@@ -0,0 +1,18 @@
+"""Easom function benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/easom.html
+"""
+
+from . import hpo_benchmark
+
+__all__ = ["install", "init", "hpo"]
+
+hpo = hpo_benchmark.benchmark
+
+
+def install(config: dict = None):
+    """Install the benchmark."""
+
+
+def init(config: dict = None):
+    """Initialize the benchmark."""
diff --git a/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py
new file mode 100644
index 0000000..8fe83cd
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py
@@ -0,0 +1,92 @@
+"""Module defining the problem and run-function of the benchmark."""
+
+import os
+import time
+
+import numpy as np
+
+from deephyper.hpo import HpProblem
+from deephyper.evaluator import profile, RunningJob
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+__all__ = ["benchmark"]
+
+
+def easom(x):
+    assert len(x) == 2
+    y = -np.cos(x[0]) * np.cos(x[1]) * np.exp(-((x[0] - np.pi) ** 2 + (x[1] - np.pi) ** 2))
+    return y
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -easom(x)
+
+
+class EasomHPOScorer(HPOScorer):
+    """A class defining performance evaluators for the Ackley problem."""
+
+    def __init__(self, offset=0):
+        self.x_min = np.array([np.pi, np.pi]) - offset
+        self.y_min = -1.0
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_min - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class EasomHPOBenchmark(HPOBenchmark):
+    def refresh_settings(self):
+        self.DEEPHYPER_BENCHMARK_OFF = int(os.environ.get("DEEPHYPER_BENCHMARK_OFFSET", 0))
+
+    @property
+    def problem(self):
+        # Grid sampling or QMC sampling to directly hit the optimum...
+        domain = (
+            -100.0 - self.DEEPHYPER_BENCHMARK_OFFSET,
+            100.0 - self.DEEPHYPER_BENCHMARK_OFFSET,
+        )
+        problem = HpProblem()
+        for i in range(2):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+        return problem
+
+    @property
+    def run_function(self):
+        return run_function
+
+    @property
+    def scorer(self):
+        return EasomHPOScorer(self.DEEPHYPER_BENCHMARK_NDIMS_SLACK)
+
+
+benchmark = EasomHPOBenchmark()

From e880fc114ccc885032d1be61d977c6d2cd28ea7a Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Fri, 14 Mar 2025 16:34:23 +0100
Subject: [PATCH 05/43] update

---
 .../lib/c_bbo/ackley/hpo_benchmark.py                  |  8 ++++----
 .../lib/c_bbo/branin/hpo_benchmark.py                  |  6 +++---
 .../lib/c_bbo/easom/hpo_benchmark.py                   | 10 +++++-----
 3 files changed, 12 insertions(+), 12 deletions(-)

diff --git a/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
index ffaab68..9ef0ac3 100644
--- a/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
+++ b/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
@@ -47,9 +47,9 @@ def __init__(
         offset=0,
     ):
         self.p_num = p_num
-        self.x_min = np.full(self.p_num, fill_value=-offset)
-        self.x_min[p_num - p_num_slack :] = np.nan
-        self.y_min = 0.0
+        self.x_max = np.full(self.p_num, fill_value=-offset)
+        self.x_max[p_num - p_num_slack :] = np.nan
+        self.y_max = 0.0
 
     def simple_regret(self, y: np.ndarray) -> np.ndarray:
         """Compute the regret of a list of given solution.
@@ -60,7 +60,7 @@ def simple_regret(self, y: np.ndarray) -> np.ndarray:
         Returns:
             np.ndarray: An array of regret values.
         """
-        return self.y_min - y
+        return self.y_max - y
 
     def cumul_regret(self, y: np.ndarray) -> np.ndarray:
         """Compute the cumulative regret of an array of ordered given solution.
diff --git a/src/deephyper_benchmark/lib/c_bbo/branin/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/branin/hpo_benchmark.py
index e935472..8004b0e 100644
--- a/src/deephyper_benchmark/lib/c_bbo/branin/hpo_benchmark.py
+++ b/src/deephyper_benchmark/lib/c_bbo/branin/hpo_benchmark.py
@@ -43,8 +43,8 @@ class BraninHPOScorer(HPOScorer):
 
     def __init__(self):
         self.p_num = 2
-        self.x_min = np.array([[-np.pi, 12.275], [np.pi, 2.275], [9.42478, 2.475]])
-        self.y_min = 0.397887
+        self.x_max = np.array([[-np.pi, 12.275], [np.pi, 2.275], [9.42478, 2.475]])
+        self.y_max = -0.397887
 
     def simple_regret(self, y: np.ndarray) -> np.ndarray:
         """Compute the regret of a list of given solution.
@@ -55,7 +55,7 @@ def simple_regret(self, y: np.ndarray) -> np.ndarray:
         Returns:
             np.ndarray: An array of regret values.
         """
-        return self.y_min - y
+        return self.y_max - y
 
     def cumul_regret(self, y: np.ndarray) -> np.ndarray:
         """Compute the cumulative regret of an array of ordered given solution.
diff --git a/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py
index 8fe83cd..4c0509c 100644
--- a/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py
+++ b/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py
@@ -37,8 +37,8 @@ class EasomHPOScorer(HPOScorer):
     """A class defining performance evaluators for the Ackley problem."""
 
     def __init__(self, offset=0):
-        self.x_min = np.array([np.pi, np.pi]) - offset
-        self.y_min = -1.0
+        self.x_max = np.array([np.pi, np.pi]) - offset
+        self.y_max = 1.0
 
     def simple_regret(self, y: np.ndarray) -> np.ndarray:
         """Compute the regret of a list of given solution.
@@ -49,7 +49,7 @@ def simple_regret(self, y: np.ndarray) -> np.ndarray:
         Returns:
             np.ndarray: An array of regret values.
         """
-        return self.y_min - y
+        return self.y_max - y
 
     def cumul_regret(self, y: np.ndarray) -> np.ndarray:
         """Compute the cumulative regret of an array of ordered given solution.
@@ -65,7 +65,7 @@ def cumul_regret(self, y: np.ndarray) -> np.ndarray:
 
 class EasomHPOBenchmark(HPOBenchmark):
     def refresh_settings(self):
-        self.DEEPHYPER_BENCHMARK_OFF = int(os.environ.get("DEEPHYPER_BENCHMARK_OFFSET", 0))
+        self.DEEPHYPER_BENCHMARK_OFFSET = float(os.environ.get("DEEPHYPER_BENCHMARK_OFFSET", 4.0))
 
     @property
     def problem(self):
@@ -86,7 +86,7 @@ def run_function(self):
 
     @property
     def scorer(self):
-        return EasomHPOScorer(self.DEEPHYPER_BENCHMARK_NDIMS_SLACK)
+        return EasomHPOScorer(self.DEEPHYPER_BENCHMARK_OFFSET)
 
 
 benchmark = EasomHPOBenchmark()

From a372f4c837355dfd0965f16e3da23ebb9290a1de Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Fri, 14 Mar 2025 16:40:00 +0100
Subject: [PATCH 06/43] adding griewank

---
 .../lib/c_bbo/griewank/__init__.py            |  18 +++
 .../lib/c_bbo/griewank/hpo_benchmark.py       | 115 ++++++++++++++++++
 2 files changed, 133 insertions(+)
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/griewank/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/griewank/hpo_benchmark.py

diff --git a/src/deephyper_benchmark/lib/c_bbo/griewank/__init__.py b/src/deephyper_benchmark/lib/c_bbo/griewank/__init__.py
new file mode 100644
index 0000000..81f454f
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/griewank/__init__.py
@@ -0,0 +1,18 @@
+"""Easom function benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/griewank.html
+"""
+
+from . import hpo_benchmark
+
+__all__ = ["install", "init", "hpo"]
+
+hpo = hpo_benchmark.benchmark
+
+
+def install(config: dict = None):
+    """Install the benchmark."""
+
+
+def init(config: dict = None):
+    """Initialize the benchmark."""
diff --git a/src/deephyper_benchmark/lib/c_bbo/griewank/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/griewank/hpo_benchmark.py
new file mode 100644
index 0000000..e583126
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/griewank/hpo_benchmark.py
@@ -0,0 +1,115 @@
+"""Module defining the problem and run-function of the benchmark."""
+
+import os
+import time
+
+import numpy as np
+
+from deephyper.hpo import HpProblem
+from deephyper.evaluator import profile, RunningJob
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+__all__ = ["benchmark"]
+
+
+def griewank(x, fr=4000):
+    n = len(x)
+    j = np.arange(1.0, n + 1)
+    s = np.sum(x**2)
+    p = np.prod(np.cos(x / np.sqrt(j)))
+    return s / fr - p + 1
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -griewank(x)
+
+
+class GriewankHPOScorer(HPOScorer):
+    """A class defining performance evaluators for the Griewank problem."""
+
+    def __init__(
+        self,
+        p_num,
+        p_num_slack,
+        offset=0,
+    ):
+        self.p_num = p_num
+        self.x_max = np.full(self.p_num, fill_value=-offset)
+        self.x_max[p_num - p_num_slack :] = np.nan
+        self.y_max = 0.0
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_max - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class GriewankHPOBenchmark(HPOBenchmark):
+    def refresh_settings(self):
+        self.DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
+        self.DEEPHYPER_BENCHMARK_OFFSET = float(os.environ.get("DEEPHYPER_BENCHMARK_OFFSET", 4.0))
+        self.DEEPHYPER_BENCHMARK_NDIMS_SLACK = int(
+            os.environ.get("DEEPHYPER_BENCHMARK_NDIMS_SLACK", 0)
+        )
+
+    @property
+    def problem(self):
+        domain = (
+            -600.0 - self.DEEPHYPER_BENCHMARK_OFFSET,
+            600.0 - self.DEEPHYPER_BENCHMARK_OFFSET,
+        )
+        problem = HpProblem()
+        for i in range(self.DEEPHYPER_BENCHMARK_NDIMS - self.DEEPHYPER_BENCHMARK_NDIMS_SLACK):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+        # Add slack/dummy dimensions (useful to test predicors which are sensitive
+        # to unimportant features)
+        for i in range(
+            self.DEEPHYPER_BENCHMARK_NDIMS - self.DEEPHYPER_BENCHMARK_NDIMS_SLACK,
+            self.DEEPHYPER_BENCHMARK_NDIMS,
+        ):
+            problem.add_hyperparameter(domain, f"z{i}")
+        return problem
+
+    @property
+    def run_function(self):
+        return run_function
+
+    @property
+    def scorer(self):
+        return GriewankHPOScorer(
+            self.DEEPHYPER_BENCHMARK_NDIMS,
+            self.DEEPHYPER_BENCHMARK_NDIMS_SLACK,
+            self.DEEPHYPER_BENCHMARK_OFFSET,
+        )
+
+
+benchmark = GriewankHPOBenchmark()

From 81874f454050387db0b738144741ffc5b11337ec Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Fri, 14 Mar 2025 16:44:55 +0100
Subject: [PATCH 07/43] add hartmann6d

---
 .../lib/c_bbo/hartmann6D/__init__.py          |  18 +++
 .../lib/c_bbo/hartmann6D/hpo_benchmark.py     | 118 ++++++++++++++++++
 2 files changed, 136 insertions(+)
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/hartmann6D/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/hartmann6D/hpo_benchmark.py

diff --git a/src/deephyper_benchmark/lib/c_bbo/hartmann6D/__init__.py b/src/deephyper_benchmark/lib/c_bbo/hartmann6D/__init__.py
new file mode 100644
index 0000000..76707df
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/hartmann6D/__init__.py
@@ -0,0 +1,18 @@
+"""Hartmann6D function benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/hart6.html
+"""
+
+from . import hpo_benchmark
+
+__all__ = ["install", "init", "hpo"]
+
+hpo = hpo_benchmark.benchmark
+
+
+def install(config: dict = None):
+    """Install the benchmark."""
+
+
+def init(config: dict = None):
+    """Initialize the benchmark."""
diff --git a/src/deephyper_benchmark/lib/c_bbo/hartmann6D/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/hartmann6D/hpo_benchmark.py
new file mode 100644
index 0000000..581ecd6
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/hartmann6D/hpo_benchmark.py
@@ -0,0 +1,118 @@
+"""Module defining the problem and run-function of the benchmark."""
+
+import time
+
+import numpy as np
+
+from deephyper.hpo import HpProblem
+from deephyper.evaluator import profile, RunningJob
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+__all__ = ["benchmark"]
+
+
+def hartmann6D(x):
+    alpha = np.array([1.0, 1.2, 3.0, 3.2])
+    A = np.array(
+        [
+            [10, 3, 17, 3.5, 1.7, 8],
+            [0.05, 10, 17, 0.1, 8, 14],
+            [3, 3.5, 1.7, 10, 17, 8],
+            [17, 8, 0.05, 10, 0.1, 14],
+        ]
+    )
+    P = 1e-4 * np.array(
+        [
+            [1312, 1696, 5569, 124, 8283, 5886],
+            [2329, 4135, 8307, 3736, 1004, 9991],
+            [2348, 1451, 3522, 2883, 3047, 6650],
+            [4047, 8828, 8732, 5743, 1091, 381],
+        ]
+    )
+    X = np.array([x for _ in range(4)])
+    inner = np.sum(np.multiply(A, np.square(X - P)), axis=1)
+    outer = np.sum(alpha * np.exp(-inner))
+    y = -(2.58 + outer) / 1.94
+    return y
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -hartmann6D(x)
+
+
+class Hartmann6DHPOScorer(HPOScorer):
+    """A class defining performance evaluators for the Hartmann6D problem."""
+
+    def __init__(self):
+        self.p_num = 6
+        self.x_max = np.array(
+            [
+                0.20169,
+                0.150011,
+                0.476874,
+                0.275332,
+                0.311652,
+                0.6573,
+            ]
+        )
+        self.y_max = 3.32237
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_max - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class Hartmann6DHPOBenchmark(HPOBenchmark):
+    @property
+    def problem(self):
+        # The original range is simetric (-32.768, 32.768) but we make it less simetric to avoid
+        # Grid sampling or QMC sampling to directly hit the optimum...
+        domain = (0.0, 1.0)
+        problem = HpProblem()
+        for i in range(6):
+            problem.add_hyperparameter(domain, f"x{i}")
+        return problem
+
+    @property
+    def run_function(self):
+        return run_function
+
+    @property
+    def scorer(self):
+        return Hartmann6DHPOScorer(
+            self.DEEPHYPER_BENCHMARK_NDIMS,
+            self.DEEPHYPER_BENCHMARK_NDIMS_SLACK,
+            self.DEEPHYPER_BENCHMARK_OFFSET,
+        )
+
+
+benchmark = Hartmann6DHPOBenchmark()

From 938d86c80b40f368aa0e26a92fd14aef98a53256 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Fri, 14 Mar 2025 17:38:42 +0100
Subject: [PATCH 08/43] upload all other benchmarks for c_bbo

---
 .../lib/c_bbo/hartmann6D/hpo_benchmark.py     |  8 +-
 .../lib/c_bbo/levy/__init__.py                | 18 ++++
 .../lib/c_bbo/levy/hpo_benchmark.py           | 94 ++++++++++++++++++
 .../lib/c_bbo/michal/__init__.py              | 18 ++++
 .../lib/c_bbo/michal/hpo_benchmark.py         | 98 +++++++++++++++++++
 .../lib/c_bbo/rosen/__init__.py               | 18 ++++
 .../lib/c_bbo/rosen/hpo_benchmark.py          | 83 ++++++++++++++++
 .../lib/c_bbo/schwefel/__init__.py            | 18 ++++
 .../lib/c_bbo/schwefel/hpo_benchmark.py       | 92 +++++++++++++++++
 .../lib/c_bbo/shekel/__init__.py              | 18 ++++
 .../lib/c_bbo/shekel/hpo_benchmark.py         | 93 ++++++++++++++++++
 11 files changed, 551 insertions(+), 7 deletions(-)
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/levy/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/levy/hpo_benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/michal/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/michal/hpo_benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/rosen/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/rosen/hpo_benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/schwefel/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/schwefel/hpo_benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/shekel/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/c_bbo/shekel/hpo_benchmark.py

diff --git a/src/deephyper_benchmark/lib/c_bbo/hartmann6D/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/hartmann6D/hpo_benchmark.py
index 581ecd6..e86b29d 100644
--- a/src/deephyper_benchmark/lib/c_bbo/hartmann6D/hpo_benchmark.py
+++ b/src/deephyper_benchmark/lib/c_bbo/hartmann6D/hpo_benchmark.py
@@ -94,8 +94,6 @@ def cumul_regret(self, y: np.ndarray) -> np.ndarray:
 class Hartmann6DHPOBenchmark(HPOBenchmark):
     @property
     def problem(self):
-        # The original range is simetric (-32.768, 32.768) but we make it less simetric to avoid
-        # Grid sampling or QMC sampling to directly hit the optimum...
         domain = (0.0, 1.0)
         problem = HpProblem()
         for i in range(6):
@@ -108,11 +106,7 @@ def run_function(self):
 
     @property
     def scorer(self):
-        return Hartmann6DHPOScorer(
-            self.DEEPHYPER_BENCHMARK_NDIMS,
-            self.DEEPHYPER_BENCHMARK_NDIMS_SLACK,
-            self.DEEPHYPER_BENCHMARK_OFFSET,
-        )
+        return Hartmann6DHPOScorer()
 
 
 benchmark = Hartmann6DHPOBenchmark()
diff --git a/src/deephyper_benchmark/lib/c_bbo/levy/__init__.py b/src/deephyper_benchmark/lib/c_bbo/levy/__init__.py
new file mode 100644
index 0000000..62fb823
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/levy/__init__.py
@@ -0,0 +1,18 @@
+"""Levy function benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/levy.html
+"""
+
+from . import hpo_benchmark
+
+__all__ = ["install", "init", "hpo"]
+
+hpo = hpo_benchmark.benchmark
+
+
+def install(config: dict = None):
+    """Install the benchmark."""
+
+
+def init(config: dict = None):
+    """Initialize the benchmark."""
diff --git a/src/deephyper_benchmark/lib/c_bbo/levy/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/levy/hpo_benchmark.py
new file mode 100644
index 0000000..95e4857
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/levy/hpo_benchmark.py
@@ -0,0 +1,94 @@
+"""Module defining the problem and run-function of the benchmark."""
+
+import os
+import time
+
+import numpy as np
+
+from deephyper.hpo import HpProblem
+from deephyper.evaluator import profile, RunningJob
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+__all__ = ["benchmark"]
+
+
+def levy(x):
+    z = 1 + (x - 1) / 4
+    return (
+        np.sin(np.pi * z[0]) ** 2
+        + np.sum((z[:-1] - 1) ** 2 * (1 + 10 * np.sin(np.pi * z[:-1] + 1) ** 2))
+        + (z[-1] - 1) ** 2 * (1 + np.sin(2 * np.pi * z[-1]) ** 2)
+    )
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -levy(x)
+
+
+class LevyHPOScorer(HPOScorer):
+    """A class defining performance evaluators for the Levy problem."""
+
+    def __init__(
+        self,
+        p_num,
+    ):
+        self.p_num = p_num
+        self.x_max = np.ones(self.p_num)
+        self.y_max = 0.0
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_max - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class LevyHPOBenchmark(HPOBenchmark):
+    def refresh_settings(self):
+        self.DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
+
+    @property
+    def problem(self):
+        domain = (-10.0, 10.0)
+        problem = HpProblem()
+        for i in range(self.DEEPHYPER_BENCHMARK_NDIMS):
+            problem.add_hyperparameter(domain, f"x{i}")
+        return problem
+
+    @property
+    def run_function(self):
+        return run_function
+
+    @property
+    def scorer(self):
+        return LevyHPOScorer(self.DEEPHYPER_BENCHMARK_NDIMS)
+
+
+benchmark = LevyHPOBenchmark()
diff --git a/src/deephyper_benchmark/lib/c_bbo/michal/__init__.py b/src/deephyper_benchmark/lib/c_bbo/michal/__init__.py
new file mode 100644
index 0000000..38fefec
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/michal/__init__.py
@@ -0,0 +1,18 @@
+"""Michalewicz function benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/michal.html
+"""
+
+from . import hpo_benchmark
+
+__all__ = ["install", "init", "hpo"]
+
+hpo = hpo_benchmark.benchmark
+
+
+def install(config: dict = None):
+    """Install the benchmark."""
+
+
+def init(config: dict = None):
+    """Initialize the benchmark."""
diff --git a/src/deephyper_benchmark/lib/c_bbo/michal/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/michal/hpo_benchmark.py
new file mode 100644
index 0000000..cd163c8
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/michal/hpo_benchmark.py
@@ -0,0 +1,98 @@
+"""Module defining the problem and run-function of the benchmark."""
+
+import os
+import time
+
+import numpy as np
+
+from deephyper.hpo import HpProblem
+from deephyper.evaluator import profile, RunningJob
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+__all__ = ["benchmark"]
+
+
+def michal(x, m=10):
+    ix2 = np.arange(1, len(x) + 1) * x**2
+    y = -np.sum(np.sin(x) * np.power(np.sin(ix2 / np.pi), 2 * m))
+    return y
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -michal(x)
+
+
+class MichalHPOScorer(HPOScorer):
+    """A class defining performance evaluators for the Michal problem."""
+
+    def __init__(
+        self,
+        p_num,
+    ):
+        assert p_num in [2, 5, 10]
+        self.p_num = p_num
+        if self.p_num == 2:
+            self.x_max = np.array([2.20, 1.57])
+            self.y_max = 1.8013
+        elif self.p_num == 5:
+            self.y_max = 4.687658
+        elif self.p_num == 10:
+            self.y_max = 9.66015
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_max - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class MichalHPOBenchmark(HPOBenchmark):
+    def refresh_settings(self):
+        self.DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
+        assert self.DEEPHYPER_BENCHMARK_NDIMS in [2, 5, 10]
+
+    @property
+    def problem(self):
+        domain = (0.0, np.pi)
+        problem = HpProblem()
+        for i in range(self.DEEPHYPER_BENCHMARK_NDIMS):
+            problem.add_hyperparameter(domain, f"x{i}")
+        return problem
+
+    @property
+    def run_function(self):
+        return run_function
+
+    @property
+    def scorer(self):
+        return MichalHPOScorer(self.DEEPHYPER_BENCHMARK_NDIMS)
+
+
+benchmark = MichalHPOBenchmark()
diff --git a/src/deephyper_benchmark/lib/c_bbo/rosen/__init__.py b/src/deephyper_benchmark/lib/c_bbo/rosen/__init__.py
new file mode 100644
index 0000000..d11dbca
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/rosen/__init__.py
@@ -0,0 +1,18 @@
+"""Rosen function benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/rosen.html
+"""
+
+from . import hpo_benchmark
+
+__all__ = ["install", "init", "hpo"]
+
+hpo = hpo_benchmark.benchmark
+
+
+def install(config: dict = None):
+    """Install the benchmark."""
+
+
+def init(config: dict = None):
+    """Initialize the benchmark."""
diff --git a/src/deephyper_benchmark/lib/c_bbo/rosen/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/rosen/hpo_benchmark.py
new file mode 100644
index 0000000..c7dc39e
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/rosen/hpo_benchmark.py
@@ -0,0 +1,83 @@
+"""Module defining the problem and run-function of the benchmark."""
+
+import os
+import time
+
+import numpy as np
+from deephyper.evaluator import RunningJob, profile
+from deephyper.hpo import HpProblem
+from scipy.optimize import rosen
+
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+__all__ = ["benchmark"]
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -rosen(x)
+
+
+class RosenHPOScorer(HPOScorer):
+    """A class defining performance evaluators for the Rosen problem."""
+
+    def __init__(self, p_num):
+        self.p_num = p_num
+        self.x_max = np.ones(self.p_num)
+        self.y_max = 0.0
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_max - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class RosenHPOBenchmark(HPOBenchmark):
+    def refresh_settings(self):
+        self.DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
+
+    @property
+    def problem(self):
+        domain = (-5.0, 10.0)
+        problem = HpProblem()
+        for i in range(self.DEEPHYPER_BENCHMARK_NDIMS):
+            problem.add_hyperparameter(domain, f"x{i}")
+        return problem
+
+    @property
+    def run_function(self):
+        return run_function
+
+    @property
+    def scorer(self):
+        return RosenHPOScorer(self.DEEPHYPER_BENCHMARK_NDIMS)
+
+
+benchmark = RosenHPOBenchmark()
diff --git a/src/deephyper_benchmark/lib/c_bbo/schwefel/__init__.py b/src/deephyper_benchmark/lib/c_bbo/schwefel/__init__.py
new file mode 100644
index 0000000..7a99827
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/schwefel/__init__.py
@@ -0,0 +1,18 @@
+"""Schwefel function benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/schwef.html
+"""
+
+from . import hpo_benchmark
+
+__all__ = ["install", "init", "hpo"]
+
+hpo = hpo_benchmark.benchmark
+
+
+def install(config: dict = None):
+    """Install the benchmark."""
+
+
+def init(config: dict = None):
+    """Initialize the benchmark."""
diff --git a/src/deephyper_benchmark/lib/c_bbo/schwefel/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/schwefel/hpo_benchmark.py
new file mode 100644
index 0000000..4f3cbec
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/schwefel/hpo_benchmark.py
@@ -0,0 +1,92 @@
+"""Module defining the problem and run-function of the benchmark."""
+
+import os
+import time
+
+import numpy as np
+
+from deephyper.hpo import HpProblem
+from deephyper.evaluator import profile, RunningJob
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+__all__ = ["benchmark"]
+
+
+def schwefel(x):  # schw.m
+    n = len(x)
+    return 418.9829 * n - sum(x * np.sin(np.sqrt(np.abs(x))))
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -schwefel(x)
+
+
+class SchwefelHPOScorer(HPOScorer):
+    """A class defining performance evaluators for the Schwefel problem."""
+
+    def __init__(
+        self,
+        p_num,
+        p_num_slack,
+        offset=0,
+    ):
+        self.p_num = p_num
+        self.x_max = np.full(self.p_num, fill_value=420.9687)
+        self.y_max = 0.0
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_max - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class SchwefelHPOBenchmark(HPOBenchmark):
+    def refresh_settings(self):
+        self.DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
+
+    @property
+    def problem(self):
+        domain = (-500.0, 500.0)
+        problem = HpProblem()
+        for i in range(self.DEEPHYPER_BENCHMARK_NDIMS):
+            problem.add_hyperparameter(domain, f"x{i}")
+        return problem
+
+    @property
+    def run_function(self):
+        return run_function
+
+    @property
+    def scorer(self):
+        return SchwefelHPOScorer(self.DEEPHYPER_BENCHMARK_NDIMS)
+
+
+benchmark = SchwefelHPOBenchmark()
diff --git a/src/deephyper_benchmark/lib/c_bbo/shekel/__init__.py b/src/deephyper_benchmark/lib/c_bbo/shekel/__init__.py
new file mode 100644
index 0000000..9ca299d
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/shekel/__init__.py
@@ -0,0 +1,18 @@
+"""Shekel function benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/shekel.html
+"""
+
+from . import hpo_benchmark
+
+__all__ = ["install", "init", "hpo"]
+
+hpo = hpo_benchmark.benchmark
+
+
+def install(config: dict = None):
+    """Install the benchmark."""
+
+
+def init(config: dict = None):
+    """Initialize the benchmark."""
diff --git a/src/deephyper_benchmark/lib/c_bbo/shekel/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/shekel/hpo_benchmark.py
new file mode 100644
index 0000000..d1b881e
--- /dev/null
+++ b/src/deephyper_benchmark/lib/c_bbo/shekel/hpo_benchmark.py
@@ -0,0 +1,93 @@
+"""Module defining the problem and run-function of the benchmark."""
+
+import os
+import time
+
+import numpy as np
+
+from deephyper.hpo import HpProblem
+from deephyper.evaluator import profile, RunningJob
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+__all__ = ["benchmark"]
+
+
+def shekel(x):
+    m = 10
+    beta = 0.1 * np.array([1, 2, 2, 4, 4, 6, 3, 7, 5, 5]).T
+    C = np.array(
+        [
+            [4, 1, 8, 6, 3, 2, 5, 8, 6, 7],
+            [4, 1, 8, 6, 7, 9, 3, 1, 2, 3.6],
+            [4, 1, 8, 6, 3, 2, 5, 8, 6, 7],
+            [4, 1, 8, 6, 7, 9, 3, 1, 2, 3.6],
+        ]
+    )
+    return -sum([1 / (np.sum((x - C[:, i]) ** 2) + beta[i]) for i in range(m)])
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -shekel(x)
+
+
+class ShekelHPOScorer(HPOScorer):
+    """A class defining performance evaluators for the Shekel problem."""
+
+    def __init__(self):
+        self.p_num = 10
+        self.x_max = np.full(self.p_num, fill_value=4.0)
+        self.y_max = 10.5364
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_max - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class ShekelHPOBenchmark(HPOBenchmark):
+    @property
+    def problem(self):
+        domain = (0.0, 10.0)
+        problem = HpProblem()
+        for i in range(10):
+            problem.add_hyperparameter(domain, f"x{i}")
+        return problem
+
+    @property
+    def run_function(self):
+        return run_function
+
+    @property
+    def scorer(self):
+        return ShekelHPOScorer()
+
+
+benchmark = ShekelHPOBenchmark()

From f18469b2f91995c944cb8feef743ebac3d4988f6 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 17 Mar 2025 16:46:28 +0100
Subject: [PATCH 09/43] adding optuna simple sequential

---
 src/deephyper_benchmark/hpo/__init__.py |  0
 src/deephyper_benchmark/hpo/_optuna.py  | 64 +++++++++++++++++++++++--
 2 files changed, 60 insertions(+), 4 deletions(-)
 create mode 100644 src/deephyper_benchmark/hpo/__init__.py

diff --git a/src/deephyper_benchmark/hpo/__init__.py b/src/deephyper_benchmark/hpo/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/hpo/_optuna.py b/src/deephyper_benchmark/hpo/_optuna.py
index b47c240..8c59a8e 100644
--- a/src/deephyper_benchmark/hpo/_optuna.py
+++ b/src/deephyper_benchmark/hpo/_optuna.py
@@ -46,6 +46,30 @@ def optuna_suggest_from_configspace(trial: Trial, config_space: ConfigurationSpa
     return config
 
 
+def optuna_dist_from_hp(hp: Hyperparameter):
+    if isinstance(hp, csh.UniformIntegerHyperparameter):
+        value = optuna.distributions.IntDistribution(low=hp.lower, high=hp.upper, log=hp.log)
+    elif isinstance(hp, csh.UniformFloatHyperparameter):
+        value = optuna.distributions.FloatDistribution(low=hp.lower, high=hp.upper, log=hp.log)
+    elif isinstance(hp, csh.CategoricalHyperparameter):
+        value = optuna.distributions.CategoricalDistribution(hp.choices)
+    elif isinstance(hp, csh.OrdinalHyperparameter):
+        value = optuna.distributions.CategoricalDistribution(hp.sequence)
+    else:
+        raise TypeError(f"Cannot convert hyperparameter of type {type(hp)}")
+
+    return value
+
+
+def optuna_dist_from_configspace(config_space: ConfigurationSpace) -> dict:
+    config = {}
+    for name in config_space:
+        value = optuna_dist_from_hp(config_space[name])
+        config[name] = value
+
+    return config
+
+
 class CheckpointSaverCallback:
     def __init__(self, log_dir=".", states=(TrialState.COMPLETE,)) -> None:
         self._log_dir = log_dir
@@ -66,12 +90,14 @@ def constraints(trial):
 
 
 # Supported samplers
-supported_samplers = ["TPE", "CMAES", "NSGAII", "DUMMY", "BOTORCH", "QMC"]
+supported_samplers = ["TPE", "CMAES", "NSGAII", "DUMMY", "BOTORCH", "QMC", "SMAC"]
 supported_pruners = ["NOP", "SHA", "HB", "MED"]
 
 
 # TODO: CMAES requires: $ pip install cmaes
 # TODO: GP requires: $ pip install torch
+# TODO: SMAC requires $ pip install optunahub smac
+
 
 class OptunaSearch(Search):
     """Wrapper for Optuna to run distributed optimization with MPI.
@@ -129,7 +155,9 @@ def __init__(
         log_dir: str = ".",
         verbose: int = 0,
         sampler: Union[str, optuna.samplers.BaseSampler] = "TPE",
+        sampler_kwargs: dict = None,
         pruner: Union[str, optuna.pruners.BasePruner] = None,
+        pruner_kwargs: dict = None,
         n_objectives: int = 1,
         study_name: str = None,
         storage: Union[str, optuna.storages.BaseStorage] = None,
@@ -166,6 +194,10 @@ def __init__(
                 )
 
         # Setup the sampler
+
+        if sampler_kwargs is None:
+            sampler_kwargs = {}
+
         if isinstance(sampler, optuna.samplers.BaseSampler):
             pass
         elif isinstance(sampler, str):
@@ -175,24 +207,32 @@ def __init__(
                     n_startup_trials=self._n_initial_points,
                     seed=sampler_seed,
                     constraints_func=self._constraints_func,
+                    **sampler_kwargs,
                 )
             elif sampler == "GP":
                 sampler = optuna.samplers.GPSampler(
                     n_startup_trials=self._n_initial_points,
                     seed=sampler_seed,
                     constraints_func=self._constraints_func,
+                    **sampler_kwargs,
                 )
             elif sampler == "CMAES":
                 sampler = optuna.samplers.CmaEsSampler(
-                    n_startup_trials=self._n_initial_points, seed=sampler_seed
+                    n_startup_trials=self._n_initial_points,
+                    seed=sampler_seed,
+                    **sampler_kwargs,
                 )
             elif sampler == "NSGAII":
                 sampler = optuna.samplers.NSGAIISampler(
                     seed=sampler_seed,
                     constraints_func=self._constraints_func,
+                    **sampler_kwargs,
                 )
             elif sampler == "DUMMY":
-                sampler = optuna.samplers.RandomSampler(seed=sampler_seed)
+                sampler = optuna.samplers.RandomSampler(
+                    seed=sampler_seed,
+                    **sampler_kwargs,
+                )
             elif sampler == "BOTORCH":
                 from optuna.integration import BoTorchSampler
 
@@ -200,9 +240,22 @@ def __init__(
                     n_startup_trials=self._n_initial_points,
                     seed=sampler_seed,
                     constraints_func=self._constraints_func,
+                    **sampler_kwargs,
                 )
             elif sampler == "QMC":
-                sampler = optuna.samplers.QMCSampler(seed=sampler_seed)
+                sampler = optuna.samplers.QMCSampler(
+                    seed=sampler_seed,
+                    **sampler_kwargs,
+                )
+            elif sampler == "SMAC":
+                import optunahub
+
+                module = optunahub.load_module("samplers/smac_sampler")
+                sampler = module.SMACSampler(
+                    search_space=optuna_dist_from_configspace(self._problem.space),
+                    seed=sampler_seed,
+                    **sampler_kwargs,
+                )
 
             else:
                 raise ValueError(
@@ -217,6 +270,9 @@ def __init__(
         self._n_objectives = n_objectives
 
         # Setup the pruner
+        if pruner_kwargs is None:
+            pruner_kwargs = {}
+
         if self._n_objectives > 1 or pruner is None:
             pruner = None
             if pruner is not None:

From db215bd7e77acabe2f1627bc171b51a0714e6a06 Mon Sep 17 00:00:00 2001
From: Gavin Wiggins <6828967+wigging@users.noreply.github.com>
Date: Mon, 17 Mar 2025 18:50:29 -0400
Subject: [PATCH 10/43] Add uv lock file for development

---
 uv.lock | 1067 +++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 1067 insertions(+)
 create mode 100644 uv.lock

diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..8976905
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,1067 @@
+version = 1
+revision = 1
+requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.13'",
+    "python_full_version == '3.12.*'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "about-time"
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From b91e4160d866b85abb8e573d20867d29ed633293 Mon Sep 17 00:00:00 2001
From: Gavin Wiggins <6828967+wigging@users.noreply.github.com>
Date: Mon, 17 Mar 2025 22:00:39 -0400
Subject: [PATCH 11/43] Add classes and examples for ackley and branin

---
 examples/bench_ackley.py                 | 19 +++++++
 examples/bench_branin.py                 | 19 +++++++
 src/deephyper_benchmark/__init__.py      |  6 ++-
 src/deephyper_benchmark/cbbo/__init__.py |  0
 src/deephyper_benchmark/cbbo/ackley.py   | 65 ++++++++++++++++++++++++
 src/deephyper_benchmark/cbbo/branin.py   | 48 +++++++++++++++++
 6 files changed, 155 insertions(+), 2 deletions(-)
 create mode 100644 examples/bench_ackley.py
 create mode 100644 examples/bench_branin.py
 create mode 100644 src/deephyper_benchmark/cbbo/__init__.py
 create mode 100644 src/deephyper_benchmark/cbbo/ackley.py
 create mode 100644 src/deephyper_benchmark/cbbo/branin.py

diff --git a/examples/bench_ackley.py b/examples/bench_ackley.py
new file mode 100644
index 0000000..fe4861f
--- /dev/null
+++ b/examples/bench_ackley.py
@@ -0,0 +1,19 @@
+"""here."""
+
+from deephyper.hpo import CBO
+from deephyper_benchmark import AckleyBenchmark
+
+
+def main():
+    """Run example."""
+
+    bench = AckleyBenchmark()
+
+    search = CBO(bench.problem, bench.run_function)
+    results = search.search(10)
+
+    print(results)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/examples/bench_branin.py b/examples/bench_branin.py
new file mode 100644
index 0000000..7b1780d
--- /dev/null
+++ b/examples/bench_branin.py
@@ -0,0 +1,19 @@
+"""here."""
+
+from deephyper.hpo import CBO
+from deephyper_benchmark import BraninBenchmark
+
+
+def main():
+    """Run example."""
+
+    bench = BraninBenchmark()
+
+    search = CBO(bench.problem, bench.run_function)
+    results = search.search(10)
+
+    print(results)
+
+
+if __name__ == "__main__":
+    main()
diff --git a/src/deephyper_benchmark/__init__.py b/src/deephyper_benchmark/__init__.py
index 7672c1f..a52dc4f 100644
--- a/src/deephyper_benchmark/__init__.py
+++ b/src/deephyper_benchmark/__init__.py
@@ -1,6 +1,8 @@
-"""DeepHyper-Benchmark package."""
+"""Public interface for deephyper_benchmark package."""
 
 from ._benchmark import Benchmark, HPOBenchmark
 from ._scorer import Scorer, HPOScorer
+from .cbbo.ackley import AckleyBenchmark
+from .cbbo.branin import BraninBenchmark
 
-__all__ = ["Benchmark", "HPOBenchmark", "Scorer", "HPOScorer"]
+__all__ = ["Benchmark", "HPOBenchmark", "Scorer", "HPOScorer", "AckleyBenchmark", "BraninBenchmark"]
diff --git a/src/deephyper_benchmark/cbbo/__init__.py b/src/deephyper_benchmark/cbbo/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/cbbo/ackley.py b/src/deephyper_benchmark/cbbo/ackley.py
new file mode 100644
index 0000000..03fc7c9
--- /dev/null
+++ b/src/deephyper_benchmark/cbbo/ackley.py
@@ -0,0 +1,65 @@
+"""here."""
+
+import numpy as np
+import time
+
+from deephyper.evaluator import profile
+from deephyper.evaluator import RunningJob
+from deephyper.hpo import HpProblem
+
+
+def ackley(x, a=20, b=0.2, c=2 * np.pi):
+    d = len(x)
+    s1 = np.sum(x**2)
+    s2 = np.sum(np.cos(c * x))
+    term1 = -a * np.exp(-b * np.sqrt(s1 / d))
+    term2 = -np.exp(s2 / d)
+    y = term1 + term2 + a + np.exp(1)
+    return y
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -ackley(x)
+
+
+class AckleyBenchmark:
+    def __init__(self, ndims=5, offset=4.0, slack=0) -> None:
+        self.ndims = ndims
+        self.offset = offset
+        self.slack = slack
+
+    @property
+    def problem(self):
+        # The original range is simetric (-32.768, 32.768) but we make it less simetric to avoid
+        # Grid sampling or QMC sampling to directly hit the optimum...
+        domain = (
+            -32.768 - self.offset,
+            32.768 - self.offset,
+        )
+        problem = HpProblem()
+        for i in range(self.ndims - self.slack):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+        # Add slack/dummy dimensions (useful to test predicors which are sensitive
+        # to unimportant features)
+        for i in range(
+            self.ndims - self.slack,
+            self.ndims,
+        ):
+            problem.add_hyperparameter(domain, f"z{i}")
+        return problem
+
+    @property
+    def run_function(self):
+        return run_function
diff --git a/src/deephyper_benchmark/cbbo/branin.py b/src/deephyper_benchmark/cbbo/branin.py
new file mode 100644
index 0000000..181b05f
--- /dev/null
+++ b/src/deephyper_benchmark/cbbo/branin.py
@@ -0,0 +1,48 @@
+"""here."""
+
+import numpy as np
+import time
+
+from deephyper.evaluator import RunningJob
+from deephyper.evaluator import profile
+from deephyper.hpo import HpProblem
+
+
+def branin(x):
+    assert len(x) == 2
+    a = 1.0
+    b = 5.1 / (4.0 * np.pi**2)
+    c = 5.0 / np.pi
+    r = 6.0
+    s = 10.0
+    t = 1.0 / (8.0 * np.pi)
+    y = a * (x[1] - b * x[0] ** 2 + c * x[0] - r) ** 2 + s * (1 - t) * np.cos(x[0]) + s
+    return y
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -branin(x)
+
+
+class BraninBenchmark:
+    @property
+    def problem(self):
+        problem = HpProblem()
+        problem.add_hyperparameter((-5.0, 10.0), "x0")
+        problem.add_hyperparameter((0.0, 15.0), "x1")
+        return problem
+
+    @property
+    def run_function(self):
+        return run_function

From 9b746aca4621c3b849e4ce1b574837eebf1db453 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 13:56:06 +0100
Subject: [PATCH 12/43] updating ackley and branin

---
 examples/bench_ackley.py                      |   8 +-
 examples/bench_branin.py                      |   8 +-
 examples/bench_combo.py                       |  26 -
 src/deephyper_benchmark/__init__.py           |   5 +-
 src/deephyper_benchmark/_benchmark.py         |   9 +-
 .../{cbbo => benchmarks}/__init__.py          |   0
 .../benchmarks/cbbo/__init__.py               |   6 +
 .../benchmarks/cbbo/ackley.py                 | 121 +++
 .../benchmarks/cbbo/branin.py                 |  90 ++
 src/deephyper_benchmark/cbbo/ackley.py        |  65 --
 src/deephyper_benchmark/cbbo/branin.py        |  48 --
 src/deephyper_benchmark/lib/__init__.py       |   0
 .../lib/autotuning/Mochi/HEPnOS/Makefile      |  11 +
 .../lib/autotuning/Mochi/HEPnOS/__init__.py   |   0
 .../lib/autotuning/Mochi/HEPnOS/benchmark.py  |  19 +
 .../lib/autotuning/Mochi/HEPnOS/hpo.py        | 281 +++++++
 .../lib/autotuning/Mochi/HEPnOS/metrics.py    |  39 +
 .../lib/autotuning/Mochi/README.md            |  13 +
 .../lib/autotuning/README.md                  |   3 +
 .../lib/c_bbo/ackley/hpo_benchmark.py         |   2 +-
 .../lib/c_bbo/easom/hpo_benchmark.py          |   2 +-
 .../lib/c_bbo/schwefel/hpo_benchmark.py       |   2 -
 .../lib/c_bbo/shekel/hpo_benchmark.py         |   4 +-
 src/deephyper_benchmark/lib/dtlz/README.md    | 130 +++
 src/deephyper_benchmark/lib/dtlz/__init__.py  |   1 +
 src/deephyper_benchmark/lib/dtlz/benchmark.py |   6 +
 src/deephyper_benchmark/lib/dtlz/hpo.py       |  61 ++
 src/deephyper_benchmark/lib/dtlz/metrics.py   | 201 +++++
 src/deephyper_benchmark/lib/dtlz/model.py     | 591 +++++++++++++
 .../lib/ecp_candle/Pilot1/Combo/Makefile      |  11 +
 .../lib/ecp_candle/Pilot1/Combo/README.md     |  54 ++
 .../lib/ecp_candle/Pilot1/Combo/__init__.py   |   1 +
 .../lib/ecp_candle/Pilot1/Combo/benchmark.py  |  28 +
 .../Pilot1/Combo/combo_default_model.yaml     |  31 +
 .../lib/ecp_candle/Pilot1/Combo/hpo.py        | 160 ++++
 .../lib/ecp_candle/Pilot1/Combo/model.py      | 782 ++++++++++++++++++
 .../ecp_candle/Pilot1/Combo/requirements.txt  |   5 +
 .../lib/fnobench/README.md                    |  64 ++
 .../lib/fnobench/__init__.py                  |   0
 .../lib/fnobench/benchmark.py                 |  17 +
 src/deephyper_benchmark/lib/fnobench/data.py  | 227 +++++
 .../lib/fnobench/data/darcy_test_16.pt        | Bin 0 -> 64999 bytes
 .../lib/fnobench/data/darcy_test_32.pt        | Bin 0 -> 256999 bytes
 .../lib/fnobench/data/darcy_train_16.pt       | Bin 0 -> 1280999 bytes
 src/deephyper_benchmark/lib/fnobench/hpo.py   | 104 +++
 src/deephyper_benchmark/lib/fnobench/model.py | 560 +++++++++++++
 .../lib/fnobench/requirements.txt             |   5 +
 .../lib/hpobench/README.md                    |  28 +
 .../lib/hpobench/tabular/__init__.py          |   0
 .../lib/hpobench/tabular/benchmark.py         |  17 +
 .../hpobench/tabular/constant_predictor.py    | 127 +++
 .../lib/hpobench/tabular/hpo.py               | 139 ++++
 .../lib/hpobench/tabular/install.sh           |  39 +
 .../lib/hpobench/tabular/metrics.py           |  78 ++
 .../lib/jahsbench/README.md                   | 176 ++++
 .../lib/jahsbench/__init__.py                 |   1 +
 .../lib/jahsbench/benchmark.py                |  22 +
 src/deephyper_benchmark/lib/jahsbench/hpo.py  |  57 ++
 .../lib/jahsbench/install.sh                  |   1 +
 .../lib/jahsbench/metrics.py                  | 119 +++
 .../lib/jahsbench/model.py                    |  80 ++
 .../lib/jahsbench/requirements.txt            |   2 +
 src/deephyper_benchmark/lib/lcbench/Makefile  |  14 +
 src/deephyper_benchmark/lib/lcbench/README.md |  19 +
 .../lib/lcbench/__init__.py                   |   2 +
 .../lib/lcbench/benchmark.py                  |  18 +
 src/deephyper_benchmark/lib/lcbench/hpo.py    |  78 ++
 src/deephyper_benchmark/lib/lcdb/README.md    |   4 +
 .../lib/lcdb/algorithm_selection/__init__.py  |   0
 .../hyperparameter_optimization/__init__.py   |   0
 .../hyperparameter_optimization/benchmark.py  |  12 +
 .../lcdb/hyperparameter_optimization/hpo.py   | 216 +++++
 .../lib/lcmbench/README.md                    |   4 +
 .../lib/lcmbench/lcdb/README.md               |   3 +
 .../lib/lcmbench/lcdb/__init__.py             |   0
 .../lib/lcmbench/lcdb/benchmark.py            |  11 +
 .../lib/lcmbench/lcdb/hpo.py                  | 128 +++
 .../lib/lcmbench/loglin2/README.md            |   5 +
 .../lib/lcmbench/loglin2/__init__.py          |   0
 .../lib/lcmbench/loglin2/benchmark.py         |  12 +
 .../lib/lcmbench/loglin2/hpo.py               |  71 ++
 .../lib/lcmbench/pow3/README.md               |   5 +
 .../lib/lcmbench/pow3/__init__.py             |   0
 .../lib/lcmbench/pow3/benchmark.py            |  12 +
 .../lib/lcmbench/pow3/hpo.py                  |  81 ++
 .../lib/pinnbench/Burgers/__init__.py         |   0
 .../lib/pinnbench/Burgers/benchmark.py        |  17 +
 .../lib/pinnbench/Burgers/hpo.py              | 111 +++
 .../lib/pinnbench/Burgers/model.py            | 411 +++++++++
 .../lib/pinnbench/Burgers/requirements.txt    |   5 +
 .../pinnbench/DiffusionReaction/__init__.py   |   1 +
 .../pinnbench/DiffusionReaction/benchmark.py  |  28 +
 .../lib/pinnbench/DiffusionReaction/hpo.py    | 206 +++++
 .../lib/pinnbench/DiffusionReaction/model.py  | 154 ++++
 .../DiffusionReaction/requirements.txt        |   6 +
 .../lib/pinnbench/README.md                   | 144 ++++
 .../lib/pinnbench/install.sh                  |  13 +
 src/deephyper_benchmark/lib/yahpo/README.md   |  13 +
 .../lib/yahpo/lcbench/__init__.py             |   0
 .../lib/yahpo/lcbench/benchmark.py            |  27 +
 .../lib/yahpo/lcbench/hpo.py                  |  75 ++
 .../lib/yahpo/lcbench/metrics.py              |  69 ++
 102 files changed, 6502 insertions(+), 160 deletions(-)
 delete mode 100644 examples/bench_combo.py
 rename src/deephyper_benchmark/{cbbo => benchmarks}/__init__.py (100%)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/__init__.py
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/ackley.py
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/branin.py
 delete mode 100644 src/deephyper_benchmark/cbbo/ackley.py
 delete mode 100644 src/deephyper_benchmark/cbbo/branin.py
 create mode 100644 src/deephyper_benchmark/lib/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/Makefile
 create mode 100644 src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/metrics.py
 create mode 100644 src/deephyper_benchmark/lib/autotuning/Mochi/README.md
 create mode 100644 src/deephyper_benchmark/lib/autotuning/README.md
 create mode 100644 src/deephyper_benchmark/lib/dtlz/README.md
 create mode 100644 src/deephyper_benchmark/lib/dtlz/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/dtlz/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/dtlz/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/dtlz/metrics.py
 create mode 100644 src/deephyper_benchmark/lib/dtlz/model.py
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/Makefile
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/README.md
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/combo_default_model.yaml
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/model.py
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/requirements.txt
 create mode 100644 src/deephyper_benchmark/lib/fnobench/README.md
 create mode 100644 src/deephyper_benchmark/lib/fnobench/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/fnobench/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/fnobench/data.py
 create mode 100644 src/deephyper_benchmark/lib/fnobench/data/darcy_test_16.pt
 create mode 100644 src/deephyper_benchmark/lib/fnobench/data/darcy_test_32.pt
 create mode 100644 src/deephyper_benchmark/lib/fnobench/data/darcy_train_16.pt
 create mode 100644 src/deephyper_benchmark/lib/fnobench/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/fnobench/model.py
 create mode 100644 src/deephyper_benchmark/lib/fnobench/requirements.txt
 create mode 100644 src/deephyper_benchmark/lib/hpobench/README.md
 create mode 100644 src/deephyper_benchmark/lib/hpobench/tabular/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/hpobench/tabular/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/hpobench/tabular/constant_predictor.py
 create mode 100644 src/deephyper_benchmark/lib/hpobench/tabular/hpo.py
 create mode 100755 src/deephyper_benchmark/lib/hpobench/tabular/install.sh
 create mode 100644 src/deephyper_benchmark/lib/hpobench/tabular/metrics.py
 create mode 100644 src/deephyper_benchmark/lib/jahsbench/README.md
 create mode 100644 src/deephyper_benchmark/lib/jahsbench/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/jahsbench/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/jahsbench/hpo.py
 create mode 100755 src/deephyper_benchmark/lib/jahsbench/install.sh
 create mode 100644 src/deephyper_benchmark/lib/jahsbench/metrics.py
 create mode 100644 src/deephyper_benchmark/lib/jahsbench/model.py
 create mode 100644 src/deephyper_benchmark/lib/jahsbench/requirements.txt
 create mode 100644 src/deephyper_benchmark/lib/lcbench/Makefile
 create mode 100644 src/deephyper_benchmark/lib/lcbench/README.md
 create mode 100644 src/deephyper_benchmark/lib/lcbench/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/lcbench/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/lcbench/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/lcdb/README.md
 create mode 100644 src/deephyper_benchmark/lib/lcdb/algorithm_selection/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/README.md
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/lcdb/README.md
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/lcdb/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/lcdb/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/lcdb/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/loglin2/README.md
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/loglin2/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/loglin2/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/loglin2/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/pow3/README.md
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/pow3/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/pow3/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/pow3/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/Burgers/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/Burgers/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/Burgers/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/Burgers/model.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/Burgers/requirements.txt
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/model.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/requirements.txt
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/README.md
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/install.sh
 create mode 100644 src/deephyper_benchmark/lib/yahpo/README.md
 create mode 100644 src/deephyper_benchmark/lib/yahpo/lcbench/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/yahpo/lcbench/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/yahpo/lcbench/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/yahpo/lcbench/metrics.py

diff --git a/examples/bench_ackley.py b/examples/bench_ackley.py
index fe4861f..74e6f4c 100644
--- a/examples/bench_ackley.py
+++ b/examples/bench_ackley.py
@@ -1,19 +1,21 @@
 """here."""
 
 from deephyper.hpo import CBO
-from deephyper_benchmark import AckleyBenchmark
+from deephyper_benchmark.benchmarks.cbbo import AckleyBenchmark
 
 
 def main():
     """Run example."""
-
     bench = AckleyBenchmark()
 
     search = CBO(bench.problem, bench.run_function)
-    results = search.search(10)
+    results = search.search(25)
 
     print(results)
 
+    cumul_regret = bench.scorer.cumul_regret(results.objective)
+    print(cumul_regret)
+
 
 if __name__ == "__main__":
     main()
diff --git a/examples/bench_branin.py b/examples/bench_branin.py
index 7b1780d..41fb347 100644
--- a/examples/bench_branin.py
+++ b/examples/bench_branin.py
@@ -1,19 +1,21 @@
 """here."""
 
 from deephyper.hpo import CBO
-from deephyper_benchmark import BraninBenchmark
+from deephyper_benchmark.benchmarks.cbbo import BraninBenchmark
 
 
 def main():
     """Run example."""
-
     bench = BraninBenchmark()
 
     search = CBO(bench.problem, bench.run_function)
-    results = search.search(10)
+    results = search.search(25)
 
     print(results)
 
+    cumul_regret = bench.scorer.cumul_regret(results.objective)
+    print(cumul_regret)
+
 
 if __name__ == "__main__":
     main()
diff --git a/examples/bench_combo.py b/examples/bench_combo.py
deleted file mode 100644
index b8fdac5..0000000
--- a/examples/bench_combo.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import logging
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(levelname)s - %(filename)s:%(funcName)s - %(message)s",
-    force=True,
-)
-
-import deephyper_benchmark as dhb
-
-# dhb.install("ECP-Candle/Pilot1/Combo")
-
-dhb.load("ECP-Candle/Pilot1/Combo")
-
-# Run training with Testing scores
-from deephyper_benchmark.lib.ecp_candle.pilot1.combo import model 
-res = model.run_pipeline(mode="test")
-print(f"{res=}")
-
-# Run HPO-pipeline with default configuration of hyperparameters
-from deephyper_benchmark.lib.ecp_candle.pilot1.combo import hpo
-config = hpo.problem.default_configuration
-res = hpo.run(config)
-print(f"{res=}")
-
-
diff --git a/src/deephyper_benchmark/__init__.py b/src/deephyper_benchmark/__init__.py
index a52dc4f..c8df6c3 100644
--- a/src/deephyper_benchmark/__init__.py
+++ b/src/deephyper_benchmark/__init__.py
@@ -2,7 +2,6 @@
 
 from ._benchmark import Benchmark, HPOBenchmark
 from ._scorer import Scorer, HPOScorer
-from .cbbo.ackley import AckleyBenchmark
-from .cbbo.branin import BraninBenchmark
 
-__all__ = ["Benchmark", "HPOBenchmark", "Scorer", "HPOScorer", "AckleyBenchmark", "BraninBenchmark"]
+
+__all__ = ["Benchmark", "HPOBenchmark", "Scorer", "HPOScorer"]
diff --git a/src/deephyper_benchmark/_benchmark.py b/src/deephyper_benchmark/_benchmark.py
index 3850d7f..219e311 100644
--- a/src/deephyper_benchmark/_benchmark.py
+++ b/src/deephyper_benchmark/_benchmark.py
@@ -2,15 +2,12 @@
 
 
 class Benchmark(abc.ABC):
-    def __init__(self):
-        super().__init__()
-        self.refresh_settings()
-
-    def refresh_settings(self):
-        """Refresh benchmark settings."""
+    """Base class for benchmarks."""
 
 
 class HPOBenchmark(Benchmark):
+    """Base class for Hyperparameter optimization benchmarks."""
+
     @property
     @abc.abstractmethod
     def problem(self):
diff --git a/src/deephyper_benchmark/cbbo/__init__.py b/src/deephyper_benchmark/benchmarks/__init__.py
similarity index 100%
rename from src/deephyper_benchmark/cbbo/__init__.py
rename to src/deephyper_benchmark/benchmarks/__init__.py
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/__init__.py b/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
new file mode 100644
index 0000000..42d103a
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
@@ -0,0 +1,6 @@
+"""here."""
+
+from .ackley import AckleyBenchmark
+from .branin import BraninBenchmark
+
+__all__ = ["AckleyBenchmark", "BraninBenchmark"]
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/ackley.py b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
new file mode 100644
index 0000000..c7d591e
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
@@ -0,0 +1,121 @@
+"""here."""
+
+import numpy as np
+import time
+
+from deephyper.evaluator import profile
+from deephyper.evaluator import RunningJob
+from deephyper.hpo import HpProblem
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+
+def ackley(x, a=20, b=0.2, c=2 * np.pi):
+    """Ackley function.
+
+    Description of the function: https://www.sfu.ca/~ssurjano/ackley.html
+    """
+    d = len(x)
+    s1 = np.sum(x**2)
+    s2 = np.sum(np.cos(c * x))
+    term1 = -a * np.exp(-b * np.sqrt(s1 / d))
+    term2 = -np.exp(s2 / d)
+    y = term1 + term2 + a + np.exp(1)
+    return y
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:  # noqa: D103
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -ackley(x)
+
+
+class AckleyScorer(HPOScorer):
+    """A class defining performance evaluators for the Ackley problem."""
+
+    def __init__(
+        self,
+        nparams,
+        nslack,
+    ):
+        self.nparams = nparams
+        self.x_max = np.full(self.nparams, fill_value=0.0)
+        self.x_max[nparams - nslack :] = np.nan
+        self.y_max = 0.0
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_max - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class AckleyBenchmark(HPOBenchmark):
+    """Ackley benchmark.
+
+    Args:
+        nparams (int, optional): the number of parameters in the problem.
+        offset (int, optional): the offset in the space of parameters.
+        nslack (int, optional): the number of additional slack parameters in the problem.
+    """
+
+    def __init__(self, nparams: int = 5, offset: int = -4.0, nslack: int = 0) -> None:
+        self.nparams = nparams
+        assert offset <= 32.768 and offset >= -32.768, (
+            "offset must be in [-32.768, 32.768] to keep the same maximum value."
+        )
+        self.offset = offset
+        self.nslack = nslack
+
+    @property
+    def problem(self):  # noqa: D102
+        # The original range is simetric (-32.768, 32.768) but we make it less simetric to avoid
+        # Grid sampling or QMC sampling to directly hit the optimum...
+        domain = (
+            -32.768 + self.offset,
+            32.768 + self.offset,
+        )
+        problem = HpProblem()
+        for i in range(self.nparams - self.nslack):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+        # Add slack/dummy dimensions (useful to test predicors which are sensitive
+        # to unimportant features)
+        for i in range(
+            self.nparams - self.nslack,
+            self.nparams,
+        ):
+            problem.add_hyperparameter(domain, f"z{i}")
+        return problem
+
+    @property
+    def run_function(self):  # noqa: D102
+        return run_function
+
+    @property
+    def scorer(self):  # noqa: D102
+        return AckleyScorer(self.nparams, self.nslack)
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/branin.py b/src/deephyper_benchmark/benchmarks/cbbo/branin.py
new file mode 100644
index 0000000..1717d78
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/branin.py
@@ -0,0 +1,90 @@
+"""here."""
+
+import numpy as np
+import time
+
+from deephyper.evaluator import RunningJob
+from deephyper.evaluator import profile
+from deephyper.hpo import HpProblem
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+
+def branin(x):
+    """Branin function.
+
+    Description of the function: https://www.sfu.ca/~ssurjano/branin.html"
+    """
+    assert len(x) == 2
+    a = 1.0
+    b = 5.1 / (4.0 * np.pi**2)
+    c = 5.0 / np.pi
+    r = 6.0
+    s = 10.0
+    t = 1.0 / (8.0 * np.pi)
+    y = a * (x[1] - b * x[0] ** 2 + c * x[0] - r) ** 2 + s * (1 - t) * np.cos(x[0]) + s
+    return y
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:  # noqa: D103
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -branin(x)
+
+
+class BraninHPOScorer(HPOScorer):
+    """A class defining performance evaluators for the Ackley problem."""
+
+    def __init__(self):
+        self.nparams = 2
+        self.x_max = np.array([[-np.pi, 12.275], [np.pi, 2.275], [9.42478, 2.475]])
+        self.y_max = -0.397887
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_max - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class BraninBenchmark(HPOBenchmark):
+    """Branin benchmark."""
+
+    @property
+    def problem(self):  # noqa: D102
+        problem = HpProblem()
+        problem.add_hyperparameter((-5.0, 10.0), "x0")
+        problem.add_hyperparameter((0.0, 15.0), "x1")
+        return problem
+
+    @property
+    def run_function(self):  # noqa: D102
+        return run_function
+
+    @property
+    def scorer(self):  # noqa: D102
+        return BraninHPOScorer()
diff --git a/src/deephyper_benchmark/cbbo/ackley.py b/src/deephyper_benchmark/cbbo/ackley.py
deleted file mode 100644
index 03fc7c9..0000000
--- a/src/deephyper_benchmark/cbbo/ackley.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""here."""
-
-import numpy as np
-import time
-
-from deephyper.evaluator import profile
-from deephyper.evaluator import RunningJob
-from deephyper.hpo import HpProblem
-
-
-def ackley(x, a=20, b=0.2, c=2 * np.pi):
-    d = len(x)
-    s1 = np.sum(x**2)
-    s2 = np.sum(np.cos(c * x))
-    term1 = -a * np.exp(-b * np.sqrt(s1 / d))
-    term2 = -np.exp(s2 / d)
-    y = term1 + term2 + a + np.exp(1)
-    return y
-
-
-@profile
-def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -ackley(x)
-
-
-class AckleyBenchmark:
-    def __init__(self, ndims=5, offset=4.0, slack=0) -> None:
-        self.ndims = ndims
-        self.offset = offset
-        self.slack = slack
-
-    @property
-    def problem(self):
-        # The original range is simetric (-32.768, 32.768) but we make it less simetric to avoid
-        # Grid sampling or QMC sampling to directly hit the optimum...
-        domain = (
-            -32.768 - self.offset,
-            32.768 - self.offset,
-        )
-        problem = HpProblem()
-        for i in range(self.ndims - self.slack):
-            problem.add_hyperparameter(domain, f"x{i}")
-
-        # Add slack/dummy dimensions (useful to test predicors which are sensitive
-        # to unimportant features)
-        for i in range(
-            self.ndims - self.slack,
-            self.ndims,
-        ):
-            problem.add_hyperparameter(domain, f"z{i}")
-        return problem
-
-    @property
-    def run_function(self):
-        return run_function
diff --git a/src/deephyper_benchmark/cbbo/branin.py b/src/deephyper_benchmark/cbbo/branin.py
deleted file mode 100644
index 181b05f..0000000
--- a/src/deephyper_benchmark/cbbo/branin.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""here."""
-
-import numpy as np
-import time
-
-from deephyper.evaluator import RunningJob
-from deephyper.evaluator import profile
-from deephyper.hpo import HpProblem
-
-
-def branin(x):
-    assert len(x) == 2
-    a = 1.0
-    b = 5.1 / (4.0 * np.pi**2)
-    c = 5.0 / np.pi
-    r = 6.0
-    s = 10.0
-    t = 1.0 / (8.0 * np.pi)
-    y = a * (x[1] - b * x[0] ** 2 + c * x[0] - r) ** 2 + s * (1 - t) * np.cos(x[0]) + s
-    return y
-
-
-@profile
-def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -branin(x)
-
-
-class BraninBenchmark:
-    @property
-    def problem(self):
-        problem = HpProblem()
-        problem.add_hyperparameter((-5.0, 10.0), "x0")
-        problem.add_hyperparameter((0.0, 15.0), "x1")
-        return problem
-
-    @property
-    def run_function(self):
-        return run_function
diff --git a/src/deephyper_benchmark/lib/__init__.py b/src/deephyper_benchmark/lib/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/Makefile b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/Makefile
new file mode 100644
index 0000000..beec148
--- /dev/null
+++ b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/Makefile
@@ -0,0 +1,11 @@
+
+VERSION=0.0.1
+
+build:
+	mkdir -p build
+	wget https://github.com/hepnos/HEPnOS-Autotuning-analysis/archive/refs/heads/main.tar.gz -O build/hepnos-autotuning-analysis.tar.gz
+	tar -xzvf build/hepnos-autotuning-analysis.tar.gz -C build
+	rm -rf build/hepnos-autotuning-analysis.tar.gz
+
+clean:
+	rm -rf build/
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/__init__.py b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/benchmark.py b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/benchmark.py
new file mode 100644
index 0000000..4727358
--- /dev/null
+++ b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/benchmark.py
@@ -0,0 +1,19 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class HEPnOS(Benchmark):
+    """
+    - Experimental data from the HEPnOS paper: https://github.com/hepnos/HEPnOS-Autotuning-analysis
+    - Experimental code from the HEPnOS paper: https://github.com/hepnos/HEPnOS-Autotuning/tree/dev-new-hepnos
+    - Paper from M. Dorier and R. Egele et al. "HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization". https://arxiv.org/pdf/2210.00798
+    """
+
+    version = "0.0.1"
+
+    requires = {
+        "makefile": {"step": "install", "type": "cmd", "cmd": "make build"},
+    }
diff --git a/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/hpo.py b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/hpo.py
new file mode 100644
index 0000000..c5ed94d
--- /dev/null
+++ b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/hpo.py
@@ -0,0 +1,281 @@
+import os
+import time
+
+import numpy as np
+import pandas as pd
+from deephyper.analysis.hpo import filter_failed_objectives
+from deephyper.evaluator import RunningJob, profile
+from deephyper.hpo import HpProblem
+from sklearn.compose import ColumnTransformer
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.preprocessing import OneHotEncoder, FunctionTransformer
+from sklearn.pipeline import Pipeline
+
+BUILD_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "build")
+DATA_DIR = os.path.join(
+    BUILD_DIR, "HEPnOS-Autotuning-analysis-main", "results", "theta"
+)
+
+DEEPHYPER_BENCHMARK_HEPNOS_MODEL = os.environ.get(
+    "DEEPHYPER_BENCHMARK_HEPNOS_MODEL", "RAND"
+)
+DEEPHYPER_BENCHMARK_HEPNOS_SCALE = int(
+    os.environ.get("DEEPHYPER_BENCHMARK_HEPNOS_SCALE", "4")
+)
+DEEPHYPER_BENCHMARK_HEPNOS_DISABLE_PEP = bool(
+    os.environ.get("DEEPHYPER_BENCHMARK_HEPNOS_DISABLE_PEP", "0")
+)
+DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS = bool(
+    os.environ.get("DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS", "0")
+)
+
+NUMERIC_VARIABLES = []
+CATEGORICAL_VARIABLES = []
+
+problem = HpProblem()
+
+
+def add_parameter(name, domain, value_type, default_value, description=""):
+    problem.add_hyperparameter(domain, name, default_value=default_value)
+    if value_type in [int, float, bool]:
+        NUMERIC_VARIABLES.append(name)
+    else:
+        CATEGORICAL_VARIABLES.append(name)
+
+
+def create_problem():
+    add_parameter(
+        "busy_spin",
+        [0, 1],
+        int,
+        0,
+        "Whether Mercury should busy-spin instead of block",
+    )
+    add_parameter(
+        "hepnos_progress_thread",
+        [0, 1],
+        int,
+        0,
+        "Whether to use a dedicated progress thread in HEPnOS",
+    )
+    add_parameter(
+        "hepnos_num_rpc_threads",
+        (0, 63),
+        int,
+        0,
+        "Number of threads used for serving RPC requests",
+    )
+    add_parameter(
+        "hepnos_num_event_databases",
+        (1, 16),
+        int,
+        1,
+        "Number of databases per process used to store events",
+    )
+    add_parameter(
+        "hepnos_num_product_databases",
+        (1, 16),
+        int,
+        1,
+        "Number of databases per process used to store products",
+    )
+    add_parameter(
+        "hepnos_num_providers",
+        (1, 32),
+        int,
+        1,
+        "Number of database providers per process",
+    )
+    add_parameter(
+        "hepnos_pool_type",
+        ["fifo", "fifo_wait"],
+        str,
+        "fifo_wait",
+        "Thread-scheduling policity used by Argobots pools",
+    )
+    add_parameter(
+        "hepnos_pes_per_node",
+        [1, 2, 4, 8, 16, 32],
+        int,
+        2,
+        "Number of HEPnOS processes per node",
+    )
+    add_parameter(
+        "loader_progress_thread",
+        [0, 1],
+        int,
+        0,
+        "Whether to use a dedicated progress thread in the Dataloader",
+    )
+    add_parameter(
+        "loader_batch_size",
+        (1, 2048, "log-uniform"),
+        int,
+        512,
+        "Size of the batches of events sent by the Dataloader to HEPnOS",
+    )
+    add_parameter(
+        "loader_pes_per_node",
+        [1, 2, 4, 8, 16],
+        int,
+        2,
+        "Number of processes per node for the Dataloader",
+    )
+    if DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS:
+        add_parameter(
+            "loader_async",
+            [0, 1],
+            int,
+            0,
+            "Whether to use the HEPnOS AsyncEngine in the Dataloader",
+        )
+        add_parameter(
+            "loader_async_threads",
+            (1, 63, "log-uniform"),
+            int,
+            1,
+            "Number of threads for the AsyncEngine to use",
+        )
+
+    if DEEPHYPER_BENCHMARK_HEPNOS_DISABLE_PEP:
+        return
+
+    add_parameter(
+        "pep_progress_thread",
+        [0, 1],
+        int,
+        0,
+        "Whether to use a dedicated progress thread in the PEP step",
+    )
+    add_parameter(
+        "pep_num_threads",
+        (1, 31),
+        int,
+        4,
+        "Number of threads used for processing in the PEP step",
+    )
+    add_parameter(
+        "pep_ibatch_size",
+        (8, 1024, "log-uniform"),
+        int,
+        128,
+        "Batch size used when PEP processes are loading events from HEPnOS",
+    )
+    add_parameter(
+        "pep_obatch_size",
+        (8, 1024, "log-uniform"),
+        int,
+        128,
+        "Batch size used when PEP processes are exchanging events among themselves",
+    )
+    add_parameter(
+        "pep_pes_per_node",
+        [1, 2, 4, 8, 16, 32],
+        int,
+        8,
+        "Number of processes per node for the PEP step",
+    )
+
+    if DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS:
+        add_parameter(
+            "pep_no_preloading",
+            [0, 1],
+            int,
+            False,
+            "Whether to disable product-preloading in PEP",
+        )
+        add_parameter(
+            "pep_no_rdma",
+            [0, 1],
+            int,
+            0,
+            "Whether to disable RDMA in PEP",
+        )
+
+
+create_problem()
+
+CSV_SUFFIX = f"{str(DEEPHYPER_BENCHMARK_HEPNOS_DISABLE_PEP).lower()}-{str(DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS).lower()}"
+CSV_SUFFIX = f"{DEEPHYPER_BENCHMARK_HEPNOS_MODEL}-{DEEPHYPER_BENCHMARK_HEPNOS_SCALE}-{CSV_SUFFIX}"
+
+
+def load_data():
+    dataframes, dataframes_with_failures = [], []
+    for i in range(1, 6):
+        csv_path = os.path.join(
+            DATA_DIR,
+            f"exp-{CSV_SUFFIX}-{i}.csv",
+        )
+        df = pd.read_csv(csv_path, index_col=None, header=0)
+        df, df_with_failures = filter_failed_objectives(df)
+        dataframes.append(df)
+        dataframes_with_failures.append(df_with_failures)
+
+    dataframes = pd.concat(dataframes, axis=0, ignore_index=True)
+    dataframes_with_failures = pd.concat(
+        dataframes_with_failures, axis=0, ignore_index=True
+    )
+
+    # The objective is -log(time) initialy so we convert it back to time
+    dataframes["objective"] = dataframes["objective"] = np.exp(
+        -dataframes["objective"].values.astype(np.float32)
+    )
+    dataframes_with_failures["objective"] = -1
+
+    dataframes = pd.concat(
+        [dataframes, dataframes_with_failures], axis=0, ignore_index=True
+    )
+
+    return dataframes
+
+
+def create_model():
+    df = load_data()
+    names = problem.hyperparameter_names
+
+    X = df.loc[:, names]
+    y = df["objective"].values
+
+    transformer = ColumnTransformer(
+        [
+            (
+                "onehot",
+                OneHotEncoder(handle_unknown="ignore"),
+                CATEGORICAL_VARIABLES,
+            ),
+            ("passthrough", FunctionTransformer(), NUMERIC_VARIABLES),
+        ]
+    )
+
+    model = KNeighborsRegressor(n_neighbors=1)
+
+    pipeline = Pipeline([("transformer", transformer), ("model", model)])
+    pipeline.fit(X, y)
+
+    return pipeline
+
+
+model = create_model()
+
+
+@profile
+def run(job: RunningJob) -> dict:
+    config = job.parameters.copy()
+
+    df = pd.DataFrame([config])
+    y_pred = -model.predict(df)[0]
+
+    if y_pred > 0:
+        y_pred = "F"
+
+    return {"objective": y_pred}
+
+
+if __name__ == "__main__":
+    print(problem)
+    df = load_data()
+    print(df)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/metrics.py b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/metrics.py
new file mode 100644
index 0000000..bc09e5a
--- /dev/null
+++ b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/metrics.py
@@ -0,0 +1,39 @@
+import os
+
+import numpy as np
+
+from .hpo import load_data, problem
+
+class PerformanceEvaluator:
+    """A class defining performance evaluators for the HEPnOS problem."""
+
+    def __init__(self):
+        """Read the current problem defn from environment vars."""
+
+        df = load_data()
+        df = df[df["objective"] > 0]
+        idx_max = df["objective"].argmin()
+        self.x_min = df[problem.hyperparameter_names].iloc[idx_max].to_dict()
+        self.y_min = df.iloc[idx_max]["objective"]
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return y - self.y_min
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
diff --git a/src/deephyper_benchmark/lib/autotuning/Mochi/README.md b/src/deephyper_benchmark/lib/autotuning/Mochi/README.md
new file mode 100644
index 0000000..3f4bb65
--- /dev/null
+++ b/src/deephyper_benchmark/lib/autotuning/Mochi/README.md
@@ -0,0 +1,13 @@
+# Mochi Benchmarks
+
+From the Mochi documentation:
+
+> The Mochi project is a collaboration between Argonne National Laboratory, Los Alamos National Laboratory, Carnegie Mellon University, and the HDF Group. The objective of this project is to explore a software defined storage approach for composing storage services that provides new levels of functionality, performance, and reliability for science applications at extreme scale.
+
+- Documentation: https://mochi.readthedocs.io/en/latest/
+
+## Installation
+
+```console
+python -c "import deephyper_benchmark as dhb; dhb.install('AutoTuning/Mochi/HEPnOS');"
+```
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/autotuning/README.md b/src/deephyper_benchmark/lib/autotuning/README.md
new file mode 100644
index 0000000..bd2e296
--- /dev/null
+++ b/src/deephyper_benchmark/lib/autotuning/README.md
@@ -0,0 +1,3 @@
+# AutoTuning Benchmarks
+
+AutoTuning corresponds to the automated configurations of software to improve performance. For example, it can be the configuration of a database, a compilator, or parallelism.
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
index 9ef0ac3..d144a9d 100644
--- a/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
+++ b/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
@@ -47,7 +47,7 @@ def __init__(
         offset=0,
     ):
         self.p_num = p_num
-        self.x_max = np.full(self.p_num, fill_value=-offset)
+        self.x_max = np.full(self.p_num, fill_value=0.0)
         self.x_max[p_num - p_num_slack :] = np.nan
         self.y_max = 0.0
 
diff --git a/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py
index 4c0509c..df5c9ff 100644
--- a/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py
+++ b/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py
@@ -37,7 +37,7 @@ class EasomHPOScorer(HPOScorer):
     """A class defining performance evaluators for the Ackley problem."""
 
     def __init__(self, offset=0):
-        self.x_max = np.array([np.pi, np.pi]) - offset
+        self.x_max = np.array([np.pi, np.pi])
         self.y_max = 1.0
 
     def simple_regret(self, y: np.ndarray) -> np.ndarray:
diff --git a/src/deephyper_benchmark/lib/c_bbo/schwefel/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/schwefel/hpo_benchmark.py
index 4f3cbec..903ba0c 100644
--- a/src/deephyper_benchmark/lib/c_bbo/schwefel/hpo_benchmark.py
+++ b/src/deephyper_benchmark/lib/c_bbo/schwefel/hpo_benchmark.py
@@ -38,8 +38,6 @@ class SchwefelHPOScorer(HPOScorer):
     def __init__(
         self,
         p_num,
-        p_num_slack,
-        offset=0,
     ):
         self.p_num = p_num
         self.x_max = np.full(self.p_num, fill_value=420.9687)
diff --git a/src/deephyper_benchmark/lib/c_bbo/shekel/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/shekel/hpo_benchmark.py
index d1b881e..d0e10ab 100644
--- a/src/deephyper_benchmark/lib/c_bbo/shekel/hpo_benchmark.py
+++ b/src/deephyper_benchmark/lib/c_bbo/shekel/hpo_benchmark.py
@@ -45,7 +45,7 @@ class ShekelHPOScorer(HPOScorer):
     """A class defining performance evaluators for the Shekel problem."""
 
     def __init__(self):
-        self.p_num = 10
+        self.p_num = 4
         self.x_max = np.full(self.p_num, fill_value=4.0)
         self.y_max = 10.5364
 
@@ -77,7 +77,7 @@ class ShekelHPOBenchmark(HPOBenchmark):
     def problem(self):
         domain = (0.0, 10.0)
         problem = HpProblem()
-        for i in range(10):
+        for i in range(4):
             problem.add_hyperparameter(domain, f"x{i}")
         return problem
 
diff --git a/src/deephyper_benchmark/lib/dtlz/README.md b/src/deephyper_benchmark/lib/dtlz/README.md
new file mode 100644
index 0000000..5c09f18
--- /dev/null
+++ b/src/deephyper_benchmark/lib/dtlz/README.md
@@ -0,0 +1,130 @@
+
+# Modified Multiobjective DTLZ Test Suite
+
+✅ **Benchmark ready to be used.**
+
+This module contains objective function implementations of the DTLZ test
+suite, derived from the implementations in
+[ParMOO](https://github.com/parmoo/parmoo).
+
+------------------------------------------------------------------------------
+
+For further reference, the DTLZ test suite was originally proposed in:
+
+    Deb, Thiele, Laumanns, and Zitzler. "Scalable test problems for
+    evolutionary multiobjective optimization" in Evolutionary Multiobjective
+    Optimization, Theoretical Advances and Applications, Ch. 6 (pp. 105--145).
+    Springer-Verlag, London, UK, 2005. Abraham, Jain, and Goldberg (Eds).
+
+The original implementation was appropriate for testing randomized algorithms,
+but for many deterministic algorithms, the global solutions represent either
+best- or worst-case scenarios, so an configurable offset was introduced in:
+
+    Chang. "Mathematical Software for Multiobjective Optimization Problems."
+    Ph.D. dissertation, Virginia Tech, Dept. of Computer Science, 2020.
+
+Note that the DTLZ problems are minimization problems. Since DeepHyper
+maximizes, the implementation herein returns the negative value for each of
+the DTLZ objectives.
+
+Our performance evaluator ``metrics`` scripts can evaluate either the
+positive or negative solutions to estimate how well we have solved the
+problem.
+
+------------------------------------------------------------------------------
+
+The full list of public classes in this module includes the 7 unconstrained
+DTLZ problems
+ * ``dtlz1``,
+ * ``dtlz2``,
+ * ``dtlz3``,
+ * ``dtlz4``,
+ * ``dtlz5``,
+ * ``dtlz6``, and
+ * ``dtlz7``
+
+which are selected by setting the environment variable
+``DEEPHYPER_BENCHMARK_DTLZ_PROB``.
+
+## Installation 
+
+To use the benchmark follow this example set of instructions:
+
+```python
+
+# Set DTLZ problem environment variables before loading
+import os
+os.environ["DEEPHYPER_BENCHMARK_NDIMS"] = "5" # 5 vars
+os.environ["DEEPHYPER_BENCHMARK_NOBJS"] = "3" # 2 objs
+os.environ["DEEPHYPER_BENCHMARK_DTLZ_PROB"] = "2" # DTLZ2 problem
+os.environ["DEEPHYPER_BENCHMARK_DTLZ_OFFSET"] = "0.6" # soln [x_o, .., x_n]=0.6
+
+# Load DTLZ benchmark suite
+import deephyper_benchmark as dhb
+dhb.load("DTLZ")
+
+# Example of running one evaluation of DTLZ problem
+from deephyper.evaluator import RunningJob
+config = dtlz.hpo.problem.default_configuration # get a default config to test
+res = dtlz.hpo.run(RunningJob(parameters=config))
+
+```
+
+## Configuration
+
+To configure the problem, set the following:
+
+- Environment variable `DEEPHYPER_BENCHMARK_PROB` with a value of `1`, `2`, ... `7` to select the DTLZ problem to run. Defaults to `2`.
+- Environment variable `DEEPHYPER_BENCHMARK_NDIMS` with an integer value to set the number of input variables. Defaults to `5`.
+- Environment variable `DEEPHYPER_BENCHMARK_NOBJS` with an integer value to set the number of objectives. Defaults to `2`
+- Environment variable `DEEPHYPER_BENCHMARK_OFFSET` with a value between `0.0` and `1.0` to select the offset of the solution to the DTLZ problem. Defaults to `0.5` for DTLZ1, ..., DTLZ5 and `0.0` for DTLZ6 and DTLZ7. One may wish to adjust this value when comparing against deterministic blackbox solvers, which may sample the center and boundaries of the input space on specific schedules.
+- Environment variable `DEEPHYPER_BENCHMARK_FAILURES` with value `0` or `1` to activate or deactivate failures. Defaults to `0`.
+
+## Metadata
+
+Since these problems are analytic, there is no metadata for this problem
+beyond the standard DeepHyper metadata (timestamp information).
+
+## Evaluating Results
+
+Evaluating the performance of a multiobjective solver is nontrivial.
+Typically, one should evaluate on two orthogonal bases:
+ 1. Quality of solutions -- What is the (average) error in the solutions
+    returned by the solver?
+ 2. Diversity of solutions -- How much of the true Pareto front is covered
+    by these solutions?
+
+To evaluate these two metrics, we use:
+ 1. Improved generational distance (GD+): Let $F_i$ be a point in the solution
+    set returned by a solver,
+    and let $Y_i$ be the nearest point to $F_i$ on the true Pareto front,
+    for $i=1,\ldots, n$.
+    Then the GD+ is $\sum_{i} D^+(F_i, Y_i) / n$.
+    Where $D^+$ denotes the improved distance function
+    $D^+(F, Y) = ||\max(F - Y, 0)||_2^2$, where the "max" is taken
+    componentwise.
+    **Note that this metric may not increase monotonically. Additionally,
+    it may be impossible to calculate for an arbitrary blackbox function,
+    and can only be calculated here since the solution known and easily
+    expressed algebraically for all of the DTLZ problems.**
+ 2. Hypervolume dominated: Let $F_i$ be defined as above, and let $R$ be
+    a pre-determined reference point such that all $F_i$ dominate $R$.
+    Then the hypervolume is given by the volume of the union of all
+    hyperboxes $B_i$ whose largest vertex is $F_i$ and smallest vertex
+    is $R$. The value (and usefulness) of the hypervolume metric is extremely
+    sensitive to the choice of $R$. Therefore, for this problem, we choose
+    $R$ to be the Nadir point for the true Pareto front. **Note that in order
+    to use the Nadir point as the reference point, we must throw out every
+    solution returned by the solver that is worse than the Nadir point. For
+    extremely difficult problems, this can result in zero hypervolume if no
+    solutions better than the Nadir point were found. This is most common
+    for DTLZ1, DTLZ3, and DTLZ7.**
+
+For a general problem, the two metrics listed above could be very difficult
+to compute and many researchers will use the hypervolume with an overly
+pessimistic reference point as a proxy for both quality and diversity.
+However, in general, the hypervolume tends to promote diversity over quality.
+For the DTLZ problems, since the shape of the true Pareto front is known,
+we can calculate each of these metrics, and both the ``gdPlus(results)`` and
+``hypervolume(results)`` functions are implemented in the ``dtlz.metrics``
+module.
diff --git a/src/deephyper_benchmark/lib/dtlz/__init__.py b/src/deephyper_benchmark/lib/dtlz/__init__.py
new file mode 100644
index 0000000..f102a9c
--- /dev/null
+++ b/src/deephyper_benchmark/lib/dtlz/__init__.py
@@ -0,0 +1 @@
+__version__ = "0.0.1"
diff --git a/src/deephyper_benchmark/lib/dtlz/benchmark.py b/src/deephyper_benchmark/lib/dtlz/benchmark.py
new file mode 100644
index 0000000..2c12251
--- /dev/null
+++ b/src/deephyper_benchmark/lib/dtlz/benchmark.py
@@ -0,0 +1,6 @@
+from deephyper_benchmark import *
+
+
+class DTLZBenchmark(Benchmark):
+
+    version = "0.0.1"
diff --git a/src/deephyper_benchmark/lib/dtlz/hpo.py b/src/deephyper_benchmark/lib/dtlz/hpo.py
new file mode 100644
index 0000000..70cd6d3
--- /dev/null
+++ b/src/deephyper_benchmark/lib/dtlz/hpo.py
@@ -0,0 +1,61 @@
+import os
+import time
+
+import numpy as np
+from deephyper.evaluator import RunningJob, profile
+from deephyper.hpo import HpProblem
+
+from . import model as dtlz
+
+# Read DTLZ problem name and acquire pointer
+dtlz_prob = os.environ.get("DEEPHYPER_BENCHMARK_DTLZ_PROB", 2)
+dtlz_prob_name = f"dtlz{dtlz_prob}"
+dtlz_class_ptr = getattr(dtlz, dtlz_prob_name)
+
+# Read problem dims and definition (or read from ENV)
+nb_dim = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
+nb_obj = int(os.environ.get("DEEPHYPER_BENCHMARK_NOBJS", 2))
+if dtlz_prob_name in ["dtlz1", "dtlz2", "dtlz3", "dtlz4", "dtlz5"]:
+    soln_offset = float(os.environ.get("DEEPHYPER_BENCHMARK_DTLZ_OFFSET", 0.5))
+elif dtlz_prob_name in ["dtlz6", "dtlz7"]:
+    soln_offset = float(os.environ.get("DEEPHYPER_BENCHMARK_DTLZ_OFFSET", 0.0))
+else:
+    raise ValueError(f"Invalid problem {dtlz_prob_name}")
+domain = (0.0, 1.0)
+
+# Failures
+DEEPHYPER_BENCHMARK_FAILURES = bool(int(os.environ.get("DEEPHYPER_BENCHMARK_FAILURES", 0)))
+
+# Create problem
+problem = HpProblem()
+dtlz_obj = dtlz_class_ptr(nb_dim, nb_obj, offset=soln_offset)
+for i in range(nb_dim):
+    problem.add_hyperparameter(domain, f"x{i}")
+
+
+@profile
+def run(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+    ff = [-fi for fi in dtlz_obj(x)]
+
+    if DEEPHYPER_BENCHMARK_FAILURES:
+        if any(xi < 0.25 for xi in x[nb_obj-1:]):
+            ff = ["F" for _ in ff]
+
+    return ff
+
+
+if __name__ == "__main__":
+    print(problem)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/dtlz/metrics.py b/src/deephyper_benchmark/lib/dtlz/metrics.py
new file mode 100644
index 0000000..3d4a305
--- /dev/null
+++ b/src/deephyper_benchmark/lib/dtlz/metrics.py
@@ -0,0 +1,201 @@
+import os
+import numpy as np
+from deephyper.skopt.moo import pareto_front, hypervolume
+
+
+class PerformanceEvaluator:
+    """ A class defining performance evaluators for the DTLZ problems.
+
+    Contains the following public methods:
+
+     * `__init__()` constructs a new instance by reading the problem defn
+       from environment variables,
+     * `hypervolume(pts)` calculates the total hypervolume dominated by
+       the current solution, using the Nadir point as the reference point
+       and filtering out solutions that do not dominate the Nadir point,
+     * `nadirPt()` calculates the Nadir point for the current problem,
+     * `numPts(pts)` calculates the number of solution points that dominate
+       the Nadir point, and
+     * `gdPlus(pts)` calculates the RMSE where the error in each point is
+       approximated by the 2-norm distance to the nearest solution point.
+
+    """
+
+    def __init__(self):
+        """ Read the current DTLZ problem defn from environment vars. """
+
+        self.p_num = os.environ.get("DEEPHYPER_BENCHMARK_DTLZ_PROB", "2")
+        self.nobjs = int(os.environ.get("DEEPHYPER_BENCHMARK_NOBJS", 2))
+
+    def hypervolume(self, pts):
+        """ Calculate the hypervolume dominated by soln, wrt the Nadir point.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            float: The total hypervolume dominated by the current solution,
+            filtering out points worse than the Nadir point and using the
+            Nadir point as the reference.
+
+        """
+
+        if np.any(pts < 0):
+            filtered_pts = -pts.copy()
+        else:
+            filtered_pts = pts.copy()
+        nadir = self.nadirPt()
+        for i in range(pts.shape[0]):
+            if np.any(filtered_pts[i, :] > nadir):
+                filtered_pts[i, :] = nadir
+        return hypervolume(filtered_pts, nadir)
+
+    def nadirPt(self):
+        """ Calculate the Nadir point for the given problem definition. """
+
+        if self.p_num == "1":
+            return np.ones(self.nobjs) * 0.5
+        elif self.p_num in ["2", "3", "4", "5", "6"]:
+            return np.ones(self.nobjs)
+        elif self.p_num == "7":
+            nadir = np.ones(self.nobjs)
+            nadir[self.nobjs - 1] = self.nobjs * 2.0
+            return nadir
+        else:
+            raise ValueError(f"DTLZ{self.p_num} is not a valid problem")
+
+    def numPts(self, pts):
+        """ Calculate the number of solutions that dominate the Nadir point.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            int: The number of fi in pts such that all(fi < self.nadirPt).
+
+        """
+
+        if np.any(pts < 0):
+            pareto_pts = pareto_front(-pts)
+        else:
+            pareto_pts = pareto_front(pts)
+        return sum([all(fi <= self.nadirPt()) for fi in pareto_pts])
+
+    def gdPlus(self, pts):
+        """ Calculate the p=1 generational distance for a given solution set.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            float: The p=1 generational distance over all points in pts.
+
+        """
+
+        if np.any(pts < 0):
+            pareto_pts = pareto_front(-pts)
+        else:
+            pareto_pts = pareto_front(pts)
+        if self.p_num == "1":
+            dists = self._dtlz1Dist(pareto_pts)
+        elif self.p_num in ["2", "3", "4", "5", "6"]:
+            dists = self._dtlz2Dist(pareto_pts)
+        elif self.p_num == "7":
+            dists = self._dtlz7Dist(pareto_pts)
+        else:
+            raise ValueError(f"DTLZ{self.p_num} is not a valid problem")
+        return np.sum(dists) / len(dists)
+
+    def _dtlz1Dist(self, pts):
+        """ Calculate the d+ from each fi to the nearest solution in DTLZ1.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            numpy.ndarray: A 1d array of distances to the nearest solution
+            point for DTLZ1.
+            
+
+        """
+
+        return np.array([np.linalg.norm(
+                             np.maximum(fi - (0.5 * fi / np.sum(fi)), 0)
+                         ) for fi in pts])
+
+    def _dtlz2Dist(self, pts):
+        """ Calculate the d+ from each fi to the nearest point on unit sphere.
+
+        Note: Works for DTLZ2-6
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            numpy.ndarray: A 1d array of distances to the surface of the
+            unit sphere.
+
+        """
+
+        return np.array([np.linalg.norm(
+                             np.maximum(fi - (fi / np.linalg.norm(fi)), 0)
+                         ) for fi in pts])
+
+    def _dtlz7Dist(self, pts):
+        """ Calculate the d+ from each fi to the nearest soln in DTLZ7.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            numpy.ndarray: A 1d array of distances to the nearest solution
+            point to DTLZ7.
+
+        """
+
+        # Project each point onto DTLZ7 solution and calculate difference
+        pts_proj = []
+        for fi in pts:
+            gx = 2.0
+            hx = (-np.sum(fi[:self.nobjs-1] *
+                  (1.0 + np.sin(3.0 * np.pi * fi[:self.nobjs-1])) / gx)
+                  + float(self.nobjs))
+            pts_proj.append(gx * hx)
+        return np.array([np.abs(
+                             np.maximum(fi[-1] - fj, 0)
+                         ) for fi, fj in zip(pts, pts_proj)])
+
+
+if __name__ == "__main__":
+    """ Driver code to test performance metrics. """
+
+    os.environ["DEEPHYPER_BENCHMARK_DTLZ_PROB"] = "1" # DTLZ1 problem
+    dtlz1_eval = PerformanceEvaluator()
+    s1 = np.array([[0.5, 0], [0, 0.5], [.25, .25], [0.2, 0.8]])
+    os.environ["DEEPHYPER_BENCHMARK_DTLZ_PROB"] = "2" # DTLZ2 problem
+    dtlz2_eval = PerformanceEvaluator()
+    s2 = np.array([[1, 0], [0, 1], [1/np.sqrt(2), 1/np.sqrt(2)], [0.25, 2]])
+    os.environ["DEEPHYPER_BENCHMARK_DTLZ_PROB"] = "7" # DTLZ7 problem
+    dtlz7_eval = PerformanceEvaluator()
+    s7 = np.array([[0, 4], [1, 3], [.5, 4], [0.5, 6]])
+
+    assert abs(dtlz1_eval.hypervolume(s1) - .0625) < 1.0e-8
+    assert np.all(np.abs(dtlz1_eval.nadirPt() - 0.5) < 1.0e-8)
+    assert dtlz1_eval.numPts(s1) == 3
+    assert abs(dtlz1_eval.gdPlus(s1)) < 1.0e-8
+
+    assert abs(dtlz2_eval.hypervolume(s2) - (1.5 - np.sqrt(2))) < 1.0e-8
+    assert np.all(np.abs(dtlz2_eval.nadirPt() - 1) < 1.0e-8)
+    assert dtlz2_eval.numPts(s2) == 3
+    assert abs(dtlz2_eval.gdPlus(s2)) < 1.0e-8
+
+    assert abs(dtlz7_eval.hypervolume(s7)) < 1.0e-8
+    assert np.all(np.abs(dtlz7_eval.nadirPt() - np.array([1, 4])) < 1.0e-8)
+    assert dtlz7_eval.numPts(s7) == 2
+    assert abs(dtlz7_eval.gdPlus(s7)) < 1.0e-8
diff --git a/src/deephyper_benchmark/lib/dtlz/model.py b/src/deephyper_benchmark/lib/dtlz/model.py
new file mode 100644
index 0000000..f7f22f4
--- /dev/null
+++ b/src/deephyper_benchmark/lib/dtlz/model.py
@@ -0,0 +1,591 @@
+"""Objective function implementations of the DTLZ test suite.
+
+Derived from the implementations in ParMOO:
+
+Chang and Wild. "ParMOO: A Python library for parallel multiobjective
+simulation optimization." Journal of Open Source Software 8(82):4468, 2023.
+
+------------------------------------------------------------------------------
+
+For further references, the DTLZ test suite was originally proposed in:
+
+Deb, Thiele, Laumanns, and Zitzler. "Scalable test problems for
+evolutionary multiobjective optimization" in Evolutionary Multiobjective
+Optimization, Theoretical Advances and Applications, Ch. 6 (pp. 105--145).
+Springer-Verlag, London, UK, 2005. Abraham, Jain, and Goldberg (Eds).
+
+The original implementation was appropriate for testing randomized algorithms,
+but for many deterministic algorithms, the global solutions represent either
+best- or worst-case scenarios, so an configurable offset was introduced in:
+
+Chang. "Mathematical Software for Multiobjective Optimization Problems."
+Ph.D. dissertation, Virginia Tech, Dept. of Computer Science, 2020.
+
+------------------------------------------------------------------------------
+
+The full list of public classes in this module includes the 7 unconstrained
+DTLZ problems:
+ * ``dtlz1``
+ * ``dtlz2``
+ * ``dtlz3``
+ * ``dtlz4``
+ * ``dtlz5``
+ * ``dtlz6``
+ * ``dtlz7``
+
+"""
+
+import numpy as np
+
+
+class __dtlz_base__:
+    """Base class implements re-used constructor.
+
+    Constructor for all DTLZ classes.
+
+    Args:
+        num_des (int): The number of design variables.
+
+        num_obj (int, optional): The number of objectives.
+
+        offset (optional, float): The location of the global minimizers
+            is x_i = offset for i = num_objectives, ..., num_des.
+            The default offset is 0.5.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.5):
+        self.n = num_des
+        self.o = num_obj
+        self.offset = offset
+        return
+
+    def __call__(self, x):
+        raise NotImplementedError("The call method must be implemented...")
+
+
+class __g1__(__dtlz_base__):
+    """Class defining 1 of 4 kernel functions used in the DTLZ problem suite.
+
+    g1 = 100 ( (n - o + 1) +
+               sum_{i=o}^n ((x_i - offset)^2 - cos(20pi(x_i - offset))) )
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method creates a new kernel.
+
+    The ``__call__`` method performs an evaluation of the g1 kernel.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.array): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            float: The output of this objective for the input x.
+
+        """
+        return (
+            1
+            + self.n
+            - self.o
+            + np.sum(
+                (x[self.o - 1 : self.n] - self.offset) ** 2
+                - np.cos(20.0 * np.pi * (x[self.o - 1 : self.n] - self.offset))
+            )
+        ) * 100.0
+
+
+class __g2__(__dtlz_base__):
+    """Class defining 2 of 4 kernel functions used in the DTLZ problem suite.
+
+    g2 = (x_o - offset)^2 + ... + (x_n - offset)^2
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the g2 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.array): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            float: The output of this objective for the input x.
+
+        """
+        return np.sum((x[self.o - 1 : self.n] - self.offset) ** 2)
+
+
+class __g3__(__dtlz_base__):
+    """Class defining 3 of 4 kernel functions used in the DTLZ problem suite.
+
+    g3 = |x_o - offset|^.1 + ... + |x_n - offset|^.1
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the g3 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.0):
+        """Constructor for g3, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.array): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            float: The output of this objective for the input x.
+
+        """
+        return np.sum(np.abs(x[self.o - 1 : self.n] - self.offset) ** 0.1)
+
+
+class __g4__(__dtlz_base__):
+    """Class defining 4 of 4 kernel functions used in the DTLZ problem suite.
+
+    g4 = 1 + (9 * (|x_o - offset| + ... + |x_n - offset|) / (n + 1 - o))
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the g4 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.0):
+        """Constructor for g4, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.array): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            float: The output of this objective for the input x.
+
+        """
+        return (
+            9 * np.sum(np.abs(x[self.o - 1 : self.n] - self.offset)) / float(self.n + 1 - self.o)
+        ) + 1.0
+
+
+class dtlz1(__dtlz_base__):
+    """Class defining the DTLZ1 problem with offset minimizer.
+
+    DTLZ1 has a linear Pareto front, with all nondominated points
+    on the hyperplane F_1 + F_2 + ... + F_o = 0.5.
+    DTLZ1 has 11^k - 1 "local" Pareto fronts where k = n - o + 1, and
+    1 "global" Pareto front.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ1 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g1__(self.n, self.o, self.offset)
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = (1.0 + ker(x)) / 2.0
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= x[j]
+            if i > 0:
+                fx[i] *= 1.0 - x[self.o - 1 - i]
+        return fx
+
+
+class dtlz2(__dtlz_base__):
+    """Class defining the DTLZ2 problem with offset minimizer.
+
+    DTLZ2 has a concave Pareto front, given by the unit sphere in
+    objective space, restricted to the positive orthant.
+    DTLZ2 has no "local" Pareto fronts, besides the true Pareto front.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ2 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g2__(self.n, self.o, self.offset)
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + ker(x)
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * x[j] / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * x[self.o - 1 - i] / 2)
+        return fx
+
+
+class dtlz3(__dtlz_base__):
+    """Class defining the DTLZ3 problem with offset minimizer.
+
+    DTLZ3 has a concave Pareto front, given by the unit sphere in
+    objective space, restricted to the positive orthant.
+    DTLZ3 has 3^k - 1 "local" Pareto fronts where k = n - o + 1, and
+    1 "global" Pareto front.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ3 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g1__(self.n, self.o, self.offset)
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + ker(x)
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * x[j] / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * x[self.o - 1 - i] / 2)
+        return fx
+
+
+class dtlz4(__dtlz_base__):
+    """Class defining the DTLZ4 problem with offset minimizer.
+
+    DTLZ4 has a concave Pareto front, given by the unit sphere in
+    objective space, restricted to the positive orthant.
+    DTLZ4 has no "local" Pareto fronts, besides the true Pareto front,
+    but by tuning the optional parameter alpha, one can adjust the
+    solution density, making it harder for MOO algorithms to produce
+    a uniform distribution of solutions.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ4 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.5, alpha=100.0):
+        """Constructor for DTLZ7, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+            alpha (optional, float or int): The uniformity parameter used for
+                controlling the uniformity of the distribution of solutions
+                across the Pareto front. Must be greater than or equal to 1.
+                A value of 1 results in DTLZ2. Default value is 100.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        self.alpha = alpha
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g2__(self.n, self.o, self.offset)
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + ker(x)
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * x[j] ** self.alpha / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * x[self.o - 1 - i] ** self.alpha / 2)
+        return fx
+
+
+class dtlz5(__dtlz_base__):
+    """Class defining the DTLZ5 problem with offset minimizer.
+
+    DTLZ5 has a lower-dimensional Pareto front embedded in the objective
+    space, given by an arc of the unit sphere in the positive orthant.
+    DTLZ5 has no "local" Pareto fronts, besides the true Pareto front.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ5 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g2__(self.n, self.o, self.offset)
+        # Calculate theta values
+        theta = np.zeros(self.o)
+        g2x = ker(x)
+        theta[0] = x[0]
+        for i in range(1, self.o):
+            theta[i] = (1 + 2 * g2x * x[i]) / (2 * (1 + g2x))
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + g2x
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * theta[j] / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * theta[self.o - 1 - i] / 2)
+        return fx
+
+
+class dtlz6(__dtlz_base__):
+    """Class defining the DTLZ6 problem with offset minimizer.
+
+    DTLZ6 has a lower-dimensional Pareto front embedded in the objective
+    space, given by an arc of the unit sphere in the positive orthant.
+    DTLZ6 has no "local" Pareto fronts, but tends to show very little
+    improvement until the algorithm is very close to its solution set.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ6 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.0):
+        """Constructor for DTLZ6, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g3__(self.n, self.o, self.offset)
+        # Calculate theta values
+        theta = np.zeros(self.o)
+        g3x = ker(x)
+        theta[0] = x[0]
+        for i in range(1, self.o):
+            theta[i] = (1 + 2 * g3x * x[i]) / (2 * (1 + g3x))
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + g3x
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * theta[j] / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * theta[self.o - 1 - i] / 2)
+        return fx
+
+
+class dtlz7(__dtlz_base__):
+    """Class defining the DTLZ7 problem with offset minimizer.
+
+    DTLZ7 has a discontinuous Pareto front, with solutions on the
+    2^(o-1) discontinuous nondominated regions of the surface:
+
+    F_m = o - F_1 (1 + sin(3pi F_1)) - ... - F_{o-1} (1 + sin3pi F_{o-1}).
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ7 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.0):
+        """Constructor for DTLZ7, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g4__(self.n, self.o, self.offset)
+        # Initialize first o-1 entries in the output array
+        fx = np.zeros(self.o)
+        fx[: self.o - 1] = x[: self.o - 1]
+        # Calculate kernel functions
+        gx = 1.0 + ker(x)
+        hx = -np.sum(x[: self.o - 1] * (1.0 + np.sin(3.0 * np.pi * x[: self.o - 1])) / gx) + float(
+            self.o
+        )
+        # Calculate the last entry in the output array
+        fx[self.o - 1] = gx * hx
+        return fx
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/Makefile b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/Makefile
new file mode 100644
index 0000000..29e6a6b
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/Makefile
@@ -0,0 +1,11 @@
+
+VERSION=0.5.1
+
+build:
+	mkdir -p build
+	wget https://github.com/ECP-CANDLE/Benchmarks/archive/refs/tags/v$(VERSION).tar.gz -O build/ecp-candle-benchmarks.tar.gz
+	tar -xzvf build/ecp-candle-benchmarks.tar.gz -C build
+	mv build/Benchmarks-$(VERSION) build/Benchmarks
+
+clean:
+	rm -rf build/
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/README.md b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/README.md
new file mode 100644
index 0000000..ba8f255
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/README.md
@@ -0,0 +1,54 @@
+
+# ECP-Candle Benchmark - Pilot1/Combo
+
+This benchmark is based on materials from the [ECP-Candle/Benchmarks](https://github.com/ECP-CANDLE/Benchmarks) repository. More precisely it is a simplified replicate of the [Pilot1/Combo](https://github.com/ECP-CANDLE/Benchmarks/tree/master/Pilot1/Combo) benchmark.
+
+
+## Installation
+
+To use the benchmark follow this example set of instructions:
+
+```python
+from deephyper_benchmark import *
+
+install("ECP-Candle/Pilot1/Combo")
+
+load("ECP-Candle/Pilot1/Combo")
+
+from deephyper_benchmark.lib.ecp_candle.pilot1.combo import hpo
+```
+
+## Configuration
+
+Different parameters can be set to configure this benchmark.
+
+- Environment variable `DEEPHYPER_BENCHMARK_MAX_EPOCHS` sets the maximum number of training epochs. Defaults to `50`.
+- Environment variable `DEEPHYPER_BENCHMARK_TIMEOUT` sets the maximum duration of training in secondes. Defaults to `1800` (i.e., 30 minutes).
+- Environment variable `DEEPHYPER_BENCHMARK_MOO` with value `0` or `1` to select if the task should be run with single or multiple objectives. **Defaults to `0` for single-objective**. The objectives are `valid_r2`, `-num_parameters_train`, `-duration_batch_inference`.
+
+## Metadata
+
+The current set of returned metadata is:
+
+- [x] `num_parameters`
+- [x] `num_parameters_train`
+- [x] `duration_train`: time in secondes taken to train the model (`model.fit(...)`).
+- [x] `duration_batch_inference`: time in secondes taken to predict one batch.
+- [x] `budget`
+- [x] `stopped`
+- [x] `train_mse`
+- [x] `train_mae`
+- [x] `train_r2`
+- [x] `train_corr`
+- [x] `valid_mse`
+- [x] `valid_mae`
+- [x] `valid_r2`
+- [x] `valid_corr`
+- [x] `test_mse`
+- [x] `test_mae`
+- [x] `test_r2`
+- [x] `test_corr`
+- [ ] `flops`
+- [ ] `latency`
+- [x] `lc_train_mse`
+- [x] `lc_valid_mse`
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/__init__.py b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/__init__.py
new file mode 100644
index 0000000..7dd6fda
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/__init__.py
@@ -0,0 +1 @@
+__version__ = "0.5.1"
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/benchmark.py b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/benchmark.py
new file mode 100644
index 0000000..4f7fb4d
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/benchmark.py
@@ -0,0 +1,28 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class ECPCandlePilot1Combo(Benchmark):
+    version = "0.5.1"
+
+    requires = {
+        "makefile": {"step": "install", "type": "cmd", "cmd": "make build"},
+        "py-pip-requirements": {
+            "step": "install",
+            "type": "pip",
+            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
+        },
+        "pkg-candle": {
+            "step": "load",
+            "type": "pythonpath",
+            "path": f"{DIR}/build/Benchmarks/common",
+        },
+        "pkg-combo": {
+            "step": "load",
+            "type": "pythonpath",
+            "path": f"{DIR}/build/Benchmarks/Pilot1/Combo",
+        },
+    }
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/combo_default_model.yaml b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/combo_default_model.yaml
new file mode 100644
index 0000000..47e4ce1
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/combo_default_model.yaml
@@ -0,0 +1,31 @@
+cell_features: ['expression']
+drug_features: ['descriptors']
+dense: [1000, 1000, 1000]
+dense_feature_layers: [1000, 1000, 1000]
+activation: 'relu'
+loss: 'mse'
+optimizer: 'adam'
+scaling: 'std'
+dropout: 0
+epochs: 1
+batch_size: 32
+valid_split: 0.2
+test_split: 0.2
+max_val_loss: 1.0
+learning_rate: 0.001
+base_lr: 0.001
+residual: False
+reduce_lr: False
+reduce_lr_factor: 0.5
+reduce_lr_patience: 5
+warmup_lr: False
+early_stopping: False
+early_stopping_patience: 5
+batch_normalization: False
+feature_subsample: 0
+rng_seed: 2017
+save_path: 'save/combo'
+gen: False
+use_combo_score: False
+verbose : True
+timeout: 1800
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/hpo.py b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/hpo.py
new file mode 100644
index 0000000..429607d
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/hpo.py
@@ -0,0 +1,160 @@
+import copy
+import os
+import traceback
+
+from deephyper.evaluator import profile
+from deephyper.hpo import HpProblem
+from deephyper.stopper.integration.tensorflow import TFKerasStopperCallback
+
+from .model import run_pipeline
+
+
+DEEPHYPER_BENCHMARK_MAX_EPOCHS = int(
+    os.environ.get("DEEPHYPER_BENCHMARK_MAX_EPOCHS", 50)
+)
+DEEPHYPER_BENCHMARK_TIMEOUT = int(os.environ.get("DEEPHYPER_BENCHMARK_TIMEOUT", 1800))
+DEEPHYPER_BENCHMARK_MOO = bool(int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 0)))
+
+
+problem = HpProblem()
+
+# Model hyperparameters
+ACTIVATIONS = [
+    "elu",
+    "gelu",
+    "hard_sigmoid",
+    "linear",
+    "relu",
+    "selu",
+    "sigmoid",
+    "softplus",
+    "softsign",
+    "swish",
+    "tanh",
+]
+default_dense = [1000, 1000, 1000]
+default_dense_feature_layers = [1000, 1000, 1000]
+
+for i in range(len(default_dense)):
+    problem.add_hyperparameter(
+        (10, 1024, "log-uniform"),
+        f"dense_{i}",
+        default_value=default_dense[i],
+    )
+
+    problem.add_hyperparameter(
+        (10, 1024, "log-uniform"),
+        f"dense_feature_layers_{i}",
+        default_value=default_dense_feature_layers[i],
+    )
+
+problem.add_hyperparameter(ACTIVATIONS, "activation", default_value="relu")
+
+# Optimization hyperparameters
+problem.add_hyperparameter(
+    [
+        "sgd",
+        "rmsprop",
+        "adagrad",
+        "adadelta",
+        "adam",
+    ],
+    "optimizer",
+    default_value="sgd",
+)
+
+problem.add_hyperparameter((0, 0.5), "dropout", default_value=0.0)
+problem.add_hyperparameter((8, 512, "log-uniform"), "batch_size", default_value=32)
+
+problem.add_hyperparameter(
+    (1e-5, 1e-2, "log-uniform"), "learning_rate", default_value=0.001
+)
+problem.add_hyperparameter((1e-5, 1e-2, "log-uniform"), "base_lr", default_value=0.001)
+problem.add_hyperparameter([True, False], "residual", default_value=False)
+
+problem.add_hyperparameter([True, False], "early_stopping", default_value=False)
+problem.add_hyperparameter((5, 20), "early_stopping_patience", default_value=5)
+
+problem.add_hyperparameter([True, False], "reduce_lr", default_value=False)
+problem.add_hyperparameter((0.1, 1.0), "reduce_lr_factor", default_value=0.5)
+problem.add_hyperparameter((5, 20), "reduce_lr_patience", default_value=5)
+
+problem.add_hyperparameter([True, False], "warmup_lr", default_value=False)
+problem.add_hyperparameter([True, False], "batch_normalization", default_value=False)
+
+problem.add_hyperparameter(
+    ["mse", "mae", "logcosh", "mape", "msle", "huber"], "loss", default_value="mse"
+)
+
+problem.add_hyperparameter(["std", "minmax", "maxabs"], "scaling", default_value="std")
+
+
+def remap_hyperparameters(config: dict):
+    """Transform input configurations of hyperparameters to the format accepted by the candle benchmark."""
+    dense = []
+    dense_feature_layers = []
+    for i in range(len(default_dense)):
+        key = f"dense_{i}"
+        dense.append(config.pop(key))
+
+        key = f"dense_feature_layers_{i}"
+        dense_feature_layers.append(config.pop(key))
+
+    config["dense"] = dense
+    config["dense_feature_layers"] = dense_feature_layers
+
+
+@profile
+def run(job, optuna_trial=None):
+    config = copy.deepcopy(job.parameters)
+
+    params = {
+        "epochs": DEEPHYPER_BENCHMARK_MAX_EPOCHS,
+        "timeout": DEEPHYPER_BENCHMARK_TIMEOUT,
+        "verbose": False,
+    }
+    if len(config) > 0:
+        remap_hyperparameters(config)
+        params.update(config)
+
+    if optuna_trial is None:
+        stopper_callback = TFKerasStopperCallback(job, monitor="val_r2", mode="max")
+    else:
+        from deephyper_benchmark.integration.optuna import KerasPruningCallback
+
+        stopper_callback = KerasPruningCallback(optuna_trial, "val_r2")
+
+    try:
+        score = run_pipeline(params, mode="valid", stopper_callback=stopper_callback)
+    except Exception as e:
+        print(traceback.format_exc())
+        score = {"objective": "F"}
+        keys = "m:num_parameters,m:num_parameters_train,m:duration_train,m:duration_batch_inference,m:budget,m:stopped,m:train_mse,m:train_mae,m:train_r2,m:train_corr,m:valid_mse,m:valid_mae,m:valid_r2,m:valid_corr,m:test_mse,m:test_mae,m:test_r2,m:test_corr,m:lc_train_mse,m:lc_valid_mse,m:lc_train_mae,m:lc_valid_mae,m:lc_train_r2,m:lc_valid_r2"
+        metadata = {k.strip("m:"): None for k in keys.split(",")}
+        score["metadata"] = metadata
+
+    # Handle multi-objective optimization (all maximized)
+    if DEEPHYPER_BENCHMARK_MOO:
+        if score["objective"] == "F":
+            score["objective"] = ["F", "F", "F"]
+        else:
+            score["objective"] = [
+                score["objective"],
+                -score["metadata"]["num_parameters_train"],
+                -score["metadata"]["duration_batch_inference"],
+            ]
+
+    return score
+
+
+def evaluate(config):
+    """Evaluate an hyperparameter configuration on training/validation and testing data."""
+
+    params = {
+        "epochs": 100,
+        "timeout": 60 * 60,
+        "verbose": True,
+    }  # 60 minutes per model
+    remap_hyperparameters(config)
+    params.update(config)
+    run_pipeline(params, mode="test")
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/model.py b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/model.py
new file mode 100644
index 0000000..5234495
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/model.py
@@ -0,0 +1,782 @@
+from __future__ import division, print_function
+
+import collections
+import logging
+import os
+import time
+import warnings
+
+import yaml
+
+try:
+    from yaml import CLoader as Loader
+except ImportError:
+    from yaml import Loader
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+DEFAULT_CONFIG = os.path.join(HERE, "combo_default_model.yaml")
+
+#! must be placed before "import candle"
+from tensorflow import keras
+from tensorflow.keras import backend as K
+from tensorflow.keras import optimizers
+from tensorflow.keras.callbacks import (
+    Callback,
+    EarlyStopping,
+    LearningRateScheduler,
+    ReduceLROnPlateau,
+)
+from tensorflow.keras.layers import Dense, Dropout, Input
+from tensorflow.keras.models import Model
+from tensorflow.keras.utils import get_custom_objects
+
+import candle
+import combo
+import NCI60
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from scipy.stats.stats import pearsonr
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
+from deephyper_benchmark.integration.keras import count_params
+from deephyper_benchmark.utils.json_utils import array_to_json
+
+
+logger = logging.getLogger(__name__)
+
+np.set_printoptions(precision=4)
+tf.compat.v1.disable_eager_execution()
+
+
+def verify_path(path):
+    folder = os.path.dirname(path)
+    if folder and not os.path.exists(folder):
+        os.makedirs(folder)
+
+
+def extension_from_parameters(args):
+    """Construct string for saving model with annotation of parameters"""
+    ext = ""
+    ext += ".A={}".format(args.activation)
+    ext += ".B={}".format(args.batch_size)
+    ext += ".E={}".format(args.epochs)
+    ext += ".O={}".format(args.optimizer)
+    # ext += '.LEN={}'.format(args.maxlen)
+    ext += ".LR={}".format(args.learning_rate)
+    ext += ".CF={}".format("".join([x[0] for x in sorted(args.cell_features)]))
+    ext += ".DF={}".format("".join([x[0] for x in sorted(args.drug_features)]))
+    if args.feature_subsample > 0:
+        ext += ".FS={}".format(args.feature_subsample)
+    if args.dropout > 0:
+        ext += ".DR={}".format(args.dropout)
+    if args.warmup_lr:
+        ext += ".wu_lr"
+    if args.reduce_lr:
+        ext += ".re_lr"
+    if args.residual:
+        ext += ".res"
+    if args.use_landmark_genes:
+        ext += ".L1000"
+    if args.gen:
+        ext += ".gen"
+    if args.use_combo_score:
+        ext += ".scr"
+    if args.use_mean_growth:
+        ext += ".mg"
+    for i, n in enumerate(args.dense):
+        if n > 0:
+            ext += ".D{}={}".format(i + 1, n)
+    if args.dense_feature_layers != args.dense:
+        for i, n in enumerate(args.dense):
+            if n > 0:
+                ext += ".FD{}={}".format(i + 1, n)
+
+    return ext
+
+
+def discretize(y, bins=5):
+    percentiles = [100 / bins * (i + 1) for i in range(bins - 1)]
+    thresholds = [np.percentile(y, x) for x in percentiles]
+    classes = np.digitize(y, thresholds)
+    return classes
+
+
+class ComboDataLoader(object):
+    """Load merged drug response, drug descriptors and cell line essay data"""
+
+    def __init__(
+        self,
+        seed,
+        valid_split=0.2,
+        test_split=0.2,
+        shuffle=True,
+        cell_features=["expression"],
+        drug_features=["descriptors"],
+        response_url=None,
+        use_landmark_genes=False,
+        use_combo_score=False,
+        use_mean_growth=False,
+        preprocess_rnaseq=None,
+        exclude_cells=[],
+        exclude_drugs=[],
+        feature_subsample=None,
+        scaling="std",
+        scramble=False,
+    ):
+        """Initialize data merging drug response, drug descriptors and cell line essay.
+           Shuffle and split training and validation set
+
+        Parameters
+        ----------
+        seed: integer
+            seed for random generation
+        val_split : float, optional (default 0.2)
+            fraction of data to use in validation
+        cell_features: list of strings from 'expression', 'expression_5platform', 'mirna', 'proteome', 'all', 'categorical' (default ['expression'])
+            use one or more cell line feature sets: gene expression, microRNA, proteome
+            use 'all' for ['expression', 'mirna', 'proteome']
+            use 'categorical' for one-hot encoded cell lines
+        drug_features: list of strings from 'descriptors', 'latent', 'all', 'categorical', 'noise' (default ['descriptors'])
+            use dragon7 descriptors, latent representations from Aspuru-Guzik's SMILES autoencoder
+            trained on NSC drugs, or both; use random features if set to noise
+            use 'categorical' for one-hot encoded drugs
+        shuffle : True or False, optional (default True)
+            if True shuffles the merged data before splitting training and validation sets
+        scramble: True or False, optional (default False)
+            if True randomly shuffle dose response data as a control
+        feature_subsample: None or integer (default None)
+            number of feature columns to use from cellline expressions and drug descriptors
+        use_landmark_genes: True or False
+            only use LINCS1000 landmark genes
+        use_combo_score: bool (default False)
+            use combination score in place of percent growth (stored in 'GROWTH' column)
+        use_mean_growth: bool (default False)
+            use mean aggregation instead of min on percent growth
+        scaling: None, 'std', 'minmax' or 'maxabs' (default 'std')
+            type of feature scaling: 'maxabs' to [-1,1], 'maxabs' to [-1, 1], 'std' for standard normalization
+        """
+
+        self._random_state = np.random.RandomState(seed)
+
+        df = NCI60.load_combo_response(
+            response_url=response_url,
+            use_combo_score=use_combo_score,
+            use_mean_growth=use_mean_growth,
+            fraction=True,
+            exclude_cells=exclude_cells,
+            exclude_drugs=exclude_drugs,
+        )
+        logger.info("Loaded {} unique (CL, D1, D2) response sets.".format(df.shape[0]))
+
+        if "all" in cell_features:
+            self.cell_features = ["expression", "mirna", "proteome"]
+        else:
+            self.cell_features = cell_features
+
+        if "all" in drug_features:
+            self.drug_features = ["descriptors", "latent"]
+        else:
+            self.drug_features = drug_features
+
+        for fea in self.cell_features:
+            if fea == "expression" or fea == "rnaseq":
+                self.df_cell_expr = NCI60.load_cell_expression_rnaseq(
+                    ncols=feature_subsample,
+                    scaling=scaling,
+                    use_landmark_genes=use_landmark_genes,
+                    preprocess_rnaseq=preprocess_rnaseq,
+                )
+                df = df.merge(self.df_cell_expr[["CELLNAME"]], on="CELLNAME")
+            elif fea == "expression_u133p2":
+                self.df_cell_expr = NCI60.load_cell_expression_u133p2(
+                    ncols=feature_subsample,
+                    scaling=scaling,
+                    use_landmark_genes=use_landmark_genes,
+                )
+                df = df.merge(self.df_cell_expr[["CELLNAME"]], on="CELLNAME")
+            elif fea == "expression_5platform":
+                self.df_cell_expr = NCI60.load_cell_expression_5platform(
+                    ncols=feature_subsample,
+                    scaling=scaling,
+                    use_landmark_genes=use_landmark_genes,
+                )
+                df = df.merge(self.df_cell_expr[["CELLNAME"]], on="CELLNAME")
+            elif fea == "mirna":
+                self.df_cell_mirna = NCI60.load_cell_mirna(
+                    ncols=feature_subsample, scaling=scaling
+                )
+                df = df.merge(self.df_cell_mirna[["CELLNAME"]], on="CELLNAME")
+            elif fea == "proteome":
+                self.df_cell_prot = NCI60.load_cell_proteome(
+                    ncols=feature_subsample, scaling=scaling
+                )
+                df = df.merge(self.df_cell_prot[["CELLNAME"]], on="CELLNAME")
+            elif fea == "categorical":
+                df_cell_ids = df[["CELLNAME"]].drop_duplicates()
+                cell_ids = df_cell_ids["CELLNAME"].map(lambda x: x.replace(":", "."))
+                df_cell_cat = pd.get_dummies(cell_ids)
+                df_cell_cat.index = df_cell_ids["CELLNAME"]
+                self.df_cell_cat = df_cell_cat.reset_index()
+
+        for fea in self.drug_features:
+            if fea == "descriptors":
+                self.df_drug_desc = NCI60.load_drug_descriptors(
+                    ncols=feature_subsample, scaling=scaling
+                )
+                df = df[
+                    df["NSC1"].isin(self.df_drug_desc["NSC"])
+                    & df["NSC2"].isin(self.df_drug_desc["NSC"])
+                ]
+            elif fea == "latent":
+                self.df_drug_auen = NCI60.load_drug_autoencoded_AG(
+                    ncols=feature_subsample, scaling=scaling
+                )
+                df = df[
+                    df["NSC1"].isin(self.df_drug_auen["NSC"])
+                    & df["NSC2"].isin(self.df_drug_auen["NSC"])
+                ]
+            elif fea == "categorical":
+                df_drug_ids = df[["NSC1"]].drop_duplicates()
+                df_drug_ids.columns = ["NSC"]
+                drug_ids = df_drug_ids["NSC"]
+                df_drug_cat = pd.get_dummies(drug_ids)
+                df_drug_cat.index = df_drug_ids["NSC"]
+                self.df_drug_cat = df_drug_cat.reset_index()
+            elif fea == "noise":
+                ids1 = df[["NSC1"]].drop_duplicates().rename(columns={"NSC1": "NSC"})
+                ids2 = df[["NSC2"]].drop_duplicates().rename(columns={"NSC2": "NSC"})
+                df_drug_ids = pd.concat([ids1, ids2]).drop_duplicates()
+                noise = np.random.normal(size=(df_drug_ids.shape[0], 500))
+                df_rand = pd.DataFrame(
+                    noise,
+                    index=df_drug_ids["NSC"],
+                    columns=["RAND-{:03d}".format(x) for x in range(500)],
+                )
+                self.df_drug_rand = df_rand.reset_index()
+
+        logger.info(
+            "Filtered down to {} rows with matching information.".format(df.shape[0])
+        )
+
+        ids1 = df[["NSC1"]].drop_duplicates().rename(columns={"NSC1": "NSC"})
+        ids2 = df[["NSC2"]].drop_duplicates().rename(columns={"NSC2": "NSC"})
+        df_drug_ids = pd.concat([ids1, ids2]).drop_duplicates().reset_index(drop=True)
+
+        n_drugs = df_drug_ids.shape[0]
+        n_valid_drugs = int(n_drugs * valid_split)
+        n_test_drugs = int(n_drugs * test_split)
+        n_train_drugs = n_drugs - n_valid_drugs - n_test_drugs
+
+        logger.info("Unique cell lines: {}".format(df["CELLNAME"].nunique()))
+        logger.info("Unique drugs: {}".format(n_drugs))
+
+        if shuffle:
+            df = df.sample(frac=1.0, random_state=seed).reset_index(drop=True)
+            df_drug_ids = df_drug_ids.sample(frac=1.0, random_state=seed).reset_index(
+                drop=True
+            )
+
+        self.df_response = df
+        self.df_drug_ids = df_drug_ids
+
+        self.train_drug_ids = df_drug_ids["NSC"][:n_train_drugs]
+        self.valid_drug_ids = df_drug_ids["NSC"][
+            n_train_drugs : n_train_drugs + n_valid_drugs
+        ]
+        self.test_drug_ids = df_drug_ids["NSC"][
+            n_train_drugs + n_valid_drugs : n_train_drugs + n_valid_drugs + n_test_drugs
+        ]
+
+        if scramble:
+            growth = df[["GROWTH"]]
+            random_growth = growth.iloc[
+                self._random_state.permutation(np.arange(growth.shape[0]))
+            ].reset_index()
+            self.df_response[["GROWTH"]] = random_growth["GROWTH"]
+            logger.warn("Randomly shuffled dose response growth values.")
+
+        logger.info("Distribution of dose response:")
+        logger.info(self.df_response[["GROWTH"]].describe())
+
+        self.total = df.shape[0]
+        self.n_valid = int(self.total * valid_split)
+        self.n_test = int(self.total * test_split)
+        self.n_train = self.total - self.n_valid - self.n_test
+        logger.info(
+            "Rows in train: {}, valid: {}, test: {}".format(
+                self.n_train, self.n_valid, self.n_test
+            )
+        )
+
+        self.cell_df_dict = {
+            "expression": "df_cell_expr",
+            "expression_5platform": "df_cell_expr",
+            "expression_u133p2": "df_cell_expr",
+            "rnaseq": "df_cell_expr",
+            "mirna": "df_cell_mirna",
+            "proteome": "df_cell_prot",
+            "categorical": "df_cell_cat",
+        }
+
+        self.drug_df_dict = {
+            "descriptors": "df_drug_desc",
+            "latent": "df_drug_auen",
+            "categorical": "df_drug_cat",
+            "noise": "df_drug_rand",
+        }
+
+        self.input_features = collections.OrderedDict()
+        self.feature_shapes = {}
+        for fea in self.cell_features:
+            feature_type = "cell." + fea
+            feature_name = "cell." + fea
+            df_cell = getattr(self, self.cell_df_dict[fea])
+            self.input_features[feature_name] = feature_type
+            self.feature_shapes[feature_type] = (df_cell.shape[1] - 1,)
+
+        for drug in ["drug1", "drug2"]:
+            for fea in self.drug_features:
+                feature_type = "drug." + fea
+                feature_name = drug + "." + fea
+                df_drug = getattr(self, self.drug_df_dict[fea])
+                self.input_features[feature_name] = feature_type
+                self.feature_shapes[feature_type] = (df_drug.shape[1] - 1,)
+
+        logger.info("Input features shapes:")
+        for k, v in self.input_features.items():
+            logger.info("  {}: {}".format(k, self.feature_shapes[v]))
+
+        self.input_dim = sum(
+            [np.prod(self.feature_shapes[x]) for x in self.input_features.values()]
+        )
+        logger.info("Total input dimensions: {}".format(self.input_dim))
+
+    def load_data_all(self, switch_drugs=False):
+        df_all = self.df_response
+        y_all = df_all["GROWTH"].values
+        x_all_list = []
+
+        for fea in self.cell_features:
+            df_cell = getattr(self, self.cell_df_dict[fea])
+            df_x_all = pd.merge(
+                df_all[["CELLNAME"]], df_cell, on="CELLNAME", how="left"
+            )
+            x_all_list.append(df_x_all.drop(["CELLNAME"], axis=1).values)
+
+        drugs = ["NSC1", "NSC2"]
+        if switch_drugs:
+            drugs = ["NSC2", "NSC1"]
+
+        for drug in drugs:
+            for fea in self.drug_features:
+                df_drug = getattr(self, self.drug_df_dict[fea])
+                df_x_all = pd.merge(
+                    df_all[[drug]], df_drug, left_on=drug, right_on="NSC", how="left"
+                )
+                x_all_list.append(df_x_all.drop([drug, "NSC"], axis=1).values)
+
+        return x_all_list, y_all, df_all
+
+    def load_data_by_index(self, train_index, valid_index, test_index):
+        x_all_list, y_all, df_all = self.load_data_all()
+
+        x_train_list = [x[train_index] for x in x_all_list]
+        x_valid_list = [x[valid_index] for x in x_all_list]
+        x_test_list = [x[test_index] for x in x_all_list]
+
+        y_train = y_all[train_index]
+        y_valid = y_all[valid_index]
+        y_test = y_all[test_index]
+
+        df_train = df_all.iloc[train_index, :]
+        df_valid = df_all.iloc[valid_index, :]
+        df_test = df_all.iloc[test_index, :]
+
+        return (
+            x_train_list,
+            y_train,
+            x_valid_list,
+            y_valid,
+            x_test_list,
+            y_test,
+            df_train,
+            df_valid,
+            df_test,
+        )
+
+    def load_data(self):
+        train_index = range(self.n_train)
+        valid_index = range(self.n_train, self.n_train + self.n_valid)
+        test_index = range(
+            self.n_train + self.n_valid, self.n_train + self.n_valid + self.n_test
+        )
+        return self.load_data_by_index(train_index, valid_index, test_index)
+
+
+def test_loader(loader):
+    (
+        x_train_list,
+        y_train,
+        x_val_list,
+        y_val,
+        x_test_list,
+        y_test,
+        _,
+        _,
+        _,
+    ) = loader.load_data()
+    print("x_train shapes:")
+    for x in x_train_list:
+        print(x.shape)
+    print("y_train shape:", y_train.shape)
+
+    print("x_valid shapes:")
+    for x in x_val_list:
+        print(x.shape)
+    print("y_valid shape:", y_val.shape)
+
+    print("x_test shapes:")
+    for x in x_test_list:
+        print(x.shape)
+    print("y_test shape:", y_test.shape)
+
+
+def r2(y_true, y_pred):
+    SS_res = K.sum(K.square(y_true - y_pred))
+    SS_tot = K.sum(K.square(y_true - K.mean(y_true)))
+    return 1 - SS_res / (SS_tot + K.epsilon())
+
+
+def mae(y_true, y_pred):
+    return keras.metrics.mean_absolute_error(y_true, y_pred)
+
+
+def mse(y_true, y_pred):
+    return keras.metrics.mean_squared_error(y_true, y_pred)
+
+
+def evaluate_prediction(y_true, y_pred):
+    mse = mean_squared_error(y_true, y_pred)
+    mae = mean_absolute_error(y_true, y_pred)
+    r2 = r2_score(y_true, y_pred)
+    corr, _ = pearsonr(y_true, y_pred)
+    return {"mse": mse, "mae": mae, "r2": r2, "corr": corr}
+
+
+def log_evaluation(metric_outputs, description="Comparing y_true and y_pred:"):
+    logger.info(description)
+    for metric, value in metric_outputs.items():
+        logger.info("  {}: {:.4f}".format(metric, value))
+
+
+class LoggingCallback(Callback):
+    def __init__(self, print_fcn=print):
+        Callback.__init__(self)
+        self.print_fcn = print_fcn
+
+    def on_epoch_end(self, epoch, logs={}):
+        msg = "[Epoch: %i] %s" % (
+            epoch,
+            ", ".join("%s: %f" % (k, v) for k, v in sorted(logs.items())),
+        )
+        self.print_fcn(msg)
+
+
+class PermanentDropout(Dropout):
+    def __init__(self, rate, **kwargs):
+        super(PermanentDropout, self).__init__(rate, **kwargs)
+        self.uses_learning_phase = False
+
+    def call(self, x, mask=None):
+        if 0.0 < self.rate < 1.0:
+            noise_shape = self._get_noise_shape(x)
+            x = K.dropout(x, self.rate, noise_shape)
+        return x
+
+
+class ModelRecorder(Callback):
+    def __init__(self, save_all_models=False):
+        Callback.__init__(self)
+        self.save_all_models = save_all_models
+        get_custom_objects()["PermanentDropout"] = PermanentDropout
+
+    def on_train_begin(self, logs={}):
+        self.val_losses = []
+        self.best_val_loss = np.Inf
+        self.best_model = None
+
+    def on_epoch_end(self, epoch, logs={}):
+        val_loss = logs.get("val_loss")
+        self.val_losses.append(val_loss)
+        if val_loss < self.best_val_loss:
+            self.best_model = keras.models.clone_model(self.model)
+            self.best_val_loss = val_loss
+
+
+def build_feature_model(
+    input_shape,
+    name="",
+    dense_layers=[1000, 1000],
+    activation="relu",
+    residual=False,
+    dropout_rate=0,
+    permanent_dropout=True,
+):
+    x_input = Input(shape=input_shape)
+    h = x_input
+    for i, layer in enumerate(dense_layers):
+        x = h
+        h = Dense(layer, activation=activation)(h)
+        if dropout_rate > 0:
+            if permanent_dropout:
+                h = PermanentDropout(dropout_rate)(h)
+            else:
+                h = Dropout(dropout_rate)(h)
+        if residual:
+            try:
+                h = keras.layers.add([h, x])
+            except ValueError:
+                pass
+    model = Model(x_input, h, name=name)
+    return model
+
+
+def build_model(loader, args, verbose=False):
+    input_models = {}
+    dropout_rate = args.dropout
+    permanent_dropout = True
+    for fea_type, shape in loader.feature_shapes.items():
+        box = build_feature_model(
+            input_shape=shape,
+            name=fea_type,
+            dense_layers=args.dense_feature_layers,
+            dropout_rate=dropout_rate,
+            permanent_dropout=permanent_dropout,
+        )
+        if verbose:
+            box.summary()
+        input_models[fea_type] = box
+
+    inputs = []
+    encoded_inputs = []
+    for fea_name, fea_type in loader.input_features.items():
+        shape = loader.feature_shapes[fea_type]
+        fea_input = Input(shape, name="input." + fea_name)
+        inputs.append(fea_input)
+        input_model = input_models[fea_type]
+        encoded = input_model(fea_input)
+        encoded_inputs.append(encoded)
+
+    merged = keras.layers.concatenate(encoded_inputs)
+
+    h = merged
+    for i, layer in enumerate(args.dense):
+        x = h
+        h = Dense(layer, activation=args.activation)(h)
+        if dropout_rate > 0:
+            if permanent_dropout:
+                h = PermanentDropout(dropout_rate)(h)
+            else:
+                h = Dropout(dropout_rate)(h)
+        if args.residual:
+            try:
+                h = keras.layers.add([h, x])
+            except ValueError:
+                pass
+    output = Dense(1)(h)
+
+    return Model(inputs, output)
+
+
+def initialize_parameters(default_model="combo_default_model.txt"):
+    # Build benchmark object
+    comboBmk = combo.BenchmarkCombo(
+        combo.file_path,
+        default_model,
+        "keras",
+        prog="combo_baseline",
+        desc="Build neural network based models to predict tumor response to drug pairs.",
+    )
+
+    # Initialize parameters
+    gParameters = candle.finalize_parameters(comboBmk)
+
+    return gParameters
+
+
+def yaml_load(path):
+    with open(path, "r") as f:
+        yaml_data = yaml.load(f, Loader=Loader)
+    return yaml_data
+
+
+def run_pipeline(config: dict = None, mode="valid", stopper_callback=None):
+    # Default Config from original Benchmark
+    params = initialize_parameters()
+
+    # Default Config from our Benchmark
+    params.update(yaml_load(DEFAULT_CONFIG))
+    params.pop("val_split")
+
+    # Ingest input configuration
+    if config:
+        params.update(config)
+
+    args = candle.ArgumentStruct(**params)
+    seed = args.rng_seed
+    candle.set_seed(seed)
+
+    loader = ComboDataLoader(
+        seed=args.rng_seed,
+        valid_split=args.valid_split,
+        test_split=args.test_split,
+        cell_features=args.cell_features,
+        drug_features=args.drug_features,
+        use_mean_growth=args.use_mean_growth,
+        response_url=args.response_url,
+        use_landmark_genes=args.use_landmark_genes,
+        preprocess_rnaseq=args.preprocess_rnaseq,
+        exclude_cells=args.exclude_cells,
+        exclude_drugs=args.exclude_drugs,
+        use_combo_score=args.use_combo_score,
+        scaling=args.scaling,
+    )
+
+    # test_loader(loader)
+
+    model = build_model(loader, args, verbose=args.verbose)
+
+    def warmup_scheduler(epoch):
+        lr = args.learning_rate or base_lr * args.batch_size / 100
+        if epoch <= 5:
+            K.set_value(model.optimizer.lr, (base_lr * (5 - epoch) + lr * epoch) / 5)
+        logger.debug("Epoch {}: lr={}".format(epoch, K.get_value(model.optimizer.lr)))
+        return K.get_value(model.optimizer.lr)
+
+    model = build_model(loader, args)
+
+    optimizer = optimizers.deserialize({"class_name": args.optimizer, "config": {}})
+    base_lr = args.base_lr or K.get_value(optimizer.lr)
+    if args.learning_rate:
+        K.set_value(optimizer.lr, args.learning_rate)
+
+    model.compile(loss=args.loss, optimizer=optimizer, metrics=[mse, mae, r2])
+
+    # calculate trainable and non-trainable params
+    params.update(candle.compute_trainable_params(model))
+
+    early_stopping = EarlyStopping(
+        monitor="val_loss", patience=args.early_stopping_patience
+    )
+
+    timeout_monitor = candle.TerminateOnTimeOut(params["timeout"])
+
+    reduce_lr = ReduceLROnPlateau(
+        monitor="val_loss",
+        factor=args.reduce_lr_factor,
+        patience=args.reduce_lr_patience,
+        min_lr=0.00001,
+    )
+    warmup_lr = LearningRateScheduler(warmup_scheduler)
+
+    callbacks = [timeout_monitor]
+    if args.early_stopping:
+        callbacks.append(early_stopping)
+    if args.reduce_lr:
+        callbacks.append(reduce_lr)
+    if args.warmup_lr:
+        callbacks.append(warmup_lr)
+
+    if stopper_callback:
+        callbacks.append(stopper_callback)
+
+    (
+        x_train_list,
+        y_train,
+        x_valid_list,
+        y_valid,
+        x_test_list,
+        y_test,
+        _,
+        _,
+        _,
+    ) = loader.load_data()
+    training_data = (x_train_list, y_train)
+    validation_data = (x_valid_list, y_valid)
+
+    num_parameters_info = count_params(model)
+
+    # Training
+    timestamp_duration = time.time() 
+    history = model.fit(
+        *training_data,
+        batch_size=args.batch_size,
+        shuffle=args.shuffle,
+        epochs=args.epochs,
+        validation_data=validation_data,
+        callbacks=callbacks,
+        verbose=args.verbose,
+    ).history
+    duration_train = time.time() - timestamp_duration
+
+
+    timestamp_duration = time.time() 
+    y_train_pred = model.predict(x_train_list, batch_size=args.batch_size).flatten()
+    duration_batch_inference = (time.time() - timestamp_duration) / np.ceil(len(y_train_pred) / args.batch_size)
+
+    scores_train = evaluate_prediction(y_train, y_train_pred)
+    y_valid_pred = model.predict(x_valid_list, batch_size=args.batch_size).flatten()
+    scores_valid = evaluate_prediction(y_valid, y_valid_pred)
+    y_test_pred = model.predict(x_test_list, batch_size=args.batch_size).flatten()
+    scores_test = evaluate_prediction(y_test, y_test_pred)
+
+    all_scores = {}
+    all_scores.update({f"train_{k}": float(v) for k, v in scores_train.items()})
+    all_scores.update({f"valid_{k}": float(v) for k, v in scores_valid.items()})
+    all_scores.update({f"test_{k}": float(v) for k, v in scores_test.items()})
+
+    if K.backend() == "tensorflow":
+        K.clear_session()
+
+    objective = all_scores["valid_r2"]  # validation R2
+    objective = max(-1, objective)
+
+    # collect learning curves
+    lc_train_mse = array_to_json(
+        np.asarray([[i + 1, l] for i, l in enumerate(history["mse"])])
+    )
+    lc_valid_mse = array_to_json(
+        np.asarray([[i + 1, l] for i, l in enumerate(history["val_mse"])])
+    )
+
+    lc_train_mae = array_to_json(
+        np.asarray([[i + 1, l] for i, l in enumerate(history["mae"])])
+    )
+    lc_valid_mae = array_to_json(
+        np.asarray([[i + 1, l] for i, l in enumerate(history["val_mae"])])
+    )
+
+    lc_train_r2 = array_to_json(
+        np.asarray([[i + 1, l] for i, l in enumerate(history["r2"])])
+    )
+    lc_valid_r2 = array_to_json(
+        np.asarray([[i + 1, l] for i, l in enumerate(history["val_r2"])])
+    )
+
+    metadata = {
+        "num_parameters": num_parameters_info["num_parameters"],
+        "num_parameters_train": num_parameters_info["num_parameters_train"],
+        "duration_train": duration_train,
+        "duration_batch_inference": duration_batch_inference,
+        "budget": len(history["loss"]),
+        "stopped": len(history["loss"]) < args.epochs,
+        "lc_train_mse": lc_train_mse,
+        "lc_valid_mse": lc_valid_mse,
+        "lc_train_mae": lc_train_mae,
+        "lc_valid_mae": lc_valid_mae,
+        "lc_train_r2": lc_train_r2,
+        "lc_valid_r2": lc_valid_r2,
+    }
+    metadata.update(all_scores)
+    return {"objective": objective, "metadata": metadata}
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/requirements.txt b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/requirements.txt
new file mode 100644
index 0000000..8c168b6
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/requirements.txt
@@ -0,0 +1,5 @@
+astropy
+patsy
+statsmodels
+numpy<=1.22 # for compatibility with numba.cuda
+numba~=0.55.2
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/fnobench/README.md b/src/deephyper_benchmark/lib/fnobench/README.md
new file mode 100644
index 0000000..fd26692
--- /dev/null
+++ b/src/deephyper_benchmark/lib/fnobench/README.md
@@ -0,0 +1,64 @@
+# FNOBench: Fourier Neural Operator Benchmark
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
+
+This is a simplified way of using deephyper to conduct the hyperparameter tuning for the TFNO model to solve the darcy flow. The FNO and the TFNO are models for training neural operators and they can be designed using the neural operator package.
+
+The Deep Hyper is a package that automates the design of neural networks using Hyperparameter Tuning and Neural Architecture Search.
+
+## Installation
+
+To use the benchmark follow this example set of instructions:
+
+```console
+python -c "import deephyper_benchmark as dhb; dhb.install('FNOBench');"
+```
+
+Then load the benchmark from Python:
+
+```python
+import deephyper_benchmark as dhb
+dhb.load("FNOBench")
+from deephyper_benchmark.lib.fnobench import hpo
+```
+
+## Configuration
+
+...
+
+## Metadata
+
+The current set of returned metadata is:
+
+- [x] `num_parameters`
+- [ ] `num_parameters_train`
+- [x] `duration_train`
+- [ ] `duration_batch_inference`
+- [x] `budget`
+- [ ] `stopped`
+- [x] `train_loss`: the training loss which is the H1Loss
+- [x] `valid_loss`: the validation loss which is the LpLoss
+- [ ] `flops`
+- [ ] `latency`
+- [ ] `lc_train_loss`
+- [ ] `lc_valid_loss`
+
+## Other Details
+
+### Training Procedure
+
+We especially edit the Trainer class from the neural operator package to include and return the validation loss and to optimize it.
+
+We also specifically edit the function that loads the data to accommodate the validation data and the test data. Note that the training and testing data file has been included, and is available for download
+
+### Optimized Losses
+
+The losses employed by the neural operator package are the H1Loss and the LpLoss and we stick with those in this benchmark for the sake of consistency.
+
+### Data Splitting Policy
+
+
+The data we use for this test is the darcy flow test, and we have it in the data folder, it is generated by the load_darcy_flow_small function. The actual function that 
+loads and splits the data into train, validation and test data is the load_darcy_pt, and how it achieves this is to load the data from the file 
+darcy_train and split it in this form, the train data takes 80% of the data, while the validation data takes 20% and saves it into (x_train, y_train); which is loaded into the trainloader, and (x_valid, y_valid); which is loaded into the validation_loader. Note that we train our model in a particular resolution (16), but we can carry out the test in other resolutions, (for this project we test in 16, 32). To get the test data, we already have the test data in the file darcy_test, and we load it similar to how we load the train data, but here we do not split the test data at all, so we have (x_test, y_test) loaded into the testloaders; testloaderS because we load for resolution 16 and resolution 32.
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/fnobench/__init__.py b/src/deephyper_benchmark/lib/fnobench/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/fnobench/benchmark.py b/src/deephyper_benchmark/lib/fnobench/benchmark.py
new file mode 100644
index 0000000..f9e85a6
--- /dev/null
+++ b/src/deephyper_benchmark/lib/fnobench/benchmark.py
@@ -0,0 +1,17 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class FNOBenchmark(Benchmark):
+    version = "0.0.1"
+
+    requires = {
+        "py-pip-requirements": {
+            "step": "install",
+            "type": "pip",
+            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
+        },
+    }
diff --git a/src/deephyper_benchmark/lib/fnobench/data.py b/src/deephyper_benchmark/lib/fnobench/data.py
new file mode 100644
index 0000000..b294151
--- /dev/null
+++ b/src/deephyper_benchmark/lib/fnobench/data.py
@@ -0,0 +1,227 @@
+from pathlib import Path
+
+import torch
+from neuralop.datasets.tensor_dataset import TensorDataset
+from neuralop.datasets.transforms import PositionalEmbedding
+from neuralop.utils import UnitGaussianNormalizer
+
+
+# this function returns the train_loader, validation_loader, test_loader and output encoder for the darcy_flow dataset
+# it was originally written in the neuralop codes but we slightly edit it to include the validation_loader
+# which is 0.2% of the whole training_data
+def load_darcy_flow_small(
+    n_train,
+    n_tests,
+    batch_size,
+    test_batch_sizes,
+    test_resolutions=[16, 32],
+    grid_boundaries=[[0, 1], [0, 1]],
+    positional_encoding=True,
+    encode_input=False,
+    encode_output=True,
+    encoding="channel-wise",
+    channel_dim=1,
+):
+    """Loads a small Darcy-Flow dataset
+
+    Training contains 1000 samples in resolution 16x16.
+    Testing contains 100 samples at resolution 16x16 and
+    50 samples at resolution 32x32.
+
+    Parameters
+    ----------
+    n_train : int
+    n_tests : int
+    batch_size : int
+    test_batch_sizes : int list
+    test_resolutions : int list, default is [16, 32],
+    grid_boundaries : int list, default is [[0,1],[0,1]],
+    positional_encoding : bool, default is True
+    encode_input : bool, default is False
+    encode_output : bool, default is True
+    encoding : 'channel-wise'
+    channel_dim : int, default is 1
+        where to put the channel dimension, defaults size is batch, channel, height, width
+
+    Returns
+    -------
+    training_dataloader, testing_dataloaders
+
+    training_dataloader : torch DataLoader
+    testing_dataloaders : dict (key: DataLoader)
+    """
+    for res in test_resolutions:
+        if res not in [16, 32]:
+            raise ValueError(
+                f"Only 32 and 64 are supported for test resolution, but got {test_resolutions=}"
+            )
+    path = Path(__file__).resolve().parent.joinpath("data")
+    return load_darcy_pt(
+        str(path),
+        n_train=n_train,
+        n_tests=n_tests,
+        batch_size=batch_size,
+        test_batch_sizes=test_batch_sizes,
+        test_resolutions=test_resolutions,
+        train_resolution=16,
+        grid_boundaries=grid_boundaries,
+        positional_encoding=positional_encoding,
+        encode_input=encode_input,
+        encode_output=encode_output,
+        encoding=encoding,
+        channel_dim=channel_dim,
+    )
+
+
+def load_darcy_pt(
+    data_path,
+    n_train,
+    n_tests,
+    batch_size,
+    test_batch_sizes,
+    test_resolutions=[32],
+    train_resolution=32,
+    grid_boundaries=[[0, 1], [0, 1]],
+    positional_encoding=True,
+    encode_input=False,
+    encode_output=True,
+    encoding="channel-wise",
+    channel_dim=1,
+):
+    """Load the Navier-Stokes dataset"""
+    n_val = int(0.2 * n_train)
+    data = torch.load(
+        Path(data_path).joinpath(f"darcy_train_{train_resolution}.pt").as_posix()
+    )
+    x_train = (
+        data["x"][0 : n_train - n_val, :, :]
+        .unsqueeze(channel_dim)
+        .type(torch.float32)
+        .clone()
+    )
+    y_train = data["y"][0 : n_train - n_val, :, :].unsqueeze(channel_dim).clone()
+    x_val = (
+        data["x"][n_train - n_val : n_train, :, :]
+        .unsqueeze(channel_dim)
+        .type(torch.float32)
+        .clone()
+    )
+    y_val = data["y"][n_train - n_val : n_train, :, :].unsqueeze(channel_dim).clone()
+    del data
+
+    idx = test_resolutions.index(train_resolution)
+    test_resolutions.pop(idx)
+    n_test = n_tests.pop(idx)
+    test_batch_size = test_batch_sizes.pop(idx)
+
+    data = torch.load(
+        Path(data_path).joinpath(f"darcy_test_{train_resolution}.pt").as_posix()
+    )
+    x_test = data["x"][:n_test, :, :].unsqueeze(channel_dim).type(torch.float32).clone()
+    y_test = data["y"][:n_test, :, :].unsqueeze(channel_dim).clone()
+    del data
+
+    if encode_input:
+        if encoding == "channel-wise":
+            reduce_dims = list(range(x_train.ndim))
+        elif encoding == "pixel-wise":
+            reduce_dims = [0]
+
+        input_encoder = UnitGaussianNormalizer(x_train, reduce_dim=reduce_dims)
+        x_train = input_encoder.encode(x_train)
+        x_test = input_encoder.encode(x_test.contiguous())
+    else:
+        input_encoder = None
+
+    if encode_output:
+        if encoding == "channel-wise":
+            reduce_dims = list(range(y_train.ndim))
+        # reduce_d = list(range(y_val.ndim))
+        elif encoding == "pixel-wise":
+            reduce_dims = [0]
+        # reduce_d=[0]
+
+        output_encoder = UnitGaussianNormalizer(y_train, reduce_dim=reduce_dims)
+        y_train = output_encoder.encode(y_train)
+    else:
+        output_encoder = None
+
+    train_db = TensorDataset(
+        x_train,
+        y_train,
+        transform_x=PositionalEmbedding(grid_boundaries, 0)
+        if positional_encoding
+        else None,
+    )
+    train_loader = torch.utils.data.DataLoader(
+        train_db,
+        batch_size=batch_size,
+        shuffle=True,
+        num_workers=0,
+        pin_memory=True,
+        persistent_workers=False,
+    )
+    val_db = TensorDataset(
+        x_val,
+        y_val,
+        transform_x=PositionalEmbedding(grid_boundaries, 0)
+        if positional_encoding
+        else None,
+    )
+    val_loader = torch.utils.data.DataLoader(
+        val_db,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=0,
+        pin_memory=True,
+        persistent_workers=False,
+    )
+    test_db = TensorDataset(
+        x_test,
+        y_test,
+        transform_x=PositionalEmbedding(grid_boundaries, 0)
+        if positional_encoding
+        else None,
+    )
+    test_loader = torch.utils.data.DataLoader(
+        test_db,
+        batch_size=test_batch_size,
+        shuffle=False,
+        num_workers=0,
+        pin_memory=True,
+        persistent_workers=False,
+    )
+    test_loaders = {train_resolution: test_loader}
+    for res, n_test, test_batch_size in zip(
+        test_resolutions, n_tests, test_batch_sizes
+    ):
+        print(
+            f"Loading test db at resolution {res} with {n_test} samples and batch-size={test_batch_size}"
+        )
+        data = torch.load(Path(data_path).joinpath(f"darcy_test_{res}.pt").as_posix())
+        x_test = (
+            data["x"][:n_test, :, :].unsqueeze(channel_dim).type(torch.float32).clone()
+        )
+        y_test = data["y"][:n_test, :, :].unsqueeze(channel_dim).clone()
+        del data
+        if input_encoder is not None:
+            x_test = input_encoder.encode(x_test)
+
+        test_db = TensorDataset(
+            x_test,
+            y_test,
+            transform_x=PositionalEmbedding(grid_boundaries, 0)
+            if positional_encoding
+            else None,
+        )
+        test_loader = torch.utils.data.DataLoader(
+            test_db,
+            batch_size=test_batch_size,
+            shuffle=False,
+            num_workers=0,
+            pin_memory=True,
+            persistent_workers=False,
+        )
+        test_loaders[res] = test_loader
+
+    return train_loader, val_loader, test_loaders, output_encoder
diff --git a/src/deephyper_benchmark/lib/fnobench/data/darcy_test_16.pt b/src/deephyper_benchmark/lib/fnobench/data/darcy_test_16.pt
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diff --git a/src/deephyper_benchmark/lib/fnobench/data/darcy_test_32.pt b/src/deephyper_benchmark/lib/fnobench/data/darcy_test_32.pt
new file mode 100644
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diff --git a/src/deephyper_benchmark/lib/fnobench/data/darcy_train_16.pt b/src/deephyper_benchmark/lib/fnobench/data/darcy_train_16.pt
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diff --git a/src/deephyper_benchmark/lib/fnobench/hpo.py b/src/deephyper_benchmark/lib/fnobench/hpo.py
new file mode 100644
index 0000000..8d3f7a9
--- /dev/null
+++ b/src/deephyper_benchmark/lib/fnobench/hpo.py
@@ -0,0 +1,104 @@
+import torch
+from deephyper.evaluator import profile
+from deephyper.hpo import HpProblem
+
+from .model import build_and_train_model
+
+# now we define the hyperparameters with which we aim to get the best configuration for solving the problem
+problem = HpProblem()
+# this is for dimension 2 darcy flow, below we list the hyperparameters and where they are being used
+# 1. n_modes_height and n_modes_width are the number of modes to keep in fourier layer along each dimension, if we are working with 3 dimensions, we have height, width, and length. It is called when you are defining the model e.g model =TFNO(n_modes=...),The dimensionality of the TFNO is inferred from ``len(n_modes)``
+problem.add_hyperparameter((2, 19), "n_modes_height", default_value=16)
+problem.add_hyperparameter((2, 19), "n_modes_width", default_value=16)
+# 2. hidden_channels: width of the FNO (i.e. number of channels). they go into the model definition
+problem.add_hyperparameter((1, 128), "hidden_channels", default_value=32)
+# 3. lifting_channels: number of hidden channels of the lifting block of the FNO, they go into the model definition
+problem.add_hyperparameter((1, 1024), "lifting_channels", default_value=256)
+# 4 projection_channels:number of hidden channels of the projection block of the FNO, they go into the model definition
+problem.add_hyperparameter((1, 1024), "projection_channels", default_value=64)
+# 5. use_mlp: Whether to use an MLP layer after each FNO block, they go into the model definition
+problem.add_hyperparameter([True, False], "use_mlp", default_value=False)
+# 6 mlp['dropout']: parameter of the MLP, they go into the model definition
+problem.add_hyperparameter((0.0, 1.0), "mlp['dropout']", default_value=0)
+# 7 mlp['expansion']: MLP parameter, they go into the model definition
+problem.add_hyperparameter((0.0, 3.0), "mlp['expansion']", default_value=0.5)
+# 8 rank Rank of the tensor factorization of the Fourier weights, they go into the model definition
+problem.add_hyperparameter((0.0, 1.0), "rank", default_value=1.0)
+# 9 factorization:Tensor factorization of the parameters weight to use, they go into the model definition
+problem.add_hyperparameter(
+    ["tucker", "cp", "tt", "dense"], "factorization", default_value="dense"
+)
+# 10 learning_rate: this is the learning_rate, goes into the optimizer definition
+problem.add_hyperparameter(
+    (1e-6, 1e-2, "log-uniform"), "opt_learning_rate", default_value=5e-3
+)
+# 11 batch_size: the batch size for the training, goes into the train_loader for loading the datasel
+problem.add_hyperparameter((2, 64), "data_batch_size", default_value=16)
+# 12 weight_decay: this is the weight_decay, goes into the optimizer definition
+problem.add_hyperparameter(
+    (1e-6, 1e-2, "log-uniform"), "opt_weight_decay", default_value=1e-4
+)
+# 13 n_layers:Number of Fourier Layers
+problem.add_hyperparameter((1, 8), "n_layers", default_value=4)
+# 14 epochs: number of epochs, goes into the trainer
+problem.add_hyperparameter((1, 1000), "opt_n_epochs", default_value=300)
+# 15 scheduler_T_max: this goes into the scheduler, it's the max number of iterations in the scheduler
+problem.add_hyperparameter([500], "opt_scheduler_T_max", default_value=500)
+# 16 n_train: no of data samples to train the model, goes into the train_loader for loading the dataset
+problem.add_hyperparameter([1000], "data_n_train", default_value=1000)
+# the next set of hyperparameters are data related and we're keeping them constant for now,and for the particular problem
+problem.add_hyperparameter([16], "data_train_resolution", default_value=16)
+problem.add_hyperparameter([True], "data_positional_encoding", default_value=True)
+problem.add_hyperparameter([True], "data_encode_input", default_value=True)
+problem.add_hyperparameter([False], "data_encode_output", default_value=False)
+problem.add_hyperparameter([666], "distributed_seed", default_value=666)
+problem.add_hyperparameter([3], "data_channels", default_value=3)
+# implementation is about how the factorization is done,
+problem.add_hyperparameter(
+    ["factorized", "reconstructed"], "implementation", default_value="factorized"
+)
+# By default, None, otherwise tanh is used before FFT in the FNO block
+problem.add_hyperparameter(["None", "tanh"], "stabilizer", default_value=None)
+# if 'full', the FNO Block runs in full precision,
+# if 'half', the FFT, contraction, and inverse FFT run in half precision
+# if 'mixed', the contraction and inverse FFT run in half precision
+problem.add_hyperparameter(
+    ["full", "half", "mixed"], "fno_block_precision", default_value="half"
+)
+# Type of skip connection to use,
+problem.add_hyperparameter(
+    ["soft-gating", "identity", "linear"], "skip", default_value="linear"
+)
+# the scheduler_patience, used for only  ReduceLROnPlateau, we're keeping it constant
+problem.add_hyperparameter([5], "opt_scheduler_patience", default_value=5)
+# type of scheduler to use
+problem.add_hyperparameter(
+    ["StepLR", "CosineAnnealingLR", "ReduceLROnPlateau"],
+    "opt_scheduler",
+    default_value="StepLR",
+)
+# step_size for the optimizer
+problem.add_hyperparameter((10, 100), "opt_step_size", default_value=60)
+# gamma for the constant, we're keeping it constant
+problem.add_hyperparameter([0.5], "opt_gamma", default_value=0.5)
+
+
+@profile
+def run(config):
+    # important to avoid memory explosion
+    torch.cuda.empty_cache()
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    _, history = build_and_train_model(config, device, verbose=0)
+    score = {
+        "objective": -history["val_loss"][-1],
+        "metadata": {
+            "train_err": history["train_err"][-1],
+            "num_parameters": history["num_parameters"],
+            "budget": history["Epochs"][-1],
+            "val_loss": history["val_loss"][-1],
+            "duration_train": sum(history["train_time"]),
+        },
+    }
+
+    return score
diff --git a/src/deephyper_benchmark/lib/fnobench/model.py b/src/deephyper_benchmark/lib/fnobench/model.py
new file mode 100644
index 0000000..8c38e42
--- /dev/null
+++ b/src/deephyper_benchmark/lib/fnobench/model.py
@@ -0,0 +1,560 @@
+import sys
+from timeit import default_timer
+
+import matplotlib.pyplot as plt
+import neuralop.mpu.comm as comm
+import numpy as np
+import torch
+import wandb
+from neuralop import H1Loss, LpLoss
+from neuralop.models import TFNO2d
+from neuralop.training.patching import MultigridPatching2D
+from neuralop.utils import count_params
+
+from .data import load_darcy_flow_small
+
+
+# this is the trainer class that trains the neural operators,
+# it is from the neuralop package, but we had to slightly
+# modify it in order to
+# 1. calculate the validation loss instead of the testing loss
+# 2. return the history of the training, including the time for each epoch, the training loss for each epoch
+# and the validation loss for each epoch
+class Trainer:
+    def __init__(
+        self,
+        model,
+        n_epochs,
+        wandb_log=True,
+        device=None,
+        mg_patching_levels=0,
+        mg_patching_padding=0,
+        mg_patching_stitching=True,
+        log_test_interval=1,
+        log_output=False,
+        use_distributed=False,
+        verbose=True,
+    ):
+        """
+        A general Trainer class to train neural-operators on given datasets
+
+        Parameters
+        ----------
+        model : nn.Module
+        n_epochs : int
+        wandb_log : bool, default is True
+        device : torch.device
+        mg_patching_levels : int, default is 0
+            if 0, no multi-grid domain decomposition is used
+            if > 0, indicates the number of levels to use
+        mg_patching_padding : float, default is 0
+            value between 0 and 1, indicates the fraction of size to use as padding on each side
+            e.g. for an image of size 64, padding=0.25 will use 16 pixels of padding on each side
+        mg_patching_stitching : bool, default is True
+            if False, the patches are not stitched back together and the loss is instead computed per patch
+        log_test_interval : int, default is 1
+            how frequently to print updates
+        log_output : bool, default is False
+            if True, and if wandb_log is also True, log output images to wandb
+        use_distributed : bool, default is False
+            whether to use DDP
+        verbose : bool, default is True
+        """
+        self.n_epochs = n_epochs
+        self.wandb_log = wandb_log
+        self.log_test_interval = log_test_interval
+        self.log_output = log_output
+        self.verbose = verbose
+        self.mg_patching_levels = mg_patching_levels
+        self.mg_patching_stitching = mg_patching_stitching
+        self.use_distributed = use_distributed
+        self.device = device
+
+        if mg_patching_levels > 0:
+            self.mg_n_patches = 2**mg_patching_levels
+            if verbose:
+                print(f"Training on {self.mg_n_patches ** 2} multi-grid patches.")
+                sys.stdout.flush()
+        else:
+            self.mg_n_patches = 1
+            mg_patching_padding = 0
+            if verbose:
+                print(f"Training on regular inputs (no multi-grid patching).")
+                sys.stdout.flush()
+
+        self.mg_patching_padding = mg_patching_padding
+        self.patcher = MultigridPatching2D(
+            model,
+            levels=mg_patching_levels,
+            padding_fraction=mg_patching_padding,
+            use_distributed=use_distributed,
+            stitching=mg_patching_stitching,
+        )
+
+    def train(
+        self,
+        train_loader,
+        validation_loader,
+        output_encoder,
+        model,
+        optimizer,
+        scheduler,
+        regularizer,
+        training_loss=None,
+        eval_losses=None,
+    ):
+        """Trains the given model on the given datasets"""
+        n_train = len(train_loader.dataset)
+        history = {"Epochs": [], "train_err": [], "train_time": [], "val_loss": []}
+        # if not isinstance(test_loaders, dict):
+        #   test_loaders = dict(test=test_loaders)
+
+        if self.verbose:
+            print(f"Training on {n_train} samples")
+            # print(f'Testing on {[len(loader.dataset) for loader in test_loaders.values()]} samples'
+            #     f'         on resolutions {[name for name in test_loaders]}.')
+            sys.stdout.flush()
+
+        if training_loss is None:
+            training_loss = LpLoss(d=2)
+
+        if eval_losses is None:  # By default just evaluate on the training loss
+            eval_losses = dict(l2=training_loss)
+
+        if output_encoder is not None:
+            output_encoder.to(self.device)
+
+        if self.use_distributed:
+            is_logger = comm.get_world_rank() == 0
+        else:
+            is_logger = True
+
+        for epoch in range(self.n_epochs):
+            avg_loss = 0
+            avg_lasso_loss = 0
+            model.train()
+            t1 = default_timer()
+            train_err = 0.0
+
+            for idx, sample in enumerate(train_loader):
+                x, y = sample["x"], sample["y"]
+
+                if epoch == 0 and idx == 0 and self.verbose and is_logger:
+                    print(f"Training on raw inputs of size {x.shape=}, {y.shape=}")
+
+                x, y = self.patcher.patch(x, y)
+
+                if epoch == 0 and idx == 0 and self.verbose and is_logger:
+                    print(f".. patched inputs of size {x.shape=}, {y.shape=}")
+
+                x = x.to(self.device)
+                y = y.to(self.device)
+
+                optimizer.zero_grad(set_to_none=True)
+                if regularizer:
+                    regularizer.reset()
+
+                out = model(x)
+                if epoch == 0 and idx == 0 and self.verbose and is_logger:
+                    print(f"Raw outputs of size {out.shape=}")
+
+                out, y = self.patcher.unpatch(out, y)
+                # Output encoding only works if output is stiched
+                if output_encoder is not None and self.mg_patching_stitching:
+                    out = output_encoder.decode(out)
+                    y = output_encoder.decode(y)
+                if epoch == 0 and idx == 0 and self.verbose and is_logger:
+                    print(f".. Processed (unpatched) outputs of size {out.shape=}")
+
+                loss = training_loss(out.float(), y)
+
+                if regularizer:
+                    loss += regularizer.loss
+
+                loss.backward()
+
+                optimizer.step()
+                train_err += loss.item()
+
+                with torch.no_grad():
+                    avg_loss += loss.item()
+                    if regularizer:
+                        avg_lasso_loss += regularizer.loss
+
+            if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
+                scheduler.step(train_err)
+            else:
+                scheduler.step()
+
+            epoch_train_time = default_timer() - t1
+            del x, y
+
+            train_err /= n_train
+            avg_loss /= self.n_epochs
+            history["Epochs"].append(epoch)
+            history["train_err"].append(train_err)
+            history["train_time"].append(epoch_train_time)
+            # if epoch % self.log_test_interval == 0:
+
+            msg = f"[{epoch}] time={epoch_train_time:.2f}, avg_loss={avg_loss:.4f}, train_err={train_err:.4f}"
+
+            values_to_log = dict(
+                train_err=train_err, time=epoch_train_time, avg_loss=avg_loss
+            )
+            # for loader_name, loader in test_loaders.items():
+            # if epoch == self.n_epochs - 1 and self.log_output:
+            #     to_log_output = True
+            # else:
+            #     to_log_output = False
+
+            loader = validation_loader
+            loader_name = ""
+            errors = self.evaluate(
+                model, eval_losses, loader, output_encoder, log_prefix=loader_name
+            )
+
+            for loss_name, loss_value in errors.items():
+                msg += f", {loss_name}={loss_value:.4f}"
+                values_to_log[loss_name] = loss_value
+            history["val_loss"].append(values_to_log[loss_name])
+            if regularizer:
+                avg_lasso_loss /= self.n_epochs
+                msg += f", avg_lasso={avg_lasso_loss:.5f}"
+
+            if self.verbose and is_logger:
+                print(msg)
+                sys.stdout.flush()
+
+                # Wandb loging
+            if self.wandb_log and is_logger:
+                for pg in optimizer.param_groups:
+                    lr = pg["lr"]
+                    values_to_log["lr"] = lr
+                wandb.log(values_to_log, step=epoch, commit=True)
+        return history
+
+    def evaluate(
+        self, model, loss_dict, data_loader, output_encoder=None, log_prefix=""
+    ):
+        """Evaluates the model on a dictionary of losses
+
+        Parameters
+        ----------
+        model : model to evaluate
+        loss_dict : dict of functions
+          each function takes as input a tuple (prediction, ground_truth)
+          and returns the corresponding loss
+        data_loader : data_loader to evaluate on
+        output_encoder : used to decode outputs if not None
+        log_prefix : str, default is ''
+            if not '', used as prefix in output dictionary
+
+        Returns
+        -------
+        errors : dict
+            dict[f'{log_prefix}_{loss_name}] = loss for loss in loss_dict
+        """
+        model.eval()
+
+        if self.use_distributed:
+            is_logger = comm.get_world_rank() == 0
+        else:
+            is_logger = True
+
+        errors = {f"{log_prefix}_{loss_name}": 0 for loss_name in loss_dict.keys()}
+
+        n_samples = 0
+        with torch.no_grad():
+            for it, sample in enumerate(data_loader):
+                x, y = sample["x"], sample["y"]
+
+                n_samples += x.size(0)
+
+                x, y = self.patcher.patch(x, y)
+                y = y.to(self.device)
+                x = x.to(self.device)
+
+                out = model(x)
+
+                out, y = self.patcher.unpatch(out, y, evaluation=True)
+
+                if output_encoder is not None:
+                    out = output_encoder.decode(out)
+
+                if (it == 0) and self.log_output and self.wandb_log and is_logger:
+                    if out.ndim == 2:
+                        img = out
+                    else:
+                        img = out.squeeze()[0]
+                    wandb.log(
+                        {
+                            f"image_{log_prefix}": wandb.Image(
+                                img.unsqueeze(-1).cpu().numpy()
+                            )
+                        },
+                        commit=False,
+                    )
+
+                for loss_name, loss in loss_dict.items():
+                    errors[f"{log_prefix}_{loss_name}"] += loss(out, y).item()
+
+        del x, y, out
+
+        for key in errors.keys():
+            errors[key] /= n_samples
+
+        return errors
+
+
+# this function takes an input configuration and uses this to build a TFNO model in a format accepted by
+# the TFNO benchmark and then trains the model
+def build_and_train_model(config: dict, device="cpu", verbose: bool = 0):
+    """
+    this function takes an input configuration and uses this to build a TFNO model in
+    a format accepted by the TFNO benchmark and then trains the model
+    """
+    default_config = {
+
+        # General
+        # For computing compression
+        # 'n_params_baseline': None,
+
+        # Distributed computing
+        'distributed_seed': 666,
+
+        # FNO related
+        "lifting_channels": 256,
+        'data_channels': 3,
+        'n_modes_height': 16,
+        'n_modes_width': 16,
+        'hidden_channels': 32,
+        'projection_channels': 64,
+        'n_layers': 4,
+        'skip': 'linear',
+        'implementation': 'factorized',
+
+        "use_mlp": False,
+        "mlp": {"dropout": 0, "expansion": 0.5},
+
+        'factorization': None,
+        'rank': 1.0,
+
+        'stabilizer': None,
+        'fno_block_precision': 'half',
+
+        # Optimizer
+
+        'opt_n_epochs': 5,
+        'opt_learning_rate': 5e-3,
+        # 'opt_training_loss': 'h1',
+        'opt_weight_decay': 1e-4,
+
+        'opt_scheduler_T_max': 500,  # For cosine only, typically take n_epochs
+        'opt_scheduler_patience': 5,  # For ReduceLROnPlateau only
+        'opt_scheduler': 'StepLR',  # Or 'CosineAnnealingLR' OR 'ReduceLROnPlateau'
+        'opt_step_size': 60,
+        'opt_gamma': 0.5,
+
+        # Dataset related
+
+        'data_batch_size': 16,
+        'data_n_train': 1000,
+        'data_train_resolution': 16,
+        'data_n_tests': [100, 50],
+        'data_test_resolutions': [16, 32],
+        'data_test_batch_sizes': [16, 16],
+        'data_positional_encoding': True,
+
+        'data_encode_input': True,
+        'data_encode_output': False,
+
+        # Patching
+        'patching': {'levels': 0},
+        'patching_levels': 0,
+        'patching_padding': 0,
+        'patching_stitching': False,
+    }
+
+    default_config.update(config)
+    config_name = 'default'
+
+   
+
+    # Loading the Darcy flow dataset
+
+    (train_loader, validation_loader, test_loaders, output_encoder) = load_darcy_flow_small(
+        n_train=default_config['data_n_train'], batch_size=default_config['data_batch_size'],
+        positional_encoding=default_config['data_positional_encoding'],
+        test_resolutions=default_config['data_test_resolutions'], n_tests=default_config['data_n_tests'],
+        test_batch_sizes=default_config['data_test_batch_sizes'],
+        encode_input=default_config['data_encode_input'], encode_output=default_config['data_encode_output'],
+        )
+
+    model = TFNO2d(data_channels=default_config["data_channels"], n_modes_height=default_config['n_modes_height'],
+                   n_modes_width=default_config["n_modes_width"],
+                   n_layers=default_config["n_layers"], lifting_channels=default_config["lifting_channels"],
+                   hidden_channels=default_config["hidden_channels"],
+                   projection_channels=default_config["projection_channels"],
+                   factorization=default_config["factorization"], rank=default_config["rank"],
+                   use_mlp=default_config["use_mlp"], mlp=default_config["mlp"], skip=default_config["skip"],
+                   implementation=default_config["implementation"],
+                   stabilizer=default_config["stabilizer"], fno_block_precision=default_config["fno_block_precision"])
+    model = model.to(device)
+
+    # Log parameter count
+    n_params = count_params(model)
+
+
+    print(f'\nn_params: {n_params}')
+    sys.stdout.flush()
+
+
+    # Create the optimizer
+    optimizer = torch.optim.Adam(model.parameters(),
+                                 lr=default_config['opt_learning_rate'],
+                                 weight_decay=default_config['opt_weight_decay'])
+
+    if default_config['opt_scheduler'] == 'ReduceLROnPlateau':
+        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=default_config['opt_gamma'],
+                                                               patience=default_config['opt_scheduler_patience'],
+                                                               mode='min')
+    elif default_config['opt_scheduler'] == 'CosineAnnealingLR':
+        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=default_config['opt_scheduler_T_max'])
+    elif default_config['opt_scheduler'] == 'StepLR':
+        scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=default_config['opt_step_size'],
+                                                    gamma=default_config['opt_gamma'])
+    else:
+        raise ValueError(f"Got { default_config['opt_scheduler']= }")
+
+    # Creating the losses
+    l2loss = LpLoss(d=2, p=2)
+    h1loss = H1Loss(d=2)
+    train_loss = h1loss
+    eval_losses = dict(h1=train_loss)
+    # if default_config['opt_training_loss'] == 'l2':
+    #     train_loss = l2loss
+    #     eval_losses = dict(l2=train_loss)
+    # elif default_config['opt_training_loss'] == 'h1':
+    #     train_loss = h1loss
+    #     eval_losses = dict(h1=train_loss)
+    # else:
+    #     raise ValueError(f"Got training_loss={default_config['opt_training_loss']} but expected one of ['l2', 'h1']")
+    # # eval_losses = {'h1': h1loss, 'l2': l2loss}
+
+
+    print('\n### MODEL ###\n', model)
+    print('\n### OPTIMIZER ###\n', optimizer)
+    print('\n### SCHEDULER ###\n', scheduler)
+    print('\n### LOSSES ###')
+    print(f'\n * Train: {train_loss}')
+    print(f'\n * Test: {eval_losses}')
+    print(f'\n### Beginning Training...\n')
+    sys.stdout.flush()
+
+    trainer = Trainer(model, n_epochs=default_config['opt_n_epochs'], device=device,
+                      mg_patching_levels=default_config['patching_levels'],
+                      mg_patching_padding=default_config['patching_padding'],
+                      mg_patching_stitching=default_config['patching_stitching'], wandb_log=False, log_test_interval=1,
+                      log_output=True, use_distributed=False, verbose=True)
+
+    history = trainer.train(train_loader, validation_loader, output_encoder, model, optimizer, scheduler,
+                            regularizer=False, training_loss=train_loss, eval_losses=eval_losses, )
+
+    history["num_parameters"] = n_params
+
+    return model, history
+
+
+
+def bestevaluate(best_config):
+    """we design the model based on the best hyperparameter configuration,
+    we then evaluate this based on the training/validation and testing data"""
+
+    (
+        train_loader,
+        validation_loader,
+        test_loaders,
+        output_encoder,
+    ) = load_darcy_flow_small(
+        n_train=best_config["data_n_train"],
+        batch_size=best_config["data_batch_size"],
+        positional_encoding=best_config["data_positional_encoding"],
+        test_resolutions=[16, 32],
+        n_tests=[100, 50],
+        test_batch_sizes=[32, 32],
+        encode_input=best_config["data_encode_input"],
+        encode_output=best_config["data_encode_output"],
+    )
+    # the build_and_train_model function
+    # gives the training and validation losses
+    best_model, best_history = build_and_train_model(best_config, verbose=1)
+    # next we generate the testing losses
+    trainer = Trainer(
+        best_model,
+        n_epochs=best_config["opt_n_epochs"],
+        device=device,
+        mg_patching_levels=best_config["patching_levels"],
+        mg_patching_padding=best_config["patching_padding"],
+        mg_patching_stitching=best_config["patching_stitching"],
+        wandb_log=best_config["wandb_log"],
+        log_test_interval=best_config["wandb_log_test_interval"],
+        log_output=best_config["wandb_log_output"],
+        use_distributed=best_config["distributed_use_distributed"],
+        verbose=True,
+    )
+    for loader_name, loader in test_loaders.items():
+        l2loss = LpLoss(d=2, p=2)
+        h1loss = H1Loss(d=2)
+        eval_losses = {"h1": h1loss, "l2": l2loss}
+        errors = trainer.evaluate(
+            best_model, eval_losses, loader, output_encoder, log_prefix=loader_name
+        )
+        for loss_name, loss_value in errors.items():
+            best_history[loss_name] = loss_value
+
+    test_samples = test_loaders[32].dataset
+
+    fig = plt.figure(figsize=(7, 7))
+    for index in range(3):
+        data = test_samples[index]
+        # Input x
+        x = data["x"]
+        # Ground-truth
+        y = data["y"]
+        # Model prediction
+        out = best_model(x.unsqueeze(0))
+
+        ax = fig.add_subplot(4, 4, index * 4 + 1)
+        ax.imshow(x[0], cmap="gray")
+        if index == 0:
+            ax.set_title("Input x")
+        plt.xticks([], [])
+        plt.yticks([], [])
+
+        ax = fig.add_subplot(4, 4, index * 4 + 2)
+        ax.imshow(y.squeeze())
+        if index == 0:
+            ax.set_title("Ground-truth y")
+        plt.xticks([], [])
+        plt.yticks([], [])
+
+        ax = fig.add_subplot(4, 4, index * 4 + 3)
+        ax.imshow(out.squeeze().detach().numpy())
+        if index == 0:
+            ax.set_title("Model prediction")
+        plt.xticks([], [])
+        plt.yticks([], [])
+
+        ax = fig.add_subplot(4, 4, index * 4 + 4)
+        err = (np.abs(y.squeeze() - out.detach().numpy())) ** 2
+        im = ax.imshow(err.squeeze(), cmap="plasma", aspect="auto")
+        plt.colorbar(im, label="$|y - \hat{y}|^2$")
+        if index == 0:
+            ax.set_title("Squared Error")
+        plt.xticks([], [])
+        plt.yticks([], [])
+
+    fig.suptitle("Inputs, ground-truth output, prediction and absolute error.", y=0.98)
+    plt.tight_layout()
+    fig.show()
+    return best_history
diff --git a/src/deephyper_benchmark/lib/fnobench/requirements.txt b/src/deephyper_benchmark/lib/fnobench/requirements.txt
new file mode 100644
index 0000000..77a79ef
--- /dev/null
+++ b/src/deephyper_benchmark/lib/fnobench/requirements.txt
@@ -0,0 +1,5 @@
+neuraloperator
+tensorly
+tensorly-torch
+wandb
+zarr
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/hpobench/README.md b/src/deephyper_benchmark/lib/hpobench/README.md
new file mode 100644
index 0000000..0ff9825
--- /dev/null
+++ b/src/deephyper_benchmark/lib/hpobench/README.md
@@ -0,0 +1,28 @@
+# HPOBench
+
+✅ **Benchmark ready to be used.**
+
+This set of benchmarks provides precomputed evaluations of neural networks for hyperparameter optimization. This is particularly useful to compare optimization schemes without training neural networks for real.
+
+## Installation
+
+To install this benchmark the following command can be run:
+
+```console
+python -c "import deephyper_benchmark as dhb; dhb.install('HPOBench/tabular');"
+```
+
+For detailed information see the [NeurIPS paper describing the HPOBench benchmark](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/93db85ed909c13838ff95ccfa94cebd9-Abstract-round2.html).
+
+## Configuration
+
+Different parameters can be set to configure this benchmark.
+
+- Environment variable `DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME` with boolean value in `[0, 1]` which set if the "recorded training time" of each training epoch should be simulated or not. **Defaults to `0`.**
+- Environment variable `DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME` with real value $> 0$. It represents the proportion of the "recorded training time" which should be used for simulation. For example, `1.0` (default) corresponds to 100% of the training time while `0.1` corresponds to 10% of the training time. **Defaults to `1.0`.**
+- Environment variable `DEEPHYPER_BENCHMARK_TASK` to select the task on which to run the benchmark with values in:
+    - **`navalpropulsion` (Default)**
+    - `parkinsonstelemonitoring`
+    - `proteinstructure`
+    - `slicelocalization`
+- Environment variable `DEEPHYPER_BENCHMARK_MOO` with value `0` or `1` to select if the task should be run with single or multiple objectives. **Defaults to `0` for single-objective**.
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/hpobench/tabular/__init__.py b/src/deephyper_benchmark/lib/hpobench/tabular/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/hpobench/tabular/benchmark.py b/src/deephyper_benchmark/lib/hpobench/tabular/benchmark.py
new file mode 100644
index 0000000..6906ef8
--- /dev/null
+++ b/src/deephyper_benchmark/lib/hpobench/tabular/benchmark.py
@@ -0,0 +1,17 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class HPOBenchTabularNavalPropulsion(Benchmark):
+    version = "0.0.1"
+
+    requires = {
+        "bash-install": {
+            "step": "install",
+            "type": "cmd",
+            "cmd": os.path.join(DIR, "install.sh"),
+        },
+    }
diff --git a/src/deephyper_benchmark/lib/hpobench/tabular/constant_predictor.py b/src/deephyper_benchmark/lib/hpobench/tabular/constant_predictor.py
new file mode 100644
index 0000000..4a42b3b
--- /dev/null
+++ b/src/deephyper_benchmark/lib/hpobench/tabular/constant_predictor.py
@@ -0,0 +1,127 @@
+from typing import Tuple
+
+import numpy as np
+import openml
+from sklearn.dummy import DummyRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+
+# Reference of HPOBench Paper: https://arxiv.org/pdf/2109.06716.pdf
+# Reference of original paper introducign these benchmarks: https://arxiv.org/pdf/1905.04970.pdf
+# Trying to reproduce as much as possible results/setup from the original paper
+
+map_task_to_openmlid = {
+    # https://archive.ics.uci.edu/dataset/316/condition+based+maintenance+of+naval+propulsion+plants
+    "navalpropulsion": "44969",
+    # https://archive.ics.uci.edu/dataset/189/parkinsons+telemonitoring
+    "parkinsonstelemonitoring": "4531",
+    # https://archive.ics.uci.edu/dataset/265/physicochemical+properties+of+protein+tertiary+structure
+    "proteinstructure": "44963",
+    # https://archive.ics.uci.edu/dataset/206/relative+location+of+ct+slices+on+axial+axis
+    "slicelocalization": "42973",
+}
+
+map_openmlid_to_task = {v: k for k, v in map_task_to_openmlid.items()}
+
+map_task_to_default_target_attribute = {
+    "navalpropulsion": ["gt_compressor_decay_state_coefficient"],
+    "parkinsonstelemonitoring": ["motor_UPDRS", "total_UPDRS"],
+    "proteinstructure": ["RMSD"],
+    "slicelocalization": ["reference"],
+}
+
+
+def load_from_openml(dataset_id: str) -> Tuple[np.ndarray, np.ndarray, dict]:
+    """Load dataset from openml library.
+
+    Args:
+        dataset_id (str): the identifier of the dataset to be loaded from the OpenML database.
+
+    Raises:
+        ValueError: if the dataset is not found.
+
+    Returns:
+        (np.ndarray, np.ndarray, dict): X, y, metadata where X is the input array, y is the target array, and metadata is a dictionary containing additional information about the data.
+    """
+    task_name = map_openmlid_to_task[dataset_id]
+    default_target_attribute = map_task_to_default_target_attribute[task_name]
+
+    dataset = openml.datasets.get_dataset(
+        dataset_id,
+        download_data=True,
+        download_qualities=True,
+        download_features_meta_data=True,
+    )
+
+    X, y, categorical_indicator, _ = dataset.get_data()
+    y = X[default_target_attribute]
+    X = X.drop(default_target_attribute, axis=1)
+    y = y.values
+
+    # Drop constant features
+    constant_features = []
+    continuous_features = []
+    for i in range(X.shape[1]):
+        values, _ = np.unique(X.values[:, i], return_counts=True)
+        if len(values) == 1:
+            constant_features.append(list(X.columns)[i])
+        elif len(values) >= 10:
+            continuous_features.append(list(X.columns)[i])
+
+    X = X.drop(constant_features, axis=1)
+
+    # Split into train and test
+    test_size = int(0.2 * len(X))
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=test_size, random_state=42
+    )
+    X_train, X_valid, y_train, y_valid = train_test_split(
+        X_train, y_train, test_size=test_size, random_state=42
+    )
+
+    # Standard Normalization on features
+    m = np.mean(X_train[continuous_features], axis=0)
+    s = np.std(X_train[continuous_features], axis=0)
+    X_train[continuous_features] = (X_train[continuous_features] - m) / s
+    X_valid[continuous_features] = (X_valid[continuous_features] - m) / s
+    X_test[continuous_features] = (X_test[continuous_features] - m) / s
+
+    # Standard Normalization on targets
+    m = np.mean(y_train, axis=0)
+    s = np.std(y_train, axis=0)
+    y_train = (y_train - m) / s
+    y_valid = (y_valid - m) / s
+    y_test = (y_test - m) / s
+
+    return X_train.values, X_valid.values, X_test.values, y_train, y_valid, y_test
+
+
+if __name__ == "__main__":
+    # Some tests
+    task = "navalpropulsion"
+    # task = "parkinsonstelemonitoring"
+    # task = "proteinstructure"
+    # task = "slicelocalization"
+    print(f"Task: {task}")
+    openmlid = map_task_to_openmlid[task]
+    X_train, X_valid, X_test, y_train, y_valid, y_test = load_from_openml(openmlid)
+
+    print("Num samples:", X_train.shape[0] + X_valid.shape[0] + X_test.shape[0])
+    print("X_train.shape", X_train.shape)
+    print("X_valid.shape", X_valid.shape)
+    print("X_test.shape", X_test.shape)
+    print("y_train.shape", y_train.shape)
+    print("y_valid.shape", y_valid.shape)
+    print("y_test.shape", y_test.shape)
+
+    model = DummyRegressor(strategy="mean")
+    model.fit(X_train, y_train)
+
+    # As the data are normalized these values are expected to be close to 1.0
+    fold = ["train", "valid", "test"]
+    for i, (X, y) in enumerate(
+        zip([X_train, X_valid, X_test], [y_train, y_valid, y_test])
+    ):
+        print(f"Fold: {fold[i]}")
+        y_pred = model.predict(X)
+        print(f"  MSE: {mean_squared_error(y, y_pred)}")
diff --git a/src/deephyper_benchmark/lib/hpobench/tabular/hpo.py b/src/deephyper_benchmark/lib/hpobench/tabular/hpo.py
new file mode 100644
index 0000000..0c662ed
--- /dev/null
+++ b/src/deephyper_benchmark/lib/hpobench/tabular/hpo.py
@@ -0,0 +1,139 @@
+import os
+import time
+
+import numpy as np
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+import hpobench.benchmarks.nas.tabular_benchmarks
+
+from deephyper.evaluator import profile, RunningJob
+from deephyper.hpo import HpProblem
+
+DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME = bool(
+    int(os.environ.get("DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME", 0))
+)
+DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME = float(
+    os.environ.get("DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME", 1.0)
+)
+DEEPHYPER_BENCHMARK_TASK = os.environ.get("DEEPHYPER_BENCHMARK_TASK", "navalpropulsion")
+DEEPHYPER_BENCHMARK_MOO = bool(int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 0)))
+
+
+map_task_to_benchmark = {
+    "navalpropulsion": "NavalPropulsionBenchmark",
+    "parkinsonstelemonitoring": "ParkinsonsTelemonitoringBenchmark",
+    "proteinstructure": "ProteinStructureBenchmark",
+    "slicelocalization": "SliceLocalizationBenchmark",
+}
+data_path = os.path.join(DIR, "build/HPOBench/data/fcnet_tabular_benchmarks/")
+
+
+benchmark_class = getattr(
+    hpobench.benchmarks.nas.tabular_benchmarks,
+    map_task_to_benchmark[DEEPHYPER_BENCHMARK_TASK],
+)
+b = benchmark_class(data_path=data_path)
+config_space = b.get_configuration_space()
+
+problem = HpProblem(config_space=config_space)
+
+
+@profile
+def run(job: RunningJob, optuna_trial=None) -> dict:
+    # otherwise failure
+    config = job.parameters
+
+    seed = int(job.id.split(".")[-1])
+    rng = np.random.RandomState(seed)
+    run_index = int(rng.choice((0, 1, 2, 3)))
+
+    benchmark_class = getattr(
+        hpobench.benchmarks.nas.tabular_benchmarks,
+        map_task_to_benchmark[DEEPHYPER_BENCHMARK_TASK],
+    )
+    b = benchmark_class(data_path=data_path)
+    budget_space = b.get_fidelity_space().get("budget")
+    min_b, max_b = budget_space.lower, budget_space.upper
+
+    # The objective is the coefficient of determination (R^2)
+    # As the target were standardized, the R^2 = 1 - MSE / MSE_baseline with MSE_baseline = 1.0
+    # The objective is maximized in deephyper
+    eval_test = b.objective_function_test(config, fidelity={"budget": max_b})
+    objective_test = 1 - eval_test["function_value"]
+    cost_eval = eval_test["cost"]
+    cost_step = cost_eval / (max_b - min_b + 1)
+
+    eval_val = b.objective_function(
+        config, fidelity={"budget": max_b}, run_index=run_index
+    )
+    objective_val = 1 - eval_val["function_value"]
+
+    other_metadata = {}
+
+    consumed_time = 0  # the cost here corresponds to time
+
+    if optuna_trial:
+        for budget_i in range(min_b, max_b + 1):
+            eval = b.objective_function(
+                config, fidelity={"budget": budget_i}, run_index=run_index
+            )
+
+            consumed_time += cost_step
+            if DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME:
+                time.sleep(cost_step * DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME)
+
+            objective_i = 1 - eval["function_value"]  # maximizing in deephyper
+
+            # Trial report is not support for MOO in Optuna
+            if DEEPHYPER_BENCHMARK_MOO:
+                continue
+
+            optuna_trial.report(objective_i, step=budget_i)
+            if optuna_trial.should_prune():
+                break
+
+        objective = objective_i
+
+    else:
+        for budget_i in range(min_b, max_b + 1):
+            eval = b.objective_function(
+                config, fidelity={"budget": budget_i}, run_index=run_index
+            )
+
+            consumed_time += cost_step
+            if DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME:
+                time.sleep(cost_step * DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME)
+
+            objective_i = 1 - eval["function_value"]  # maximizing in deephyper
+            job.record(budget_i, objective_i)
+            if job.stopped():
+                break
+
+        objective = job.objective
+
+        if hasattr(job, "stopper") and hasattr(job.stopper, "infos_stopped"):
+            other_metadata["infos_stopped"] = job.stopper.infos_stopped
+
+    metadata = {
+        "budget": budget_i,
+        "stopped": budget_i < max_b,
+        "objective_test": objective_test,
+        "objective_val": objective_val,
+    }
+    metadata.update(other_metadata)
+
+    return {
+        "objective": (objective, -consumed_time)
+        if DEEPHYPER_BENCHMARK_MOO
+        else objective,
+        "metadata": metadata,
+    }
+
+
+if __name__ == "__main__":
+    print(problem)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/hpobench/tabular/install.sh b/src/deephyper_benchmark/lib/hpobench/tabular/install.sh
new file mode 100755
index 0000000..1eb5d47
--- /dev/null
+++ b/src/deephyper_benchmark/lib/hpobench/tabular/install.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+
+set -x
+
+mkdir build/ && cd build
+
+# Clone HPOBench repository
+git clone https://github.com/automl/HPOBench.git
+cd HPOBench/
+
+# Freeze commit used for the benchmark
+git checkout d8b45b1eca9a61c63fe79cdfbe509f77d3f5c779
+
+# Relax the constraint on the Python version required
+sed -i '' 's/>=3.6, <=3.10/>=3.6, <3.12/g' setup.py
+
+# Download data
+mkdir data && cd data/
+
+# For tabular benchmarks
+wget http://ml4aad.org/wp-content/uploads/2019/01/fcnet_tabular_benchmarks.tar.gz
+tar xf fcnet_tabular_benchmarks.tar.gz
+
+# Install other dependencies
+cd ..
+pip install "."
+pip install git+https://github.com/google-research/nasbench.git@master
+
+git clone https://github.com/automl/nas_benchmarks.git
+cd nas_benchmarks
+
+# Freeze commit used for the benchmark
+git checkout 1b09906ba3f522f15766b75643423acccd9db3a5
+
+# Comment out the last line of the file
+sed -i '' '5 s/./#&/' tabular_benchmarks/__init__.py
+
+python setup.py install
+
diff --git a/src/deephyper_benchmark/lib/hpobench/tabular/metrics.py b/src/deephyper_benchmark/lib/hpobench/tabular/metrics.py
new file mode 100644
index 0000000..d135e38
--- /dev/null
+++ b/src/deephyper_benchmark/lib/hpobench/tabular/metrics.py
@@ -0,0 +1,78 @@
+import json
+import os
+
+import numpy as np
+
+from .hpo import b as BENCHMARK
+
+
+class PerformanceEvaluator:
+    """A class defining performance evaluators for the DTLZ problems."""
+
+    def __init__(self):
+        """Read the current problem defn from environment vars."""
+
+        # Retrieve the Best Configuration
+        configs, te, ve = [], [], []
+        for k in BENCHMARK.benchmark.data.keys():
+            configs.append(json.loads(k))
+            te.append(np.min(BENCHMARK.benchmark.data[k]["final_test_error"]))
+            ve.append(np.min(np.mean(BENCHMARK.benchmark.data[k]["valid_mse"], axis=0)))
+
+        idx_opt_ve = np.argmin(ve)
+        idx_opt_te = np.argmin(te)
+
+        # Configuration, Validation Error, Test Error based on the best validation error
+        self.x_min_on_valid = dict(configs[idx_opt_ve])
+        self.y_min_valid_on_valid = ve[idx_opt_ve]
+        self.y_min_test_on_valid = te[idx_opt_ve]
+
+        # Configuration, Validation Error, Test Error based on the best final test error
+        # only available for this benchmark
+        self.x_min_on_test = dict(configs[idx_opt_te])
+        self.y_min_valid_on_test = ve[idx_opt_te]
+        self.y_min_test_on_test = te[idx_opt_te]
+
+    def simple_regret_valid(self, y_valid: np.ndarray) -> np.ndarray:
+        """Compute the regret of the objective (validation RMSE) of a list of ordered solutions.
+
+        Args:
+            y_valid (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return y_valid - self.y_min_valid_on_valid
+
+    def cumul_regret_valid(self, y_valid: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of the objective (validation RMSE) aon n array of ordered solutions.
+
+        Args:
+            y_valid (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret_valid(y_valid))
+
+    def simple_regret_test(self, y_test: np.ndarray) -> np.ndarray:
+        """Compute the regret of the test RMSE of a list of ordered solutions.
+
+        Args:
+            y_test (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return y_test - self.y_min_test_on_test
+
+    def cumul_regret_test(self, y_test: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of the test RMSE aon n array of ordered solutions.
+
+        Args:
+            y_test (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret_test(y_test))
diff --git a/src/deephyper_benchmark/lib/jahsbench/README.md b/src/deephyper_benchmark/lib/jahsbench/README.md
new file mode 100644
index 0000000..26ac239
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/README.md
@@ -0,0 +1,176 @@
+# JAHS Benchmark Suite
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
+
+This module contains a DeepHyper wrapper for
+ [JAHS-Bench-201](https://github.com/automl/jahs_bench_201).
+
+JAHSBench implements a random forest surrogate model, trained on real-world
+performance data for neural networks trained on three standard benchmark
+problems:
+ - ``fashion_mnist`` (**default**)
+ - ``cifar10`` 
+ - ``colorectal_histology``
+
+Using these models as surrogates for the true performance, we can use this
+benchmark problem to study the performance of AutoML techniques on joint
+architecture-hyperparameter search tasks at minimal expense.
+
+The models allow us to tune 2 continuous training hyperparameters
+ - ``LearningRate`` and
+ - ``WeightDecay``,
+
+2 categorical training hyperparameters
+ - ``Activation`` and
+ - ``TrivialAugment``,
+
+and 5 categorical architecture parameters
+ - ``Op{i}`` for ``i=0, ..., 4``.
+
+For DeepHyper's implementation, we have added 9th integer-valued parameter,
+which is the number of epochs trained
+ - ``nepochs``.
+
+When run with the option ``wait=True``, ``JAHSBench`` will wait for an
+amount of time proportional to the ``runtime`` field returned by
+JAHS-Bench-201's surrogates. By default, this is 1% of the true runtime.
+
+The benchmark can be run to tune a single objective (``valid-acc``) or
+three objectives (``valid-acc``, ``latency``, and ``size_MB``).
+*Note that in the original JAHS-Bench-201 benchmark, there are only 2
+objectives and ``size_MB`` is not included as an objective.*
+
+For further information, see:
+
+```
+    @inproceedings{NEURIPS2022_fd78f2f6,
+        author = {Bansal, Archit and Stoll, Danny and Janowski, Maciej and Zela, Arber and Hutter, Frank},
+        booktitle = {Advances in Neural Information Processing Systems},
+        editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
+        pages = {38788--38802},
+        publisher = {Curran Associates, Inc.},
+        title = {JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search},
+        url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/fd78f2f65881c1c7ce47e26b040cf48f-Paper-Datasets_and_Benchmarks.pdf},
+        volume = {35},
+        year = {2022}
+    }
+```
+
+## Installation
+
+To install this benchmark the following command can be run:
+```
+python -c "import deephyper_benchmark as dhb; dhb.install('JAHSBench');"
+```
+
+## Configuration
+
+Prior to initialize the problem, set the following environment variables
+to configure the JAHS-Bench problem:
+- ``DEEPHYPER_BENCHMARK_MOO`` to `0` for single objective runs or `1` for
+  multiobjective runs. Defaults to `1`.
+- ``DEEPHYPER_BENCHMARK_JAHS_PROB`` to one of the following:
+  `fashion_mnist` (default), `cifar10`, or `colorectal_histology`. 
+
+## Metadata
+
+In addition to DeepHyper's standard metadata (timestamps), the following metadata
+is produced by JAHS-Bench-201, and recorded by DeepHyper:
+- ``m:size_MB``,
+- ``m:runtime``,
+- ``m:latency``,
+- ``m:FLOPS``,
+- ``m:valid-acc``,
+- ``m:train-acc``, and
+- ``m:test-acc``.
+
+The ``valid-acc`` is used to compute the objective in the single objective
+case.
+Additionally, the negative values of the ``latency`` and ``size_MB`` are
+used in the multiobjective case.
+All metadata is recorded regardless of the case.
+
+## Usage
+
+To use the benchmark follow this example set of instructions:
+
+```python
+
+import deephyper_benchmark as dhb
+
+
+# Load JAHS-bench-201
+dhb.load("JAHSBench")
+
+from deephyper_benchmark.lib.jahsbench import hpo
+
+# Example of running one evaluation of JAHSBench
+from deephyper.evaluator import RunningJob
+config = hpo.problem.jahs_obj.__sample__() # get a default config to test
+res = hpo.run(RunningJob(parameters=config))
+
+```
+
+Note that JAHS-Bench-201 uses XGBoost, which may not be compatible with older
+versions of MacOS.
+Additionally, the surrogate data has been pickled with an older version
+of scikit-learn and newer versions will fail to correctly load the surrogate
+models.
+
+For more information, see the following GitHub issues:
+ - https://github.com/automl/jahs_bench_201/issues/6
+ - https://github.com/automl/jahs_bench_201/issues/18
+
+## Evaluating Results
+
+To evaluate the results, the AutoML team recommends using the validation
+error for single-objective runs or the hypervolume metric over both
+validation error and evaluation latency for multiobjective-runs.
+See their
+[Evaluation Protocol](https://automl.github.io/jahs_bench_201/evaluation_protocol)
+for more details.
+
+**For multiobjective problems:**
+In their original benchmark, no recommended reference point is given,
+as discussed in
+[this GitHub issue](https://github.com/automl/jahs_bench_201/issues/19).
+Since we have already modified the original problem, we have taken the
+liberty of assigning a reference point.
+This reference point was chosen to include all true solutions for the
+``latency`` and ``size_MB`` objectives.
+These values were chosen by observing the true Pareto front for the raw data.
+However, we have purposefully created a lower bound on the "interesting range"
+for the ``valid-acc`` objective, which is higher than it was before.
+In particular, no accuracies less than 95% will be considered when
+computing hypervolume scores for the Fashion MNIST dataset,
+no accuracies less than 90% are considered for the CIFAR-10 dataset,
+and no accuracies less than 93% are considered for the colorectal histology
+dataset.
+When solving any of these problems via DeepHyper, the ``moo_lower_bounds``
+argument should be set accordingly.
+To evaluate hypervolume with these reference points, use our metrics as
+shown below
+
+```python
+
+from deephyper_benchmark.lib.jahsbench import metrics
+evaluator = metrics.PerformanceEvaluator()
+hv = evaluator.hypervolume(res)
+
+```
+
+## Additional Problem Details
+
+Each problem in JAHS-Bench-201 is given by a XGBoost **surrogate** for a
+joint neural network architecture/hyperparameter search problem.
+Surrogates are given for networks trained to solve image classification tasks
+for three different datasets.
+These datasets are listed below and have the following properties:
+
+- ``fashion_mnist`` is based on the Fashion MNIST dataset (https://github.com/zalandoresearch/fashion-mnist).
+  This problem has lower bound of 95% on the "interesting range" of validation accuracies, and the Kendall's tau for the XGBoost model is over 92% for all 3 objectives (92.2% for the ``valid-acc`` objective).
+- ``cifar10`` is based on the Cifar-10 dataset (https://www.cs.toronto.edu/~kriz/cifar.html)
+  This problem has lower bound of 90% on the "interesting range" of validation accuracies, and the Kendall's tau for the XGBoost model is over 89% for all 3 objectives (89% for the ``valid-acc`` objective).
+- ``colorecal-histology`` is based on the colorectal histology dataset (https://zenodo.org/record/53169#.XGZemKwzbmG).
+  This problem has lower bound of 93% on the "interesting range" of validation accuracies, and the Kendall's tau for the XGBoost model is considerably lower than with the other datasets (just 68.2% for the ``valid-acc`` objective), so it may not be an accurate representation of the true problem.
diff --git a/src/deephyper_benchmark/lib/jahsbench/__init__.py b/src/deephyper_benchmark/lib/jahsbench/__init__.py
new file mode 100644
index 0000000..f102a9c
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/__init__.py
@@ -0,0 +1 @@
+__version__ = "0.0.1"
diff --git a/src/deephyper_benchmark/lib/jahsbench/benchmark.py b/src/deephyper_benchmark/lib/jahsbench/benchmark.py
new file mode 100644
index 0000000..2b99dc3
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/benchmark.py
@@ -0,0 +1,22 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class JAHS201Benchmark(Benchmark):
+    version = "0.0.1"
+    
+    requires = {
+        "py-pip-requirements": {
+            "step": "install",
+            "type": "pip",
+            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
+        },
+        "bash-install": {
+            "step": "install",
+            "type": "cmd",
+            "cmd": "cd . && bash " + os.path.join(DIR, "./install.sh"),
+        },
+    }
diff --git a/src/deephyper_benchmark/lib/jahsbench/hpo.py b/src/deephyper_benchmark/lib/jahsbench/hpo.py
new file mode 100644
index 0000000..4e8738a
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/hpo.py
@@ -0,0 +1,57 @@
+import os
+import numpy as np
+import time
+
+from deephyper.evaluator import profile, RunningJob
+from deephyper.hpo import HpProblem
+from . import model
+
+
+# Read in whether to do single- or multi-objectives
+multiobj = int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 1))
+prob_name = os.environ.get("DEEPHYPER_BENCHMARK_JAHS_PROB", "fashion_mnist")
+
+# Create problem
+problem = HpProblem()
+jahs_obj = model.jahs_bench(dataset=prob_name)
+# 2 continuous hyperparameters
+problem.add_hyperparameter((1e-3, 1.0, "log-uniform"), "LearningRate")
+problem.add_hyperparameter((1e-5, 1e-3, "log-uniform"), "WeightDecay")
+# 2 categorical hyperparameters
+problem.add_hyperparameter(["ReLU", "Hardswish", "Mish"], "Activation")
+problem.add_hyperparameter(["on", "off"], "TrivialAugment")
+# 6 categorical architecture design variables
+for i in range(1, 7):
+    problem.add_hyperparameter([0, 1, 2, 3, 4], f"Op{i}")
+# 1 integer hyperparameter number of training epochs (1 to 200)
+problem.add_hyperparameter((1, 200), "nepochs")
+
+
+@profile
+def run(job: RunningJob, sleep=False, sleep_scale=0.01) -> dict:
+    config = job.parameters
+    result = jahs_obj(config)
+
+    if sleep:
+        t_sleep = result["runtime"] * sleep_scale
+        time.sleep(t_sleep)
+
+    dh_data = {}
+    dh_data["metadata"] = result
+    if multiobj:
+        dh_data["objective"] = [
+            result["valid-acc"],
+            -result["latency"],
+            -result["size_MB"],
+        ]
+    else:
+        dh_data["objective"] = result["valid-acc"]
+    return dh_data
+
+
+if __name__ == "__main__":
+    print(problem)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/jahsbench/install.sh b/src/deephyper_benchmark/lib/jahsbench/install.sh
new file mode 100755
index 0000000..76a97b8
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/install.sh
@@ -0,0 +1 @@
+python -m jahs_bench.download --target surrogates
diff --git a/src/deephyper_benchmark/lib/jahsbench/metrics.py b/src/deephyper_benchmark/lib/jahsbench/metrics.py
new file mode 100644
index 0000000..f6040f7
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/metrics.py
@@ -0,0 +1,119 @@
+import numpy as np
+from deephyper.skopt.moo import pareto_front, hypervolume
+
+
+class PerformanceEvaluator:
+    """ A class defining performance evaluators for JAHS Bench 201 problems.
+
+    Contains the following public methods:
+
+     * `__init__()` constructs a new instance by reading the problem
+       definition from environment variables,
+     * `hypervolume(pts)` calculates the total hypervolume dominated by
+       the current point set, w.r.t. the reference point and filtering out
+       solutions that do not dominate the reference point,
+     * `refPt()` calculates a reference point for the current problem, and
+     * `numPts(pts)` calculates the number of solution points that dominate
+       the reference point.
+
+    """
+
+    def __init__(self):
+        """ Read the current JAHS-Bench-201 problem definition and initialize. """
+
+        import os
+
+        multiobj = int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 1))
+        prob_name = os.environ.get("DEEPHYPER_BENCHMARK_JAHS_PROB", "fashion_mnist")
+        self.p_name = prob_name
+        if multiobj:
+            self.nobjs = 3
+        else:
+            self.nobjs = 1
+
+    def hypervolume(self, pts):
+        """ Calculate the hypervolume dominated, w.r.t. the reference point.
+
+        Args:
+            pts (numpy.ndarray): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            float: The total hypervolume dominated by the current solution
+            w.r.t. the reference point, after filtering out points worse than
+            the reference point.
+
+        """
+
+        if self.nobjs < 2:
+            raise ValueError("Cannot calculate hypervolume for 1 objective")
+        if pts.size > 0 and pts[0, 0] > 0:
+            filtered_pts = -pts.copy()
+        else:
+            filtered_pts = pts.copy()
+        rp = self.refPt()
+        for i in range(pts.shape[0]):
+            if np.any(filtered_pts[i, :] > rp):
+                filtered_pts[i, :] = rp
+        return hypervolume(filtered_pts, rp)
+
+    def refPt(self):
+        """ Calculate the reference point for the given problem definition. """
+
+        if self.p_name in ["fashion_mnist"]:
+            rp = np.ones(self.nobjs)
+            rp[0] = -95
+            if self.nobjs > 1:
+                rp[1] = 1.75
+                rp[2] = 0.6
+            return rp
+        elif self.p_name in ["cifar10"]:
+            rp = np.ones(self.nobjs)
+            rp[0] = -90
+            if self.nobjs > 1:
+                rp[1] = 4.0
+                rp[2] = 0.9
+            return rp
+        elif self.p_name in ["colorectal_histology"]:
+            rp = np.ones(self.nobjs)
+            rp[0] = -93
+            if self.nobjs > 1:
+                rp[1] = 4.0
+                rp[2] = 0.4
+            return rp
+        else:
+            raise ValueError(f"{self.p_name} is not a valid problem")
+
+    def numPts(self, pts):
+        """ Calculate the number of solutions that dominate the reference point.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            int: The number of fi in pts such that all(fi < self.refPt).
+
+        """
+
+        if np.any(pts < 0):
+            pareto_pts = pareto_front(-pts)
+        else:
+            pareto_pts = pareto_front(pts)
+        return sum([all(fi <= self.refPt()) for fi in pareto_pts])
+
+
+if __name__ == "__main__":
+    """ Driver code to test performance metrics. """
+
+    result = np.array([[90, -1, -2],
+                       [94, -0.1, -0.2],
+                       [96, -0.75, -0.1],
+                       [99.0, -0.5, -200.0]])
+
+    evaluator = PerformanceEvaluator()
+
+    assert abs(evaluator.hypervolume(result) - 0.5) < 1.0e-8
+    assert evaluator.numPts(result) == 1
+    assert np.all(np.abs(evaluator.refPt() - np.array([-95.0, 1.75, 0.6]))
+                  < 1.0e-8)
diff --git a/src/deephyper_benchmark/lib/jahsbench/model.py b/src/deephyper_benchmark/lib/jahsbench/model.py
new file mode 100644
index 0000000..6f0eef4
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/model.py
@@ -0,0 +1,80 @@
+""" This module contains objective function implementations of the JAHS 201
+benchmark suite, implemented as DeepHyper compatible models.
+
+"""
+
+import numpy as np
+
+class jahs_bench:
+    """ A callable class implementing the JAHS benchmark problems. """
+
+    def __init__(self, dataset="fashion_mnist"):
+        """ Import and configure the jahs-bench module.
+
+        Args:
+            dataset (str): One of "cifar10", "colorectal_histology",
+                or "fashion_mnist" (default)
+
+        """
+
+        from jahs_bench.api import Benchmark
+        import os
+
+        ### JAHS bench settings ###
+        MODEL_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+                                  "jahs_bench_data")
+        # Define the benchmark
+        self.benchmark = Benchmark(
+                task=dataset,
+                save_dir=MODEL_PATH,
+                kind="surrogate",
+                download=False
+            )
+
+    def __sample__(self):
+        """ Randomly sample a JAHS-Bench-201 configuration.
+
+        Returns:
+            dict: A configuration dictionary.
+
+        """
+
+        config = self.benchmark.sample_config()
+        config['nepochs'] = np.random.randint(1, 200)
+        return config
+
+    def __call__(self, x):
+        """ DeepHyper compatible objective function calling jahs-bench.
+
+        Args:
+            x (dict): Configuration dictionary with same keys as jahs-bench.
+
+        Returns:
+            tuple of floats: In order: accuracy (to be maximized), latency
+            (to be minimized).
+
+        """
+
+        # Default config
+        config = {
+            'Optimizer': 'SGD',
+            'N': 5,
+            'W': 16,
+            'Resolution': 1.0,
+        }
+        # Update config using x
+        for key in x.keys():
+            config[key] = x[key]
+        # Special rule for setting "TrivialAugment"
+        if x['TrivialAugment'] == "on":
+            config['TrivialAugment'] = True
+        else:
+            config['TrivialAugment'] = False
+        # Check for nepochs
+        nepochs = 200
+        if 'nepochs' in x.keys():
+            nepochs = x['nepochs']
+        # Evaluate and return
+        fx = np.zeros(2)
+        result = self.benchmark(config, nepochs=nepochs)
+        return result[nepochs]
diff --git a/src/deephyper_benchmark/lib/jahsbench/requirements.txt b/src/deephyper_benchmark/lib/jahsbench/requirements.txt
new file mode 100644
index 0000000..702f41c
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/requirements.txt
@@ -0,0 +1,2 @@
+jahs-bench
+xgboost
diff --git a/src/deephyper_benchmark/lib/lcbench/Makefile b/src/deephyper_benchmark/lib/lcbench/Makefile
new file mode 100644
index 0000000..cb13f98
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcbench/Makefile
@@ -0,0 +1,14 @@
+
+COMMIT=8de69dc0e7f7b4baee7129d599e74cc3e09b6d06
+
+build:
+	mkdir -p build
+	wget https://github.com/automl/LCBench/archive/$(COMMIT).zip -O build/LCBench.zip
+	unzip build/LCBench.zip -d build/
+	mv build/LCBench-$(COMMIT) build/LCBench
+	mkdir -p build/LCBench/data
+	wget https://figshare.com/ndownloader/files/21188607 -O build/LCBench/data/data_2k.zip
+	unzip build/LCBench/data/data_2k.zip -d build/LCBench/data/
+
+clean:
+	rm -rf build/
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcbench/README.md b/src/deephyper_benchmark/lib/lcbench/README.md
new file mode 100644
index 0000000..14ff7ae
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcbench/README.md
@@ -0,0 +1,19 @@
+# LCBench
+
+✅ **Benchmark ready to be used.**
+
+This benchmark is only compatible with random search and only interesting to benchmark multi-fidelity algorithms that do not consider hyperparameter optimization. Candidate learners are represented by an id.
+
+## Installation
+
+To install the `LCBench` benchmark, run:
+```console
+python -c "import deephyper_benchmark as dhb; dhb.install('LCBench');"
+```
+
+Run the hyperparameter search
+```
+import deephyper_benchmark as dhb
+dhb.load("LCBench")
+from deephyper_benchmark.lib.lcbench import hpo
+```
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcbench/__init__.py b/src/deephyper_benchmark/lib/lcbench/__init__.py
new file mode 100644
index 0000000..68a63b1
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcbench/__init__.py
@@ -0,0 +1,2 @@
+
+datasets = ['APSFailure', 'Amazon_employee_access', 'Australian', 'Fashion-MNIST', 'KDDCup09_appetency', 'MiniBooNE', 'adult', 'airlines', 'albert', 'bank-marketing', 'blood-transfusion-service-center', 'car', 'christine', 'cnae-9', 'connect-4', 'covertype', 'credit-g', 'dionis', 'fabert', 'helena', 'higgs', 'jannis', 'jasmine', 'jungle_chess_2pcs_raw_endgame_complete', 'kc1', 'kr-vs-kp', 'mfeat-factors', 'nomao', 'numerai28.6', 'phoneme', 'segment', 'shuttle', 'sylvine', 'vehicle', 'volkert']
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcbench/benchmark.py b/src/deephyper_benchmark/lib/lcbench/benchmark.py
new file mode 100644
index 0000000..18ddea6
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcbench/benchmark.py
@@ -0,0 +1,18 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class LCBench(Benchmark):
+    version = "0.0.1"
+
+    requires = {
+        "makefile": {"step": "install", "type": "cmd", "cmd": "make build"},
+        "lcdbench-api": {
+            "step": "load",
+            "type": "pythonpath",
+            "path": f"{DIR}/../build/LCBench/",
+        },
+    }
diff --git a/src/deephyper_benchmark/lib/lcbench/hpo.py b/src/deephyper_benchmark/lib/lcbench/hpo.py
new file mode 100644
index 0000000..9902dc0
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcbench/hpo.py
@@ -0,0 +1,78 @@
+import os
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+from deephyper.evaluator import profile, RunningJob
+from deephyper.hpo import HpProblem
+
+dataset = os.environ.get("DEEPHYPER_BENCHMARK_LCBENCH_DATASET", "APSFailure")
+
+data_path = os.path.join(DIR, "../build/LCBench/data/data_2k.json")
+
+from api import Benchmark as LCBenchBenchmark
+bench = LCBenchBenchmark(data_path, cache=True, cache_dir=os.path.join(DIR, "../build/LCBench/data/"))
+
+num_elements = 2000
+
+problem = HpProblem()
+problem.add_hyperparameter((0, num_elements - 1), "index")
+
+
+@profile
+def run(job: RunningJob, optuna_trial=None) -> dict:
+
+    
+    # otherwise failure
+    idx = job.parameters["index"]
+    y_valid = bench.query(dataset, "Train/val_cross_entropy", idx)[1:51]
+    y_test  = bench.query(dataset, "Train/test_cross_entropy", idx)[1:51]
+
+    min_b, max_b = 1, 50 
+
+    objective_val = -y_valid[-1]
+    objective_test = -y_test[-1]
+
+    other_metadata = {}
+
+    if optuna_trial:
+
+        for budget_i in range(min_b, max_b + 1):
+            objective_i = -y_valid[budget_i-1] # maximizing in deephyper
+            optuna_trial.report(objective_i, step=budget_i)
+            if optuna_trial.should_prune():
+                break
+
+        objective = objective_i
+
+    else:
+
+        for budget_i in range(min_b, max_b + 1):
+            objective_i = -y_valid[budget_i-1]  # maximizing in deephyper
+            job.record(budget_i, objective_i)
+            if job.stopped():
+                break
+
+        objective = job.objective
+
+        if hasattr(job, "stopper") and hasattr(job.stopper, "infos_stopped"):
+            other_metadata["infos_stopped"] = job.stopper.infos_stopped
+
+    metadata = {
+        "budget": budget_i,
+        "stopped": budget_i < max_b,
+        "objective_test": objective_test,
+        "objective_val": objective_val,
+    }
+    metadata.update(other_metadata)
+    return {
+        "objective": objective,
+        "metadata": metadata,
+    }
+
+
+if __name__ == "__main__":
+    print(problem)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/lcdb/README.md b/src/deephyper_benchmark/lib/lcdb/README.md
new file mode 100644
index 0000000..a9d798d
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcdb/README.md
@@ -0,0 +1,4 @@
+# Learning Curve Databased Benchmark
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
diff --git a/src/deephyper_benchmark/lib/lcdb/algorithm_selection/__init__.py b/src/deephyper_benchmark/lib/lcdb/algorithm_selection/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/__init__.py b/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/benchmark.py b/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/benchmark.py
new file mode 100644
index 0000000..377c4c8
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/benchmark.py
@@ -0,0 +1,12 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class LCDBHyperparameterOptimization(Benchmark):
+    """LCDB 2.0
+    """
+
+    version = "0.0.1"
diff --git a/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/hpo.py b/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/hpo.py
new file mode 100644
index 0000000..45100ae
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/hpo.py
@@ -0,0 +1,216 @@
+import gzip
+import os
+import time
+
+import numpy as np
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+from deephyper.evaluator import RunningJob, profile
+from deephyper.hpo import HpProblem
+from lcdb.analysis import read_csv_results
+from lcdb.analysis.json import (
+    QueryAnchorValues,
+    QueryMetricValuesFromAnchors,
+    QueryEpochValues,
+    QueryMetricValuesFromEpochs,
+)
+from lcdb.analysis.score import balanced_accuracy_from_confusion_matrix
+from lcdb.utils import import_attr_from_module
+
+# DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME = bool(
+#     int(os.environ.get("DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME", 0))
+# )
+# DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME = float(
+#     os.environ.get("DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME", 1.0)
+# )
+
+# DEEPHYPER_BENCHMARK_FIDELITY = os.environ.get("DEEPHYPER_BENCHMARK_FIDELITY", "anchor")
+# DEEPHYPER_BENCHMARK_WORKFLOW = "lcdb.workflow.sklearn.KNNWorkflow"
+# DEEPHYPER_BENCHMARK_CSV = os.environ.get(
+#     "DEEPHYPER_BENCHMARK_CSV",
+#     "/Users/romainegele/Documents/Research/LCDB/lcdb/publications/2023-neurips/experiments/alcf/polaris/knn/output/lcdb.workflow.sklearn.KNNWorkflow/3/42-42-42/results.csv.gz",
+# )
+DEEPHYPER_BENCHMARK_MAX_FIDELITY = int(
+    os.environ.get("DEEPHYPER_BENCHMARK_MAX_FIDELITY", 100)
+)
+DEEPHYPER_BENCHMARK_FIDELITY = os.environ.get("DEEPHYPER_BENCHMARK_FIDELITY", "epoch")
+DEEPHYPER_BENCHMARK_CSV = os.environ.get(
+    "DEEPHYPER_BENCHMARK_CSV",
+    "/Users/romainegele/Documents/Research/LCDB/lcdb/publications/2023-neurips/experiments/alcf/polaris/densenn/output/lcdb.workflow.keras.DenseNNWorkflow/3/42-42-42/results.csv.gz",
+)
+
+
+# WorkflowClass = import_attr_from_module(DEEPHYPER_BENCHMARK_WORKFLOW)
+# config_space = WorkflowClass.config_space()
+# problem = HpProblem(config_space=config_space)
+problem = HpProblem()
+
+with gzip.GzipFile(DEEPHYPER_BENCHMARK_CSV, "rb") as f:
+    r_df, r_df_failed = read_csv_results(f)
+DEEPHYPER_BENCHMARK_DATA = r_df
+
+if DEEPHYPER_BENCHMARK_FIDELITY == "anchor":
+    query_anchor_values = QueryAnchorValues()
+    query_confusion_matrix_values_on_val = QueryMetricValuesFromAnchors(
+        "confusion_matrix", split_name="val"
+    )
+    query_confusion_matrix_values_on_test = QueryMetricValuesFromAnchors(
+        "confusion_matrix", split_name="test"
+    )
+elif DEEPHYPER_BENCHMARK_FIDELITY == "epoch":
+    # Load the learning "epoch"-curve at last epoch
+    query_anchor_values_ = QueryEpochValues()
+    query_confusion_matrix_values_on_val_ = QueryMetricValuesFromEpochs(
+        "confusion_matrix", split_name="val"
+    )
+    query_confusion_matrix_values_on_test_ = QueryMetricValuesFromEpochs(
+        "confusion_matrix", split_name="test"
+    )
+    query_anchor_values = lambda x: query_anchor_values_(x)[-1]
+    query_confusion_matrix_values_on_val = (
+        lambda x: query_confusion_matrix_values_on_val_(x)[-1]
+    )
+    query_confusion_matrix_values_on_test = (
+        lambda x: query_confusion_matrix_values_on_test_(x)[-1]
+    )
+
+    source = DEEPHYPER_BENCHMARK_DATA["m:json"]
+    values = source.apply(lambda x: query_confusion_matrix_values_on_val(x)).to_list()
+    mask = [len(x) > 0 for x in values]
+    DEEPHYPER_BENCHMARK_DATA = DEEPHYPER_BENCHMARK_DATA[mask]
+
+    source = DEEPHYPER_BENCHMARK_DATA["m:json"]
+    values = source.apply(lambda x: query_confusion_matrix_values_on_test(x)).to_list()
+    mask = [len(x) > 0 for x in values]
+    DEEPHYPER_BENCHMARK_DATA = DEEPHYPER_BENCHMARK_DATA[mask]
+else:
+    raise ValueError(f"Unknown fidelity: {DEEPHYPER_BENCHMARK_FIDELITY}")
+
+problem.add_hyperparameter((0, len(DEEPHYPER_BENCHMARK_DATA) - 1), "eval_id")
+
+
+# TODO: To test LCPFN with accuracy metric
+def query_balanced_accuracy_values_on_val(x):
+    cm = query_confusion_matrix_values_on_val(x)
+    return list(map(balanced_accuracy_from_confusion_matrix, cm))
+
+
+def query_coefficient_of_determiniation_values_on_val(x):
+    cm = query_confusion_matrix_values_on_val(x)
+    num_classes = len(cm[0])
+    balanced_error_rate_baseline = 1 - 1 / num_classes
+    return list(
+        map(
+            lambda x: 1
+            - (1 - balanced_accuracy_from_confusion_matrix(x))
+            / balanced_error_rate_baseline,
+            cm,
+        )
+    )
+
+
+def query_coefficient_of_determiniation_values_on_test(x):
+    cm = query_confusion_matrix_values_on_test(x)
+    num_classes = len(cm[0])
+    balanced_error_rate_baseline = 1 - 1 / num_classes
+    return list(
+        map(
+            lambda x: 1
+            - (1 - balanced_accuracy_from_confusion_matrix(x))
+            / balanced_error_rate_baseline,
+            cm,
+        )
+    )
+
+
+@profile
+def run(job: RunningJob, optuna_trial=None) -> dict:
+    # otherwise failure
+    eval_id = job.parameters["eval_id"]
+
+    source = DEEPHYPER_BENCHMARK_DATA.iloc[eval_id]["m:json"]
+    anchor_values = query_anchor_values(source)
+
+    # TODO: to test with LCPFN
+    objective_values = query_balanced_accuracy_values_on_val(source)
+
+    objective_val_values = query_coefficient_of_determiniation_values_on_val(source)
+    objective_test_values = query_coefficient_of_determiniation_values_on_test(source)
+    length = min(len(objective_val_values), len(objective_test_values))
+    anchor_values = anchor_values[:length]
+    objective_values = objective_values[:length]
+    objective_val_values = objective_val_values[:length]
+    objective_test_values = objective_test_values[:length]
+
+    objective_values = np.array(objective_values)
+    objective_val_values = np.array(objective_val_values)
+    objective_test_values = np.array(objective_test_values)
+    anchor_values = np.array(anchor_values)
+
+    mask = anchor_values <= DEEPHYPER_BENCHMARK_MAX_FIDELITY
+    anchor_values = anchor_values[mask].tolist()
+    objective_values = objective_values[mask].tolist()
+    objective_val_values = objective_val_values[mask].tolist()
+    objective_test_values = objective_test_values[mask].tolist()
+
+    if anchor_values[-1] <= DEEPHYPER_BENCHMARK_MAX_FIDELITY:
+        anchor_values.append(DEEPHYPER_BENCHMARK_MAX_FIDELITY)
+        objective_values.append(objective_values[-1])
+        objective_val_values.append(objective_val_values[-1])
+        objective_test_values.append(objective_test_values[-1])
+
+    min_b, max_b = anchor_values[0], anchor_values[-1]
+
+    other_metadata = {}
+
+    consumed_time = 0  # the cost here corresponds to time
+
+    if optuna_trial:
+        for i, budget_i in enumerate(anchor_values):
+            objective_i = objective_val_values[i]
+
+            optuna_trial.report(objective_i, step=budget_i)
+            if optuna_trial.should_prune():
+                break
+
+        objective = objective_i
+
+    else:
+        for i, budget_i in enumerate(anchor_values):
+            # objective_i = objective_val_values[i]
+            # TODO: to test with LCPFN
+            objective_i = objective_values[i]
+
+            job.record(budget_i, objective_i)
+            if job.stopped():
+                break
+
+        objective = job.objective
+
+        if hasattr(job, "stopper") and hasattr(job.stopper, "infos_stopped"):
+            other_metadata["infos_stopped"] = job.stopper.infos_stopped
+
+    metadata = {
+        "budget": budget_i,
+        "stopped": budget_i < max_b,
+        "objective_test": objective_test_values[-1],
+        "objective_val": objective_val_values[-1],
+    }
+    metadata.update(other_metadata)
+
+    return {
+        "objective": objective,
+        "metadata": metadata,
+    }
+
+
+if __name__ == "__main__":
+    print(problem)
+    # default_config = problem.default_configuration
+    for eval_id in range(10):
+        default_config = dict(eval_id=eval_id)
+        print(f"{default_config=}")
+        result = run(RunningJob(parameters=default_config))
+        print(f"{result=}")
+        print()
diff --git a/src/deephyper_benchmark/lib/lcmbench/README.md b/src/deephyper_benchmark/lib/lcmbench/README.md
new file mode 100644
index 0000000..f65fb4a
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/README.md
@@ -0,0 +1,4 @@
+# LCMBench: Learning Curve Models Benchmark
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
diff --git a/src/deephyper_benchmark/lib/lcmbench/lcdb/README.md b/src/deephyper_benchmark/lib/lcmbench/lcdb/README.md
new file mode 100644
index 0000000..046e4b5
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/lcdb/README.md
@@ -0,0 +1,3 @@
+# LCDB: Learning Curve DataBase
+
+Benchmark based on the LCDB project: https://github.com/fmohr/lcdb
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcmbench/lcdb/__init__.py b/src/deephyper_benchmark/lib/lcmbench/lcdb/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/lcmbench/lcdb/benchmark.py b/src/deephyper_benchmark/lib/lcmbench/lcdb/benchmark.py
new file mode 100644
index 0000000..8067a67
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/lcdb/benchmark.py
@@ -0,0 +1,11 @@
+from deephyper_benchmark import *
+
+
+class LCDBBenchmark(Benchmark):
+
+    version = "0.0.1"
+
+    requires = {
+        "py-lcdb": {"type": "pip", "name": "lcdb"},
+        "py-func-timeout": {"type": "pip", "name": "func-timeout"},
+    }
diff --git a/src/deephyper_benchmark/lib/lcmbench/lcdb/hpo.py b/src/deephyper_benchmark/lib/lcmbench/lcdb/hpo.py
new file mode 100644
index 0000000..280ce6e
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/lcdb/hpo.py
@@ -0,0 +1,128 @@
+import numpy as np
+
+import lcdb
+
+from deephyper.evaluator import profile, RunningJob
+from deephyper.hpo import HpProblem
+
+
+df_meta = lcdb.get_meta_features()
+
+all_openmlid = df_meta["openmlid"].tolist()
+all_name = df_meta["Name"].tolist()
+all_algorithms = [
+    "SVC_linear",
+    "SVC_poly",
+    "SVC_rbf",
+    "SVC_sigmoid",
+    "sklearn.tree.DecisionTreeClassifier",
+    "sklearn.tree.ExtraTreeClassifier",
+    "sklearn.linear_model.LogisticRegression",
+    "sklearn.linear_model.PassiveAggressiveClassifier",
+    "sklearn.linear_model.Perceptron",
+    "sklearn.linear_model.RidgeClassifier",
+    "sklearn.linear_model.SGDClassifier",
+    "sklearn.neural_network.MLPClassifier",
+    # "sklearn.discriminant_analysis.LinearDiscriminantAnalysis",
+    # "sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis",
+    "sklearn.naive_bayes.BernoulliNB",
+    # "sklearn.naive_bayes.MultinomialNB", # often missing for large datasets
+    "sklearn.neighbors.KNeighborsClassifier",
+    "sklearn.ensemble.ExtraTreesClassifier",
+    "sklearn.ensemble.RandomForestClassifier",
+    "sklearn.ensemble.GradientBoostingClassifier",
+]
+
+problem = HpProblem()
+problem.add_hyperparameter(all_algorithms, "model")
+
+
+@profile
+def run(job: RunningJob, optuna_trial=None, task_id=3) -> dict:
+
+    model = job.parameters["model"]
+
+    try:
+        curve = lcdb.get_curve(task_id, model, metric="accuracy")
+    except Exception:
+        if optuna_trial:
+            objective = 0  # accuracy 0
+        else:
+            objective = [[1], [0]]  # accuracy 0 at step 1
+        return {
+            "objective": objective,
+            "metadata": {
+                "budget": budget_i,
+                "stopped": budget_i < anchors[-1],
+                "duration": cum_time,
+            },
+        }
+
+    anchors, _, scores_valid, _ = curve
+    _, times = lcdb.get_train_times(task_id, model)
+
+    # scores_valid = [list(v) for v in scores_valid]
+    # times = [list(t) for t in times]
+
+    anchors = np.array(anchors).tolist()
+    # print(model, type(scores_valid), type(scores_valid[0]), type(scores_valid[0][0]))
+    # scores_valid = np.array(np.array(scores_valid).tolist())
+    # times = np.array(np.array(times).tolist())
+
+    # print("->", np.ndim(scores_valid))
+    # print("->", scores_valid)
+    # print("->", np.shape(scores_valid))
+
+    # if np.ndim(scores_valid) == 2:
+    # scores_valid = np.mean(scores_valid, axis=1)
+    scores_valid = np.array([v[0] for v in scores_valid])
+    times = np.array([t[0] for t in times])
+
+    scores_valid = scores_valid.tolist()
+    times = times.tolist()
+
+    cum_time = 0
+
+    if optuna_trial:
+
+        for i, budget_i in enumerate(anchors):
+            objective_i = scores_valid[i]
+            cum_time += times[i]
+            optuna_trial.report(objective_i, step=budget_i)
+            if optuna_trial.should_prune():
+                break
+
+        return {
+            "objective": objective_i,
+            "metadata": {
+                "budget": budget_i,
+                "stopped": budget_i < anchors[-1],
+                "duration": cum_time,
+            },
+        }
+
+    else:
+
+        for i, budget_i in enumerate(anchors):
+            objective_i = scores_valid[i]
+            cum_time += times[i]
+            job.record(budget_i, objective_i)
+            if job.stopped():
+                break
+
+        return {
+            "objective": job.observations,
+            "metadata": {
+                "budget": budget_i,
+                "stopped": budget_i < anchors[-1],
+                "duration": cum_time,
+            },
+        }
+
+
+if __name__ == "__main__":
+    print(problem)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/lcmbench/loglin2/README.md b/src/deephyper_benchmark/lib/lcmbench/loglin2/README.md
new file mode 100644
index 0000000..e9bd949
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/loglin2/README.md
@@ -0,0 +1,5 @@
+# `LogLin2`
+
+Benchmark for Multi-Fidelity Black-Box Optimization.
+
+$$f_\text{loglin2}(z;\rho) = b(z)^{\rho_1} \cdot \exp{\rho_0}$$
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcmbench/loglin2/__init__.py b/src/deephyper_benchmark/lib/lcmbench/loglin2/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/lcmbench/loglin2/benchmark.py b/src/deephyper_benchmark/lib/lcmbench/loglin2/benchmark.py
new file mode 100644
index 0000000..ca8cd5b
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/loglin2/benchmark.py
@@ -0,0 +1,12 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class LogLin2Benchmark(Benchmark):
+
+    version = "0.0.1"
+
+    requires = {}
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcmbench/loglin2/hpo.py b/src/deephyper_benchmark/lib/lcmbench/loglin2/hpo.py
new file mode 100644
index 0000000..2382caa
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/loglin2/hpo.py
@@ -0,0 +1,71 @@
+import os
+
+import numpy as np
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+from deephyper.evaluator import profile, RunningJob
+from deephyper.hpo import HpProblem
+
+
+problem = HpProblem()
+problem.add_hyperparameter((0.0, 1.0), "rho_0")
+problem.add_hyperparameter((0.0, 1.0/25.0), "rho_1")
+
+
+def f_loglin2(b, rho):
+    return np.power(b, rho[1]) * np.exp(rho[0])
+
+
+@profile
+def run(job: RunningJob, optuna_trial=None) -> dict:
+
+    # otherwise failure
+    min_b, max_b = 1, 100
+
+    rho = [job.parameters["rho_0"], job.parameters["rho_1"]]
+    f = lambda b: f_loglin2(b, rho)
+
+    objective_test = f(max_b)
+
+    if optuna_trial:
+
+        for budget_i in range(min_b, max_b + 1):
+            objective_i = f(budget_i)
+            optuna_trial.report(objective_i, step=budget_i)
+            if optuna_trial.should_prune():
+                break
+
+        return {
+            "objective": objective_i,
+            "metadata": {
+                "budget": budget_i,
+                "stopped": budget_i < max_b,
+                "objective_test": objective_test,
+            },
+        }
+
+    else:
+
+        for budget_i in range(min_b, max_b + 1):
+            objective_i = f(budget_i)
+            job.record(budget_i, objective_i)
+            if job.stopped():
+                break
+
+        return {
+            "objective": job.observations,
+            "metadata": {
+                "budget": budget_i,
+                "stopped": budget_i < max_b,
+                "objective_test": objective_test,
+            },
+        }
+
+
+if __name__ == "__main__":
+    print(problem)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/lcmbench/pow3/README.md b/src/deephyper_benchmark/lib/lcmbench/pow3/README.md
new file mode 100644
index 0000000..01c296a
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/pow3/README.md
@@ -0,0 +1,5 @@
+# `Pow3`: Inverse Power Law with 3 Parameters
+
+Inverse-Power Law benchmark for Multi-Fidelity Black-Box Optimization.
+
+$$f_\text{pow3}(b;\rho) = \rho_0 + \rho_1 \cdot b^{-\rho_2}$$
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcmbench/pow3/__init__.py b/src/deephyper_benchmark/lib/lcmbench/pow3/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/lcmbench/pow3/benchmark.py b/src/deephyper_benchmark/lib/lcmbench/pow3/benchmark.py
new file mode 100644
index 0000000..e740b42
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/pow3/benchmark.py
@@ -0,0 +1,12 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class Pow3Benchmark(Benchmark):
+
+    version = "0.0.1"
+
+    requires = {}
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcmbench/pow3/hpo.py b/src/deephyper_benchmark/lib/lcmbench/pow3/hpo.py
new file mode 100644
index 0000000..2f994a3
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/pow3/hpo.py
@@ -0,0 +1,81 @@
+import os
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+from deephyper.evaluator import profile, RunningJob
+from deephyper.hpo import HpProblem
+
+
+problem = HpProblem()
+problem.add_hyperparameter((1e-6, 10, "log-uniform"), "alpha")
+problem.add_hyperparameter((1.0, 10.0), "beta")
+problem.add_hyperparameter((0.1, 0.5), "gamma")
+
+# optimum is at
+# C(s=100, alpha=1e-6, beta=1.0, gamma=0.5) -> 0.100001
+
+
+def ipl_curve(s, alpha, beta, gamma):
+    """Inverse Power-Law Model"""
+    return alpha + beta * s**-gamma
+
+
+@profile
+def run(job: RunningJob, optuna_trial=None) -> dict:
+
+    print(f"{job.id=}")
+
+    # otherwise failure
+    min_b, max_b = 1, 1000
+
+    alpha = job.parameters["alpha"]
+    beta = job.parameters["beta"]
+    gamma = job.parameters["gamma"]
+
+    objective_test = -ipl_curve(max_b, alpha, beta, gamma)
+
+    if optuna_trial:
+
+        for budget_i in range(min_b, max_b + 1):
+            objective_i = -ipl_curve(
+                budget_i, alpha, beta, gamma
+            )  # maximisation in deephyper
+            optuna_trial.report(objective_i, step=budget_i)
+            if optuna_trial.should_prune():
+                break
+
+        return {
+            "objective": objective_i,
+            "metadata": {
+                "budget": budget_i,
+                "stopped": budget_i < max_b,
+                "objective_test": objective_test,
+            },
+        }
+
+    else:
+
+        for budget_i in range(min_b, max_b + 1):
+            objective_i = -ipl_curve(
+                budget_i, alpha, beta, gamma
+            )  # maximisation in deephyper
+            job.record(budget_i, objective_i)
+            if job.stopped():
+                break
+
+        return {
+            "objective": job.observations,
+            "metadata": {
+                "budget": budget_i,
+                "stopped": budget_i < max_b,
+                "objective_test": objective_test,
+            },
+        }
+
+
+if __name__ == "__main__":
+    print(problem)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/pinnbench/Burgers/__init__.py b/src/deephyper_benchmark/lib/pinnbench/Burgers/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/pinnbench/Burgers/benchmark.py b/src/deephyper_benchmark/lib/pinnbench/Burgers/benchmark.py
new file mode 100644
index 0000000..cfc92cf
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/Burgers/benchmark.py
@@ -0,0 +1,17 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class PINNDiffusionReactionBenchmark(Benchmark):
+    version = "0.0.1"
+
+    requires = {
+        "py-pip-requirements": {
+            "step": "install",
+            "type": "pip",
+            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
+        },
+    }
diff --git a/src/deephyper_benchmark/lib/pinnbench/Burgers/hpo.py b/src/deephyper_benchmark/lib/pinnbench/Burgers/hpo.py
new file mode 100644
index 0000000..43ab995
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/Burgers/hpo.py
@@ -0,0 +1,111 @@
+import numpy as np
+
+from deephyper.hpo import HpProblem
+from deephyper.search.hps import CBO
+from .model import get_data, PINN, BurgerSupervisor, plotter
+from deephyper.stopper import LCModelStopper
+from deephyper.evaluator import profile, RunningJob
+
+
+@profile
+def run(job: RunningJob) -> dict:
+    # load data
+    nu = 0.01 / np.pi
+    train, val, test = get_data(NT=200, NX=128, X_U=1, X_L=-1, T_max=1, Nu=nu)
+
+    config = job.parameters
+    min_b = 1
+    max_b = 1000
+
+    num_layers = config["num_layers"]
+    hidden_dim = config["hidden_dim"]
+    output_dim = 1
+    epochs = 100
+    lr = config["lr"]
+    alpha = config["alpha"]
+    activation = config["activation"]
+    net = PINN(
+        num_layers=num_layers,
+        hidden_dim=hidden_dim,
+        output_dim=output_dim,
+        act_fn=activation,
+    )
+    sup = BurgerSupervisor(nu, net, epochs, lr, alpha)
+
+    for budget_i in range(min_b, max_b + 1):
+        train_loss, eval = sup.step(train, val)
+        objective_i = -eval  # maximizing in deephyper
+        job.record(budget_i, objective_i)
+        if job.stopped():
+            break
+    objective = job.objective
+
+    # calculate test scores
+    pred_test = sup.net(test["x"][:, 0], test["x"][:, 1])
+    test_loss = np.mean((test["y"] - pred_test) ** 2)
+
+    metadata = {
+        "num_parameters": sup.net.count_params(),
+        "train_loss": train_loss,
+        "val_loss": eval,
+        "test_loss": test_loss,
+        "budget": budget_i,
+        "stopped": budget_i < max_b,
+        "infos_stopped": job.stopper.infos_stopped
+        if hasattr(job, "stopper") and hasattr(job.stopper, "infos_stopped")
+        else None,
+    }
+    return {"objective": objective, "metadata": metadata}
+
+
+# define the search space
+problem = HpProblem()
+problem.add_hyperparameter((5, 20), "num_layers", default_value=5)
+problem.add_hyperparameter((1e-5, 1e-2), "lr", default_value=0.01)
+problem.add_hyperparameter((5, 50), "hidden_dim", default_value=5)
+problem.add_hyperparameter((0.0, 5.0), "alpha", default_value=0.5)
+problem.add_hyperparameter(
+    ["relu", "leaky_relu", "tanh", "elu", "gelu", "sigmoid"],
+    "activation",
+    default_value="tanh",
+)
+
+
+def evaluate(config):
+    """
+    Evaluate an hyperparameter configuration
+    on training/validation and testing data.
+    """
+    nu = 0.01 / np.pi
+    train, val, test = get_data(NT=200, NX=100, X_U=1.0, X_L=-1.0, T_max=1, Nu=nu)
+    num_layers = config["num_layers"]
+    hidden_dim = config["hidden_dim"]
+    output_dim = 1
+    epochs = config["epochs"]
+    lr = config["lr"]
+    alpha = config["alpha"]
+    activation = config["activation"]
+    net = PINN(
+        num_layers=num_layers,
+        hidden_dim=hidden_dim,
+        output_dim=output_dim,
+        act_fn=activation,
+    )
+    sup = BurgerSupervisor(nu=nu, net=net, epochs=epochs, lr=lr, alpha=alpha)
+    val_f_loss = sup.train(train, test)
+
+
+if __name__ == "__main__":
+    import time
+
+    start_time = time.time()
+    stopper = LCModelStopper(max_steps=1000, lc_model="mmf4")
+    scheduler = {
+        "type": "periodic-exp-decay",
+        "periode": 25,
+        "rate": 0.1,
+    }
+
+    default_config = problem.default_configuration
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/pinnbench/Burgers/model.py b/src/deephyper_benchmark/lib/pinnbench/Burgers/model.py
new file mode 100644
index 0000000..fb2a476
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/Burgers/model.py
@@ -0,0 +1,411 @@
+import numpy as np
+import tensorflow as tf
+import phi.flow as pl
+from scipy.stats import qmc
+
+
+def BurgersSolver(
+    NT: int, NX: int, X_U: float, X_L: float, T_max: float, Nu: float, IN_cond
+):
+    """
+    Burgers equation solver with Phiflow,
+    partially adopted from
+    https://physicsbaseddeeplearning.org/overview-burgers-forw.html
+
+    args:
+        NT: number of points in the t-direction.
+        NX: number of points in the x-direction.
+        X_U: the upper bound of x range.
+        X_L: the lower bound of x range.
+        T_max: the maximum time lenght.
+        Nu: viscosity.
+        IN_cond: the initial condition function.
+    """
+    DX = (X_U - X_L) / NX
+    DT = T_max / NT
+    x_ = np.linspace(X_L, X_U, NX)
+    t_ = np.linspace(0, T_max, NT)
+    xx, tt = np.meshgrid(x_, t_)
+    input_domain = np.stack((xx.flatten(), tt.flatten()), axis=1)
+
+    x_range = np.linspace(X_L + DX / 2.0, X_U - DX / 2.0, NX)
+    INITIAL = IN_cond(x_range)
+    INITIAL = pl.math.tensor(INITIAL, pl.spatial("x"))  # convert to phiflow tensor
+    velocity = pl.CenteredGrid(
+        INITIAL, pl.extrapolation.PERIODIC, x=NX, bounds=pl.Box(x=(X_L, X_U))
+    )
+    vt = pl.advect.semi_lagrangian(velocity, velocity, DT)
+
+    velocities = [velocity]
+    age = 0.0
+    for i in range(NT - 1):
+        v1 = pl.diffuse.explicit(velocities[-1], Nu, DT)
+        v2 = pl.advect.semi_lagrangian(v1, v1, DT)
+        age += DT
+        velocities.append(v2)
+    vels = np.array([v.values.numpy("x,vector") for v in velocities])
+    u = np.squeeze(vels)
+    return x_, t_, u
+
+
+def sample_latin_hypercube(self):
+    bc_engine = qmc.LatinHypercube(d=1)
+    t_d = bc_engine.random(n=50)
+    t_i = np.zeros((50, 1))
+    t_d = np.append(t_i, t_d, axis=0)
+    return
+
+
+def split_data(x_, t_, u, show_points=False):
+    """
+    Split data into train, val, and test.
+        train: input coordinates on the boundaries and its solution (boundary conditions)
+                and the input coordinates of collocation points
+        val: the collocation points (in training) with solution
+        test: the entire domain and its solution.
+    """
+    rd = np.random.RandomState(0)  # set local random seed
+    lhc = qmc.LatinHypercube(d=1, seed=0)
+
+    NUM_IC_PT = 50
+    NUM_BC_PT = 50
+    NUM_COL_PT = 10000
+
+    # x_ic_idx = np.array(rd.uniform(size=NUM_IC_PT) * len(x_), dtype=int)
+    x_ic_idx = np.array(lhc.random(n=NUM_IC_PT) * len(x_), dtype=int).flatten()
+    # print("sampled IC indices are:", x_ic_idx)
+    x_ic = x_[x_ic_idx]
+    input_ic = np.stack((x_ic, np.zeros(len(x_ic))), axis=1)
+    u_ic = u[0, x_ic_idx].reshape(-1, 1)
+
+    # t_bc_idx = np.array(rd.uniform(size=NUM_BC_PT//2) * len(t_), dtype=int)
+    t_bc_idx = np.array(lhc.random(n=NUM_BC_PT) * len(t_), dtype=int).flatten()
+    t_bc = t_[t_bc_idx]
+
+    input_bc_upper = np.stack(
+        [np.repeat(x_[-1], len(t_bc) // 2), t_bc[: NUM_BC_PT // 2]], axis=1
+    )
+    input_bc_lower = np.stack(
+        [np.repeat(x_[0], len(t_bc) // 2), t_bc[NUM_BC_PT // 2 :]], axis=1
+    )
+    input_bc = np.vstack([input_bc_lower, input_bc_upper])
+
+    u_bc_upper = u[t_bc_idx, -1]
+    u_bc_lower = u[t_bc_idx, 0]
+    u_bc = np.hstack([u_bc_lower, u_bc_upper]).reshape(-1, 1)
+
+    t_col_idx = np.array(rd.uniform(size=NUM_COL_PT) * len(t_), dtype=int)
+    x_col_idx = np.array(rd.uniform(size=NUM_COL_PT) * len(x_), dtype=int)
+    input_col = np.stack((x_[x_col_idx], t_[t_col_idx]), axis=1)
+    u_col = u[t_col_idx, x_col_idx].reshape(-1, 1)
+
+    x_u_train = np.vstack([input_ic, input_bc])
+    y_u_train = np.vstack([u_ic, u_bc])
+    idx = np.arange(0, NUM_IC_PT + NUM_BC_PT)
+    rd.shuffle(idx)
+    x_u_train = x_u_train[idx]
+    y_u_train = y_u_train[idx]
+
+    x_f_train = input_col
+
+    # prepare val data
+    rd2 = np.random.RandomState(42)
+    v_t_col_idx = np.array(rd2.uniform(size=NUM_COL_PT) * len(t_), dtype=int)
+    v_x_col_idx = np.array(rd2.uniform(size=NUM_COL_PT) * len(x_), dtype=int)
+    x_val = np.stack((x_[v_x_col_idx], t_[v_t_col_idx]), axis=1)
+    y_val = u[v_t_col_idx, v_x_col_idx].reshape(-1, 1)
+
+    # prepare testing data
+    xx, tt = np.meshgrid(x_, t_)
+    input_domain = np.stack([xx.flatten(), tt.flatten()], axis=1)
+    y_test = u.flatten().reshape(-1, 1)
+
+    train_data = {"x_u": x_u_train, "y_u": y_u_train, "x_f": x_f_train}
+    val_data = {"x": x_f_train, "y": u_col}
+    test_data = {"x": input_domain, "y": y_test}
+
+    if show_points == True:
+        import matplotlib.pyplot as plt
+
+        # plt.scatter(input_ic[:, 1], input_ic[:,0])
+        # plt.scatter(input_bc[:, 1], input_bc[:,0])
+        # plt.scatter(x_f_train[:,1], x_f_train[:,0])
+        plt.scatter(x_val[:, 1], x_val[:, 0])
+        plt.show()
+
+    return train_data, val_data, test_data
+
+
+def get_data(NT, NX, X_U, X_L, T_max, Nu):
+    def init_cond(x):
+        return -np.sin(np.pi * x)
+
+    x_, t_, u = BurgersSolver(
+        NT=NT, NX=NX, X_U=X_U, X_L=X_L, T_max=T_max, Nu=Nu, IN_cond=init_cond
+    )
+    train, val, test = split_data(x_, t_, u)
+    return train, val, test
+
+
+class PINN(tf.keras.Model):
+    def __init__(self, num_layers, hidden_dim, output_dim, act_fn):
+        super().__init__()
+        # tf.random.set_seed(0)
+        tf.keras.utils.set_random_seed(0)
+        if act_fn == "relu":
+            self.act_fn = tf.nn.relu
+        elif act_fn == "leaky_relu":
+            self.act_fn = tf.nn.leaky_relu
+        elif act_fn == "elu":
+            self.act_fn = tf.nn.elu
+        elif act_fn == "gelu":
+            self.act_fn = tf.nn.gelu
+        elif act_fn == "tanh":
+            self.act_fn = tf.nn.tanh
+        elif act_fn == "sigmoid":
+            self.act_fn = tf.nn.sigmoid
+        else:
+            raise ValueError("%s is not in the activation function list" % act_fn)
+
+        self.lys = [
+            tf.keras.layers.Dense(
+                units=hidden_dim,
+                activation=self.act_fn,
+                kernel_initializer="glorot_uniform",
+                bias_initializer="zeros",
+            )
+        ]
+        for n in range(num_layers):
+            self.lys.append(
+                tf.keras.layers.Dense(
+                    units=hidden_dim,
+                    activation=self.act_fn,
+                    kernel_initializer="glorot_uniform",
+                    bias_initializer="zeros",
+                )
+            )
+        self.lys.append(
+            tf.keras.layers.Dense(
+                units=output_dim,
+                activation=None,
+                kernel_initializer="glorot_uniform",
+                bias_initializer="zeros",
+            )
+        )
+
+    def call(self, x, t):
+        x = tf.cast(x, tf.float32)
+        t = tf.cast(t, tf.float32)
+
+        x_concat = tf.stack((x, t), axis=1)
+        for layer in self.lys:
+            x_concat = layer(x_concat)
+        return x_concat
+
+
+class BurgerSupervisor:
+    def __init__(self, nu, net, epochs, lr, alpha):
+        self.nu = nu
+        self.net = net  # the net predicting u from x, t pairs.
+        self.epochs = epochs
+
+        self.alpha = alpha
+        self.optimizer = tf.keras.optimizers.Adam(learning_rate=lr)
+
+        self.loss = tf.losses.MeanSquaredError()
+
+    @tf.function
+    def f_net(self, x, t):
+        x = tf.cast(x, tf.float32)
+        t = tf.cast(t, tf.float32)
+        u0 = tf.squeeze(self.net(x, t))
+        u_t = tf.gradients(u0, t)[0]
+        u_x = tf.gradients(u0, x)[0]
+        u_xx = tf.gradients(u_x, x)[0]
+        F = u_t + u0 * u_x - (0.01 / np.pi) * u_xx
+        return F
+
+    def step(self, train, val):
+        x_u_train = tf.cast(train["x_u"], tf.float32)
+        x_f_train = tf.cast(train["x_f"], tf.float32)
+        y_train = tf.cast(train["y_u"], tf.float32)
+
+        x_val = tf.cast(val["x"], tf.float32)
+        y_val = tf.cast(val["y"], tf.float32)
+        x_u = x_u_train[:, 0]
+        t_u = x_u_train[:, 1]
+
+        x_f = x_f_train[:, 0]
+        t_f = x_f_train[:, 1]
+        with tf.GradientTape() as t:
+            u_out = self.net(x_u, t_u)
+            f_out = self.f_net(x_f, t_f)
+            loss_u = self.loss(y_train, u_out)
+            loss_f = self.loss(0, f_out)
+            tot_loss = loss_u + self.alpha * loss_f
+
+        grads = t.gradient(tot_loss, self.net.trainable_weights)
+        self.optimizer.apply_gradients(zip(grads, self.net.trainable_weights))
+        train_loss = loss_u.numpy() + loss_f.numpy()
+        val_pred = self.net(x_val[:, 0], x_val[:, 1])
+        val_u_loss = self.loss(y_val, val_pred).numpy()
+        return train_loss, val_u_loss
+
+    def train(self, train, val):
+        x_u_train = tf.cast(train["x_u"], tf.float32)
+        x_f_train = tf.cast(train["x_f"], tf.float32)
+        y_train = tf.cast(train["y_u"], tf.float32)
+
+        x_val = tf.cast(val["x"], tf.float32)
+        y_val = tf.cast(val["y"], tf.float32)
+        x_u = x_u_train[:, 0]
+        t_u = x_u_train[:, 1]
+
+        x_f = x_f_train[:, 0]
+        t_f = x_f_train[:, 1]
+        for i in range(self.epochs):
+            train_ls = []
+            with tf.GradientTape() as t:
+                u_out = self.net(x_u, t_u)
+                f_out = self.f_net(x_f, t_f)
+                loss_u = self.loss(y_train, u_out)
+                loss_f = self.loss(0, f_out)
+                tot_loss = loss_u + self.alpha * loss_f
+                train_ls.append(tot_loss)
+            grads = t.gradient(tot_loss, self.net.trainable_weights)
+            self.optimizer.apply_gradients(zip(grads, self.net.trainable_weights))
+            obj = loss_u.numpy() + loss_f.numpy()
+            # print('epoch {}, obj is {}'.format(i, obj))
+            # validation loss
+            val_r2 = self.r2(y_val, self.net(x_val[:, 0], x_val[:, 1]))
+            val_f_loss = self.loss(0, self.f_net(x_val[:, 0], x_val[:, 1])).numpy()
+            val_pred = self.net(x_val[:, 0], x_val[:, 1])
+            val_u_loss = self.loss(y_val, val_pred).numpy()
+            # val_f_loss = self.loss(0, self.f_net(x_val[:, 0], x_val[:, 1])).numpy()
+            val_loss = val_u_loss
+            print(
+                "Epoch %d, training loss: (%.4f, %.4f), obj loss %.4f"
+                % (i + 1, loss_u.numpy(), loss_f.numpy(), val_loss)
+            )
+        return val_loss  # return the objective.
+
+    def r2(self, true, pred):
+        true = tf.cast(true, tf.float32)
+        rss = tf.reduce_sum((true - pred) ** 2)
+        tss = tf.reduce_sum((true - tf.reduce_mean(true)) ** 2)
+        return 1 - rss / tss
+
+    def test(self, x_test, y_test):
+        out = self.net(x_test)
+        r2 = self.r2(y_test, out)
+        f_loss = self.f_loss(x_test)
+        return r2, f_loss
+
+
+def plotter(x_, u_, name, NUMX, NUMT, **kwargs):
+    """
+    x_: shape=[num_points, 2]. First dim being x and second t.
+    u_: shape=[num_points,]
+    NUMX: number of points the in x-direction.
+    NUMT: number of points the in t-direction.
+    """
+    import matplotlib.animation as animation
+
+    u_ = u_.reshape(NUMT, NUMX).T
+    xv = x_[:, 0].reshape(NUMT, NUMX)
+    tv = x_[:, 1].reshape(NUMT, NUMX)
+
+    import matplotlib.pyplot as plt
+
+    fig, ax = plt.subplots(2, 2, figsize=(16, 9))
+    ax[0, 0].imshow(
+        u_,
+        interpolation="nearest",
+        cmap="rainbow",
+        extent=[tv.min(), tv.max(), xv.min(), xv.max()],
+        origin="lower",
+        aspect="auto",
+    )
+    ax[0, 0].set_xlabel("t")
+    ax[0, 0].set_ylabel("x")
+
+    (line,) = ax[1, 0].plot(xv[0], u_[:, 0])
+    text = ax[1, 0].text(0.8, 0.9, " ", transform=ax[1, 0].transAxes)
+    ax[1, 0].set_xlabel("x")
+    ax[1, 0].set_ylabel("u")
+
+    u_true = kwargs["u_true"].reshape(NUMT, NUMX).T
+    ax[0, 1].set_title("True", fontsize=24)
+    ax[0, 0].set_title("Predicted", fontsize=24)
+    ax[0, 1].imshow(
+        u_true,
+        interpolation="nearest",
+        cmap="rainbow",
+        extent=[tv.min(), tv.max(), xv.min(), xv.max()],
+        origin="lower",
+        aspect="auto",
+    )
+    ax[0, 1].set_xlabel("t")
+    ax[0, 1].set_ylabel("x")
+
+    (line_true,) = ax[1, 1].plot(xv[0], u_true[:, 0])
+    text_true = ax[1, 1].text(0.8, 0.9, " ", transform=ax[1, 1].transAxes)
+    ax[1, 0].set_xlabel("x")
+    ax[1, 0].set_ylabel("u")
+
+    T = np.linspace(x_[:, 1].min(), x_[:, 1].max(), NUMT)
+
+    def animate(t):
+        line.set_ydata(u_[:, t])
+        line_true.set_ydata(u_true[:, t])
+        text.set_text("t= %.3f" % T[t])
+        text_true.set_text("t= %.3f" % T[t])
+        return line, text, line_true, text_true
+
+    ani = animation.FuncAnimation(fig, animate, frames=u_.shape[1], interval=50)
+    plt.tight_layout()
+    writergif = animation.PillowWriter(fps=60)
+    ani.save("./Burgers_" + name + ".gif", writer=writergif)
+
+
+if __name__ == "__main__":
+    import time
+    import pickle
+
+    start_time = time.time()
+    config = {
+        "num_layers": 17,
+        "hidden_dim": 9,
+        "epochs": 1000,
+        "lr": 0.00580381,
+        "alpha": 0.050742893,
+        "activation": "tanh",
+    }
+
+    nu = 0.01 / np.pi
+    train, val, test = get_data(NT=200, NX=128, X_U=1, X_L=-1, T_max=1, Nu=nu)
+    with open("./data/data.pkl", "rb") as f:
+        data = pickle.load(f)
+
+    train = data["train"]
+    val = data["val"]
+
+    num_layers = config["num_layers"]
+    hidden_dim = config["hidden_dim"]
+    output_dim = 1
+    epochs = config["epochs"]
+    lr = config["lr"]
+    alpha = config["alpha"]
+    activation = config["activation"]
+    net = PINN(
+        num_layers=num_layers,
+        hidden_dim=hidden_dim,
+        output_dim=output_dim,
+        act_fn=activation,
+    )
+    sup = BurgerSupervisor(nu, net, epochs, lr, alpha)
+    val_f_loss = sup.train(train, val)
+    print("--- %s seconds ---" % (time.time() - start_time))
+    print("objective is ", val_f_loss)
+    # pred_test = net(test['x'][:,0], test['x'][:,1]).numpy()
+    # plotter(test['x'], pred_test, 'true_pred_hps-adbo', 128, 200, u_true=test['y'])
diff --git a/src/deephyper_benchmark/lib/pinnbench/Burgers/requirements.txt b/src/deephyper_benchmark/lib/pinnbench/Burgers/requirements.txt
new file mode 100644
index 0000000..ff2206e
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/Burgers/requirements.txt
@@ -0,0 +1,5 @@
+deephyper>=0.5.0
+matplotlib==3.4.2
+numpy==1.23.4
+phiflow==2.2.7
+tensorflow==2.10.0
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/__init__.py b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/__init__.py
new file mode 100644
index 0000000..dc5340c
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/__init__.py
@@ -0,0 +1 @@
+from . import hpo
diff --git a/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/benchmark.py b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/benchmark.py
new file mode 100644
index 0000000..bacef6c
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/benchmark.py
@@ -0,0 +1,28 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class PINNDiffusionReactionBenchmark(Benchmark):
+    version = "0.0.1"
+
+    requires = {
+        "py-pip-requirements": {
+            "step": "install",
+            "type": "pip",
+            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
+        },
+        "bash-install": {
+            "step": "install",
+            "type": "cmd",
+            "cmd": "cd .. && bash " + os.path.join(DIR, "./install.sh"),
+        },
+        "deepxde-backend": {
+            "step": "load",
+            "type": "env",
+            "key": "DDE_BACKEND",
+            "value": "pytorch",
+        },
+    }
diff --git a/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/hpo.py b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/hpo.py
new file mode 100644
index 0000000..7df1633
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/hpo.py
@@ -0,0 +1,206 @@
+import os
+
+import numpy as np
+import torch
+from deephyper.evaluator import RunningJob, profile
+from deephyper.hpo import HpProblem
+from deephyper.stopper.integration.deepxde import DeepXDEStopperCallback
+from deepxde.callbacks import EarlyStopping
+from fvcore.nn import FlopCountAnalysis
+from pdebench.models.pinn.train import run_training
+
+from deephyper_benchmark.integration.torch import count_params
+from deephyper_benchmark.utils.json_utils import array_to_json
+
+from .model import FNN, ACTIVATIONS
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+DEEPHYPER_BENCHMARK_MOO = bool(int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 0)))
+
+# define the search space
+problem = HpProblem()
+problem.add_hyperparameter((5, 20), "num_layers", default_value=10)
+problem.add_hyperparameter((5, 100), "num_neurons", default_value=10)
+problem.add_hyperparameter((100, 1000), "epochs", default_value=100)
+problem.add_hyperparameter(
+    list(ACTIVATIONS.keys()),
+    "activation",
+    default_value="relu",
+)
+problem.add_hyperparameter([True, False], "skip_co", default_value=False)
+problem.add_hyperparameter((0.0, 0.5), "dropout", default_value=0)
+
+# Regularization hyperparameters
+problem.add_hyperparameter([True, False], "batch_norm", default_value=False)
+problem.add_hyperparameter(["None", "l2"], "regularization", default_value="None")
+problem.add_hyperparameter(
+    (1e-5, 1.0, "log-uniform"), "weight_decay", default_value=0.01
+)
+problem.add_hyperparameter(
+    ["Glorot normal", "Glorot uniform", "He normal", "He uniform"],
+    "kernel_initializer",
+    default_value="Glorot normal",
+)
+
+# Optimization hyperparameters
+problem.add_hyperparameter(["None", "step"], "decay", default_value="None")
+problem.add_hyperparameter((1, 100), "decay_step_size", default_value=5)
+problem.add_hyperparameter((1e-5, 1.0, "log-uniform"), "decay_gamma", default_value=0.1)
+
+problem.add_hyperparameter(
+    ["adam", "sgd", "rmsprop", "adamw"], "optimizer", default_value="adam"
+)
+problem.add_hyperparameter(
+    (1e-5, 1e-1, "log-uniform"), "learning_rate", default_value=0.01
+)
+
+# Loss weights is tuned only if MOO is activated.
+if DEEPHYPER_BENCHMARK_MOO:
+    problem.add_hyperparameter((0.1, 0.9), "loss_weights", default_value=0.5)
+
+
+@profile
+def run(job: RunningJob) -> dict:
+    config = job.parameters.copy()
+    dataset = "2D_diff-react_NA_NA"
+
+    for k, v in config.items():
+        if v == "None":
+            config[k] = None
+
+    if "loss_weights" not in config:
+        lw = 0.5
+    else:
+        lw = config["loss_weights"]
+    config["loss_weights"] = np.array([lw, lw, 1 - lw, 1 - lw, 1 - lw, 1 - lw])
+
+    # https://github.com/lululxvi/deepxde/blob/master/deepxde/optimizers/pytorch/optimizers.py
+    if config["decay"] == "step":
+        config["decay"] = ("step", config["decay_step_size"], config["decay_gamma"])
+
+    # To avoid error: https://github.com/lululxvi/deepxde/blob/master/deepxde/optimizers/pytorch/optimizers.py#L45C4-L45C4
+    if config["optimizer"] == "adamw":
+        config["regularization"] = "l2"
+
+    stopper_callback = DeepXDEStopperCallback(job)
+
+    error_type = None
+    try:
+        (
+            val_loss,
+            test_loss,
+            losshistory,
+            model,
+            duration_batch_inference,
+        ) = run_training(
+            net_class=FNN,
+            scenario="diff-react",
+            epochs=config["epochs"],
+            learning_rate=config["learning_rate"],
+            model_update=500,
+            root_path=os.path.join(
+                DIR, "../build/PDEBench-DH/pdebench/data/" + dataset
+            ),
+            flnm=dataset + ".h5",
+            config=config,
+            seed="0000",
+            callbacks=stopper_callback,
+        )
+    except torch.cuda.OutOfMemoryError:
+        error_type = "F_OOM"
+
+    # Managing failures to learn memory constraints
+    if error_type is not None:
+        metadata = {
+            "num_parameters": None,
+            "num_parameters_train": None,
+            "val_loss": None,
+            "test_rmse": None,
+            "budget": None,
+            "stopped": None,
+            "lc_train_loss": None,
+            "lc_val_loss": None,
+            "flops": None,
+            "duration_batch_inference": None,
+        }
+
+        if DEEPHYPER_BENCHMARK_MOO:
+            objective = [error_type for _ in range(3)]
+        else:
+            objective = error_type
+
+        return {"objective": objective, "metadata": metadata}
+
+    param_count = count_params(model)
+    flops = FlopCountAnalysis(model, inputs=(torch.randn(1, 3))).total()
+
+    train_ls = np.array(losshistory.loss_train).sum(axis=1)
+    val_ls = np.array(losshistory.loss_test).sum(axis=1)
+    steps = np.array(losshistory.steps)
+    lc_train_X = np.stack([steps, train_ls], axis=1)
+    lc_val_X = np.stack([steps, val_ls], axis=1)
+    lc_train_X_json = array_to_json(lc_train_X)
+    lc_val_X_json = array_to_json(lc_val_X)
+
+    if DEEPHYPER_BENCHMARK_MOO:
+        print("Optimizing multiple objectives...")
+
+        if np.isnan(val_loss[:2]).any() or np.isinf(val_loss[:2]).any():
+            objective_0 = "F"
+        else:
+            objective_0 = -(val_loss[:2] / config["loss_weights"][:2]).sum()
+
+        if np.isnan(val_loss[2:]).any() or np.isinf(val_loss[2:]).any():
+            objective_1 = "F"
+        else:
+            objective_1 = -(val_loss[2:] / config["loss_weights"][2:]).sum()
+
+        objective = [
+            objective_0,
+            objective_1,
+            -flops,
+        ]
+    else:
+        objective = (
+            "F"
+            if np.isnan(val_loss).any() or np.isinf(val_loss).any()
+            else -(val_loss / config["loss_weights"]).sum()
+        )
+    metadata = {
+        "num_parameters": param_count["num_parameters"],
+        "num_parameters_train": param_count["num_parameters_train"],
+        "val_loss": array_to_json(val_loss),  # array of 4 elements
+        "test_rmse": float(test_loss[0]),
+        "budget": stopper_callback.budget,
+        "stopped": stopper_callback.stopped,
+        "lc_train_loss": lc_train_X_json,
+        "lc_val_loss": lc_val_X_json,
+        "flops": flops,
+        "duration_batch_inference": duration_batch_inference,  # add the inference time in seconds
+    }
+
+    return {"objective": objective, "metadata": metadata}
+
+
+def evaluate(config):
+    """
+    Evaluate an hyperparameter configuration
+    on training/validation and testing data.
+    """
+    callbacks = EarlyStopping(patience=100_000)
+    DIR = os.path.dirname(os.path.abspath(__file__))
+    dataset = os.environ.get("DEEPHYPER_BENCHMARK_DATASET")
+
+    val_loss, test_loss, losshistory, model = run_training(
+        net_class=FNN,
+        scenario="diff-react",
+        epochs=config["epochs"],
+        learning_rate=config["lr"],
+        model_update=1,
+        root_path=os.path.join(DIR, "../build/PDEBench-DH/pdebench/data/" + dataset),
+        flnm="2D_diff-react_NA_NA.h5",
+        config=config,
+        seed="0000",
+        callbacks=callbacks,
+    )
+    return val_loss, test_loss, losshistory
diff --git a/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/model.py b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/model.py
new file mode 100644
index 0000000..ff9e3d0
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/model.py
@@ -0,0 +1,154 @@
+import torch
+import torch.nn as nn
+
+from deepxde.nn import NN
+from deepxde.nn import initializers
+from deepxde import config
+
+INITIALIZERS = initializers
+
+class Sin(nn.Module):
+    def __init__(self):
+        super(Sin, self).__init__()
+
+    def forward(self, x):
+        return torch.sin(x)
+
+ACTIVATIONS = {
+        # "id": nn.Identity,
+        "elu": nn.ELU,
+        "relu": nn.ReLU,
+        "selu": nn.SELU,
+        "sigmoid": nn.Sigmoid,
+        "silu": nn.SiLU, # same as swish
+        "sin": Sin, 
+        "tanh": nn.Tanh,
+        "hardswish": nn.Hardswish,
+        "leakyrelu": nn.LeakyReLU,
+        "mish": nn.Mish,
+        "softplus": nn.Softplus,
+}
+
+
+class FNN(NN):
+    """Fully-connected neural network."""
+
+    def __init__(
+        self,
+        input_dim: int,
+        output_dim: int,
+        num_layers: int = 5,
+        num_neurons: int = 30,
+        activation: str = "elu",
+        kernel_initializer: str = "Glorot normal",
+        batch_norm: bool = False,
+        skip_co: bool = False,
+        dropout_rate: float = 0.0,
+        regularization: str = None,
+        weight_decay: float = 0.01,
+        **kwargs,
+    ):
+        super(FNN, self).__init__()
+
+        if regularization is None:
+            self.regularizer = None
+        else:
+            self.regularizer = [regularization, weight_decay]
+
+        layer_sizes = [input_dim] + [num_neurons for _ in range(num_layers)]
+
+        initializer = INITIALIZERS.get(kernel_initializer)
+        initializer_zero = INITIALIZERS.get("zeros")
+
+        self.linears = nn.Sequential()
+        for i in range(1, len(layer_sizes)):
+            if skip_co:
+                self.linears.append(
+                    SkipConnection(
+                        in_dim=layer_sizes[i - 1],
+                        out_dim=layer_sizes[i],
+                        batch_norm=batch_norm,
+                        kernel_initializer=kernel_initializer,
+                        activation=activation,
+                    )
+                )
+
+            else:
+                linear_module = nn.Linear(
+                    layer_sizes[i - 1], layer_sizes[i], dtype=config.real(torch)
+                )
+                initializer(linear_module.weight)
+                initializer_zero(linear_module.bias)
+                self.linears.append(linear_module)
+                if batch_norm:
+                    self.linears.append(
+                        nn.BatchNorm1d(layer_sizes[i], dtype=config.real(torch))
+                    )
+                
+                if activation != "id":
+                    self.linears.append(ACTIVATIONS.get(activation)())
+
+            self.linears.append(nn.Dropout(p=dropout_rate))
+
+        self.linears.append(
+            nn.Linear(layer_sizes[-1], output_dim, dtype=config.real(torch))
+        )
+
+    def forward(self, inputs):
+        x = inputs
+        if self._input_transform is not None:
+            x = self._input_transform(x)
+        x = self.linears(x)
+        if self._output_transform is not None:
+            x = self._output_transform(inputs, x)
+        return x
+
+
+class SkipConnection(nn.Module):
+    def __init__(
+        self,
+        in_dim,
+        out_dim,
+        kernel_initializer="Glorot normal",
+        batch_norm=False,
+        activation="elu",
+    ) -> None:
+        super(SkipConnection, self).__init__()
+
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        initializer = INITIALIZERS.get(kernel_initializer)
+        initializer_zero = INITIALIZERS.get("zeros")
+
+        if self.in_dim != self.out_dim:
+            self.map = nn.Linear(in_dim, out_dim)
+            initializer(self.map.weight)
+            initializer_zero(self.map.bias)
+
+        self.block = nn.Sequential()
+        
+        linear_module = nn.Linear(in_dim, out_dim, dtype=config.real(torch))
+        initializer(linear_module.weight)
+        initializer_zero(linear_module.bias)
+        self.block.append(linear_module)
+
+        if batch_norm:
+            self.block.append(nn.BatchNorm1d(out_dim, dtype=config.real(torch)))
+
+        if activation != "id":
+            self.block.append(ACTIVATIONS.get(activation)())
+        
+        if activation != "id":
+            self.act = ACTIVATIONS.get(activation)()
+        else:
+            self.act = None
+
+    def forward(self, x):
+        residual = x
+        if self.in_dim != self.out_dim:
+            residual = self.map(residual)
+        out = self.block(x) + residual
+        if self.act is not None:
+            out = self.act(out)
+        return out
diff --git a/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/requirements.txt b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/requirements.txt
new file mode 100644
index 0000000..3ad0199
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/requirements.txt
@@ -0,0 +1,6 @@
+DeepXDE>=1.8.2
+numpy>=1.24.2
+omegaconf>=2.3.0
+packaging>=23.0
+torch>=2.0.0
+fvcore>=0.1.5
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/pinnbench/README.md b/src/deephyper_benchmark/lib/pinnbench/README.md
new file mode 100644
index 0000000..520a485
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/README.md
@@ -0,0 +1,144 @@
+# Physics-informed Neural Networks Benchmark
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
+
+Physics-Informed Neural Networks (PINNs) are a class of machine learning models that combine the strengths of neural networks and physics-based modeling. PINNs are used to solve partial differential equations (PDEs) and other physical problems by learning a solution directly from data.
+
+The basic idea behind PINNs is to use a neural network to approximate the solution to a PDE, while also enforcing the underlying physical laws that govern the problem. This is achieved by incorporating the PDE as a constraint in the neural network training process. More details can be found in the [original work](https://arxiv.org/abs/1711.10561).
+
+This set of benchmarks seeks to incorporate AutoML workflow into the development of PINNs with DeepHyper. The PINN benchmark problems support Hyperparameter Optimization (HPO), Neural Architecture Search (NAS), and Multi-fidelity evaluations. 
+
+The currently available problems are 
+
+<!-- [Burgers Equation](#burgers-equation) -->
+
+- [Diffusion-reaction Equation](#diffusion-reaction-equation) (Dataset Size: 13 GB)
+
+<!-- 
+## Burgers Equation
+### Installation
+To install
+
+```
+python -c "import deephyper_benchmark as dhb; dhb.install('PINNBench/Burgers');"
+```
+-->
+
+
+<!-- PINNs are neural networks are a type of machine learning method that combines deep neural networks with physical equations to solve complex physical problems. In the case of 1-D Burgers Equation, training a PINN, $\hat{u}$, is to minimize a compound loss function. The 1-D Burgers equation has the following form:
+
+$$\frac{\partial u }{\partial t} + u\frac{\partial u}{\partial x} = \nu \frac{\partial^2 u}{\partial x^2}$$
+
+The loss functions has two component,
+$$L_{tot} = L_u + \alpha L_f$$
+
+the solution loss for specified initial and boundary conditions,
+$$L_u = \vert \vert \hat{u} - u \vert \vert^2_2$$
+
+and the loss according to the governing equation (PDE loss) for every other points in the domain,
+$$L_f  = \vert \vert \frac{\partial \hat{u}}{\partial t} + \hat{u}\frac{\partial \hat{u}}{\partial x} - \nu \frac{\partial^2 \hat{u}}{\partial x^2})\vert \vert^2_2.$$ -->
+
+
+<!-- ### Hyperparameter Search
+<!-- ### Hyperparameter Search
+The objective of this benchmark is to optimize a set of hyperparameters for a feedforward neural network, including `num_layers`, `lr` (the learning rate of the optimizer), `hidden_dim` (the number of neurons in the hidden layers), `alpha` (the PDE loss coefficient), and `activation` (the activation function). The optimization is performed by minimizing the negative total loss value from the validation dataset.
+
+To improve the efficiency of the search, the benchmark uses MCModelStopper to predict the training dynamics and perform early stopping when the predicted subsequent iterations are unlikely to lead to a further improvement in model performance. The maximum budget, `max_b`, for this search is 1000 epochs.
+
+```python
+for budget_i in range(min_b, max_b + 1):
+        train_loss, eval = sup.step(train, val)
+        objective_i = -eval  # maximizing in deephyper
+        job.record(budget_i, objective_i)
+        if job.stopped():
+            break
+```
+### Defualt configuration and results
+The default configuration of the PINN.
+
+| | |
+|:--|:--|
+|`num_layers`| 5 |
+|`lr` | 0.01|
+| `hidden_dim`| 5|
+|`alpha`| 5 |
+|`activation`| `tanh`|
+
+```
+result={'objective': -0.06480624, 'metadata': {'timestamp_start': 1680036315.47377, 'timestamp_end': 1680036380.2708638, 'num_parameters': 171, 'train_loss': 0.06834503, 'val_loss': 0.06480624, 'test_loss': 0.066540696, 'budget': 1000, 'stopped': False, 'infos_stopped': None}}
+``` --> 
+
+
+## Diffusion-reaction Equation
+
+This benchmark is based on **modified** [`PDEBench`](https://github.com/pdebench/PDEBench) and [`DeepXDE`](https://github.com/lululxvi/deepxde). 
+
+### Installation
+
+To install the **modified** `PDEBench` and this benchmark, run:
+```
+python -c "import deephyper_benchmark as dhb; dhb.install('PINNBench/DiffusionReaction');"
+```
+
+Run the hyperparameter search
+```
+import os
+os.environ['DEEPHYPER_BENCHMARK_MOO'] = '1' # enable multi-objective optimization
+
+import deephyper_benchmark as dhb
+diff_react = dhb.load("PINNBench/DiffusionReaction")
+
+from deephyper.evaluator import RunningJob
+config = diff_react.hpo.problem.default_configuration # get a default config to test
+res = diff_react.hpo.run(RunningJob(parameters=config))
+```
+
+
+### Configuration
+
+It is necessary to configure `DeepXDE` to use `PyTorch` backend. The instructions can be found [here](https://deepxde.readthedocs.io/en/latest/user/installation.html#working-with-different-backends).
+
+### Metadata
+
+- [x] `num_parameters`: integer value of the number of parameters in the neural network.
+- [x] `num_parameters_train`: integer value of the number of **trainable** parameters of the neural network.
+- [x] `budget`: scalar value (float/int) of the budget consumed by the neural network. Therefore the budget should be defined for each benchmark (e.g., number of epochs in general).
+- [x] `stopped`: boolean value indicating if the evaluation was stopped before consuming the maximum budget.
+- [x] `train_loss`:  scalar value of the training metrics (replace `X` by the metric name, 1 key per metric).
+- [x] `valid_loss`: scalar value of the validation metrics (replace `X` by the metric name, 1 key per metric).
+- [x] `test_rmse`: scalar value of the testing metrics (replace `X` by the metric name, 1 key per metric).
+- [x] `flops`: number of flops of the model such as computed in `fvcore.nn.FlopCountAnalysis(...).total()` (See [documentation](https://detectron2.readthedocs.io/en/latest/modules/fvcore.html#module-fvcore.nn)).
+- [ ] `latency`: TO BE CLARIFIED
+- [x] `lc_train_loss`: recorded learning curves of the trained model, the `bi` variables are the budget value (e.g., epochs/batches), and the `yi` values are the recorded metric. `X` in `train_X` is replaced by the name of the metric such as `train_loss` or `train_accuracy`. The format is `[[b0, y0], [b1, y1], ...]`.
+- [x] `lc_valid_loss`: Same as `lc_train_X` but for validation data.
+- [x] `duration_batch_inference`: average inference time for a single data point.
+
+### Other details
+
+#### Supported datasets
+
+The currently available dataset is `2D_diff-react_NA_NA`. The rest datasets from PDEBench (see [list](https://github.com/iamyixuan/PDEBench-DH/tree/main/pdebench/data_download) )will be supported in the future.
+
+#### Supported hyperparameters
+
+- [x] `num_layers`: number of layers (or other building blocks) in the network.
+- [x] `lr`: learning rate for the optimizer.
+- [x] `num_neurons`: number of neurons per layer.
+- [x] `epochs`: number of maximum epochs for training.
+- [x] `activation`: activation functions.
+- [x] `skip_co`: if using skip connection (residual block).
+- [x] `dropout_rate`: dropout rate.
+- [x] `optimizer`: choices of the optimizer.
+- [x] `weight_decay`: magnitude of L2 regularization.
+- [x] `initialization`: initialization strategy for network weights.
+- [x] `loss_weights`: weights assigned to the PDE loss.
+  
+#### Multi-objective Optimization (MOO)
+
+MOO is supported for the `DiffusionReaction` benchmark. To enable MOO, set environment variable `DEEPHYPER_BENCHMARK_MOO=1`. There are five minimized objectives in this case:
+
+- `objective_0`: Validation PDE loss.
+- `objective_1`: Validation boundary and initial condition solution loss.
+- `objective_2`: Batch inference duration (in seconds).
+- `objective_3`: FLOPS from [FVCORE Package](https://github.com/facebookresearch/fvcore/blob/main/docs/flop_count.md).
diff --git a/src/deephyper_benchmark/lib/pinnbench/install.sh b/src/deephyper_benchmark/lib/pinnbench/install.sh
new file mode 100644
index 0000000..d28fea3
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/install.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+
+
+mkdir build/ && cd build/
+# install modified PDEBench
+git clone https://github.com/iamyixuan/PDEBench-DH.git
+cd PDEBench-DH/
+python -m pip install -e . 
+
+# generate data
+mkdir ./pdebench/data/
+cd ./pdebench/data_gen/
+python gen_diff_react.py
diff --git a/src/deephyper_benchmark/lib/yahpo/README.md b/src/deephyper_benchmark/lib/yahpo/README.md
new file mode 100644
index 0000000..6fbb7c4
--- /dev/null
+++ b/src/deephyper_benchmark/lib/yahpo/README.md
@@ -0,0 +1,13 @@
+# YAHPO Gym - Surrogate Benchmark for Hyperparameter Optimization
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
+
+* [YAHPO Gym - Github](https://github.com/slds-lmu/yahpo_gym)
+
+
+## Installation
+
+```console
+python -c "import deephyper_benchmark as dhb; dhb.install('YAHPO/lcbench');"
+```
diff --git a/src/deephyper_benchmark/lib/yahpo/lcbench/__init__.py b/src/deephyper_benchmark/lib/yahpo/lcbench/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/yahpo/lcbench/benchmark.py b/src/deephyper_benchmark/lib/yahpo/lcbench/benchmark.py
new file mode 100644
index 0000000..18ec8d7
--- /dev/null
+++ b/src/deephyper_benchmark/lib/yahpo/lcbench/benchmark.py
@@ -0,0 +1,27 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class YAHPOLCBench(Benchmark):
+    version = "0.0.1"
+
+    data_dir = os.path.join(DIR, "..", "build", "data")
+    requires = {
+        "download-data": {
+            "step": "install",
+            "type": "cmd",
+            "cmd": f"git clone https://github.com/slds-lmu/yahpo_data.git {data_dir}",
+        },
+        "pip-yahpo-gym": {"step": "install", "type": "pip", "args": "install yahpo-gym"},
+    }
+
+    def install(self):
+        super().install()
+
+        from yahpo_gym import local_config
+
+        local_config.init_config()
+        local_config.set_data_path(self.data_dir)
diff --git a/src/deephyper_benchmark/lib/yahpo/lcbench/hpo.py b/src/deephyper_benchmark/lib/yahpo/lcbench/hpo.py
new file mode 100644
index 0000000..539ba7a
--- /dev/null
+++ b/src/deephyper_benchmark/lib/yahpo/lcbench/hpo.py
@@ -0,0 +1,75 @@
+import os
+
+from ConfigSpace import ConfigurationSpace
+from deephyper.evaluator import profile, RunningJob
+from deephyper.hpo import HpProblem
+
+from yahpo_gym import benchmark_set
+import yahpo_gym.benchmarks.lcbench
+
+DEEPHYPER_BENCHMARK_INSTANCE = os.environ.get("DEEPHYPER_BENCHMARK_INSTANCE", "3945")
+
+
+bench = benchmark_set.BenchmarkSet("lcbench")
+bench.set_instance(DEEPHYPER_BENCHMARK_INSTANCE)
+
+config_space = ConfigurationSpace()
+config_space.add_hyperparameters(
+    [
+        hp
+        for hp in bench.config_space.get_hyperparameters()
+        if not (hp.name in ["OpenML_task_id", "epoch"])
+    ]
+)
+problem = HpProblem(config_space=config_space)
+
+
+@profile()
+def run(job: RunningJob) -> dict:
+    config = job.parameters.copy()
+    config["OpenML_task_id"] = DEEPHYPER_BENCHMARK_INSTANCE
+
+    # Min/Max budget (here epochs)
+    min_b, max_b = 2, 51
+
+    # Run the benchmark (batch for better performance)
+    def update_config(config, budget):
+        config = config.copy()
+        config["epoch"] = budget
+        return config
+
+    outputs = [
+        bench.objective_function(update_config(config, budget=b))
+        for b in range(min_b, max_b + 1)
+    ]
+
+    for i, out_i in enumerate(outputs):
+        out_i = out_i[0]
+        budget_i = i + 1
+        objective_i = -out_i["val_cross_entropy"]
+
+        job.record(budget_i, objective_i)
+        if job.stopped():
+            break
+
+    return {
+        "objective": objective_i,
+        "metadata": {
+            "budget": budget_i,
+            "stopped": budget_i < len(outputs),
+            "test_balanced_accuracy": outputs[-1][0]["test_balanced_accuracy"],
+            "val_balanced_accuracy": outputs[-1][0]["val_balanced_accuracy"],
+            "test_cross_entropy": outputs[-1][0]["test_cross_entropy"],
+            "val_cross_entropy": outputs[-1][0]["val_cross_entropy"],
+            "time": outputs[-1][0]["time"],
+        },
+    }
+
+
+if __name__ == "__main__":
+    print(problem)
+    config = bench.config_space.sample_configuration(1).get_dictionary()
+    config.pop("OpenML_task_id")
+    print(config)
+    result = run(RunningJob(id=0, parameters=config))
+    print(result)
diff --git a/src/deephyper_benchmark/lib/yahpo/lcbench/metrics.py b/src/deephyper_benchmark/lib/yahpo/lcbench/metrics.py
new file mode 100644
index 0000000..92d0359
--- /dev/null
+++ b/src/deephyper_benchmark/lib/yahpo/lcbench/metrics.py
@@ -0,0 +1,69 @@
+import json
+import os
+
+import numpy as np
+
+from .hpo import bench as BENCHMARK
+
+
+class PerformanceEvaluator:
+    """A class defining performance evaluators for the YAHPO/lcbench problems."""
+
+    def __init__(self):
+        """Read the current problem defn from environment vars."""
+
+        df = BENCHMARK.target_stats
+
+        self.x_min = None
+
+        self.y_metric = "cross_entropy"
+
+        cond = (df["metric"] == "val_cross_entropy") & (df["statistic"] == "min")
+        self.y_min_valid = df[cond].iloc[0]["value"]
+
+        cond = (df["metric"] == "test_cross_entropy") & (df["statistic"] == "min")
+        self.y_min_test = df[cond].iloc[0]["value"]
+
+    def simple_regret_valid(self, y_valid: np.ndarray) -> np.ndarray:
+        """Compute the regret of the objective (validation RMSE) of a list of ordered solutions.
+
+        Args:
+            y_valid (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return y_valid - self.y_min_valid
+
+    def cumul_regret_valid(self, y_valid: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of the objective (validation RMSE) aon n array of ordered solutions.
+
+        Args:
+            y_valid (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret_valid(y_valid))
+
+    def simple_regret_test(self, y_test: np.ndarray) -> np.ndarray:
+        """Compute the regret of the test RMSE of a list of ordered solutions.
+
+        Args:
+            y_test (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return y_test - self.y_min_test
+
+    def cumul_regret_test(self, y_test: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of the test RMSE aon n array of ordered solutions.
+
+        Args:
+            y_test (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret_test(y_test))

From 86f23760119ff73c35b49eae280a4a2072117dc3 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 13:57:29 +0100
Subject: [PATCH 13/43] adding readme to cbbo

---
 .../benchmarks/cbbo/README.md                 | 20 +++++++++++++++++++
 1 file changed, 20 insertions(+)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/README.md

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/README.md b/src/deephyper_benchmark/benchmarks/cbbo/README.md
new file mode 100644
index 0000000..6ecf11d
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/README.md
@@ -0,0 +1,20 @@
+# C-BBO: Continuous Black-Box Optimization
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
+
+Set of continuous function benchmarks. The `DEEPHYPER_BENCHMARK_NDIMS` environment variable defines the number of dimensions of the problem.
+
+| Function Name | Number of Dimensions  |                   Comment                   |
+| ------------- | --------------------- | ------------------------------------------- |
+| ackley        | $\infty$ (default 5)  | Many local minima and single global optimum |
+| branin        | 2                     | Three global optimum                        |
+| cossin        | 1                     | Many local minima, good for visualisation.  |
+| easom         | 2                     | Almost flat everywhere                      |
+| griewank      | $\infty$ (default 5)  |                                             |
+| hartmann6D    | 6                     |                                             |
+| levy          | $\infty$ (default 5)  |                                             |
+| michal        | $\infty$ (default 2)  |                                             |
+| rosen         | $\infty$ (default 5)  |                                             |
+| schwefel      | $\infty$ (default 5)  |                                             |
+| shekel        | 4                     | Many local minima with flat areas           |
\ No newline at end of file

From c844f6a1318d9c5686fc48d0b625ee0c3fd8422e Mon Sep 17 00:00:00 2001
From: Gavin Wiggins <6828967+wigging@users.noreply.github.com>
Date: Mon, 24 Mar 2025 09:40:26 -0400
Subject: [PATCH 14/43] Cleanup pyproject contents

---
 pyproject.toml | 23 +++++++++--------------
 1 file changed, 9 insertions(+), 14 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index f692c9b..ad84ffa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,6 @@ build-backend = "hatchling.build"
 name = "deephyper-benchmark"
 version = "0.0.2"
 dependencies = [
-    # "deephyper",
     "deephyper>=0.9.3",
 ]
 requires-python = ">=3.10"
@@ -16,7 +15,7 @@ authors = [
 maintainers = [
     { name = "Romain Egele", email = "regele@ornl.gov" },
 ]
-description = "Some description..."
+description = "Benchmarks for the DeepHyper package"
 readme = "README.md"
 license = { file = "LICENSE" }
 classifiers = [
@@ -29,12 +28,6 @@ classifiers = [
     "Programming Language :: Python :: 3.13",
 ]
 
-[project.optional-dependencies]
-
-
-[project.scripts]
-
-
 [project.urls]
 Documentation = "http://deephyper.readthedocs.io"
 Changes = "https://github.com/deephyper/deephyper/releases"
@@ -42,10 +35,6 @@ Forum = "https://github.com/deephyper/deephyper/discussions"
 GitHub = "https://github.com/deephyper/deephyper"
 Issues = "https://github.com/deephyper/deephyper/issues"
 
-[tool.pytest.ini_options]
-norecursedirs = ".git"
-
-
 [tool.coverage.paths]
 source = ["src/", "*/site-packages"]
 
@@ -56,9 +45,15 @@ source = ["deephyper"]
 [tool.coverage.report]
 exclude_lines = ["if __name__ == '__main__':"]
 
+[tool.pytest.ini_options]
+norecursedirs = ".git"
+
+[tool.pyright]
+pythonVersion = "3.13"
+venvPath = "."
+venv = ".venv"
+
 [tool.ruff]
-exclude = [
-]
 line-length = 100
 
 [tool.ruff.lint]

From 86bbde2d1d19c6adabcfd62a304d16d8fe96e879 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 14:56:10 +0100
Subject: [PATCH 15/43] adding refactored dtlz benchmark

---
 .../benchmarks/dtlz/README.md                 | 130 ++++
 .../benchmarks/dtlz/__init__.py               |   5 +
 .../benchmarks/dtlz/dtlz.py                   | 288 +++++++++
 .../benchmarks/dtlz/model.py                  | 591 ++++++++++++++++++
 4 files changed, 1014 insertions(+)
 create mode 100644 src/deephyper_benchmark/benchmarks/dtlz/README.md
 create mode 100644 src/deephyper_benchmark/benchmarks/dtlz/__init__.py
 create mode 100644 src/deephyper_benchmark/benchmarks/dtlz/dtlz.py
 create mode 100644 src/deephyper_benchmark/benchmarks/dtlz/model.py

diff --git a/src/deephyper_benchmark/benchmarks/dtlz/README.md b/src/deephyper_benchmark/benchmarks/dtlz/README.md
new file mode 100644
index 0000000..5c09f18
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/dtlz/README.md
@@ -0,0 +1,130 @@
+
+# Modified Multiobjective DTLZ Test Suite
+
+✅ **Benchmark ready to be used.**
+
+This module contains objective function implementations of the DTLZ test
+suite, derived from the implementations in
+[ParMOO](https://github.com/parmoo/parmoo).
+
+------------------------------------------------------------------------------
+
+For further reference, the DTLZ test suite was originally proposed in:
+
+    Deb, Thiele, Laumanns, and Zitzler. "Scalable test problems for
+    evolutionary multiobjective optimization" in Evolutionary Multiobjective
+    Optimization, Theoretical Advances and Applications, Ch. 6 (pp. 105--145).
+    Springer-Verlag, London, UK, 2005. Abraham, Jain, and Goldberg (Eds).
+
+The original implementation was appropriate for testing randomized algorithms,
+but for many deterministic algorithms, the global solutions represent either
+best- or worst-case scenarios, so an configurable offset was introduced in:
+
+    Chang. "Mathematical Software for Multiobjective Optimization Problems."
+    Ph.D. dissertation, Virginia Tech, Dept. of Computer Science, 2020.
+
+Note that the DTLZ problems are minimization problems. Since DeepHyper
+maximizes, the implementation herein returns the negative value for each of
+the DTLZ objectives.
+
+Our performance evaluator ``metrics`` scripts can evaluate either the
+positive or negative solutions to estimate how well we have solved the
+problem.
+
+------------------------------------------------------------------------------
+
+The full list of public classes in this module includes the 7 unconstrained
+DTLZ problems
+ * ``dtlz1``,
+ * ``dtlz2``,
+ * ``dtlz3``,
+ * ``dtlz4``,
+ * ``dtlz5``,
+ * ``dtlz6``, and
+ * ``dtlz7``
+
+which are selected by setting the environment variable
+``DEEPHYPER_BENCHMARK_DTLZ_PROB``.
+
+## Installation 
+
+To use the benchmark follow this example set of instructions:
+
+```python
+
+# Set DTLZ problem environment variables before loading
+import os
+os.environ["DEEPHYPER_BENCHMARK_NDIMS"] = "5" # 5 vars
+os.environ["DEEPHYPER_BENCHMARK_NOBJS"] = "3" # 2 objs
+os.environ["DEEPHYPER_BENCHMARK_DTLZ_PROB"] = "2" # DTLZ2 problem
+os.environ["DEEPHYPER_BENCHMARK_DTLZ_OFFSET"] = "0.6" # soln [x_o, .., x_n]=0.6
+
+# Load DTLZ benchmark suite
+import deephyper_benchmark as dhb
+dhb.load("DTLZ")
+
+# Example of running one evaluation of DTLZ problem
+from deephyper.evaluator import RunningJob
+config = dtlz.hpo.problem.default_configuration # get a default config to test
+res = dtlz.hpo.run(RunningJob(parameters=config))
+
+```
+
+## Configuration
+
+To configure the problem, set the following:
+
+- Environment variable `DEEPHYPER_BENCHMARK_PROB` with a value of `1`, `2`, ... `7` to select the DTLZ problem to run. Defaults to `2`.
+- Environment variable `DEEPHYPER_BENCHMARK_NDIMS` with an integer value to set the number of input variables. Defaults to `5`.
+- Environment variable `DEEPHYPER_BENCHMARK_NOBJS` with an integer value to set the number of objectives. Defaults to `2`
+- Environment variable `DEEPHYPER_BENCHMARK_OFFSET` with a value between `0.0` and `1.0` to select the offset of the solution to the DTLZ problem. Defaults to `0.5` for DTLZ1, ..., DTLZ5 and `0.0` for DTLZ6 and DTLZ7. One may wish to adjust this value when comparing against deterministic blackbox solvers, which may sample the center and boundaries of the input space on specific schedules.
+- Environment variable `DEEPHYPER_BENCHMARK_FAILURES` with value `0` or `1` to activate or deactivate failures. Defaults to `0`.
+
+## Metadata
+
+Since these problems are analytic, there is no metadata for this problem
+beyond the standard DeepHyper metadata (timestamp information).
+
+## Evaluating Results
+
+Evaluating the performance of a multiobjective solver is nontrivial.
+Typically, one should evaluate on two orthogonal bases:
+ 1. Quality of solutions -- What is the (average) error in the solutions
+    returned by the solver?
+ 2. Diversity of solutions -- How much of the true Pareto front is covered
+    by these solutions?
+
+To evaluate these two metrics, we use:
+ 1. Improved generational distance (GD+): Let $F_i$ be a point in the solution
+    set returned by a solver,
+    and let $Y_i$ be the nearest point to $F_i$ on the true Pareto front,
+    for $i=1,\ldots, n$.
+    Then the GD+ is $\sum_{i} D^+(F_i, Y_i) / n$.
+    Where $D^+$ denotes the improved distance function
+    $D^+(F, Y) = ||\max(F - Y, 0)||_2^2$, where the "max" is taken
+    componentwise.
+    **Note that this metric may not increase monotonically. Additionally,
+    it may be impossible to calculate for an arbitrary blackbox function,
+    and can only be calculated here since the solution known and easily
+    expressed algebraically for all of the DTLZ problems.**
+ 2. Hypervolume dominated: Let $F_i$ be defined as above, and let $R$ be
+    a pre-determined reference point such that all $F_i$ dominate $R$.
+    Then the hypervolume is given by the volume of the union of all
+    hyperboxes $B_i$ whose largest vertex is $F_i$ and smallest vertex
+    is $R$. The value (and usefulness) of the hypervolume metric is extremely
+    sensitive to the choice of $R$. Therefore, for this problem, we choose
+    $R$ to be the Nadir point for the true Pareto front. **Note that in order
+    to use the Nadir point as the reference point, we must throw out every
+    solution returned by the solver that is worse than the Nadir point. For
+    extremely difficult problems, this can result in zero hypervolume if no
+    solutions better than the Nadir point were found. This is most common
+    for DTLZ1, DTLZ3, and DTLZ7.**
+
+For a general problem, the two metrics listed above could be very difficult
+to compute and many researchers will use the hypervolume with an overly
+pessimistic reference point as a proxy for both quality and diversity.
+However, in general, the hypervolume tends to promote diversity over quality.
+For the DTLZ problems, since the shape of the true Pareto front is known,
+we can calculate each of these metrics, and both the ``gdPlus(results)`` and
+``hypervolume(results)`` functions are implemented in the ``dtlz.metrics``
+module.
diff --git a/src/deephyper_benchmark/benchmarks/dtlz/__init__.py b/src/deephyper_benchmark/benchmarks/dtlz/__init__.py
new file mode 100644
index 0000000..13c7f55
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/dtlz/__init__.py
@@ -0,0 +1,5 @@
+"""here."""
+
+from .dtlz import DTLZBenchmark
+
+__all__ = ["DTLZBenchmark"]
diff --git a/src/deephyper_benchmark/benchmarks/dtlz/dtlz.py b/src/deephyper_benchmark/benchmarks/dtlz/dtlz.py
new file mode 100644
index 0000000..3f86c3f
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/dtlz/dtlz.py
@@ -0,0 +1,288 @@
+"""here."""
+
+import functools
+import time
+from typing import Optional
+
+import numpy as np
+from deephyper.evaluator import RunningJob, profile
+from deephyper.hpo import HpProblem
+from deephyper.skopt.moo import hypervolume, pareto_front
+
+from deephyper_benchmark import HPOBenchmark, Scorer
+
+from . import model as dtlz
+
+
+@profile
+def run_function(  # noqa: D103
+    job: RunningJob,
+    dtlz_obj,
+    nobj: int,
+    with_failures=False,
+    sleep=False,
+    sleep_mean=60,
+    sleep_noise=20,
+) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+    ff = [-fi for fi in dtlz_obj(x)]
+
+    if with_failures:
+        if any(xi < 0.25 for xi in x[nobj - 1 :]):
+            ff = ["F" for _ in ff]
+
+    return ff
+
+
+class DTLZScorer(Scorer):
+    """A class defining performance evaluators for the DTLZ problems.
+
+    Contains the following public methods:
+
+     * `hypervolume(pts)` calculates the total hypervolume dominated by
+       the current solution, using the Nadir point as the reference point
+       and filtering out solutions that do not dominate the Nadir point,
+     * `nadirPt()` calculates the Nadir point for the current problem,
+     * `numPts(pts)` calculates the number of solution points that dominate
+       the Nadir point, and
+     * `gdPlus(pts)` calculates the RMSE where the error in each point is
+       approximated by the 2-norm distance to the nearest solution point.
+
+    """
+
+    def __init__(self, prob_id: int, nobj: int):
+        """Read the current DTLZ problem defn from environment vars."""
+        self.prob_id = prob_id
+        self.nobj = nobj
+
+    def hypervolume(self, pts):
+        """Calculate the hypervolume dominated by soln, wrt the Nadir point.
+
+        Args:
+            pts (numpy.ndarray): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            float: The total hypervolume dominated by the current solution,
+            filtering out points worse than the Nadir point and using the
+            Nadir point as the reference.
+
+        """
+        if np.any(pts < 0):
+            filtered_pts = -pts.copy()
+        else:
+            filtered_pts = pts.copy()
+        nadir = self.nadirPt()
+        for i in range(pts.shape[0]):
+            if np.any(filtered_pts[i, :] > nadir):
+                filtered_pts[i, :] = nadir
+        return hypervolume(filtered_pts, nadir)
+
+    def nadirPt(self):
+        """Calculate the Nadir point for the given problem definition."""
+        if self.prob_id == 1:
+            return np.ones(self.nobj) * 0.5
+        elif self.prob_id in [2, 3, 4, 5, 6]:
+            return np.ones(self.nobj)
+        elif self.prob_id == 7:
+            nadir = np.ones(self.nobj)
+            nadir[self.nobj - 1] = self.nobj * 2.0
+            return nadir
+        else:
+            raise ValueError(f"DTLZ{self.prob_id} is not a valid problem")
+
+    def numPts(self, pts):
+        """Calculate the number of solutions that dominate the Nadir point.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            int: The number of fi in pts such that all(fi < self.nadirPt).
+
+        """
+        if np.any(pts < 0):
+            pareto_pts = pareto_front(-pts)
+        else:
+            pareto_pts = pareto_front(pts)
+        return sum([all(fi <= self.nadirPt()) for fi in pareto_pts])
+
+    def gdPlus(self, pts):
+        """Calculate the p=1 generational distance for a given solution set.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            float: The p=1 generational distance over all points in pts.
+
+        """
+        if np.any(pts < 0):
+            pareto_pts = pareto_front(-pts)
+        else:
+            pareto_pts = pareto_front(pts)
+        if self.prob_id == "1":
+            dists = self._dtlz1Dist(pareto_pts)
+        elif self.prob_id in ["2", "3", "4", "5", "6"]:
+            dists = self._dtlz2Dist(pareto_pts)
+        elif self.prob_id == "7":
+            dists = self._dtlz7Dist(pareto_pts)
+        else:
+            raise ValueError(f"DTLZ{self.prob_id} is not a valid problem")
+        return np.sum(dists) / len(dists)
+
+    def _dtlz1Dist(self, pts):
+        """Calculate the d+ from each fi to the nearest solution in DTLZ1.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            numpy.ndarray: A 1d array of distances to the nearest solution
+            point for DTLZ1.
+        """
+        return np.array([np.linalg.norm(np.maximum(fi - (0.5 * fi / np.sum(fi)), 0)) for fi in pts])
+
+    def _dtlz2Dist(self, pts):
+        """Calculate the d+ from each fi to the nearest point on unit sphere.
+
+        Note: Works for DTLZ2-6
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            numpy.ndarray: A 1d array of distances to the surface of the
+            unit sphere.
+
+        """
+        return np.array(
+            [np.linalg.norm(np.maximum(fi - (fi / np.linalg.norm(fi)), 0)) for fi in pts]
+        )
+
+    def _dtlz7Dist(self, pts):
+        """Calculate the d+ from each fi to the nearest soln in DTLZ7.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            numpy.ndarray: A 1d array of distances to the nearest solution
+            point to DTLZ7.
+
+        """
+        # Project each point onto DTLZ7 solution and calculate difference
+        pts_proj = []
+        for fi in pts:
+            gx = 2.0
+            hx = -np.sum(
+                fi[: self.nobj - 1] * (1.0 + np.sin(3.0 * np.pi * fi[: self.nobj - 1])) / gx
+            ) + float(self.nobj)
+            pts_proj.append(gx * hx)
+        return np.array([np.abs(np.maximum(fi[-1] - fj, 0)) for fi, fj in zip(pts, pts_proj)])
+
+    def hypervolume_iter(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        scores = []
+        for i in range(1, y.shape[0] + 1):
+            scores.append(self.hypervolume(y[:i]))
+        scores = np.asarray(scores)
+        return scores
+
+    def gdPlus_iter(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        scores = []
+        for i in range(1, y.shape[0] + 1):
+            scores.append(self.gdPlus(y[:i]))
+        scores = np.asarray(scores)
+        return scores
+
+
+class DTLZBenchmark(HPOBenchmark):
+    """DTLZ benchmark.
+
+    Args:
+        nparams (int, optional): the number of parameters in the problem.
+        offset (int, optional): the offset in the space of parameters.
+        nslack (int, optional): the number of additional slack parameters in the problem.
+    """
+
+    def __init__(
+        self,
+        prob_id: int = 2,
+        nparams: int = 5,
+        nobj: int = 2,
+        offset: Optional[int] = None,
+        with_failures: bool = False,
+    ) -> None:
+        self.nparams = nparams
+        self.nobj = nobj
+        self.offset = offset
+        self.with_failures = with_failures
+        # Read DTLZ problem name and acquire pointer
+
+        self.prob_id = prob_id
+        self.prob_name = f"dtlz{self.prob_id}"
+        self.dtlz_class = getattr(dtlz, self.prob_name)
+
+        # Read problem dims and definition (or read from ENV)
+        if self.prob_name in ["dtlz1", "dtlz2", "dtlz3", "dtlz4", "dtlz5"]:
+            if self.offset is None:
+                self.offset = 0.5
+        elif self.prob_name in ["dtlz6", "dtlz7"]:
+            if self.offset is None:
+                self.offset = 0.0
+        else:
+            raise ValueError(f"Invalid problem {self.prob_name}")
+
+        self.dtlz_obj = self.dtlz_class(self.nparams, self.nobj, offset=self.offset)
+
+    @property
+    def problem(self):  # noqa: D102
+        domain = (0.0, 1.0)
+        # Create problem
+        problem = HpProblem()
+        for i in range(self.nparams):
+            problem.add_hyperparameter(domain, f"x{i}")
+        return problem
+
+    @property
+    def run_function(self):  # noqa: D102
+        run_function_ = functools.partial(
+            run_function,
+            dtlz_obj=self.dtlz_obj,
+            nobj=self.nobj,
+            with_failures=self.with_failures,
+        )
+        return run_function_
+
+    @property
+    def scorer(self):  # noqa: D102
+        return DTLZScorer(self.prob_id, self.nobj)
diff --git a/src/deephyper_benchmark/benchmarks/dtlz/model.py b/src/deephyper_benchmark/benchmarks/dtlz/model.py
new file mode 100644
index 0000000..f7f22f4
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/dtlz/model.py
@@ -0,0 +1,591 @@
+"""Objective function implementations of the DTLZ test suite.
+
+Derived from the implementations in ParMOO:
+
+Chang and Wild. "ParMOO: A Python library for parallel multiobjective
+simulation optimization." Journal of Open Source Software 8(82):4468, 2023.
+
+------------------------------------------------------------------------------
+
+For further references, the DTLZ test suite was originally proposed in:
+
+Deb, Thiele, Laumanns, and Zitzler. "Scalable test problems for
+evolutionary multiobjective optimization" in Evolutionary Multiobjective
+Optimization, Theoretical Advances and Applications, Ch. 6 (pp. 105--145).
+Springer-Verlag, London, UK, 2005. Abraham, Jain, and Goldberg (Eds).
+
+The original implementation was appropriate for testing randomized algorithms,
+but for many deterministic algorithms, the global solutions represent either
+best- or worst-case scenarios, so an configurable offset was introduced in:
+
+Chang. "Mathematical Software for Multiobjective Optimization Problems."
+Ph.D. dissertation, Virginia Tech, Dept. of Computer Science, 2020.
+
+------------------------------------------------------------------------------
+
+The full list of public classes in this module includes the 7 unconstrained
+DTLZ problems:
+ * ``dtlz1``
+ * ``dtlz2``
+ * ``dtlz3``
+ * ``dtlz4``
+ * ``dtlz5``
+ * ``dtlz6``
+ * ``dtlz7``
+
+"""
+
+import numpy as np
+
+
+class __dtlz_base__:
+    """Base class implements re-used constructor.
+
+    Constructor for all DTLZ classes.
+
+    Args:
+        num_des (int): The number of design variables.
+
+        num_obj (int, optional): The number of objectives.
+
+        offset (optional, float): The location of the global minimizers
+            is x_i = offset for i = num_objectives, ..., num_des.
+            The default offset is 0.5.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.5):
+        self.n = num_des
+        self.o = num_obj
+        self.offset = offset
+        return
+
+    def __call__(self, x):
+        raise NotImplementedError("The call method must be implemented...")
+
+
+class __g1__(__dtlz_base__):
+    """Class defining 1 of 4 kernel functions used in the DTLZ problem suite.
+
+    g1 = 100 ( (n - o + 1) +
+               sum_{i=o}^n ((x_i - offset)^2 - cos(20pi(x_i - offset))) )
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method creates a new kernel.
+
+    The ``__call__`` method performs an evaluation of the g1 kernel.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.array): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            float: The output of this objective for the input x.
+
+        """
+        return (
+            1
+            + self.n
+            - self.o
+            + np.sum(
+                (x[self.o - 1 : self.n] - self.offset) ** 2
+                - np.cos(20.0 * np.pi * (x[self.o - 1 : self.n] - self.offset))
+            )
+        ) * 100.0
+
+
+class __g2__(__dtlz_base__):
+    """Class defining 2 of 4 kernel functions used in the DTLZ problem suite.
+
+    g2 = (x_o - offset)^2 + ... + (x_n - offset)^2
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the g2 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.array): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            float: The output of this objective for the input x.
+
+        """
+        return np.sum((x[self.o - 1 : self.n] - self.offset) ** 2)
+
+
+class __g3__(__dtlz_base__):
+    """Class defining 3 of 4 kernel functions used in the DTLZ problem suite.
+
+    g3 = |x_o - offset|^.1 + ... + |x_n - offset|^.1
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the g3 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.0):
+        """Constructor for g3, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.array): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            float: The output of this objective for the input x.
+
+        """
+        return np.sum(np.abs(x[self.o - 1 : self.n] - self.offset) ** 0.1)
+
+
+class __g4__(__dtlz_base__):
+    """Class defining 4 of 4 kernel functions used in the DTLZ problem suite.
+
+    g4 = 1 + (9 * (|x_o - offset| + ... + |x_n - offset|) / (n + 1 - o))
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the g4 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.0):
+        """Constructor for g4, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.array): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            float: The output of this objective for the input x.
+
+        """
+        return (
+            9 * np.sum(np.abs(x[self.o - 1 : self.n] - self.offset)) / float(self.n + 1 - self.o)
+        ) + 1.0
+
+
+class dtlz1(__dtlz_base__):
+    """Class defining the DTLZ1 problem with offset minimizer.
+
+    DTLZ1 has a linear Pareto front, with all nondominated points
+    on the hyperplane F_1 + F_2 + ... + F_o = 0.5.
+    DTLZ1 has 11^k - 1 "local" Pareto fronts where k = n - o + 1, and
+    1 "global" Pareto front.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ1 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g1__(self.n, self.o, self.offset)
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = (1.0 + ker(x)) / 2.0
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= x[j]
+            if i > 0:
+                fx[i] *= 1.0 - x[self.o - 1 - i]
+        return fx
+
+
+class dtlz2(__dtlz_base__):
+    """Class defining the DTLZ2 problem with offset minimizer.
+
+    DTLZ2 has a concave Pareto front, given by the unit sphere in
+    objective space, restricted to the positive orthant.
+    DTLZ2 has no "local" Pareto fronts, besides the true Pareto front.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ2 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g2__(self.n, self.o, self.offset)
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + ker(x)
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * x[j] / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * x[self.o - 1 - i] / 2)
+        return fx
+
+
+class dtlz3(__dtlz_base__):
+    """Class defining the DTLZ3 problem with offset minimizer.
+
+    DTLZ3 has a concave Pareto front, given by the unit sphere in
+    objective space, restricted to the positive orthant.
+    DTLZ3 has 3^k - 1 "local" Pareto fronts where k = n - o + 1, and
+    1 "global" Pareto front.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ3 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g1__(self.n, self.o, self.offset)
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + ker(x)
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * x[j] / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * x[self.o - 1 - i] / 2)
+        return fx
+
+
+class dtlz4(__dtlz_base__):
+    """Class defining the DTLZ4 problem with offset minimizer.
+
+    DTLZ4 has a concave Pareto front, given by the unit sphere in
+    objective space, restricted to the positive orthant.
+    DTLZ4 has no "local" Pareto fronts, besides the true Pareto front,
+    but by tuning the optional parameter alpha, one can adjust the
+    solution density, making it harder for MOO algorithms to produce
+    a uniform distribution of solutions.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ4 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.5, alpha=100.0):
+        """Constructor for DTLZ7, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+            alpha (optional, float or int): The uniformity parameter used for
+                controlling the uniformity of the distribution of solutions
+                across the Pareto front. Must be greater than or equal to 1.
+                A value of 1 results in DTLZ2. Default value is 100.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        self.alpha = alpha
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g2__(self.n, self.o, self.offset)
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + ker(x)
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * x[j] ** self.alpha / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * x[self.o - 1 - i] ** self.alpha / 2)
+        return fx
+
+
+class dtlz5(__dtlz_base__):
+    """Class defining the DTLZ5 problem with offset minimizer.
+
+    DTLZ5 has a lower-dimensional Pareto front embedded in the objective
+    space, given by an arc of the unit sphere in the positive orthant.
+    DTLZ5 has no "local" Pareto fronts, besides the true Pareto front.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ5 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g2__(self.n, self.o, self.offset)
+        # Calculate theta values
+        theta = np.zeros(self.o)
+        g2x = ker(x)
+        theta[0] = x[0]
+        for i in range(1, self.o):
+            theta[i] = (1 + 2 * g2x * x[i]) / (2 * (1 + g2x))
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + g2x
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * theta[j] / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * theta[self.o - 1 - i] / 2)
+        return fx
+
+
+class dtlz6(__dtlz_base__):
+    """Class defining the DTLZ6 problem with offset minimizer.
+
+    DTLZ6 has a lower-dimensional Pareto front embedded in the objective
+    space, given by an arc of the unit sphere in the positive orthant.
+    DTLZ6 has no "local" Pareto fronts, but tends to show very little
+    improvement until the algorithm is very close to its solution set.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ6 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.0):
+        """Constructor for DTLZ6, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g3__(self.n, self.o, self.offset)
+        # Calculate theta values
+        theta = np.zeros(self.o)
+        g3x = ker(x)
+        theta[0] = x[0]
+        for i in range(1, self.o):
+            theta[i] = (1 + 2 * g3x * x[i]) / (2 * (1 + g3x))
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + g3x
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * theta[j] / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * theta[self.o - 1 - i] / 2)
+        return fx
+
+
+class dtlz7(__dtlz_base__):
+    """Class defining the DTLZ7 problem with offset minimizer.
+
+    DTLZ7 has a discontinuous Pareto front, with solutions on the
+    2^(o-1) discontinuous nondominated regions of the surface:
+
+    F_m = o - F_1 (1 + sin(3pi F_1)) - ... - F_{o-1} (1 + sin3pi F_{o-1}).
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ7 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.0):
+        """Constructor for DTLZ7, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g4__(self.n, self.o, self.offset)
+        # Initialize first o-1 entries in the output array
+        fx = np.zeros(self.o)
+        fx[: self.o - 1] = x[: self.o - 1]
+        # Calculate kernel functions
+        gx = 1.0 + ker(x)
+        hx = -np.sum(x[: self.o - 1] * (1.0 + np.sin(3.0 * np.pi * x[: self.o - 1])) / gx) + float(
+            self.o
+        )
+        # Calculate the last entry in the output array
+        fx[self.o - 1] = gx * hx
+        return fx

From 95ce3b7b07168ec0f1768d47feeb6b4bad89bfab Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 14:56:21 +0100
Subject: [PATCH 16/43] adding refactored dtlz benchmark example

---
 examples/bench_dtlz.py | 76 +++++++++++++++---------------------------
 1 file changed, 27 insertions(+), 49 deletions(-)

diff --git a/examples/bench_dtlz.py b/examples/bench_dtlz.py
index 24a527a..a5339ff 100644
--- a/examples/bench_dtlz.py
+++ b/examples/bench_dtlz.py
@@ -1,50 +1,28 @@
-# Setup info-level logging
-import logging
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(levelname)s - %(filename)s:%(funcName)s - " + \
-           "%(message)s",
-    force=True,
-)
-
-# Set DTLZ problem environment variables
-import os
-os.environ["DEEPHYPER_BENCHMARK_NDIMS"] = "5" # 5 vars
-os.environ["DEEPHYPER_BENCHMARK_NOBJS"] = "3" # 2 objs
-os.environ["DEEPHYPER_BENCHMARK_DTLZ_PROB"] = "2" # DTLZ2 problem
-os.environ["DEEPHYPER_BENCHMARK_DTLZ_OFFSET"] = "0.6" # [x_o, .., x_d]*=0.6
-
-# Load DTLZ benchmark suite, nothing to install
-import deephyper_benchmark as dhb
-dhb.load("DTLZ")
-
-
-# Necessary IF statement otherwise it will enter in a infinite loop
-# when loading the 'run' function from a subprocess
-if __name__ == "__main__":
-    from deephyper.problem import HpProblem
-    from deephyper.search.hps import CBO
-
-    # Run HPO-pipeline with default configuration of hyperparameters
-    from deephyper_benchmark.lib.dtlz import hpo
-    from deephyper.evaluator import RunningJob, Evaluator
-    config = hpo.problem.default_configuration
-    print(config)
-    res = hpo.run(RunningJob(parameters=config))
-    print(f"{res=}")
-
-    # define the evaluator to distribute the computation
-    evaluator = Evaluator.create(
-        hpo.run,
-        method="process",
-        method_kwargs={
-            "num_workers": 2,
-        },
-    )
-
-    # define your search and execute it
-    search = CBO(hpo.problem, evaluator)
-
-    # solve with 100 evals
-    results = search.search(max_evals=100)
+"""here."""
+
+import numpy as np
+from deephyper.hpo import CBO
+from deephyper_benchmark.benchmarks.dtlz import DTLZBenchmark
+
+
+def main():
+    """Run example."""
+    nobj = 2
+    bench = DTLZBenchmark(nobj=nobj)
+
+    search = CBO(bench.problem, bench.run_function, acq_optimizer="sampling")
+    results = search.search(25)
+
     print(results)
+
+    obj = results[[f"objective_{i}" for i in range(nobj)]].values
+    print(np.shape(obj))
+    hvi = bench.scorer.hypervolume(obj)
+    print(hvi)
+
+    hvi_iter = bench.scorer.hypervolume_iter(obj)
+    print(hvi_iter)
+
+
+if __name__ == "__main__":
+    main()

From 16adf9cd5908ab1fcd37918cde13f2030639ae2a Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 13:56:06 +0100
Subject: [PATCH 17/43] updating ackley and branin

---
 examples/bench_ackley.py                      |   8 +-
 examples/bench_branin.py                      |   8 +-
 examples/bench_combo.py                       |  26 -
 src/deephyper_benchmark/__init__.py           |   5 +-
 src/deephyper_benchmark/_benchmark.py         |   9 +-
 .../{cbbo => benchmarks}/__init__.py          |   0
 .../benchmarks/cbbo/__init__.py               |   6 +
 .../benchmarks/cbbo/ackley.py                 | 121 +++
 .../benchmarks/cbbo/branin.py                 |  90 ++
 src/deephyper_benchmark/cbbo/ackley.py        |  65 --
 src/deephyper_benchmark/cbbo/branin.py        |  48 --
 src/deephyper_benchmark/lib/__init__.py       |   0
 .../lib/autotuning/Mochi/HEPnOS/Makefile      |  11 +
 .../lib/autotuning/Mochi/HEPnOS/__init__.py   |   0
 .../lib/autotuning/Mochi/HEPnOS/benchmark.py  |  19 +
 .../lib/autotuning/Mochi/HEPnOS/hpo.py        | 281 +++++++
 .../lib/autotuning/Mochi/HEPnOS/metrics.py    |  39 +
 .../lib/autotuning/Mochi/README.md            |  13 +
 .../lib/autotuning/README.md                  |   3 +
 .../lib/c_bbo/ackley/hpo_benchmark.py         |   2 +-
 .../lib/c_bbo/easom/hpo_benchmark.py          |   2 +-
 .../lib/c_bbo/schwefel/hpo_benchmark.py       |   2 -
 .../lib/c_bbo/shekel/hpo_benchmark.py         |   4 +-
 src/deephyper_benchmark/lib/dtlz/README.md    | 130 +++
 src/deephyper_benchmark/lib/dtlz/__init__.py  |   1 +
 src/deephyper_benchmark/lib/dtlz/benchmark.py |   6 +
 src/deephyper_benchmark/lib/dtlz/hpo.py       |  61 ++
 src/deephyper_benchmark/lib/dtlz/metrics.py   | 201 +++++
 src/deephyper_benchmark/lib/dtlz/model.py     | 591 +++++++++++++
 .../lib/ecp_candle/Pilot1/Combo/Makefile      |  11 +
 .../lib/ecp_candle/Pilot1/Combo/README.md     |  54 ++
 .../lib/ecp_candle/Pilot1/Combo/__init__.py   |   1 +
 .../lib/ecp_candle/Pilot1/Combo/benchmark.py  |  28 +
 .../Pilot1/Combo/combo_default_model.yaml     |  31 +
 .../lib/ecp_candle/Pilot1/Combo/hpo.py        | 160 ++++
 .../lib/ecp_candle/Pilot1/Combo/model.py      | 782 ++++++++++++++++++
 .../ecp_candle/Pilot1/Combo/requirements.txt  |   5 +
 .../lib/fnobench/README.md                    |  64 ++
 .../lib/fnobench/__init__.py                  |   0
 .../lib/fnobench/benchmark.py                 |  17 +
 src/deephyper_benchmark/lib/fnobench/data.py  | 227 +++++
 .../lib/fnobench/data/darcy_test_16.pt        | Bin 0 -> 64999 bytes
 .../lib/fnobench/data/darcy_test_32.pt        | Bin 0 -> 256999 bytes
 .../lib/fnobench/data/darcy_train_16.pt       | Bin 0 -> 1280999 bytes
 src/deephyper_benchmark/lib/fnobench/hpo.py   | 104 +++
 src/deephyper_benchmark/lib/fnobench/model.py | 560 +++++++++++++
 .../lib/fnobench/requirements.txt             |   5 +
 .../lib/hpobench/README.md                    |  28 +
 .../lib/hpobench/tabular/__init__.py          |   0
 .../lib/hpobench/tabular/benchmark.py         |  17 +
 .../hpobench/tabular/constant_predictor.py    | 127 +++
 .../lib/hpobench/tabular/hpo.py               | 139 ++++
 .../lib/hpobench/tabular/install.sh           |  39 +
 .../lib/hpobench/tabular/metrics.py           |  78 ++
 .../lib/jahsbench/README.md                   | 176 ++++
 .../lib/jahsbench/__init__.py                 |   1 +
 .../lib/jahsbench/benchmark.py                |  22 +
 src/deephyper_benchmark/lib/jahsbench/hpo.py  |  57 ++
 .../lib/jahsbench/install.sh                  |   1 +
 .../lib/jahsbench/metrics.py                  | 119 +++
 .../lib/jahsbench/model.py                    |  80 ++
 .../lib/jahsbench/requirements.txt            |   2 +
 src/deephyper_benchmark/lib/lcbench/Makefile  |  14 +
 src/deephyper_benchmark/lib/lcbench/README.md |  19 +
 .../lib/lcbench/__init__.py                   |   2 +
 .../lib/lcbench/benchmark.py                  |  18 +
 src/deephyper_benchmark/lib/lcbench/hpo.py    |  78 ++
 src/deephyper_benchmark/lib/lcdb/README.md    |   4 +
 .../lib/lcdb/algorithm_selection/__init__.py  |   0
 .../hyperparameter_optimization/__init__.py   |   0
 .../hyperparameter_optimization/benchmark.py  |  12 +
 .../lcdb/hyperparameter_optimization/hpo.py   | 216 +++++
 .../lib/lcmbench/README.md                    |   4 +
 .../lib/lcmbench/lcdb/README.md               |   3 +
 .../lib/lcmbench/lcdb/__init__.py             |   0
 .../lib/lcmbench/lcdb/benchmark.py            |  11 +
 .../lib/lcmbench/lcdb/hpo.py                  | 128 +++
 .../lib/lcmbench/loglin2/README.md            |   5 +
 .../lib/lcmbench/loglin2/__init__.py          |   0
 .../lib/lcmbench/loglin2/benchmark.py         |  12 +
 .../lib/lcmbench/loglin2/hpo.py               |  71 ++
 .../lib/lcmbench/pow3/README.md               |   5 +
 .../lib/lcmbench/pow3/__init__.py             |   0
 .../lib/lcmbench/pow3/benchmark.py            |  12 +
 .../lib/lcmbench/pow3/hpo.py                  |  81 ++
 .../lib/pinnbench/Burgers/__init__.py         |   0
 .../lib/pinnbench/Burgers/benchmark.py        |  17 +
 .../lib/pinnbench/Burgers/hpo.py              | 111 +++
 .../lib/pinnbench/Burgers/model.py            | 411 +++++++++
 .../lib/pinnbench/Burgers/requirements.txt    |   5 +
 .../pinnbench/DiffusionReaction/__init__.py   |   1 +
 .../pinnbench/DiffusionReaction/benchmark.py  |  28 +
 .../lib/pinnbench/DiffusionReaction/hpo.py    | 206 +++++
 .../lib/pinnbench/DiffusionReaction/model.py  | 154 ++++
 .../DiffusionReaction/requirements.txt        |   6 +
 .../lib/pinnbench/README.md                   | 144 ++++
 .../lib/pinnbench/install.sh                  |  13 +
 src/deephyper_benchmark/lib/yahpo/README.md   |  13 +
 .../lib/yahpo/lcbench/__init__.py             |   0
 .../lib/yahpo/lcbench/benchmark.py            |  27 +
 .../lib/yahpo/lcbench/hpo.py                  |  75 ++
 .../lib/yahpo/lcbench/metrics.py              |  69 ++
 102 files changed, 6502 insertions(+), 160 deletions(-)
 delete mode 100644 examples/bench_combo.py
 rename src/deephyper_benchmark/{cbbo => benchmarks}/__init__.py (100%)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/__init__.py
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/ackley.py
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/branin.py
 delete mode 100644 src/deephyper_benchmark/cbbo/ackley.py
 delete mode 100644 src/deephyper_benchmark/cbbo/branin.py
 create mode 100644 src/deephyper_benchmark/lib/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/Makefile
 create mode 100644 src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/metrics.py
 create mode 100644 src/deephyper_benchmark/lib/autotuning/Mochi/README.md
 create mode 100644 src/deephyper_benchmark/lib/autotuning/README.md
 create mode 100644 src/deephyper_benchmark/lib/dtlz/README.md
 create mode 100644 src/deephyper_benchmark/lib/dtlz/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/dtlz/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/dtlz/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/dtlz/metrics.py
 create mode 100644 src/deephyper_benchmark/lib/dtlz/model.py
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/Makefile
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/README.md
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/combo_default_model.yaml
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/model.py
 create mode 100644 src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/requirements.txt
 create mode 100644 src/deephyper_benchmark/lib/fnobench/README.md
 create mode 100644 src/deephyper_benchmark/lib/fnobench/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/fnobench/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/fnobench/data.py
 create mode 100644 src/deephyper_benchmark/lib/fnobench/data/darcy_test_16.pt
 create mode 100644 src/deephyper_benchmark/lib/fnobench/data/darcy_test_32.pt
 create mode 100644 src/deephyper_benchmark/lib/fnobench/data/darcy_train_16.pt
 create mode 100644 src/deephyper_benchmark/lib/fnobench/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/fnobench/model.py
 create mode 100644 src/deephyper_benchmark/lib/fnobench/requirements.txt
 create mode 100644 src/deephyper_benchmark/lib/hpobench/README.md
 create mode 100644 src/deephyper_benchmark/lib/hpobench/tabular/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/hpobench/tabular/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/hpobench/tabular/constant_predictor.py
 create mode 100644 src/deephyper_benchmark/lib/hpobench/tabular/hpo.py
 create mode 100755 src/deephyper_benchmark/lib/hpobench/tabular/install.sh
 create mode 100644 src/deephyper_benchmark/lib/hpobench/tabular/metrics.py
 create mode 100644 src/deephyper_benchmark/lib/jahsbench/README.md
 create mode 100644 src/deephyper_benchmark/lib/jahsbench/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/jahsbench/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/jahsbench/hpo.py
 create mode 100755 src/deephyper_benchmark/lib/jahsbench/install.sh
 create mode 100644 src/deephyper_benchmark/lib/jahsbench/metrics.py
 create mode 100644 src/deephyper_benchmark/lib/jahsbench/model.py
 create mode 100644 src/deephyper_benchmark/lib/jahsbench/requirements.txt
 create mode 100644 src/deephyper_benchmark/lib/lcbench/Makefile
 create mode 100644 src/deephyper_benchmark/lib/lcbench/README.md
 create mode 100644 src/deephyper_benchmark/lib/lcbench/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/lcbench/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/lcbench/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/lcdb/README.md
 create mode 100644 src/deephyper_benchmark/lib/lcdb/algorithm_selection/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/README.md
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/lcdb/README.md
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/lcdb/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/lcdb/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/lcdb/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/loglin2/README.md
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/loglin2/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/loglin2/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/loglin2/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/pow3/README.md
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/pow3/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/pow3/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/lcmbench/pow3/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/Burgers/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/Burgers/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/Burgers/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/Burgers/model.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/Burgers/requirements.txt
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/model.py
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/requirements.txt
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/README.md
 create mode 100644 src/deephyper_benchmark/lib/pinnbench/install.sh
 create mode 100644 src/deephyper_benchmark/lib/yahpo/README.md
 create mode 100644 src/deephyper_benchmark/lib/yahpo/lcbench/__init__.py
 create mode 100644 src/deephyper_benchmark/lib/yahpo/lcbench/benchmark.py
 create mode 100644 src/deephyper_benchmark/lib/yahpo/lcbench/hpo.py
 create mode 100644 src/deephyper_benchmark/lib/yahpo/lcbench/metrics.py

diff --git a/examples/bench_ackley.py b/examples/bench_ackley.py
index fe4861f..74e6f4c 100644
--- a/examples/bench_ackley.py
+++ b/examples/bench_ackley.py
@@ -1,19 +1,21 @@
 """here."""
 
 from deephyper.hpo import CBO
-from deephyper_benchmark import AckleyBenchmark
+from deephyper_benchmark.benchmarks.cbbo import AckleyBenchmark
 
 
 def main():
     """Run example."""
-
     bench = AckleyBenchmark()
 
     search = CBO(bench.problem, bench.run_function)
-    results = search.search(10)
+    results = search.search(25)
 
     print(results)
 
+    cumul_regret = bench.scorer.cumul_regret(results.objective)
+    print(cumul_regret)
+
 
 if __name__ == "__main__":
     main()
diff --git a/examples/bench_branin.py b/examples/bench_branin.py
index 7b1780d..41fb347 100644
--- a/examples/bench_branin.py
+++ b/examples/bench_branin.py
@@ -1,19 +1,21 @@
 """here."""
 
 from deephyper.hpo import CBO
-from deephyper_benchmark import BraninBenchmark
+from deephyper_benchmark.benchmarks.cbbo import BraninBenchmark
 
 
 def main():
     """Run example."""
-
     bench = BraninBenchmark()
 
     search = CBO(bench.problem, bench.run_function)
-    results = search.search(10)
+    results = search.search(25)
 
     print(results)
 
+    cumul_regret = bench.scorer.cumul_regret(results.objective)
+    print(cumul_regret)
+
 
 if __name__ == "__main__":
     main()
diff --git a/examples/bench_combo.py b/examples/bench_combo.py
deleted file mode 100644
index b8fdac5..0000000
--- a/examples/bench_combo.py
+++ /dev/null
@@ -1,26 +0,0 @@
-import logging
-
-logging.basicConfig(
-    level=logging.INFO,
-    format="%(asctime)s - %(levelname)s - %(filename)s:%(funcName)s - %(message)s",
-    force=True,
-)
-
-import deephyper_benchmark as dhb
-
-# dhb.install("ECP-Candle/Pilot1/Combo")
-
-dhb.load("ECP-Candle/Pilot1/Combo")
-
-# Run training with Testing scores
-from deephyper_benchmark.lib.ecp_candle.pilot1.combo import model 
-res = model.run_pipeline(mode="test")
-print(f"{res=}")
-
-# Run HPO-pipeline with default configuration of hyperparameters
-from deephyper_benchmark.lib.ecp_candle.pilot1.combo import hpo
-config = hpo.problem.default_configuration
-res = hpo.run(config)
-print(f"{res=}")
-
-
diff --git a/src/deephyper_benchmark/__init__.py b/src/deephyper_benchmark/__init__.py
index a52dc4f..c8df6c3 100644
--- a/src/deephyper_benchmark/__init__.py
+++ b/src/deephyper_benchmark/__init__.py
@@ -2,7 +2,6 @@
 
 from ._benchmark import Benchmark, HPOBenchmark
 from ._scorer import Scorer, HPOScorer
-from .cbbo.ackley import AckleyBenchmark
-from .cbbo.branin import BraninBenchmark
 
-__all__ = ["Benchmark", "HPOBenchmark", "Scorer", "HPOScorer", "AckleyBenchmark", "BraninBenchmark"]
+
+__all__ = ["Benchmark", "HPOBenchmark", "Scorer", "HPOScorer"]
diff --git a/src/deephyper_benchmark/_benchmark.py b/src/deephyper_benchmark/_benchmark.py
index 3850d7f..219e311 100644
--- a/src/deephyper_benchmark/_benchmark.py
+++ b/src/deephyper_benchmark/_benchmark.py
@@ -2,15 +2,12 @@
 
 
 class Benchmark(abc.ABC):
-    def __init__(self):
-        super().__init__()
-        self.refresh_settings()
-
-    def refresh_settings(self):
-        """Refresh benchmark settings."""
+    """Base class for benchmarks."""
 
 
 class HPOBenchmark(Benchmark):
+    """Base class for Hyperparameter optimization benchmarks."""
+
     @property
     @abc.abstractmethod
     def problem(self):
diff --git a/src/deephyper_benchmark/cbbo/__init__.py b/src/deephyper_benchmark/benchmarks/__init__.py
similarity index 100%
rename from src/deephyper_benchmark/cbbo/__init__.py
rename to src/deephyper_benchmark/benchmarks/__init__.py
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/__init__.py b/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
new file mode 100644
index 0000000..42d103a
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
@@ -0,0 +1,6 @@
+"""here."""
+
+from .ackley import AckleyBenchmark
+from .branin import BraninBenchmark
+
+__all__ = ["AckleyBenchmark", "BraninBenchmark"]
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/ackley.py b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
new file mode 100644
index 0000000..c7d591e
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
@@ -0,0 +1,121 @@
+"""here."""
+
+import numpy as np
+import time
+
+from deephyper.evaluator import profile
+from deephyper.evaluator import RunningJob
+from deephyper.hpo import HpProblem
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+
+def ackley(x, a=20, b=0.2, c=2 * np.pi):
+    """Ackley function.
+
+    Description of the function: https://www.sfu.ca/~ssurjano/ackley.html
+    """
+    d = len(x)
+    s1 = np.sum(x**2)
+    s2 = np.sum(np.cos(c * x))
+    term1 = -a * np.exp(-b * np.sqrt(s1 / d))
+    term2 = -np.exp(s2 / d)
+    y = term1 + term2 + a + np.exp(1)
+    return y
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:  # noqa: D103
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -ackley(x)
+
+
+class AckleyScorer(HPOScorer):
+    """A class defining performance evaluators for the Ackley problem."""
+
+    def __init__(
+        self,
+        nparams,
+        nslack,
+    ):
+        self.nparams = nparams
+        self.x_max = np.full(self.nparams, fill_value=0.0)
+        self.x_max[nparams - nslack :] = np.nan
+        self.y_max = 0.0
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_max - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class AckleyBenchmark(HPOBenchmark):
+    """Ackley benchmark.
+
+    Args:
+        nparams (int, optional): the number of parameters in the problem.
+        offset (int, optional): the offset in the space of parameters.
+        nslack (int, optional): the number of additional slack parameters in the problem.
+    """
+
+    def __init__(self, nparams: int = 5, offset: int = -4.0, nslack: int = 0) -> None:
+        self.nparams = nparams
+        assert offset <= 32.768 and offset >= -32.768, (
+            "offset must be in [-32.768, 32.768] to keep the same maximum value."
+        )
+        self.offset = offset
+        self.nslack = nslack
+
+    @property
+    def problem(self):  # noqa: D102
+        # The original range is simetric (-32.768, 32.768) but we make it less simetric to avoid
+        # Grid sampling or QMC sampling to directly hit the optimum...
+        domain = (
+            -32.768 + self.offset,
+            32.768 + self.offset,
+        )
+        problem = HpProblem()
+        for i in range(self.nparams - self.nslack):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+        # Add slack/dummy dimensions (useful to test predicors which are sensitive
+        # to unimportant features)
+        for i in range(
+            self.nparams - self.nslack,
+            self.nparams,
+        ):
+            problem.add_hyperparameter(domain, f"z{i}")
+        return problem
+
+    @property
+    def run_function(self):  # noqa: D102
+        return run_function
+
+    @property
+    def scorer(self):  # noqa: D102
+        return AckleyScorer(self.nparams, self.nslack)
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/branin.py b/src/deephyper_benchmark/benchmarks/cbbo/branin.py
new file mode 100644
index 0000000..1717d78
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/branin.py
@@ -0,0 +1,90 @@
+"""here."""
+
+import numpy as np
+import time
+
+from deephyper.evaluator import RunningJob
+from deephyper.evaluator import profile
+from deephyper.hpo import HpProblem
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+
+def branin(x):
+    """Branin function.
+
+    Description of the function: https://www.sfu.ca/~ssurjano/branin.html"
+    """
+    assert len(x) == 2
+    a = 1.0
+    b = 5.1 / (4.0 * np.pi**2)
+    c = 5.0 / np.pi
+    r = 6.0
+    s = 10.0
+    t = 1.0 / (8.0 * np.pi)
+    y = a * (x[1] - b * x[0] ** 2 + c * x[0] - r) ** 2 + s * (1 - t) * np.cos(x[0]) + s
+    return y
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:  # noqa: D103
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return -branin(x)
+
+
+class BraninHPOScorer(HPOScorer):
+    """A class defining performance evaluators for the Ackley problem."""
+
+    def __init__(self):
+        self.nparams = 2
+        self.x_max = np.array([[-np.pi, 12.275], [np.pi, 2.275], [9.42478, 2.475]])
+        self.y_max = -0.397887
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_max - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class BraninBenchmark(HPOBenchmark):
+    """Branin benchmark."""
+
+    @property
+    def problem(self):  # noqa: D102
+        problem = HpProblem()
+        problem.add_hyperparameter((-5.0, 10.0), "x0")
+        problem.add_hyperparameter((0.0, 15.0), "x1")
+        return problem
+
+    @property
+    def run_function(self):  # noqa: D102
+        return run_function
+
+    @property
+    def scorer(self):  # noqa: D102
+        return BraninHPOScorer()
diff --git a/src/deephyper_benchmark/cbbo/ackley.py b/src/deephyper_benchmark/cbbo/ackley.py
deleted file mode 100644
index 03fc7c9..0000000
--- a/src/deephyper_benchmark/cbbo/ackley.py
+++ /dev/null
@@ -1,65 +0,0 @@
-"""here."""
-
-import numpy as np
-import time
-
-from deephyper.evaluator import profile
-from deephyper.evaluator import RunningJob
-from deephyper.hpo import HpProblem
-
-
-def ackley(x, a=20, b=0.2, c=2 * np.pi):
-    d = len(x)
-    s1 = np.sum(x**2)
-    s2 = np.sum(np.cos(c * x))
-    term1 = -a * np.exp(-b * np.sqrt(s1 / d))
-    term2 = -np.exp(s2 / d)
-    y = term1 + term2 + a + np.exp(1)
-    return y
-
-
-@profile
-def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -ackley(x)
-
-
-class AckleyBenchmark:
-    def __init__(self, ndims=5, offset=4.0, slack=0) -> None:
-        self.ndims = ndims
-        self.offset = offset
-        self.slack = slack
-
-    @property
-    def problem(self):
-        # The original range is simetric (-32.768, 32.768) but we make it less simetric to avoid
-        # Grid sampling or QMC sampling to directly hit the optimum...
-        domain = (
-            -32.768 - self.offset,
-            32.768 - self.offset,
-        )
-        problem = HpProblem()
-        for i in range(self.ndims - self.slack):
-            problem.add_hyperparameter(domain, f"x{i}")
-
-        # Add slack/dummy dimensions (useful to test predicors which are sensitive
-        # to unimportant features)
-        for i in range(
-            self.ndims - self.slack,
-            self.ndims,
-        ):
-            problem.add_hyperparameter(domain, f"z{i}")
-        return problem
-
-    @property
-    def run_function(self):
-        return run_function
diff --git a/src/deephyper_benchmark/cbbo/branin.py b/src/deephyper_benchmark/cbbo/branin.py
deleted file mode 100644
index 181b05f..0000000
--- a/src/deephyper_benchmark/cbbo/branin.py
+++ /dev/null
@@ -1,48 +0,0 @@
-"""here."""
-
-import numpy as np
-import time
-
-from deephyper.evaluator import RunningJob
-from deephyper.evaluator import profile
-from deephyper.hpo import HpProblem
-
-
-def branin(x):
-    assert len(x) == 2
-    a = 1.0
-    b = 5.1 / (4.0 * np.pi**2)
-    c = 5.0 / np.pi
-    r = 6.0
-    s = 10.0
-    t = 1.0 / (8.0 * np.pi)
-    y = a * (x[1] - b * x[0] ** 2 + c * x[0] - r) ** 2 + s * (1 - t) * np.cos(x[0]) + s
-    return y
-
-
-@profile
-def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -branin(x)
-
-
-class BraninBenchmark:
-    @property
-    def problem(self):
-        problem = HpProblem()
-        problem.add_hyperparameter((-5.0, 10.0), "x0")
-        problem.add_hyperparameter((0.0, 15.0), "x1")
-        return problem
-
-    @property
-    def run_function(self):
-        return run_function
diff --git a/src/deephyper_benchmark/lib/__init__.py b/src/deephyper_benchmark/lib/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/Makefile b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/Makefile
new file mode 100644
index 0000000..beec148
--- /dev/null
+++ b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/Makefile
@@ -0,0 +1,11 @@
+
+VERSION=0.0.1
+
+build:
+	mkdir -p build
+	wget https://github.com/hepnos/HEPnOS-Autotuning-analysis/archive/refs/heads/main.tar.gz -O build/hepnos-autotuning-analysis.tar.gz
+	tar -xzvf build/hepnos-autotuning-analysis.tar.gz -C build
+	rm -rf build/hepnos-autotuning-analysis.tar.gz
+
+clean:
+	rm -rf build/
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/__init__.py b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/benchmark.py b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/benchmark.py
new file mode 100644
index 0000000..4727358
--- /dev/null
+++ b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/benchmark.py
@@ -0,0 +1,19 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class HEPnOS(Benchmark):
+    """
+    - Experimental data from the HEPnOS paper: https://github.com/hepnos/HEPnOS-Autotuning-analysis
+    - Experimental code from the HEPnOS paper: https://github.com/hepnos/HEPnOS-Autotuning/tree/dev-new-hepnos
+    - Paper from M. Dorier and R. Egele et al. "HPC Storage Service Autotuning Using Variational-Autoencoder-Guided Asynchronous Bayesian Optimization". https://arxiv.org/pdf/2210.00798
+    """
+
+    version = "0.0.1"
+
+    requires = {
+        "makefile": {"step": "install", "type": "cmd", "cmd": "make build"},
+    }
diff --git a/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/hpo.py b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/hpo.py
new file mode 100644
index 0000000..c5ed94d
--- /dev/null
+++ b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/hpo.py
@@ -0,0 +1,281 @@
+import os
+import time
+
+import numpy as np
+import pandas as pd
+from deephyper.analysis.hpo import filter_failed_objectives
+from deephyper.evaluator import RunningJob, profile
+from deephyper.hpo import HpProblem
+from sklearn.compose import ColumnTransformer
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.preprocessing import OneHotEncoder, FunctionTransformer
+from sklearn.pipeline import Pipeline
+
+BUILD_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "build")
+DATA_DIR = os.path.join(
+    BUILD_DIR, "HEPnOS-Autotuning-analysis-main", "results", "theta"
+)
+
+DEEPHYPER_BENCHMARK_HEPNOS_MODEL = os.environ.get(
+    "DEEPHYPER_BENCHMARK_HEPNOS_MODEL", "RAND"
+)
+DEEPHYPER_BENCHMARK_HEPNOS_SCALE = int(
+    os.environ.get("DEEPHYPER_BENCHMARK_HEPNOS_SCALE", "4")
+)
+DEEPHYPER_BENCHMARK_HEPNOS_DISABLE_PEP = bool(
+    os.environ.get("DEEPHYPER_BENCHMARK_HEPNOS_DISABLE_PEP", "0")
+)
+DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS = bool(
+    os.environ.get("DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS", "0")
+)
+
+NUMERIC_VARIABLES = []
+CATEGORICAL_VARIABLES = []
+
+problem = HpProblem()
+
+
+def add_parameter(name, domain, value_type, default_value, description=""):
+    problem.add_hyperparameter(domain, name, default_value=default_value)
+    if value_type in [int, float, bool]:
+        NUMERIC_VARIABLES.append(name)
+    else:
+        CATEGORICAL_VARIABLES.append(name)
+
+
+def create_problem():
+    add_parameter(
+        "busy_spin",
+        [0, 1],
+        int,
+        0,
+        "Whether Mercury should busy-spin instead of block",
+    )
+    add_parameter(
+        "hepnos_progress_thread",
+        [0, 1],
+        int,
+        0,
+        "Whether to use a dedicated progress thread in HEPnOS",
+    )
+    add_parameter(
+        "hepnos_num_rpc_threads",
+        (0, 63),
+        int,
+        0,
+        "Number of threads used for serving RPC requests",
+    )
+    add_parameter(
+        "hepnos_num_event_databases",
+        (1, 16),
+        int,
+        1,
+        "Number of databases per process used to store events",
+    )
+    add_parameter(
+        "hepnos_num_product_databases",
+        (1, 16),
+        int,
+        1,
+        "Number of databases per process used to store products",
+    )
+    add_parameter(
+        "hepnos_num_providers",
+        (1, 32),
+        int,
+        1,
+        "Number of database providers per process",
+    )
+    add_parameter(
+        "hepnos_pool_type",
+        ["fifo", "fifo_wait"],
+        str,
+        "fifo_wait",
+        "Thread-scheduling policity used by Argobots pools",
+    )
+    add_parameter(
+        "hepnos_pes_per_node",
+        [1, 2, 4, 8, 16, 32],
+        int,
+        2,
+        "Number of HEPnOS processes per node",
+    )
+    add_parameter(
+        "loader_progress_thread",
+        [0, 1],
+        int,
+        0,
+        "Whether to use a dedicated progress thread in the Dataloader",
+    )
+    add_parameter(
+        "loader_batch_size",
+        (1, 2048, "log-uniform"),
+        int,
+        512,
+        "Size of the batches of events sent by the Dataloader to HEPnOS",
+    )
+    add_parameter(
+        "loader_pes_per_node",
+        [1, 2, 4, 8, 16],
+        int,
+        2,
+        "Number of processes per node for the Dataloader",
+    )
+    if DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS:
+        add_parameter(
+            "loader_async",
+            [0, 1],
+            int,
+            0,
+            "Whether to use the HEPnOS AsyncEngine in the Dataloader",
+        )
+        add_parameter(
+            "loader_async_threads",
+            (1, 63, "log-uniform"),
+            int,
+            1,
+            "Number of threads for the AsyncEngine to use",
+        )
+
+    if DEEPHYPER_BENCHMARK_HEPNOS_DISABLE_PEP:
+        return
+
+    add_parameter(
+        "pep_progress_thread",
+        [0, 1],
+        int,
+        0,
+        "Whether to use a dedicated progress thread in the PEP step",
+    )
+    add_parameter(
+        "pep_num_threads",
+        (1, 31),
+        int,
+        4,
+        "Number of threads used for processing in the PEP step",
+    )
+    add_parameter(
+        "pep_ibatch_size",
+        (8, 1024, "log-uniform"),
+        int,
+        128,
+        "Batch size used when PEP processes are loading events from HEPnOS",
+    )
+    add_parameter(
+        "pep_obatch_size",
+        (8, 1024, "log-uniform"),
+        int,
+        128,
+        "Batch size used when PEP processes are exchanging events among themselves",
+    )
+    add_parameter(
+        "pep_pes_per_node",
+        [1, 2, 4, 8, 16, 32],
+        int,
+        8,
+        "Number of processes per node for the PEP step",
+    )
+
+    if DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS:
+        add_parameter(
+            "pep_no_preloading",
+            [0, 1],
+            int,
+            False,
+            "Whether to disable product-preloading in PEP",
+        )
+        add_parameter(
+            "pep_no_rdma",
+            [0, 1],
+            int,
+            0,
+            "Whether to disable RDMA in PEP",
+        )
+
+
+create_problem()
+
+CSV_SUFFIX = f"{str(DEEPHYPER_BENCHMARK_HEPNOS_DISABLE_PEP).lower()}-{str(DEEPHYPER_BENCHMARK_HEPNOS_MORE_PARAMS).lower()}"
+CSV_SUFFIX = f"{DEEPHYPER_BENCHMARK_HEPNOS_MODEL}-{DEEPHYPER_BENCHMARK_HEPNOS_SCALE}-{CSV_SUFFIX}"
+
+
+def load_data():
+    dataframes, dataframes_with_failures = [], []
+    for i in range(1, 6):
+        csv_path = os.path.join(
+            DATA_DIR,
+            f"exp-{CSV_SUFFIX}-{i}.csv",
+        )
+        df = pd.read_csv(csv_path, index_col=None, header=0)
+        df, df_with_failures = filter_failed_objectives(df)
+        dataframes.append(df)
+        dataframes_with_failures.append(df_with_failures)
+
+    dataframes = pd.concat(dataframes, axis=0, ignore_index=True)
+    dataframes_with_failures = pd.concat(
+        dataframes_with_failures, axis=0, ignore_index=True
+    )
+
+    # The objective is -log(time) initialy so we convert it back to time
+    dataframes["objective"] = dataframes["objective"] = np.exp(
+        -dataframes["objective"].values.astype(np.float32)
+    )
+    dataframes_with_failures["objective"] = -1
+
+    dataframes = pd.concat(
+        [dataframes, dataframes_with_failures], axis=0, ignore_index=True
+    )
+
+    return dataframes
+
+
+def create_model():
+    df = load_data()
+    names = problem.hyperparameter_names
+
+    X = df.loc[:, names]
+    y = df["objective"].values
+
+    transformer = ColumnTransformer(
+        [
+            (
+                "onehot",
+                OneHotEncoder(handle_unknown="ignore"),
+                CATEGORICAL_VARIABLES,
+            ),
+            ("passthrough", FunctionTransformer(), NUMERIC_VARIABLES),
+        ]
+    )
+
+    model = KNeighborsRegressor(n_neighbors=1)
+
+    pipeline = Pipeline([("transformer", transformer), ("model", model)])
+    pipeline.fit(X, y)
+
+    return pipeline
+
+
+model = create_model()
+
+
+@profile
+def run(job: RunningJob) -> dict:
+    config = job.parameters.copy()
+
+    df = pd.DataFrame([config])
+    y_pred = -model.predict(df)[0]
+
+    if y_pred > 0:
+        y_pred = "F"
+
+    return {"objective": y_pred}
+
+
+if __name__ == "__main__":
+    print(problem)
+    df = load_data()
+    print(df)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/metrics.py b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/metrics.py
new file mode 100644
index 0000000..bc09e5a
--- /dev/null
+++ b/src/deephyper_benchmark/lib/autotuning/Mochi/HEPnOS/metrics.py
@@ -0,0 +1,39 @@
+import os
+
+import numpy as np
+
+from .hpo import load_data, problem
+
+class PerformanceEvaluator:
+    """A class defining performance evaluators for the HEPnOS problem."""
+
+    def __init__(self):
+        """Read the current problem defn from environment vars."""
+
+        df = load_data()
+        df = df[df["objective"] > 0]
+        idx_max = df["objective"].argmin()
+        self.x_min = df[problem.hyperparameter_names].iloc[idx_max].to_dict()
+        self.y_min = df.iloc[idx_max]["objective"]
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return y - self.y_min
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
diff --git a/src/deephyper_benchmark/lib/autotuning/Mochi/README.md b/src/deephyper_benchmark/lib/autotuning/Mochi/README.md
new file mode 100644
index 0000000..3f4bb65
--- /dev/null
+++ b/src/deephyper_benchmark/lib/autotuning/Mochi/README.md
@@ -0,0 +1,13 @@
+# Mochi Benchmarks
+
+From the Mochi documentation:
+
+> The Mochi project is a collaboration between Argonne National Laboratory, Los Alamos National Laboratory, Carnegie Mellon University, and the HDF Group. The objective of this project is to explore a software defined storage approach for composing storage services that provides new levels of functionality, performance, and reliability for science applications at extreme scale.
+
+- Documentation: https://mochi.readthedocs.io/en/latest/
+
+## Installation
+
+```console
+python -c "import deephyper_benchmark as dhb; dhb.install('AutoTuning/Mochi/HEPnOS');"
+```
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/autotuning/README.md b/src/deephyper_benchmark/lib/autotuning/README.md
new file mode 100644
index 0000000..bd2e296
--- /dev/null
+++ b/src/deephyper_benchmark/lib/autotuning/README.md
@@ -0,0 +1,3 @@
+# AutoTuning Benchmarks
+
+AutoTuning corresponds to the automated configurations of software to improve performance. For example, it can be the configuration of a database, a compilator, or parallelism.
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
index 9ef0ac3..d144a9d 100644
--- a/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
+++ b/src/deephyper_benchmark/lib/c_bbo/ackley/hpo_benchmark.py
@@ -47,7 +47,7 @@ def __init__(
         offset=0,
     ):
         self.p_num = p_num
-        self.x_max = np.full(self.p_num, fill_value=-offset)
+        self.x_max = np.full(self.p_num, fill_value=0.0)
         self.x_max[p_num - p_num_slack :] = np.nan
         self.y_max = 0.0
 
diff --git a/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py
index 4c0509c..df5c9ff 100644
--- a/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py
+++ b/src/deephyper_benchmark/lib/c_bbo/easom/hpo_benchmark.py
@@ -37,7 +37,7 @@ class EasomHPOScorer(HPOScorer):
     """A class defining performance evaluators for the Ackley problem."""
 
     def __init__(self, offset=0):
-        self.x_max = np.array([np.pi, np.pi]) - offset
+        self.x_max = np.array([np.pi, np.pi])
         self.y_max = 1.0
 
     def simple_regret(self, y: np.ndarray) -> np.ndarray:
diff --git a/src/deephyper_benchmark/lib/c_bbo/schwefel/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/schwefel/hpo_benchmark.py
index 4f3cbec..903ba0c 100644
--- a/src/deephyper_benchmark/lib/c_bbo/schwefel/hpo_benchmark.py
+++ b/src/deephyper_benchmark/lib/c_bbo/schwefel/hpo_benchmark.py
@@ -38,8 +38,6 @@ class SchwefelHPOScorer(HPOScorer):
     def __init__(
         self,
         p_num,
-        p_num_slack,
-        offset=0,
     ):
         self.p_num = p_num
         self.x_max = np.full(self.p_num, fill_value=420.9687)
diff --git a/src/deephyper_benchmark/lib/c_bbo/shekel/hpo_benchmark.py b/src/deephyper_benchmark/lib/c_bbo/shekel/hpo_benchmark.py
index d1b881e..d0e10ab 100644
--- a/src/deephyper_benchmark/lib/c_bbo/shekel/hpo_benchmark.py
+++ b/src/deephyper_benchmark/lib/c_bbo/shekel/hpo_benchmark.py
@@ -45,7 +45,7 @@ class ShekelHPOScorer(HPOScorer):
     """A class defining performance evaluators for the Shekel problem."""
 
     def __init__(self):
-        self.p_num = 10
+        self.p_num = 4
         self.x_max = np.full(self.p_num, fill_value=4.0)
         self.y_max = 10.5364
 
@@ -77,7 +77,7 @@ class ShekelHPOBenchmark(HPOBenchmark):
     def problem(self):
         domain = (0.0, 10.0)
         problem = HpProblem()
-        for i in range(10):
+        for i in range(4):
             problem.add_hyperparameter(domain, f"x{i}")
         return problem
 
diff --git a/src/deephyper_benchmark/lib/dtlz/README.md b/src/deephyper_benchmark/lib/dtlz/README.md
new file mode 100644
index 0000000..5c09f18
--- /dev/null
+++ b/src/deephyper_benchmark/lib/dtlz/README.md
@@ -0,0 +1,130 @@
+
+# Modified Multiobjective DTLZ Test Suite
+
+✅ **Benchmark ready to be used.**
+
+This module contains objective function implementations of the DTLZ test
+suite, derived from the implementations in
+[ParMOO](https://github.com/parmoo/parmoo).
+
+------------------------------------------------------------------------------
+
+For further reference, the DTLZ test suite was originally proposed in:
+
+    Deb, Thiele, Laumanns, and Zitzler. "Scalable test problems for
+    evolutionary multiobjective optimization" in Evolutionary Multiobjective
+    Optimization, Theoretical Advances and Applications, Ch. 6 (pp. 105--145).
+    Springer-Verlag, London, UK, 2005. Abraham, Jain, and Goldberg (Eds).
+
+The original implementation was appropriate for testing randomized algorithms,
+but for many deterministic algorithms, the global solutions represent either
+best- or worst-case scenarios, so an configurable offset was introduced in:
+
+    Chang. "Mathematical Software for Multiobjective Optimization Problems."
+    Ph.D. dissertation, Virginia Tech, Dept. of Computer Science, 2020.
+
+Note that the DTLZ problems are minimization problems. Since DeepHyper
+maximizes, the implementation herein returns the negative value for each of
+the DTLZ objectives.
+
+Our performance evaluator ``metrics`` scripts can evaluate either the
+positive or negative solutions to estimate how well we have solved the
+problem.
+
+------------------------------------------------------------------------------
+
+The full list of public classes in this module includes the 7 unconstrained
+DTLZ problems
+ * ``dtlz1``,
+ * ``dtlz2``,
+ * ``dtlz3``,
+ * ``dtlz4``,
+ * ``dtlz5``,
+ * ``dtlz6``, and
+ * ``dtlz7``
+
+which are selected by setting the environment variable
+``DEEPHYPER_BENCHMARK_DTLZ_PROB``.
+
+## Installation 
+
+To use the benchmark follow this example set of instructions:
+
+```python
+
+# Set DTLZ problem environment variables before loading
+import os
+os.environ["DEEPHYPER_BENCHMARK_NDIMS"] = "5" # 5 vars
+os.environ["DEEPHYPER_BENCHMARK_NOBJS"] = "3" # 2 objs
+os.environ["DEEPHYPER_BENCHMARK_DTLZ_PROB"] = "2" # DTLZ2 problem
+os.environ["DEEPHYPER_BENCHMARK_DTLZ_OFFSET"] = "0.6" # soln [x_o, .., x_n]=0.6
+
+# Load DTLZ benchmark suite
+import deephyper_benchmark as dhb
+dhb.load("DTLZ")
+
+# Example of running one evaluation of DTLZ problem
+from deephyper.evaluator import RunningJob
+config = dtlz.hpo.problem.default_configuration # get a default config to test
+res = dtlz.hpo.run(RunningJob(parameters=config))
+
+```
+
+## Configuration
+
+To configure the problem, set the following:
+
+- Environment variable `DEEPHYPER_BENCHMARK_PROB` with a value of `1`, `2`, ... `7` to select the DTLZ problem to run. Defaults to `2`.
+- Environment variable `DEEPHYPER_BENCHMARK_NDIMS` with an integer value to set the number of input variables. Defaults to `5`.
+- Environment variable `DEEPHYPER_BENCHMARK_NOBJS` with an integer value to set the number of objectives. Defaults to `2`
+- Environment variable `DEEPHYPER_BENCHMARK_OFFSET` with a value between `0.0` and `1.0` to select the offset of the solution to the DTLZ problem. Defaults to `0.5` for DTLZ1, ..., DTLZ5 and `0.0` for DTLZ6 and DTLZ7. One may wish to adjust this value when comparing against deterministic blackbox solvers, which may sample the center and boundaries of the input space on specific schedules.
+- Environment variable `DEEPHYPER_BENCHMARK_FAILURES` with value `0` or `1` to activate or deactivate failures. Defaults to `0`.
+
+## Metadata
+
+Since these problems are analytic, there is no metadata for this problem
+beyond the standard DeepHyper metadata (timestamp information).
+
+## Evaluating Results
+
+Evaluating the performance of a multiobjective solver is nontrivial.
+Typically, one should evaluate on two orthogonal bases:
+ 1. Quality of solutions -- What is the (average) error in the solutions
+    returned by the solver?
+ 2. Diversity of solutions -- How much of the true Pareto front is covered
+    by these solutions?
+
+To evaluate these two metrics, we use:
+ 1. Improved generational distance (GD+): Let $F_i$ be a point in the solution
+    set returned by a solver,
+    and let $Y_i$ be the nearest point to $F_i$ on the true Pareto front,
+    for $i=1,\ldots, n$.
+    Then the GD+ is $\sum_{i} D^+(F_i, Y_i) / n$.
+    Where $D^+$ denotes the improved distance function
+    $D^+(F, Y) = ||\max(F - Y, 0)||_2^2$, where the "max" is taken
+    componentwise.
+    **Note that this metric may not increase monotonically. Additionally,
+    it may be impossible to calculate for an arbitrary blackbox function,
+    and can only be calculated here since the solution known and easily
+    expressed algebraically for all of the DTLZ problems.**
+ 2. Hypervolume dominated: Let $F_i$ be defined as above, and let $R$ be
+    a pre-determined reference point such that all $F_i$ dominate $R$.
+    Then the hypervolume is given by the volume of the union of all
+    hyperboxes $B_i$ whose largest vertex is $F_i$ and smallest vertex
+    is $R$. The value (and usefulness) of the hypervolume metric is extremely
+    sensitive to the choice of $R$. Therefore, for this problem, we choose
+    $R$ to be the Nadir point for the true Pareto front. **Note that in order
+    to use the Nadir point as the reference point, we must throw out every
+    solution returned by the solver that is worse than the Nadir point. For
+    extremely difficult problems, this can result in zero hypervolume if no
+    solutions better than the Nadir point were found. This is most common
+    for DTLZ1, DTLZ3, and DTLZ7.**
+
+For a general problem, the two metrics listed above could be very difficult
+to compute and many researchers will use the hypervolume with an overly
+pessimistic reference point as a proxy for both quality and diversity.
+However, in general, the hypervolume tends to promote diversity over quality.
+For the DTLZ problems, since the shape of the true Pareto front is known,
+we can calculate each of these metrics, and both the ``gdPlus(results)`` and
+``hypervolume(results)`` functions are implemented in the ``dtlz.metrics``
+module.
diff --git a/src/deephyper_benchmark/lib/dtlz/__init__.py b/src/deephyper_benchmark/lib/dtlz/__init__.py
new file mode 100644
index 0000000..f102a9c
--- /dev/null
+++ b/src/deephyper_benchmark/lib/dtlz/__init__.py
@@ -0,0 +1 @@
+__version__ = "0.0.1"
diff --git a/src/deephyper_benchmark/lib/dtlz/benchmark.py b/src/deephyper_benchmark/lib/dtlz/benchmark.py
new file mode 100644
index 0000000..2c12251
--- /dev/null
+++ b/src/deephyper_benchmark/lib/dtlz/benchmark.py
@@ -0,0 +1,6 @@
+from deephyper_benchmark import *
+
+
+class DTLZBenchmark(Benchmark):
+
+    version = "0.0.1"
diff --git a/src/deephyper_benchmark/lib/dtlz/hpo.py b/src/deephyper_benchmark/lib/dtlz/hpo.py
new file mode 100644
index 0000000..70cd6d3
--- /dev/null
+++ b/src/deephyper_benchmark/lib/dtlz/hpo.py
@@ -0,0 +1,61 @@
+import os
+import time
+
+import numpy as np
+from deephyper.evaluator import RunningJob, profile
+from deephyper.hpo import HpProblem
+
+from . import model as dtlz
+
+# Read DTLZ problem name and acquire pointer
+dtlz_prob = os.environ.get("DEEPHYPER_BENCHMARK_DTLZ_PROB", 2)
+dtlz_prob_name = f"dtlz{dtlz_prob}"
+dtlz_class_ptr = getattr(dtlz, dtlz_prob_name)
+
+# Read problem dims and definition (or read from ENV)
+nb_dim = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 5))
+nb_obj = int(os.environ.get("DEEPHYPER_BENCHMARK_NOBJS", 2))
+if dtlz_prob_name in ["dtlz1", "dtlz2", "dtlz3", "dtlz4", "dtlz5"]:
+    soln_offset = float(os.environ.get("DEEPHYPER_BENCHMARK_DTLZ_OFFSET", 0.5))
+elif dtlz_prob_name in ["dtlz6", "dtlz7"]:
+    soln_offset = float(os.environ.get("DEEPHYPER_BENCHMARK_DTLZ_OFFSET", 0.0))
+else:
+    raise ValueError(f"Invalid problem {dtlz_prob_name}")
+domain = (0.0, 1.0)
+
+# Failures
+DEEPHYPER_BENCHMARK_FAILURES = bool(int(os.environ.get("DEEPHYPER_BENCHMARK_FAILURES", 0)))
+
+# Create problem
+problem = HpProblem()
+dtlz_obj = dtlz_class_ptr(nb_dim, nb_obj, offset=soln_offset)
+for i in range(nb_dim):
+    problem.add_hyperparameter(domain, f"x{i}")
+
+
+@profile
+def run(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+    ff = [-fi for fi in dtlz_obj(x)]
+
+    if DEEPHYPER_BENCHMARK_FAILURES:
+        if any(xi < 0.25 for xi in x[nb_obj-1:]):
+            ff = ["F" for _ in ff]
+
+    return ff
+
+
+if __name__ == "__main__":
+    print(problem)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/dtlz/metrics.py b/src/deephyper_benchmark/lib/dtlz/metrics.py
new file mode 100644
index 0000000..3d4a305
--- /dev/null
+++ b/src/deephyper_benchmark/lib/dtlz/metrics.py
@@ -0,0 +1,201 @@
+import os
+import numpy as np
+from deephyper.skopt.moo import pareto_front, hypervolume
+
+
+class PerformanceEvaluator:
+    """ A class defining performance evaluators for the DTLZ problems.
+
+    Contains the following public methods:
+
+     * `__init__()` constructs a new instance by reading the problem defn
+       from environment variables,
+     * `hypervolume(pts)` calculates the total hypervolume dominated by
+       the current solution, using the Nadir point as the reference point
+       and filtering out solutions that do not dominate the Nadir point,
+     * `nadirPt()` calculates the Nadir point for the current problem,
+     * `numPts(pts)` calculates the number of solution points that dominate
+       the Nadir point, and
+     * `gdPlus(pts)` calculates the RMSE where the error in each point is
+       approximated by the 2-norm distance to the nearest solution point.
+
+    """
+
+    def __init__(self):
+        """ Read the current DTLZ problem defn from environment vars. """
+
+        self.p_num = os.environ.get("DEEPHYPER_BENCHMARK_DTLZ_PROB", "2")
+        self.nobjs = int(os.environ.get("DEEPHYPER_BENCHMARK_NOBJS", 2))
+
+    def hypervolume(self, pts):
+        """ Calculate the hypervolume dominated by soln, wrt the Nadir point.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            float: The total hypervolume dominated by the current solution,
+            filtering out points worse than the Nadir point and using the
+            Nadir point as the reference.
+
+        """
+
+        if np.any(pts < 0):
+            filtered_pts = -pts.copy()
+        else:
+            filtered_pts = pts.copy()
+        nadir = self.nadirPt()
+        for i in range(pts.shape[0]):
+            if np.any(filtered_pts[i, :] > nadir):
+                filtered_pts[i, :] = nadir
+        return hypervolume(filtered_pts, nadir)
+
+    def nadirPt(self):
+        """ Calculate the Nadir point for the given problem definition. """
+
+        if self.p_num == "1":
+            return np.ones(self.nobjs) * 0.5
+        elif self.p_num in ["2", "3", "4", "5", "6"]:
+            return np.ones(self.nobjs)
+        elif self.p_num == "7":
+            nadir = np.ones(self.nobjs)
+            nadir[self.nobjs - 1] = self.nobjs * 2.0
+            return nadir
+        else:
+            raise ValueError(f"DTLZ{self.p_num} is not a valid problem")
+
+    def numPts(self, pts):
+        """ Calculate the number of solutions that dominate the Nadir point.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            int: The number of fi in pts such that all(fi < self.nadirPt).
+
+        """
+
+        if np.any(pts < 0):
+            pareto_pts = pareto_front(-pts)
+        else:
+            pareto_pts = pareto_front(pts)
+        return sum([all(fi <= self.nadirPt()) for fi in pareto_pts])
+
+    def gdPlus(self, pts):
+        """ Calculate the p=1 generational distance for a given solution set.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            float: The p=1 generational distance over all points in pts.
+
+        """
+
+        if np.any(pts < 0):
+            pareto_pts = pareto_front(-pts)
+        else:
+            pareto_pts = pareto_front(pts)
+        if self.p_num == "1":
+            dists = self._dtlz1Dist(pareto_pts)
+        elif self.p_num in ["2", "3", "4", "5", "6"]:
+            dists = self._dtlz2Dist(pareto_pts)
+        elif self.p_num == "7":
+            dists = self._dtlz7Dist(pareto_pts)
+        else:
+            raise ValueError(f"DTLZ{self.p_num} is not a valid problem")
+        return np.sum(dists) / len(dists)
+
+    def _dtlz1Dist(self, pts):
+        """ Calculate the d+ from each fi to the nearest solution in DTLZ1.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            numpy.ndarray: A 1d array of distances to the nearest solution
+            point for DTLZ1.
+            
+
+        """
+
+        return np.array([np.linalg.norm(
+                             np.maximum(fi - (0.5 * fi / np.sum(fi)), 0)
+                         ) for fi in pts])
+
+    def _dtlz2Dist(self, pts):
+        """ Calculate the d+ from each fi to the nearest point on unit sphere.
+
+        Note: Works for DTLZ2-6
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            numpy.ndarray: A 1d array of distances to the surface of the
+            unit sphere.
+
+        """
+
+        return np.array([np.linalg.norm(
+                             np.maximum(fi - (fi / np.linalg.norm(fi)), 0)
+                         ) for fi in pts])
+
+    def _dtlz7Dist(self, pts):
+        """ Calculate the d+ from each fi to the nearest soln in DTLZ7.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            numpy.ndarray: A 1d array of distances to the nearest solution
+            point to DTLZ7.
+
+        """
+
+        # Project each point onto DTLZ7 solution and calculate difference
+        pts_proj = []
+        for fi in pts:
+            gx = 2.0
+            hx = (-np.sum(fi[:self.nobjs-1] *
+                  (1.0 + np.sin(3.0 * np.pi * fi[:self.nobjs-1])) / gx)
+                  + float(self.nobjs))
+            pts_proj.append(gx * hx)
+        return np.array([np.abs(
+                             np.maximum(fi[-1] - fj, 0)
+                         ) for fi, fj in zip(pts, pts_proj)])
+
+
+if __name__ == "__main__":
+    """ Driver code to test performance metrics. """
+
+    os.environ["DEEPHYPER_BENCHMARK_DTLZ_PROB"] = "1" # DTLZ1 problem
+    dtlz1_eval = PerformanceEvaluator()
+    s1 = np.array([[0.5, 0], [0, 0.5], [.25, .25], [0.2, 0.8]])
+    os.environ["DEEPHYPER_BENCHMARK_DTLZ_PROB"] = "2" # DTLZ2 problem
+    dtlz2_eval = PerformanceEvaluator()
+    s2 = np.array([[1, 0], [0, 1], [1/np.sqrt(2), 1/np.sqrt(2)], [0.25, 2]])
+    os.environ["DEEPHYPER_BENCHMARK_DTLZ_PROB"] = "7" # DTLZ7 problem
+    dtlz7_eval = PerformanceEvaluator()
+    s7 = np.array([[0, 4], [1, 3], [.5, 4], [0.5, 6]])
+
+    assert abs(dtlz1_eval.hypervolume(s1) - .0625) < 1.0e-8
+    assert np.all(np.abs(dtlz1_eval.nadirPt() - 0.5) < 1.0e-8)
+    assert dtlz1_eval.numPts(s1) == 3
+    assert abs(dtlz1_eval.gdPlus(s1)) < 1.0e-8
+
+    assert abs(dtlz2_eval.hypervolume(s2) - (1.5 - np.sqrt(2))) < 1.0e-8
+    assert np.all(np.abs(dtlz2_eval.nadirPt() - 1) < 1.0e-8)
+    assert dtlz2_eval.numPts(s2) == 3
+    assert abs(dtlz2_eval.gdPlus(s2)) < 1.0e-8
+
+    assert abs(dtlz7_eval.hypervolume(s7)) < 1.0e-8
+    assert np.all(np.abs(dtlz7_eval.nadirPt() - np.array([1, 4])) < 1.0e-8)
+    assert dtlz7_eval.numPts(s7) == 2
+    assert abs(dtlz7_eval.gdPlus(s7)) < 1.0e-8
diff --git a/src/deephyper_benchmark/lib/dtlz/model.py b/src/deephyper_benchmark/lib/dtlz/model.py
new file mode 100644
index 0000000..f7f22f4
--- /dev/null
+++ b/src/deephyper_benchmark/lib/dtlz/model.py
@@ -0,0 +1,591 @@
+"""Objective function implementations of the DTLZ test suite.
+
+Derived from the implementations in ParMOO:
+
+Chang and Wild. "ParMOO: A Python library for parallel multiobjective
+simulation optimization." Journal of Open Source Software 8(82):4468, 2023.
+
+------------------------------------------------------------------------------
+
+For further references, the DTLZ test suite was originally proposed in:
+
+Deb, Thiele, Laumanns, and Zitzler. "Scalable test problems for
+evolutionary multiobjective optimization" in Evolutionary Multiobjective
+Optimization, Theoretical Advances and Applications, Ch. 6 (pp. 105--145).
+Springer-Verlag, London, UK, 2005. Abraham, Jain, and Goldberg (Eds).
+
+The original implementation was appropriate for testing randomized algorithms,
+but for many deterministic algorithms, the global solutions represent either
+best- or worst-case scenarios, so an configurable offset was introduced in:
+
+Chang. "Mathematical Software for Multiobjective Optimization Problems."
+Ph.D. dissertation, Virginia Tech, Dept. of Computer Science, 2020.
+
+------------------------------------------------------------------------------
+
+The full list of public classes in this module includes the 7 unconstrained
+DTLZ problems:
+ * ``dtlz1``
+ * ``dtlz2``
+ * ``dtlz3``
+ * ``dtlz4``
+ * ``dtlz5``
+ * ``dtlz6``
+ * ``dtlz7``
+
+"""
+
+import numpy as np
+
+
+class __dtlz_base__:
+    """Base class implements re-used constructor.
+
+    Constructor for all DTLZ classes.
+
+    Args:
+        num_des (int): The number of design variables.
+
+        num_obj (int, optional): The number of objectives.
+
+        offset (optional, float): The location of the global minimizers
+            is x_i = offset for i = num_objectives, ..., num_des.
+            The default offset is 0.5.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.5):
+        self.n = num_des
+        self.o = num_obj
+        self.offset = offset
+        return
+
+    def __call__(self, x):
+        raise NotImplementedError("The call method must be implemented...")
+
+
+class __g1__(__dtlz_base__):
+    """Class defining 1 of 4 kernel functions used in the DTLZ problem suite.
+
+    g1 = 100 ( (n - o + 1) +
+               sum_{i=o}^n ((x_i - offset)^2 - cos(20pi(x_i - offset))) )
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method creates a new kernel.
+
+    The ``__call__`` method performs an evaluation of the g1 kernel.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.array): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            float: The output of this objective for the input x.
+
+        """
+        return (
+            1
+            + self.n
+            - self.o
+            + np.sum(
+                (x[self.o - 1 : self.n] - self.offset) ** 2
+                - np.cos(20.0 * np.pi * (x[self.o - 1 : self.n] - self.offset))
+            )
+        ) * 100.0
+
+
+class __g2__(__dtlz_base__):
+    """Class defining 2 of 4 kernel functions used in the DTLZ problem suite.
+
+    g2 = (x_o - offset)^2 + ... + (x_n - offset)^2
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the g2 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.array): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            float: The output of this objective for the input x.
+
+        """
+        return np.sum((x[self.o - 1 : self.n] - self.offset) ** 2)
+
+
+class __g3__(__dtlz_base__):
+    """Class defining 3 of 4 kernel functions used in the DTLZ problem suite.
+
+    g3 = |x_o - offset|^.1 + ... + |x_n - offset|^.1
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the g3 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.0):
+        """Constructor for g3, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.array): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            float: The output of this objective for the input x.
+
+        """
+        return np.sum(np.abs(x[self.o - 1 : self.n] - self.offset) ** 0.1)
+
+
+class __g4__(__dtlz_base__):
+    """Class defining 4 of 4 kernel functions used in the DTLZ problem suite.
+
+    g4 = 1 + (9 * (|x_o - offset| + ... + |x_n - offset|) / (n + 1 - o))
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the g4 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.0):
+        """Constructor for g4, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.array): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            float: The output of this objective for the input x.
+
+        """
+        return (
+            9 * np.sum(np.abs(x[self.o - 1 : self.n] - self.offset)) / float(self.n + 1 - self.o)
+        ) + 1.0
+
+
+class dtlz1(__dtlz_base__):
+    """Class defining the DTLZ1 problem with offset minimizer.
+
+    DTLZ1 has a linear Pareto front, with all nondominated points
+    on the hyperplane F_1 + F_2 + ... + F_o = 0.5.
+    DTLZ1 has 11^k - 1 "local" Pareto fronts where k = n - o + 1, and
+    1 "global" Pareto front.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ1 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g1__(self.n, self.o, self.offset)
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = (1.0 + ker(x)) / 2.0
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= x[j]
+            if i > 0:
+                fx[i] *= 1.0 - x[self.o - 1 - i]
+        return fx
+
+
+class dtlz2(__dtlz_base__):
+    """Class defining the DTLZ2 problem with offset minimizer.
+
+    DTLZ2 has a concave Pareto front, given by the unit sphere in
+    objective space, restricted to the positive orthant.
+    DTLZ2 has no "local" Pareto fronts, besides the true Pareto front.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ2 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g2__(self.n, self.o, self.offset)
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + ker(x)
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * x[j] / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * x[self.o - 1 - i] / 2)
+        return fx
+
+
+class dtlz3(__dtlz_base__):
+    """Class defining the DTLZ3 problem with offset minimizer.
+
+    DTLZ3 has a concave Pareto front, given by the unit sphere in
+    objective space, restricted to the positive orthant.
+    DTLZ3 has 3^k - 1 "local" Pareto fronts where k = n - o + 1, and
+    1 "global" Pareto front.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ3 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g1__(self.n, self.o, self.offset)
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + ker(x)
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * x[j] / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * x[self.o - 1 - i] / 2)
+        return fx
+
+
+class dtlz4(__dtlz_base__):
+    """Class defining the DTLZ4 problem with offset minimizer.
+
+    DTLZ4 has a concave Pareto front, given by the unit sphere in
+    objective space, restricted to the positive orthant.
+    DTLZ4 has no "local" Pareto fronts, besides the true Pareto front,
+    but by tuning the optional parameter alpha, one can adjust the
+    solution density, making it harder for MOO algorithms to produce
+    a uniform distribution of solutions.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ4 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.5, alpha=100.0):
+        """Constructor for DTLZ7, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+            alpha (optional, float or int): The uniformity parameter used for
+                controlling the uniformity of the distribution of solutions
+                across the Pareto front. Must be greater than or equal to 1.
+                A value of 1 results in DTLZ2. Default value is 100.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        self.alpha = alpha
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g2__(self.n, self.o, self.offset)
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + ker(x)
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * x[j] ** self.alpha / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * x[self.o - 1 - i] ** self.alpha / 2)
+        return fx
+
+
+class dtlz5(__dtlz_base__):
+    """Class defining the DTLZ5 problem with offset minimizer.
+
+    DTLZ5 has a lower-dimensional Pareto front embedded in the objective
+    space, given by an arc of the unit sphere in the positive orthant.
+    DTLZ5 has no "local" Pareto fronts, besides the true Pareto front.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ5 problem.
+
+    """
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g2__(self.n, self.o, self.offset)
+        # Calculate theta values
+        theta = np.zeros(self.o)
+        g2x = ker(x)
+        theta[0] = x[0]
+        for i in range(1, self.o):
+            theta[i] = (1 + 2 * g2x * x[i]) / (2 * (1 + g2x))
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + g2x
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * theta[j] / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * theta[self.o - 1 - i] / 2)
+        return fx
+
+
+class dtlz6(__dtlz_base__):
+    """Class defining the DTLZ6 problem with offset minimizer.
+
+    DTLZ6 has a lower-dimensional Pareto front embedded in the objective
+    space, given by an arc of the unit sphere in the positive orthant.
+    DTLZ6 has no "local" Pareto fronts, but tends to show very little
+    improvement until the algorithm is very close to its solution set.
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ6 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.0):
+        """Constructor for DTLZ6, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g3__(self.n, self.o, self.offset)
+        # Calculate theta values
+        theta = np.zeros(self.o)
+        g3x = ker(x)
+        theta[0] = x[0]
+        for i in range(1, self.o):
+            theta[i] = (1 + 2 * g3x * x[i]) / (2 * (1 + g3x))
+        # Initialize output array
+        fx = np.zeros(self.o)
+        fx[:] = 1.0 + g3x
+        # Calculate the output array
+        for i in range(self.o):
+            for j in range(self.o - 1 - i):
+                fx[i] *= np.cos(np.pi * theta[j] / 2)
+            if i > 0:
+                fx[i] *= np.sin(np.pi * theta[self.o - 1 - i] / 2)
+        return fx
+
+
+class dtlz7(__dtlz_base__):
+    """Class defining the DTLZ7 problem with offset minimizer.
+
+    DTLZ7 has a discontinuous Pareto front, with solutions on the
+    2^(o-1) discontinuous nondominated regions of the surface:
+
+    F_m = o - F_1 (1 + sin(3pi F_1)) - ... - F_{o-1} (1 + sin3pi F_{o-1}).
+
+    Contains 2 methods:
+     * ``__init__(num_des, num_obj)``
+     * ``__call__(x)``
+
+    The ``__init__`` method inherits from the __dtlz_base__ ABC.
+
+    The ``__call__`` method performs an evaluation of the DTLZ7 problem.
+
+    """
+
+    def __init__(self, num_des, num_obj=3, offset=0.0):
+        """Constructor for DTLZ7, with modified default offset.
+
+        Args:
+            num_des (int): The number of design variables.
+
+            num_obj (int, optional): The number of objectives.
+
+            offset (optional, float): The location of the global minimizers
+                is x_i = offset for i = num_objectives, ..., num_des.
+                The default offset is 0.0.
+
+        """
+        super().__init__(num_des=num_des, num_obj=num_obj, offset=offset)
+        return
+
+    def __call__(self, x):
+        """Define objective evaluation.
+
+        Args:
+            x (numpy.ndarray): A numpy.ndarray containing the design point
+                to evaluate.
+
+        Returns:
+            numpy float array: The output of this objective for the input x.
+
+        """
+        # Initialize kernel function
+        ker = __g4__(self.n, self.o, self.offset)
+        # Initialize first o-1 entries in the output array
+        fx = np.zeros(self.o)
+        fx[: self.o - 1] = x[: self.o - 1]
+        # Calculate kernel functions
+        gx = 1.0 + ker(x)
+        hx = -np.sum(x[: self.o - 1] * (1.0 + np.sin(3.0 * np.pi * x[: self.o - 1])) / gx) + float(
+            self.o
+        )
+        # Calculate the last entry in the output array
+        fx[self.o - 1] = gx * hx
+        return fx
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/Makefile b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/Makefile
new file mode 100644
index 0000000..29e6a6b
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/Makefile
@@ -0,0 +1,11 @@
+
+VERSION=0.5.1
+
+build:
+	mkdir -p build
+	wget https://github.com/ECP-CANDLE/Benchmarks/archive/refs/tags/v$(VERSION).tar.gz -O build/ecp-candle-benchmarks.tar.gz
+	tar -xzvf build/ecp-candle-benchmarks.tar.gz -C build
+	mv build/Benchmarks-$(VERSION) build/Benchmarks
+
+clean:
+	rm -rf build/
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/README.md b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/README.md
new file mode 100644
index 0000000..ba8f255
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/README.md
@@ -0,0 +1,54 @@
+
+# ECP-Candle Benchmark - Pilot1/Combo
+
+This benchmark is based on materials from the [ECP-Candle/Benchmarks](https://github.com/ECP-CANDLE/Benchmarks) repository. More precisely it is a simplified replicate of the [Pilot1/Combo](https://github.com/ECP-CANDLE/Benchmarks/tree/master/Pilot1/Combo) benchmark.
+
+
+## Installation
+
+To use the benchmark follow this example set of instructions:
+
+```python
+from deephyper_benchmark import *
+
+install("ECP-Candle/Pilot1/Combo")
+
+load("ECP-Candle/Pilot1/Combo")
+
+from deephyper_benchmark.lib.ecp_candle.pilot1.combo import hpo
+```
+
+## Configuration
+
+Different parameters can be set to configure this benchmark.
+
+- Environment variable `DEEPHYPER_BENCHMARK_MAX_EPOCHS` sets the maximum number of training epochs. Defaults to `50`.
+- Environment variable `DEEPHYPER_BENCHMARK_TIMEOUT` sets the maximum duration of training in secondes. Defaults to `1800` (i.e., 30 minutes).
+- Environment variable `DEEPHYPER_BENCHMARK_MOO` with value `0` or `1` to select if the task should be run with single or multiple objectives. **Defaults to `0` for single-objective**. The objectives are `valid_r2`, `-num_parameters_train`, `-duration_batch_inference`.
+
+## Metadata
+
+The current set of returned metadata is:
+
+- [x] `num_parameters`
+- [x] `num_parameters_train`
+- [x] `duration_train`: time in secondes taken to train the model (`model.fit(...)`).
+- [x] `duration_batch_inference`: time in secondes taken to predict one batch.
+- [x] `budget`
+- [x] `stopped`
+- [x] `train_mse`
+- [x] `train_mae`
+- [x] `train_r2`
+- [x] `train_corr`
+- [x] `valid_mse`
+- [x] `valid_mae`
+- [x] `valid_r2`
+- [x] `valid_corr`
+- [x] `test_mse`
+- [x] `test_mae`
+- [x] `test_r2`
+- [x] `test_corr`
+- [ ] `flops`
+- [ ] `latency`
+- [x] `lc_train_mse`
+- [x] `lc_valid_mse`
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/__init__.py b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/__init__.py
new file mode 100644
index 0000000..7dd6fda
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/__init__.py
@@ -0,0 +1 @@
+__version__ = "0.5.1"
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/benchmark.py b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/benchmark.py
new file mode 100644
index 0000000..4f7fb4d
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/benchmark.py
@@ -0,0 +1,28 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class ECPCandlePilot1Combo(Benchmark):
+    version = "0.5.1"
+
+    requires = {
+        "makefile": {"step": "install", "type": "cmd", "cmd": "make build"},
+        "py-pip-requirements": {
+            "step": "install",
+            "type": "pip",
+            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
+        },
+        "pkg-candle": {
+            "step": "load",
+            "type": "pythonpath",
+            "path": f"{DIR}/build/Benchmarks/common",
+        },
+        "pkg-combo": {
+            "step": "load",
+            "type": "pythonpath",
+            "path": f"{DIR}/build/Benchmarks/Pilot1/Combo",
+        },
+    }
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/combo_default_model.yaml b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/combo_default_model.yaml
new file mode 100644
index 0000000..47e4ce1
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/combo_default_model.yaml
@@ -0,0 +1,31 @@
+cell_features: ['expression']
+drug_features: ['descriptors']
+dense: [1000, 1000, 1000]
+dense_feature_layers: [1000, 1000, 1000]
+activation: 'relu'
+loss: 'mse'
+optimizer: 'adam'
+scaling: 'std'
+dropout: 0
+epochs: 1
+batch_size: 32
+valid_split: 0.2
+test_split: 0.2
+max_val_loss: 1.0
+learning_rate: 0.001
+base_lr: 0.001
+residual: False
+reduce_lr: False
+reduce_lr_factor: 0.5
+reduce_lr_patience: 5
+warmup_lr: False
+early_stopping: False
+early_stopping_patience: 5
+batch_normalization: False
+feature_subsample: 0
+rng_seed: 2017
+save_path: 'save/combo'
+gen: False
+use_combo_score: False
+verbose : True
+timeout: 1800
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/hpo.py b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/hpo.py
new file mode 100644
index 0000000..429607d
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/hpo.py
@@ -0,0 +1,160 @@
+import copy
+import os
+import traceback
+
+from deephyper.evaluator import profile
+from deephyper.hpo import HpProblem
+from deephyper.stopper.integration.tensorflow import TFKerasStopperCallback
+
+from .model import run_pipeline
+
+
+DEEPHYPER_BENCHMARK_MAX_EPOCHS = int(
+    os.environ.get("DEEPHYPER_BENCHMARK_MAX_EPOCHS", 50)
+)
+DEEPHYPER_BENCHMARK_TIMEOUT = int(os.environ.get("DEEPHYPER_BENCHMARK_TIMEOUT", 1800))
+DEEPHYPER_BENCHMARK_MOO = bool(int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 0)))
+
+
+problem = HpProblem()
+
+# Model hyperparameters
+ACTIVATIONS = [
+    "elu",
+    "gelu",
+    "hard_sigmoid",
+    "linear",
+    "relu",
+    "selu",
+    "sigmoid",
+    "softplus",
+    "softsign",
+    "swish",
+    "tanh",
+]
+default_dense = [1000, 1000, 1000]
+default_dense_feature_layers = [1000, 1000, 1000]
+
+for i in range(len(default_dense)):
+    problem.add_hyperparameter(
+        (10, 1024, "log-uniform"),
+        f"dense_{i}",
+        default_value=default_dense[i],
+    )
+
+    problem.add_hyperparameter(
+        (10, 1024, "log-uniform"),
+        f"dense_feature_layers_{i}",
+        default_value=default_dense_feature_layers[i],
+    )
+
+problem.add_hyperparameter(ACTIVATIONS, "activation", default_value="relu")
+
+# Optimization hyperparameters
+problem.add_hyperparameter(
+    [
+        "sgd",
+        "rmsprop",
+        "adagrad",
+        "adadelta",
+        "adam",
+    ],
+    "optimizer",
+    default_value="sgd",
+)
+
+problem.add_hyperparameter((0, 0.5), "dropout", default_value=0.0)
+problem.add_hyperparameter((8, 512, "log-uniform"), "batch_size", default_value=32)
+
+problem.add_hyperparameter(
+    (1e-5, 1e-2, "log-uniform"), "learning_rate", default_value=0.001
+)
+problem.add_hyperparameter((1e-5, 1e-2, "log-uniform"), "base_lr", default_value=0.001)
+problem.add_hyperparameter([True, False], "residual", default_value=False)
+
+problem.add_hyperparameter([True, False], "early_stopping", default_value=False)
+problem.add_hyperparameter((5, 20), "early_stopping_patience", default_value=5)
+
+problem.add_hyperparameter([True, False], "reduce_lr", default_value=False)
+problem.add_hyperparameter((0.1, 1.0), "reduce_lr_factor", default_value=0.5)
+problem.add_hyperparameter((5, 20), "reduce_lr_patience", default_value=5)
+
+problem.add_hyperparameter([True, False], "warmup_lr", default_value=False)
+problem.add_hyperparameter([True, False], "batch_normalization", default_value=False)
+
+problem.add_hyperparameter(
+    ["mse", "mae", "logcosh", "mape", "msle", "huber"], "loss", default_value="mse"
+)
+
+problem.add_hyperparameter(["std", "minmax", "maxabs"], "scaling", default_value="std")
+
+
+def remap_hyperparameters(config: dict):
+    """Transform input configurations of hyperparameters to the format accepted by the candle benchmark."""
+    dense = []
+    dense_feature_layers = []
+    for i in range(len(default_dense)):
+        key = f"dense_{i}"
+        dense.append(config.pop(key))
+
+        key = f"dense_feature_layers_{i}"
+        dense_feature_layers.append(config.pop(key))
+
+    config["dense"] = dense
+    config["dense_feature_layers"] = dense_feature_layers
+
+
+@profile
+def run(job, optuna_trial=None):
+    config = copy.deepcopy(job.parameters)
+
+    params = {
+        "epochs": DEEPHYPER_BENCHMARK_MAX_EPOCHS,
+        "timeout": DEEPHYPER_BENCHMARK_TIMEOUT,
+        "verbose": False,
+    }
+    if len(config) > 0:
+        remap_hyperparameters(config)
+        params.update(config)
+
+    if optuna_trial is None:
+        stopper_callback = TFKerasStopperCallback(job, monitor="val_r2", mode="max")
+    else:
+        from deephyper_benchmark.integration.optuna import KerasPruningCallback
+
+        stopper_callback = KerasPruningCallback(optuna_trial, "val_r2")
+
+    try:
+        score = run_pipeline(params, mode="valid", stopper_callback=stopper_callback)
+    except Exception as e:
+        print(traceback.format_exc())
+        score = {"objective": "F"}
+        keys = "m:num_parameters,m:num_parameters_train,m:duration_train,m:duration_batch_inference,m:budget,m:stopped,m:train_mse,m:train_mae,m:train_r2,m:train_corr,m:valid_mse,m:valid_mae,m:valid_r2,m:valid_corr,m:test_mse,m:test_mae,m:test_r2,m:test_corr,m:lc_train_mse,m:lc_valid_mse,m:lc_train_mae,m:lc_valid_mae,m:lc_train_r2,m:lc_valid_r2"
+        metadata = {k.strip("m:"): None for k in keys.split(",")}
+        score["metadata"] = metadata
+
+    # Handle multi-objective optimization (all maximized)
+    if DEEPHYPER_BENCHMARK_MOO:
+        if score["objective"] == "F":
+            score["objective"] = ["F", "F", "F"]
+        else:
+            score["objective"] = [
+                score["objective"],
+                -score["metadata"]["num_parameters_train"],
+                -score["metadata"]["duration_batch_inference"],
+            ]
+
+    return score
+
+
+def evaluate(config):
+    """Evaluate an hyperparameter configuration on training/validation and testing data."""
+
+    params = {
+        "epochs": 100,
+        "timeout": 60 * 60,
+        "verbose": True,
+    }  # 60 minutes per model
+    remap_hyperparameters(config)
+    params.update(config)
+    run_pipeline(params, mode="test")
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/model.py b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/model.py
new file mode 100644
index 0000000..5234495
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/model.py
@@ -0,0 +1,782 @@
+from __future__ import division, print_function
+
+import collections
+import logging
+import os
+import time
+import warnings
+
+import yaml
+
+try:
+    from yaml import CLoader as Loader
+except ImportError:
+    from yaml import Loader
+
+HERE = os.path.dirname(os.path.abspath(__file__))
+DEFAULT_CONFIG = os.path.join(HERE, "combo_default_model.yaml")
+
+#! must be placed before "import candle"
+from tensorflow import keras
+from tensorflow.keras import backend as K
+from tensorflow.keras import optimizers
+from tensorflow.keras.callbacks import (
+    Callback,
+    EarlyStopping,
+    LearningRateScheduler,
+    ReduceLROnPlateau,
+)
+from tensorflow.keras.layers import Dense, Dropout, Input
+from tensorflow.keras.models import Model
+from tensorflow.keras.utils import get_custom_objects
+
+import candle
+import combo
+import NCI60
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from scipy.stats.stats import pearsonr
+from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
+
+from deephyper_benchmark.integration.keras import count_params
+from deephyper_benchmark.utils.json_utils import array_to_json
+
+
+logger = logging.getLogger(__name__)
+
+np.set_printoptions(precision=4)
+tf.compat.v1.disable_eager_execution()
+
+
+def verify_path(path):
+    folder = os.path.dirname(path)
+    if folder and not os.path.exists(folder):
+        os.makedirs(folder)
+
+
+def extension_from_parameters(args):
+    """Construct string for saving model with annotation of parameters"""
+    ext = ""
+    ext += ".A={}".format(args.activation)
+    ext += ".B={}".format(args.batch_size)
+    ext += ".E={}".format(args.epochs)
+    ext += ".O={}".format(args.optimizer)
+    # ext += '.LEN={}'.format(args.maxlen)
+    ext += ".LR={}".format(args.learning_rate)
+    ext += ".CF={}".format("".join([x[0] for x in sorted(args.cell_features)]))
+    ext += ".DF={}".format("".join([x[0] for x in sorted(args.drug_features)]))
+    if args.feature_subsample > 0:
+        ext += ".FS={}".format(args.feature_subsample)
+    if args.dropout > 0:
+        ext += ".DR={}".format(args.dropout)
+    if args.warmup_lr:
+        ext += ".wu_lr"
+    if args.reduce_lr:
+        ext += ".re_lr"
+    if args.residual:
+        ext += ".res"
+    if args.use_landmark_genes:
+        ext += ".L1000"
+    if args.gen:
+        ext += ".gen"
+    if args.use_combo_score:
+        ext += ".scr"
+    if args.use_mean_growth:
+        ext += ".mg"
+    for i, n in enumerate(args.dense):
+        if n > 0:
+            ext += ".D{}={}".format(i + 1, n)
+    if args.dense_feature_layers != args.dense:
+        for i, n in enumerate(args.dense):
+            if n > 0:
+                ext += ".FD{}={}".format(i + 1, n)
+
+    return ext
+
+
+def discretize(y, bins=5):
+    percentiles = [100 / bins * (i + 1) for i in range(bins - 1)]
+    thresholds = [np.percentile(y, x) for x in percentiles]
+    classes = np.digitize(y, thresholds)
+    return classes
+
+
+class ComboDataLoader(object):
+    """Load merged drug response, drug descriptors and cell line essay data"""
+
+    def __init__(
+        self,
+        seed,
+        valid_split=0.2,
+        test_split=0.2,
+        shuffle=True,
+        cell_features=["expression"],
+        drug_features=["descriptors"],
+        response_url=None,
+        use_landmark_genes=False,
+        use_combo_score=False,
+        use_mean_growth=False,
+        preprocess_rnaseq=None,
+        exclude_cells=[],
+        exclude_drugs=[],
+        feature_subsample=None,
+        scaling="std",
+        scramble=False,
+    ):
+        """Initialize data merging drug response, drug descriptors and cell line essay.
+           Shuffle and split training and validation set
+
+        Parameters
+        ----------
+        seed: integer
+            seed for random generation
+        val_split : float, optional (default 0.2)
+            fraction of data to use in validation
+        cell_features: list of strings from 'expression', 'expression_5platform', 'mirna', 'proteome', 'all', 'categorical' (default ['expression'])
+            use one or more cell line feature sets: gene expression, microRNA, proteome
+            use 'all' for ['expression', 'mirna', 'proteome']
+            use 'categorical' for one-hot encoded cell lines
+        drug_features: list of strings from 'descriptors', 'latent', 'all', 'categorical', 'noise' (default ['descriptors'])
+            use dragon7 descriptors, latent representations from Aspuru-Guzik's SMILES autoencoder
+            trained on NSC drugs, or both; use random features if set to noise
+            use 'categorical' for one-hot encoded drugs
+        shuffle : True or False, optional (default True)
+            if True shuffles the merged data before splitting training and validation sets
+        scramble: True or False, optional (default False)
+            if True randomly shuffle dose response data as a control
+        feature_subsample: None or integer (default None)
+            number of feature columns to use from cellline expressions and drug descriptors
+        use_landmark_genes: True or False
+            only use LINCS1000 landmark genes
+        use_combo_score: bool (default False)
+            use combination score in place of percent growth (stored in 'GROWTH' column)
+        use_mean_growth: bool (default False)
+            use mean aggregation instead of min on percent growth
+        scaling: None, 'std', 'minmax' or 'maxabs' (default 'std')
+            type of feature scaling: 'maxabs' to [-1,1], 'maxabs' to [-1, 1], 'std' for standard normalization
+        """
+
+        self._random_state = np.random.RandomState(seed)
+
+        df = NCI60.load_combo_response(
+            response_url=response_url,
+            use_combo_score=use_combo_score,
+            use_mean_growth=use_mean_growth,
+            fraction=True,
+            exclude_cells=exclude_cells,
+            exclude_drugs=exclude_drugs,
+        )
+        logger.info("Loaded {} unique (CL, D1, D2) response sets.".format(df.shape[0]))
+
+        if "all" in cell_features:
+            self.cell_features = ["expression", "mirna", "proteome"]
+        else:
+            self.cell_features = cell_features
+
+        if "all" in drug_features:
+            self.drug_features = ["descriptors", "latent"]
+        else:
+            self.drug_features = drug_features
+
+        for fea in self.cell_features:
+            if fea == "expression" or fea == "rnaseq":
+                self.df_cell_expr = NCI60.load_cell_expression_rnaseq(
+                    ncols=feature_subsample,
+                    scaling=scaling,
+                    use_landmark_genes=use_landmark_genes,
+                    preprocess_rnaseq=preprocess_rnaseq,
+                )
+                df = df.merge(self.df_cell_expr[["CELLNAME"]], on="CELLNAME")
+            elif fea == "expression_u133p2":
+                self.df_cell_expr = NCI60.load_cell_expression_u133p2(
+                    ncols=feature_subsample,
+                    scaling=scaling,
+                    use_landmark_genes=use_landmark_genes,
+                )
+                df = df.merge(self.df_cell_expr[["CELLNAME"]], on="CELLNAME")
+            elif fea == "expression_5platform":
+                self.df_cell_expr = NCI60.load_cell_expression_5platform(
+                    ncols=feature_subsample,
+                    scaling=scaling,
+                    use_landmark_genes=use_landmark_genes,
+                )
+                df = df.merge(self.df_cell_expr[["CELLNAME"]], on="CELLNAME")
+            elif fea == "mirna":
+                self.df_cell_mirna = NCI60.load_cell_mirna(
+                    ncols=feature_subsample, scaling=scaling
+                )
+                df = df.merge(self.df_cell_mirna[["CELLNAME"]], on="CELLNAME")
+            elif fea == "proteome":
+                self.df_cell_prot = NCI60.load_cell_proteome(
+                    ncols=feature_subsample, scaling=scaling
+                )
+                df = df.merge(self.df_cell_prot[["CELLNAME"]], on="CELLNAME")
+            elif fea == "categorical":
+                df_cell_ids = df[["CELLNAME"]].drop_duplicates()
+                cell_ids = df_cell_ids["CELLNAME"].map(lambda x: x.replace(":", "."))
+                df_cell_cat = pd.get_dummies(cell_ids)
+                df_cell_cat.index = df_cell_ids["CELLNAME"]
+                self.df_cell_cat = df_cell_cat.reset_index()
+
+        for fea in self.drug_features:
+            if fea == "descriptors":
+                self.df_drug_desc = NCI60.load_drug_descriptors(
+                    ncols=feature_subsample, scaling=scaling
+                )
+                df = df[
+                    df["NSC1"].isin(self.df_drug_desc["NSC"])
+                    & df["NSC2"].isin(self.df_drug_desc["NSC"])
+                ]
+            elif fea == "latent":
+                self.df_drug_auen = NCI60.load_drug_autoencoded_AG(
+                    ncols=feature_subsample, scaling=scaling
+                )
+                df = df[
+                    df["NSC1"].isin(self.df_drug_auen["NSC"])
+                    & df["NSC2"].isin(self.df_drug_auen["NSC"])
+                ]
+            elif fea == "categorical":
+                df_drug_ids = df[["NSC1"]].drop_duplicates()
+                df_drug_ids.columns = ["NSC"]
+                drug_ids = df_drug_ids["NSC"]
+                df_drug_cat = pd.get_dummies(drug_ids)
+                df_drug_cat.index = df_drug_ids["NSC"]
+                self.df_drug_cat = df_drug_cat.reset_index()
+            elif fea == "noise":
+                ids1 = df[["NSC1"]].drop_duplicates().rename(columns={"NSC1": "NSC"})
+                ids2 = df[["NSC2"]].drop_duplicates().rename(columns={"NSC2": "NSC"})
+                df_drug_ids = pd.concat([ids1, ids2]).drop_duplicates()
+                noise = np.random.normal(size=(df_drug_ids.shape[0], 500))
+                df_rand = pd.DataFrame(
+                    noise,
+                    index=df_drug_ids["NSC"],
+                    columns=["RAND-{:03d}".format(x) for x in range(500)],
+                )
+                self.df_drug_rand = df_rand.reset_index()
+
+        logger.info(
+            "Filtered down to {} rows with matching information.".format(df.shape[0])
+        )
+
+        ids1 = df[["NSC1"]].drop_duplicates().rename(columns={"NSC1": "NSC"})
+        ids2 = df[["NSC2"]].drop_duplicates().rename(columns={"NSC2": "NSC"})
+        df_drug_ids = pd.concat([ids1, ids2]).drop_duplicates().reset_index(drop=True)
+
+        n_drugs = df_drug_ids.shape[0]
+        n_valid_drugs = int(n_drugs * valid_split)
+        n_test_drugs = int(n_drugs * test_split)
+        n_train_drugs = n_drugs - n_valid_drugs - n_test_drugs
+
+        logger.info("Unique cell lines: {}".format(df["CELLNAME"].nunique()))
+        logger.info("Unique drugs: {}".format(n_drugs))
+
+        if shuffle:
+            df = df.sample(frac=1.0, random_state=seed).reset_index(drop=True)
+            df_drug_ids = df_drug_ids.sample(frac=1.0, random_state=seed).reset_index(
+                drop=True
+            )
+
+        self.df_response = df
+        self.df_drug_ids = df_drug_ids
+
+        self.train_drug_ids = df_drug_ids["NSC"][:n_train_drugs]
+        self.valid_drug_ids = df_drug_ids["NSC"][
+            n_train_drugs : n_train_drugs + n_valid_drugs
+        ]
+        self.test_drug_ids = df_drug_ids["NSC"][
+            n_train_drugs + n_valid_drugs : n_train_drugs + n_valid_drugs + n_test_drugs
+        ]
+
+        if scramble:
+            growth = df[["GROWTH"]]
+            random_growth = growth.iloc[
+                self._random_state.permutation(np.arange(growth.shape[0]))
+            ].reset_index()
+            self.df_response[["GROWTH"]] = random_growth["GROWTH"]
+            logger.warn("Randomly shuffled dose response growth values.")
+
+        logger.info("Distribution of dose response:")
+        logger.info(self.df_response[["GROWTH"]].describe())
+
+        self.total = df.shape[0]
+        self.n_valid = int(self.total * valid_split)
+        self.n_test = int(self.total * test_split)
+        self.n_train = self.total - self.n_valid - self.n_test
+        logger.info(
+            "Rows in train: {}, valid: {}, test: {}".format(
+                self.n_train, self.n_valid, self.n_test
+            )
+        )
+
+        self.cell_df_dict = {
+            "expression": "df_cell_expr",
+            "expression_5platform": "df_cell_expr",
+            "expression_u133p2": "df_cell_expr",
+            "rnaseq": "df_cell_expr",
+            "mirna": "df_cell_mirna",
+            "proteome": "df_cell_prot",
+            "categorical": "df_cell_cat",
+        }
+
+        self.drug_df_dict = {
+            "descriptors": "df_drug_desc",
+            "latent": "df_drug_auen",
+            "categorical": "df_drug_cat",
+            "noise": "df_drug_rand",
+        }
+
+        self.input_features = collections.OrderedDict()
+        self.feature_shapes = {}
+        for fea in self.cell_features:
+            feature_type = "cell." + fea
+            feature_name = "cell." + fea
+            df_cell = getattr(self, self.cell_df_dict[fea])
+            self.input_features[feature_name] = feature_type
+            self.feature_shapes[feature_type] = (df_cell.shape[1] - 1,)
+
+        for drug in ["drug1", "drug2"]:
+            for fea in self.drug_features:
+                feature_type = "drug." + fea
+                feature_name = drug + "." + fea
+                df_drug = getattr(self, self.drug_df_dict[fea])
+                self.input_features[feature_name] = feature_type
+                self.feature_shapes[feature_type] = (df_drug.shape[1] - 1,)
+
+        logger.info("Input features shapes:")
+        for k, v in self.input_features.items():
+            logger.info("  {}: {}".format(k, self.feature_shapes[v]))
+
+        self.input_dim = sum(
+            [np.prod(self.feature_shapes[x]) for x in self.input_features.values()]
+        )
+        logger.info("Total input dimensions: {}".format(self.input_dim))
+
+    def load_data_all(self, switch_drugs=False):
+        df_all = self.df_response
+        y_all = df_all["GROWTH"].values
+        x_all_list = []
+
+        for fea in self.cell_features:
+            df_cell = getattr(self, self.cell_df_dict[fea])
+            df_x_all = pd.merge(
+                df_all[["CELLNAME"]], df_cell, on="CELLNAME", how="left"
+            )
+            x_all_list.append(df_x_all.drop(["CELLNAME"], axis=1).values)
+
+        drugs = ["NSC1", "NSC2"]
+        if switch_drugs:
+            drugs = ["NSC2", "NSC1"]
+
+        for drug in drugs:
+            for fea in self.drug_features:
+                df_drug = getattr(self, self.drug_df_dict[fea])
+                df_x_all = pd.merge(
+                    df_all[[drug]], df_drug, left_on=drug, right_on="NSC", how="left"
+                )
+                x_all_list.append(df_x_all.drop([drug, "NSC"], axis=1).values)
+
+        return x_all_list, y_all, df_all
+
+    def load_data_by_index(self, train_index, valid_index, test_index):
+        x_all_list, y_all, df_all = self.load_data_all()
+
+        x_train_list = [x[train_index] for x in x_all_list]
+        x_valid_list = [x[valid_index] for x in x_all_list]
+        x_test_list = [x[test_index] for x in x_all_list]
+
+        y_train = y_all[train_index]
+        y_valid = y_all[valid_index]
+        y_test = y_all[test_index]
+
+        df_train = df_all.iloc[train_index, :]
+        df_valid = df_all.iloc[valid_index, :]
+        df_test = df_all.iloc[test_index, :]
+
+        return (
+            x_train_list,
+            y_train,
+            x_valid_list,
+            y_valid,
+            x_test_list,
+            y_test,
+            df_train,
+            df_valid,
+            df_test,
+        )
+
+    def load_data(self):
+        train_index = range(self.n_train)
+        valid_index = range(self.n_train, self.n_train + self.n_valid)
+        test_index = range(
+            self.n_train + self.n_valid, self.n_train + self.n_valid + self.n_test
+        )
+        return self.load_data_by_index(train_index, valid_index, test_index)
+
+
+def test_loader(loader):
+    (
+        x_train_list,
+        y_train,
+        x_val_list,
+        y_val,
+        x_test_list,
+        y_test,
+        _,
+        _,
+        _,
+    ) = loader.load_data()
+    print("x_train shapes:")
+    for x in x_train_list:
+        print(x.shape)
+    print("y_train shape:", y_train.shape)
+
+    print("x_valid shapes:")
+    for x in x_val_list:
+        print(x.shape)
+    print("y_valid shape:", y_val.shape)
+
+    print("x_test shapes:")
+    for x in x_test_list:
+        print(x.shape)
+    print("y_test shape:", y_test.shape)
+
+
+def r2(y_true, y_pred):
+    SS_res = K.sum(K.square(y_true - y_pred))
+    SS_tot = K.sum(K.square(y_true - K.mean(y_true)))
+    return 1 - SS_res / (SS_tot + K.epsilon())
+
+
+def mae(y_true, y_pred):
+    return keras.metrics.mean_absolute_error(y_true, y_pred)
+
+
+def mse(y_true, y_pred):
+    return keras.metrics.mean_squared_error(y_true, y_pred)
+
+
+def evaluate_prediction(y_true, y_pred):
+    mse = mean_squared_error(y_true, y_pred)
+    mae = mean_absolute_error(y_true, y_pred)
+    r2 = r2_score(y_true, y_pred)
+    corr, _ = pearsonr(y_true, y_pred)
+    return {"mse": mse, "mae": mae, "r2": r2, "corr": corr}
+
+
+def log_evaluation(metric_outputs, description="Comparing y_true and y_pred:"):
+    logger.info(description)
+    for metric, value in metric_outputs.items():
+        logger.info("  {}: {:.4f}".format(metric, value))
+
+
+class LoggingCallback(Callback):
+    def __init__(self, print_fcn=print):
+        Callback.__init__(self)
+        self.print_fcn = print_fcn
+
+    def on_epoch_end(self, epoch, logs={}):
+        msg = "[Epoch: %i] %s" % (
+            epoch,
+            ", ".join("%s: %f" % (k, v) for k, v in sorted(logs.items())),
+        )
+        self.print_fcn(msg)
+
+
+class PermanentDropout(Dropout):
+    def __init__(self, rate, **kwargs):
+        super(PermanentDropout, self).__init__(rate, **kwargs)
+        self.uses_learning_phase = False
+
+    def call(self, x, mask=None):
+        if 0.0 < self.rate < 1.0:
+            noise_shape = self._get_noise_shape(x)
+            x = K.dropout(x, self.rate, noise_shape)
+        return x
+
+
+class ModelRecorder(Callback):
+    def __init__(self, save_all_models=False):
+        Callback.__init__(self)
+        self.save_all_models = save_all_models
+        get_custom_objects()["PermanentDropout"] = PermanentDropout
+
+    def on_train_begin(self, logs={}):
+        self.val_losses = []
+        self.best_val_loss = np.Inf
+        self.best_model = None
+
+    def on_epoch_end(self, epoch, logs={}):
+        val_loss = logs.get("val_loss")
+        self.val_losses.append(val_loss)
+        if val_loss < self.best_val_loss:
+            self.best_model = keras.models.clone_model(self.model)
+            self.best_val_loss = val_loss
+
+
+def build_feature_model(
+    input_shape,
+    name="",
+    dense_layers=[1000, 1000],
+    activation="relu",
+    residual=False,
+    dropout_rate=0,
+    permanent_dropout=True,
+):
+    x_input = Input(shape=input_shape)
+    h = x_input
+    for i, layer in enumerate(dense_layers):
+        x = h
+        h = Dense(layer, activation=activation)(h)
+        if dropout_rate > 0:
+            if permanent_dropout:
+                h = PermanentDropout(dropout_rate)(h)
+            else:
+                h = Dropout(dropout_rate)(h)
+        if residual:
+            try:
+                h = keras.layers.add([h, x])
+            except ValueError:
+                pass
+    model = Model(x_input, h, name=name)
+    return model
+
+
+def build_model(loader, args, verbose=False):
+    input_models = {}
+    dropout_rate = args.dropout
+    permanent_dropout = True
+    for fea_type, shape in loader.feature_shapes.items():
+        box = build_feature_model(
+            input_shape=shape,
+            name=fea_type,
+            dense_layers=args.dense_feature_layers,
+            dropout_rate=dropout_rate,
+            permanent_dropout=permanent_dropout,
+        )
+        if verbose:
+            box.summary()
+        input_models[fea_type] = box
+
+    inputs = []
+    encoded_inputs = []
+    for fea_name, fea_type in loader.input_features.items():
+        shape = loader.feature_shapes[fea_type]
+        fea_input = Input(shape, name="input." + fea_name)
+        inputs.append(fea_input)
+        input_model = input_models[fea_type]
+        encoded = input_model(fea_input)
+        encoded_inputs.append(encoded)
+
+    merged = keras.layers.concatenate(encoded_inputs)
+
+    h = merged
+    for i, layer in enumerate(args.dense):
+        x = h
+        h = Dense(layer, activation=args.activation)(h)
+        if dropout_rate > 0:
+            if permanent_dropout:
+                h = PermanentDropout(dropout_rate)(h)
+            else:
+                h = Dropout(dropout_rate)(h)
+        if args.residual:
+            try:
+                h = keras.layers.add([h, x])
+            except ValueError:
+                pass
+    output = Dense(1)(h)
+
+    return Model(inputs, output)
+
+
+def initialize_parameters(default_model="combo_default_model.txt"):
+    # Build benchmark object
+    comboBmk = combo.BenchmarkCombo(
+        combo.file_path,
+        default_model,
+        "keras",
+        prog="combo_baseline",
+        desc="Build neural network based models to predict tumor response to drug pairs.",
+    )
+
+    # Initialize parameters
+    gParameters = candle.finalize_parameters(comboBmk)
+
+    return gParameters
+
+
+def yaml_load(path):
+    with open(path, "r") as f:
+        yaml_data = yaml.load(f, Loader=Loader)
+    return yaml_data
+
+
+def run_pipeline(config: dict = None, mode="valid", stopper_callback=None):
+    # Default Config from original Benchmark
+    params = initialize_parameters()
+
+    # Default Config from our Benchmark
+    params.update(yaml_load(DEFAULT_CONFIG))
+    params.pop("val_split")
+
+    # Ingest input configuration
+    if config:
+        params.update(config)
+
+    args = candle.ArgumentStruct(**params)
+    seed = args.rng_seed
+    candle.set_seed(seed)
+
+    loader = ComboDataLoader(
+        seed=args.rng_seed,
+        valid_split=args.valid_split,
+        test_split=args.test_split,
+        cell_features=args.cell_features,
+        drug_features=args.drug_features,
+        use_mean_growth=args.use_mean_growth,
+        response_url=args.response_url,
+        use_landmark_genes=args.use_landmark_genes,
+        preprocess_rnaseq=args.preprocess_rnaseq,
+        exclude_cells=args.exclude_cells,
+        exclude_drugs=args.exclude_drugs,
+        use_combo_score=args.use_combo_score,
+        scaling=args.scaling,
+    )
+
+    # test_loader(loader)
+
+    model = build_model(loader, args, verbose=args.verbose)
+
+    def warmup_scheduler(epoch):
+        lr = args.learning_rate or base_lr * args.batch_size / 100
+        if epoch <= 5:
+            K.set_value(model.optimizer.lr, (base_lr * (5 - epoch) + lr * epoch) / 5)
+        logger.debug("Epoch {}: lr={}".format(epoch, K.get_value(model.optimizer.lr)))
+        return K.get_value(model.optimizer.lr)
+
+    model = build_model(loader, args)
+
+    optimizer = optimizers.deserialize({"class_name": args.optimizer, "config": {}})
+    base_lr = args.base_lr or K.get_value(optimizer.lr)
+    if args.learning_rate:
+        K.set_value(optimizer.lr, args.learning_rate)
+
+    model.compile(loss=args.loss, optimizer=optimizer, metrics=[mse, mae, r2])
+
+    # calculate trainable and non-trainable params
+    params.update(candle.compute_trainable_params(model))
+
+    early_stopping = EarlyStopping(
+        monitor="val_loss", patience=args.early_stopping_patience
+    )
+
+    timeout_monitor = candle.TerminateOnTimeOut(params["timeout"])
+
+    reduce_lr = ReduceLROnPlateau(
+        monitor="val_loss",
+        factor=args.reduce_lr_factor,
+        patience=args.reduce_lr_patience,
+        min_lr=0.00001,
+    )
+    warmup_lr = LearningRateScheduler(warmup_scheduler)
+
+    callbacks = [timeout_monitor]
+    if args.early_stopping:
+        callbacks.append(early_stopping)
+    if args.reduce_lr:
+        callbacks.append(reduce_lr)
+    if args.warmup_lr:
+        callbacks.append(warmup_lr)
+
+    if stopper_callback:
+        callbacks.append(stopper_callback)
+
+    (
+        x_train_list,
+        y_train,
+        x_valid_list,
+        y_valid,
+        x_test_list,
+        y_test,
+        _,
+        _,
+        _,
+    ) = loader.load_data()
+    training_data = (x_train_list, y_train)
+    validation_data = (x_valid_list, y_valid)
+
+    num_parameters_info = count_params(model)
+
+    # Training
+    timestamp_duration = time.time() 
+    history = model.fit(
+        *training_data,
+        batch_size=args.batch_size,
+        shuffle=args.shuffle,
+        epochs=args.epochs,
+        validation_data=validation_data,
+        callbacks=callbacks,
+        verbose=args.verbose,
+    ).history
+    duration_train = time.time() - timestamp_duration
+
+
+    timestamp_duration = time.time() 
+    y_train_pred = model.predict(x_train_list, batch_size=args.batch_size).flatten()
+    duration_batch_inference = (time.time() - timestamp_duration) / np.ceil(len(y_train_pred) / args.batch_size)
+
+    scores_train = evaluate_prediction(y_train, y_train_pred)
+    y_valid_pred = model.predict(x_valid_list, batch_size=args.batch_size).flatten()
+    scores_valid = evaluate_prediction(y_valid, y_valid_pred)
+    y_test_pred = model.predict(x_test_list, batch_size=args.batch_size).flatten()
+    scores_test = evaluate_prediction(y_test, y_test_pred)
+
+    all_scores = {}
+    all_scores.update({f"train_{k}": float(v) for k, v in scores_train.items()})
+    all_scores.update({f"valid_{k}": float(v) for k, v in scores_valid.items()})
+    all_scores.update({f"test_{k}": float(v) for k, v in scores_test.items()})
+
+    if K.backend() == "tensorflow":
+        K.clear_session()
+
+    objective = all_scores["valid_r2"]  # validation R2
+    objective = max(-1, objective)
+
+    # collect learning curves
+    lc_train_mse = array_to_json(
+        np.asarray([[i + 1, l] for i, l in enumerate(history["mse"])])
+    )
+    lc_valid_mse = array_to_json(
+        np.asarray([[i + 1, l] for i, l in enumerate(history["val_mse"])])
+    )
+
+    lc_train_mae = array_to_json(
+        np.asarray([[i + 1, l] for i, l in enumerate(history["mae"])])
+    )
+    lc_valid_mae = array_to_json(
+        np.asarray([[i + 1, l] for i, l in enumerate(history["val_mae"])])
+    )
+
+    lc_train_r2 = array_to_json(
+        np.asarray([[i + 1, l] for i, l in enumerate(history["r2"])])
+    )
+    lc_valid_r2 = array_to_json(
+        np.asarray([[i + 1, l] for i, l in enumerate(history["val_r2"])])
+    )
+
+    metadata = {
+        "num_parameters": num_parameters_info["num_parameters"],
+        "num_parameters_train": num_parameters_info["num_parameters_train"],
+        "duration_train": duration_train,
+        "duration_batch_inference": duration_batch_inference,
+        "budget": len(history["loss"]),
+        "stopped": len(history["loss"]) < args.epochs,
+        "lc_train_mse": lc_train_mse,
+        "lc_valid_mse": lc_valid_mse,
+        "lc_train_mae": lc_train_mae,
+        "lc_valid_mae": lc_valid_mae,
+        "lc_train_r2": lc_train_r2,
+        "lc_valid_r2": lc_valid_r2,
+    }
+    metadata.update(all_scores)
+    return {"objective": objective, "metadata": metadata}
diff --git a/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/requirements.txt b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/requirements.txt
new file mode 100644
index 0000000..8c168b6
--- /dev/null
+++ b/src/deephyper_benchmark/lib/ecp_candle/Pilot1/Combo/requirements.txt
@@ -0,0 +1,5 @@
+astropy
+patsy
+statsmodels
+numpy<=1.22 # for compatibility with numba.cuda
+numba~=0.55.2
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/fnobench/README.md b/src/deephyper_benchmark/lib/fnobench/README.md
new file mode 100644
index 0000000..fd26692
--- /dev/null
+++ b/src/deephyper_benchmark/lib/fnobench/README.md
@@ -0,0 +1,64 @@
+# FNOBench: Fourier Neural Operator Benchmark
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
+
+This is a simplified way of using deephyper to conduct the hyperparameter tuning for the TFNO model to solve the darcy flow. The FNO and the TFNO are models for training neural operators and they can be designed using the neural operator package.
+
+The Deep Hyper is a package that automates the design of neural networks using Hyperparameter Tuning and Neural Architecture Search.
+
+## Installation
+
+To use the benchmark follow this example set of instructions:
+
+```console
+python -c "import deephyper_benchmark as dhb; dhb.install('FNOBench');"
+```
+
+Then load the benchmark from Python:
+
+```python
+import deephyper_benchmark as dhb
+dhb.load("FNOBench")
+from deephyper_benchmark.lib.fnobench import hpo
+```
+
+## Configuration
+
+...
+
+## Metadata
+
+The current set of returned metadata is:
+
+- [x] `num_parameters`
+- [ ] `num_parameters_train`
+- [x] `duration_train`
+- [ ] `duration_batch_inference`
+- [x] `budget`
+- [ ] `stopped`
+- [x] `train_loss`: the training loss which is the H1Loss
+- [x] `valid_loss`: the validation loss which is the LpLoss
+- [ ] `flops`
+- [ ] `latency`
+- [ ] `lc_train_loss`
+- [ ] `lc_valid_loss`
+
+## Other Details
+
+### Training Procedure
+
+We especially edit the Trainer class from the neural operator package to include and return the validation loss and to optimize it.
+
+We also specifically edit the function that loads the data to accommodate the validation data and the test data. Note that the training and testing data file has been included, and is available for download
+
+### Optimized Losses
+
+The losses employed by the neural operator package are the H1Loss and the LpLoss and we stick with those in this benchmark for the sake of consistency.
+
+### Data Splitting Policy
+
+
+The data we use for this test is the darcy flow test, and we have it in the data folder, it is generated by the load_darcy_flow_small function. The actual function that 
+loads and splits the data into train, validation and test data is the load_darcy_pt, and how it achieves this is to load the data from the file 
+darcy_train and split it in this form, the train data takes 80% of the data, while the validation data takes 20% and saves it into (x_train, y_train); which is loaded into the trainloader, and (x_valid, y_valid); which is loaded into the validation_loader. Note that we train our model in a particular resolution (16), but we can carry out the test in other resolutions, (for this project we test in 16, 32). To get the test data, we already have the test data in the file darcy_test, and we load it similar to how we load the train data, but here we do not split the test data at all, so we have (x_test, y_test) loaded into the testloaders; testloaderS because we load for resolution 16 and resolution 32.
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/fnobench/__init__.py b/src/deephyper_benchmark/lib/fnobench/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/fnobench/benchmark.py b/src/deephyper_benchmark/lib/fnobench/benchmark.py
new file mode 100644
index 0000000..f9e85a6
--- /dev/null
+++ b/src/deephyper_benchmark/lib/fnobench/benchmark.py
@@ -0,0 +1,17 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class FNOBenchmark(Benchmark):
+    version = "0.0.1"
+
+    requires = {
+        "py-pip-requirements": {
+            "step": "install",
+            "type": "pip",
+            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
+        },
+    }
diff --git a/src/deephyper_benchmark/lib/fnobench/data.py b/src/deephyper_benchmark/lib/fnobench/data.py
new file mode 100644
index 0000000..b294151
--- /dev/null
+++ b/src/deephyper_benchmark/lib/fnobench/data.py
@@ -0,0 +1,227 @@
+from pathlib import Path
+
+import torch
+from neuralop.datasets.tensor_dataset import TensorDataset
+from neuralop.datasets.transforms import PositionalEmbedding
+from neuralop.utils import UnitGaussianNormalizer
+
+
+# this function returns the train_loader, validation_loader, test_loader and output encoder for the darcy_flow dataset
+# it was originally written in the neuralop codes but we slightly edit it to include the validation_loader
+# which is 0.2% of the whole training_data
+def load_darcy_flow_small(
+    n_train,
+    n_tests,
+    batch_size,
+    test_batch_sizes,
+    test_resolutions=[16, 32],
+    grid_boundaries=[[0, 1], [0, 1]],
+    positional_encoding=True,
+    encode_input=False,
+    encode_output=True,
+    encoding="channel-wise",
+    channel_dim=1,
+):
+    """Loads a small Darcy-Flow dataset
+
+    Training contains 1000 samples in resolution 16x16.
+    Testing contains 100 samples at resolution 16x16 and
+    50 samples at resolution 32x32.
+
+    Parameters
+    ----------
+    n_train : int
+    n_tests : int
+    batch_size : int
+    test_batch_sizes : int list
+    test_resolutions : int list, default is [16, 32],
+    grid_boundaries : int list, default is [[0,1],[0,1]],
+    positional_encoding : bool, default is True
+    encode_input : bool, default is False
+    encode_output : bool, default is True
+    encoding : 'channel-wise'
+    channel_dim : int, default is 1
+        where to put the channel dimension, defaults size is batch, channel, height, width
+
+    Returns
+    -------
+    training_dataloader, testing_dataloaders
+
+    training_dataloader : torch DataLoader
+    testing_dataloaders : dict (key: DataLoader)
+    """
+    for res in test_resolutions:
+        if res not in [16, 32]:
+            raise ValueError(
+                f"Only 32 and 64 are supported for test resolution, but got {test_resolutions=}"
+            )
+    path = Path(__file__).resolve().parent.joinpath("data")
+    return load_darcy_pt(
+        str(path),
+        n_train=n_train,
+        n_tests=n_tests,
+        batch_size=batch_size,
+        test_batch_sizes=test_batch_sizes,
+        test_resolutions=test_resolutions,
+        train_resolution=16,
+        grid_boundaries=grid_boundaries,
+        positional_encoding=positional_encoding,
+        encode_input=encode_input,
+        encode_output=encode_output,
+        encoding=encoding,
+        channel_dim=channel_dim,
+    )
+
+
+def load_darcy_pt(
+    data_path,
+    n_train,
+    n_tests,
+    batch_size,
+    test_batch_sizes,
+    test_resolutions=[32],
+    train_resolution=32,
+    grid_boundaries=[[0, 1], [0, 1]],
+    positional_encoding=True,
+    encode_input=False,
+    encode_output=True,
+    encoding="channel-wise",
+    channel_dim=1,
+):
+    """Load the Navier-Stokes dataset"""
+    n_val = int(0.2 * n_train)
+    data = torch.load(
+        Path(data_path).joinpath(f"darcy_train_{train_resolution}.pt").as_posix()
+    )
+    x_train = (
+        data["x"][0 : n_train - n_val, :, :]
+        .unsqueeze(channel_dim)
+        .type(torch.float32)
+        .clone()
+    )
+    y_train = data["y"][0 : n_train - n_val, :, :].unsqueeze(channel_dim).clone()
+    x_val = (
+        data["x"][n_train - n_val : n_train, :, :]
+        .unsqueeze(channel_dim)
+        .type(torch.float32)
+        .clone()
+    )
+    y_val = data["y"][n_train - n_val : n_train, :, :].unsqueeze(channel_dim).clone()
+    del data
+
+    idx = test_resolutions.index(train_resolution)
+    test_resolutions.pop(idx)
+    n_test = n_tests.pop(idx)
+    test_batch_size = test_batch_sizes.pop(idx)
+
+    data = torch.load(
+        Path(data_path).joinpath(f"darcy_test_{train_resolution}.pt").as_posix()
+    )
+    x_test = data["x"][:n_test, :, :].unsqueeze(channel_dim).type(torch.float32).clone()
+    y_test = data["y"][:n_test, :, :].unsqueeze(channel_dim).clone()
+    del data
+
+    if encode_input:
+        if encoding == "channel-wise":
+            reduce_dims = list(range(x_train.ndim))
+        elif encoding == "pixel-wise":
+            reduce_dims = [0]
+
+        input_encoder = UnitGaussianNormalizer(x_train, reduce_dim=reduce_dims)
+        x_train = input_encoder.encode(x_train)
+        x_test = input_encoder.encode(x_test.contiguous())
+    else:
+        input_encoder = None
+
+    if encode_output:
+        if encoding == "channel-wise":
+            reduce_dims = list(range(y_train.ndim))
+        # reduce_d = list(range(y_val.ndim))
+        elif encoding == "pixel-wise":
+            reduce_dims = [0]
+        # reduce_d=[0]
+
+        output_encoder = UnitGaussianNormalizer(y_train, reduce_dim=reduce_dims)
+        y_train = output_encoder.encode(y_train)
+    else:
+        output_encoder = None
+
+    train_db = TensorDataset(
+        x_train,
+        y_train,
+        transform_x=PositionalEmbedding(grid_boundaries, 0)
+        if positional_encoding
+        else None,
+    )
+    train_loader = torch.utils.data.DataLoader(
+        train_db,
+        batch_size=batch_size,
+        shuffle=True,
+        num_workers=0,
+        pin_memory=True,
+        persistent_workers=False,
+    )
+    val_db = TensorDataset(
+        x_val,
+        y_val,
+        transform_x=PositionalEmbedding(grid_boundaries, 0)
+        if positional_encoding
+        else None,
+    )
+    val_loader = torch.utils.data.DataLoader(
+        val_db,
+        batch_size=batch_size,
+        shuffle=False,
+        num_workers=0,
+        pin_memory=True,
+        persistent_workers=False,
+    )
+    test_db = TensorDataset(
+        x_test,
+        y_test,
+        transform_x=PositionalEmbedding(grid_boundaries, 0)
+        if positional_encoding
+        else None,
+    )
+    test_loader = torch.utils.data.DataLoader(
+        test_db,
+        batch_size=test_batch_size,
+        shuffle=False,
+        num_workers=0,
+        pin_memory=True,
+        persistent_workers=False,
+    )
+    test_loaders = {train_resolution: test_loader}
+    for res, n_test, test_batch_size in zip(
+        test_resolutions, n_tests, test_batch_sizes
+    ):
+        print(
+            f"Loading test db at resolution {res} with {n_test} samples and batch-size={test_batch_size}"
+        )
+        data = torch.load(Path(data_path).joinpath(f"darcy_test_{res}.pt").as_posix())
+        x_test = (
+            data["x"][:n_test, :, :].unsqueeze(channel_dim).type(torch.float32).clone()
+        )
+        y_test = data["y"][:n_test, :, :].unsqueeze(channel_dim).clone()
+        del data
+        if input_encoder is not None:
+            x_test = input_encoder.encode(x_test)
+
+        test_db = TensorDataset(
+            x_test,
+            y_test,
+            transform_x=PositionalEmbedding(grid_boundaries, 0)
+            if positional_encoding
+            else None,
+        )
+        test_loader = torch.utils.data.DataLoader(
+            test_db,
+            batch_size=test_batch_size,
+            shuffle=False,
+            num_workers=0,
+            pin_memory=True,
+            persistent_workers=False,
+        )
+        test_loaders[res] = test_loader
+
+    return train_loader, val_loader, test_loaders, output_encoder
diff --git a/src/deephyper_benchmark/lib/fnobench/data/darcy_test_16.pt b/src/deephyper_benchmark/lib/fnobench/data/darcy_test_16.pt
new file mode 100644
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diff --git a/src/deephyper_benchmark/lib/fnobench/data/darcy_test_32.pt b/src/deephyper_benchmark/lib/fnobench/data/darcy_test_32.pt
new file mode 100644
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diff --git a/src/deephyper_benchmark/lib/fnobench/data/darcy_train_16.pt b/src/deephyper_benchmark/lib/fnobench/data/darcy_train_16.pt
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diff --git a/src/deephyper_benchmark/lib/fnobench/hpo.py b/src/deephyper_benchmark/lib/fnobench/hpo.py
new file mode 100644
index 0000000..8d3f7a9
--- /dev/null
+++ b/src/deephyper_benchmark/lib/fnobench/hpo.py
@@ -0,0 +1,104 @@
+import torch
+from deephyper.evaluator import profile
+from deephyper.hpo import HpProblem
+
+from .model import build_and_train_model
+
+# now we define the hyperparameters with which we aim to get the best configuration for solving the problem
+problem = HpProblem()
+# this is for dimension 2 darcy flow, below we list the hyperparameters and where they are being used
+# 1. n_modes_height and n_modes_width are the number of modes to keep in fourier layer along each dimension, if we are working with 3 dimensions, we have height, width, and length. It is called when you are defining the model e.g model =TFNO(n_modes=...),The dimensionality of the TFNO is inferred from ``len(n_modes)``
+problem.add_hyperparameter((2, 19), "n_modes_height", default_value=16)
+problem.add_hyperparameter((2, 19), "n_modes_width", default_value=16)
+# 2. hidden_channels: width of the FNO (i.e. number of channels). they go into the model definition
+problem.add_hyperparameter((1, 128), "hidden_channels", default_value=32)
+# 3. lifting_channels: number of hidden channels of the lifting block of the FNO, they go into the model definition
+problem.add_hyperparameter((1, 1024), "lifting_channels", default_value=256)
+# 4 projection_channels:number of hidden channels of the projection block of the FNO, they go into the model definition
+problem.add_hyperparameter((1, 1024), "projection_channels", default_value=64)
+# 5. use_mlp: Whether to use an MLP layer after each FNO block, they go into the model definition
+problem.add_hyperparameter([True, False], "use_mlp", default_value=False)
+# 6 mlp['dropout']: parameter of the MLP, they go into the model definition
+problem.add_hyperparameter((0.0, 1.0), "mlp['dropout']", default_value=0)
+# 7 mlp['expansion']: MLP parameter, they go into the model definition
+problem.add_hyperparameter((0.0, 3.0), "mlp['expansion']", default_value=0.5)
+# 8 rank Rank of the tensor factorization of the Fourier weights, they go into the model definition
+problem.add_hyperparameter((0.0, 1.0), "rank", default_value=1.0)
+# 9 factorization:Tensor factorization of the parameters weight to use, they go into the model definition
+problem.add_hyperparameter(
+    ["tucker", "cp", "tt", "dense"], "factorization", default_value="dense"
+)
+# 10 learning_rate: this is the learning_rate, goes into the optimizer definition
+problem.add_hyperparameter(
+    (1e-6, 1e-2, "log-uniform"), "opt_learning_rate", default_value=5e-3
+)
+# 11 batch_size: the batch size for the training, goes into the train_loader for loading the datasel
+problem.add_hyperparameter((2, 64), "data_batch_size", default_value=16)
+# 12 weight_decay: this is the weight_decay, goes into the optimizer definition
+problem.add_hyperparameter(
+    (1e-6, 1e-2, "log-uniform"), "opt_weight_decay", default_value=1e-4
+)
+# 13 n_layers:Number of Fourier Layers
+problem.add_hyperparameter((1, 8), "n_layers", default_value=4)
+# 14 epochs: number of epochs, goes into the trainer
+problem.add_hyperparameter((1, 1000), "opt_n_epochs", default_value=300)
+# 15 scheduler_T_max: this goes into the scheduler, it's the max number of iterations in the scheduler
+problem.add_hyperparameter([500], "opt_scheduler_T_max", default_value=500)
+# 16 n_train: no of data samples to train the model, goes into the train_loader for loading the dataset
+problem.add_hyperparameter([1000], "data_n_train", default_value=1000)
+# the next set of hyperparameters are data related and we're keeping them constant for now,and for the particular problem
+problem.add_hyperparameter([16], "data_train_resolution", default_value=16)
+problem.add_hyperparameter([True], "data_positional_encoding", default_value=True)
+problem.add_hyperparameter([True], "data_encode_input", default_value=True)
+problem.add_hyperparameter([False], "data_encode_output", default_value=False)
+problem.add_hyperparameter([666], "distributed_seed", default_value=666)
+problem.add_hyperparameter([3], "data_channels", default_value=3)
+# implementation is about how the factorization is done,
+problem.add_hyperparameter(
+    ["factorized", "reconstructed"], "implementation", default_value="factorized"
+)
+# By default, None, otherwise tanh is used before FFT in the FNO block
+problem.add_hyperparameter(["None", "tanh"], "stabilizer", default_value=None)
+# if 'full', the FNO Block runs in full precision,
+# if 'half', the FFT, contraction, and inverse FFT run in half precision
+# if 'mixed', the contraction and inverse FFT run in half precision
+problem.add_hyperparameter(
+    ["full", "half", "mixed"], "fno_block_precision", default_value="half"
+)
+# Type of skip connection to use,
+problem.add_hyperparameter(
+    ["soft-gating", "identity", "linear"], "skip", default_value="linear"
+)
+# the scheduler_patience, used for only  ReduceLROnPlateau, we're keeping it constant
+problem.add_hyperparameter([5], "opt_scheduler_patience", default_value=5)
+# type of scheduler to use
+problem.add_hyperparameter(
+    ["StepLR", "CosineAnnealingLR", "ReduceLROnPlateau"],
+    "opt_scheduler",
+    default_value="StepLR",
+)
+# step_size for the optimizer
+problem.add_hyperparameter((10, 100), "opt_step_size", default_value=60)
+# gamma for the constant, we're keeping it constant
+problem.add_hyperparameter([0.5], "opt_gamma", default_value=0.5)
+
+
+@profile
+def run(config):
+    # important to avoid memory explosion
+    torch.cuda.empty_cache()
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+    _, history = build_and_train_model(config, device, verbose=0)
+    score = {
+        "objective": -history["val_loss"][-1],
+        "metadata": {
+            "train_err": history["train_err"][-1],
+            "num_parameters": history["num_parameters"],
+            "budget": history["Epochs"][-1],
+            "val_loss": history["val_loss"][-1],
+            "duration_train": sum(history["train_time"]),
+        },
+    }
+
+    return score
diff --git a/src/deephyper_benchmark/lib/fnobench/model.py b/src/deephyper_benchmark/lib/fnobench/model.py
new file mode 100644
index 0000000..8c38e42
--- /dev/null
+++ b/src/deephyper_benchmark/lib/fnobench/model.py
@@ -0,0 +1,560 @@
+import sys
+from timeit import default_timer
+
+import matplotlib.pyplot as plt
+import neuralop.mpu.comm as comm
+import numpy as np
+import torch
+import wandb
+from neuralop import H1Loss, LpLoss
+from neuralop.models import TFNO2d
+from neuralop.training.patching import MultigridPatching2D
+from neuralop.utils import count_params
+
+from .data import load_darcy_flow_small
+
+
+# this is the trainer class that trains the neural operators,
+# it is from the neuralop package, but we had to slightly
+# modify it in order to
+# 1. calculate the validation loss instead of the testing loss
+# 2. return the history of the training, including the time for each epoch, the training loss for each epoch
+# and the validation loss for each epoch
+class Trainer:
+    def __init__(
+        self,
+        model,
+        n_epochs,
+        wandb_log=True,
+        device=None,
+        mg_patching_levels=0,
+        mg_patching_padding=0,
+        mg_patching_stitching=True,
+        log_test_interval=1,
+        log_output=False,
+        use_distributed=False,
+        verbose=True,
+    ):
+        """
+        A general Trainer class to train neural-operators on given datasets
+
+        Parameters
+        ----------
+        model : nn.Module
+        n_epochs : int
+        wandb_log : bool, default is True
+        device : torch.device
+        mg_patching_levels : int, default is 0
+            if 0, no multi-grid domain decomposition is used
+            if > 0, indicates the number of levels to use
+        mg_patching_padding : float, default is 0
+            value between 0 and 1, indicates the fraction of size to use as padding on each side
+            e.g. for an image of size 64, padding=0.25 will use 16 pixels of padding on each side
+        mg_patching_stitching : bool, default is True
+            if False, the patches are not stitched back together and the loss is instead computed per patch
+        log_test_interval : int, default is 1
+            how frequently to print updates
+        log_output : bool, default is False
+            if True, and if wandb_log is also True, log output images to wandb
+        use_distributed : bool, default is False
+            whether to use DDP
+        verbose : bool, default is True
+        """
+        self.n_epochs = n_epochs
+        self.wandb_log = wandb_log
+        self.log_test_interval = log_test_interval
+        self.log_output = log_output
+        self.verbose = verbose
+        self.mg_patching_levels = mg_patching_levels
+        self.mg_patching_stitching = mg_patching_stitching
+        self.use_distributed = use_distributed
+        self.device = device
+
+        if mg_patching_levels > 0:
+            self.mg_n_patches = 2**mg_patching_levels
+            if verbose:
+                print(f"Training on {self.mg_n_patches ** 2} multi-grid patches.")
+                sys.stdout.flush()
+        else:
+            self.mg_n_patches = 1
+            mg_patching_padding = 0
+            if verbose:
+                print(f"Training on regular inputs (no multi-grid patching).")
+                sys.stdout.flush()
+
+        self.mg_patching_padding = mg_patching_padding
+        self.patcher = MultigridPatching2D(
+            model,
+            levels=mg_patching_levels,
+            padding_fraction=mg_patching_padding,
+            use_distributed=use_distributed,
+            stitching=mg_patching_stitching,
+        )
+
+    def train(
+        self,
+        train_loader,
+        validation_loader,
+        output_encoder,
+        model,
+        optimizer,
+        scheduler,
+        regularizer,
+        training_loss=None,
+        eval_losses=None,
+    ):
+        """Trains the given model on the given datasets"""
+        n_train = len(train_loader.dataset)
+        history = {"Epochs": [], "train_err": [], "train_time": [], "val_loss": []}
+        # if not isinstance(test_loaders, dict):
+        #   test_loaders = dict(test=test_loaders)
+
+        if self.verbose:
+            print(f"Training on {n_train} samples")
+            # print(f'Testing on {[len(loader.dataset) for loader in test_loaders.values()]} samples'
+            #     f'         on resolutions {[name for name in test_loaders]}.')
+            sys.stdout.flush()
+
+        if training_loss is None:
+            training_loss = LpLoss(d=2)
+
+        if eval_losses is None:  # By default just evaluate on the training loss
+            eval_losses = dict(l2=training_loss)
+
+        if output_encoder is not None:
+            output_encoder.to(self.device)
+
+        if self.use_distributed:
+            is_logger = comm.get_world_rank() == 0
+        else:
+            is_logger = True
+
+        for epoch in range(self.n_epochs):
+            avg_loss = 0
+            avg_lasso_loss = 0
+            model.train()
+            t1 = default_timer()
+            train_err = 0.0
+
+            for idx, sample in enumerate(train_loader):
+                x, y = sample["x"], sample["y"]
+
+                if epoch == 0 and idx == 0 and self.verbose and is_logger:
+                    print(f"Training on raw inputs of size {x.shape=}, {y.shape=}")
+
+                x, y = self.patcher.patch(x, y)
+
+                if epoch == 0 and idx == 0 and self.verbose and is_logger:
+                    print(f".. patched inputs of size {x.shape=}, {y.shape=}")
+
+                x = x.to(self.device)
+                y = y.to(self.device)
+
+                optimizer.zero_grad(set_to_none=True)
+                if regularizer:
+                    regularizer.reset()
+
+                out = model(x)
+                if epoch == 0 and idx == 0 and self.verbose and is_logger:
+                    print(f"Raw outputs of size {out.shape=}")
+
+                out, y = self.patcher.unpatch(out, y)
+                # Output encoding only works if output is stiched
+                if output_encoder is not None and self.mg_patching_stitching:
+                    out = output_encoder.decode(out)
+                    y = output_encoder.decode(y)
+                if epoch == 0 and idx == 0 and self.verbose and is_logger:
+                    print(f".. Processed (unpatched) outputs of size {out.shape=}")
+
+                loss = training_loss(out.float(), y)
+
+                if regularizer:
+                    loss += regularizer.loss
+
+                loss.backward()
+
+                optimizer.step()
+                train_err += loss.item()
+
+                with torch.no_grad():
+                    avg_loss += loss.item()
+                    if regularizer:
+                        avg_lasso_loss += regularizer.loss
+
+            if isinstance(scheduler, torch.optim.lr_scheduler.ReduceLROnPlateau):
+                scheduler.step(train_err)
+            else:
+                scheduler.step()
+
+            epoch_train_time = default_timer() - t1
+            del x, y
+
+            train_err /= n_train
+            avg_loss /= self.n_epochs
+            history["Epochs"].append(epoch)
+            history["train_err"].append(train_err)
+            history["train_time"].append(epoch_train_time)
+            # if epoch % self.log_test_interval == 0:
+
+            msg = f"[{epoch}] time={epoch_train_time:.2f}, avg_loss={avg_loss:.4f}, train_err={train_err:.4f}"
+
+            values_to_log = dict(
+                train_err=train_err, time=epoch_train_time, avg_loss=avg_loss
+            )
+            # for loader_name, loader in test_loaders.items():
+            # if epoch == self.n_epochs - 1 and self.log_output:
+            #     to_log_output = True
+            # else:
+            #     to_log_output = False
+
+            loader = validation_loader
+            loader_name = ""
+            errors = self.evaluate(
+                model, eval_losses, loader, output_encoder, log_prefix=loader_name
+            )
+
+            for loss_name, loss_value in errors.items():
+                msg += f", {loss_name}={loss_value:.4f}"
+                values_to_log[loss_name] = loss_value
+            history["val_loss"].append(values_to_log[loss_name])
+            if regularizer:
+                avg_lasso_loss /= self.n_epochs
+                msg += f", avg_lasso={avg_lasso_loss:.5f}"
+
+            if self.verbose and is_logger:
+                print(msg)
+                sys.stdout.flush()
+
+                # Wandb loging
+            if self.wandb_log and is_logger:
+                for pg in optimizer.param_groups:
+                    lr = pg["lr"]
+                    values_to_log["lr"] = lr
+                wandb.log(values_to_log, step=epoch, commit=True)
+        return history
+
+    def evaluate(
+        self, model, loss_dict, data_loader, output_encoder=None, log_prefix=""
+    ):
+        """Evaluates the model on a dictionary of losses
+
+        Parameters
+        ----------
+        model : model to evaluate
+        loss_dict : dict of functions
+          each function takes as input a tuple (prediction, ground_truth)
+          and returns the corresponding loss
+        data_loader : data_loader to evaluate on
+        output_encoder : used to decode outputs if not None
+        log_prefix : str, default is ''
+            if not '', used as prefix in output dictionary
+
+        Returns
+        -------
+        errors : dict
+            dict[f'{log_prefix}_{loss_name}] = loss for loss in loss_dict
+        """
+        model.eval()
+
+        if self.use_distributed:
+            is_logger = comm.get_world_rank() == 0
+        else:
+            is_logger = True
+
+        errors = {f"{log_prefix}_{loss_name}": 0 for loss_name in loss_dict.keys()}
+
+        n_samples = 0
+        with torch.no_grad():
+            for it, sample in enumerate(data_loader):
+                x, y = sample["x"], sample["y"]
+
+                n_samples += x.size(0)
+
+                x, y = self.patcher.patch(x, y)
+                y = y.to(self.device)
+                x = x.to(self.device)
+
+                out = model(x)
+
+                out, y = self.patcher.unpatch(out, y, evaluation=True)
+
+                if output_encoder is not None:
+                    out = output_encoder.decode(out)
+
+                if (it == 0) and self.log_output and self.wandb_log and is_logger:
+                    if out.ndim == 2:
+                        img = out
+                    else:
+                        img = out.squeeze()[0]
+                    wandb.log(
+                        {
+                            f"image_{log_prefix}": wandb.Image(
+                                img.unsqueeze(-1).cpu().numpy()
+                            )
+                        },
+                        commit=False,
+                    )
+
+                for loss_name, loss in loss_dict.items():
+                    errors[f"{log_prefix}_{loss_name}"] += loss(out, y).item()
+
+        del x, y, out
+
+        for key in errors.keys():
+            errors[key] /= n_samples
+
+        return errors
+
+
+# this function takes an input configuration and uses this to build a TFNO model in a format accepted by
+# the TFNO benchmark and then trains the model
+def build_and_train_model(config: dict, device="cpu", verbose: bool = 0):
+    """
+    this function takes an input configuration and uses this to build a TFNO model in
+    a format accepted by the TFNO benchmark and then trains the model
+    """
+    default_config = {
+
+        # General
+        # For computing compression
+        # 'n_params_baseline': None,
+
+        # Distributed computing
+        'distributed_seed': 666,
+
+        # FNO related
+        "lifting_channels": 256,
+        'data_channels': 3,
+        'n_modes_height': 16,
+        'n_modes_width': 16,
+        'hidden_channels': 32,
+        'projection_channels': 64,
+        'n_layers': 4,
+        'skip': 'linear',
+        'implementation': 'factorized',
+
+        "use_mlp": False,
+        "mlp": {"dropout": 0, "expansion": 0.5},
+
+        'factorization': None,
+        'rank': 1.0,
+
+        'stabilizer': None,
+        'fno_block_precision': 'half',
+
+        # Optimizer
+
+        'opt_n_epochs': 5,
+        'opt_learning_rate': 5e-3,
+        # 'opt_training_loss': 'h1',
+        'opt_weight_decay': 1e-4,
+
+        'opt_scheduler_T_max': 500,  # For cosine only, typically take n_epochs
+        'opt_scheduler_patience': 5,  # For ReduceLROnPlateau only
+        'opt_scheduler': 'StepLR',  # Or 'CosineAnnealingLR' OR 'ReduceLROnPlateau'
+        'opt_step_size': 60,
+        'opt_gamma': 0.5,
+
+        # Dataset related
+
+        'data_batch_size': 16,
+        'data_n_train': 1000,
+        'data_train_resolution': 16,
+        'data_n_tests': [100, 50],
+        'data_test_resolutions': [16, 32],
+        'data_test_batch_sizes': [16, 16],
+        'data_positional_encoding': True,
+
+        'data_encode_input': True,
+        'data_encode_output': False,
+
+        # Patching
+        'patching': {'levels': 0},
+        'patching_levels': 0,
+        'patching_padding': 0,
+        'patching_stitching': False,
+    }
+
+    default_config.update(config)
+    config_name = 'default'
+
+   
+
+    # Loading the Darcy flow dataset
+
+    (train_loader, validation_loader, test_loaders, output_encoder) = load_darcy_flow_small(
+        n_train=default_config['data_n_train'], batch_size=default_config['data_batch_size'],
+        positional_encoding=default_config['data_positional_encoding'],
+        test_resolutions=default_config['data_test_resolutions'], n_tests=default_config['data_n_tests'],
+        test_batch_sizes=default_config['data_test_batch_sizes'],
+        encode_input=default_config['data_encode_input'], encode_output=default_config['data_encode_output'],
+        )
+
+    model = TFNO2d(data_channels=default_config["data_channels"], n_modes_height=default_config['n_modes_height'],
+                   n_modes_width=default_config["n_modes_width"],
+                   n_layers=default_config["n_layers"], lifting_channels=default_config["lifting_channels"],
+                   hidden_channels=default_config["hidden_channels"],
+                   projection_channels=default_config["projection_channels"],
+                   factorization=default_config["factorization"], rank=default_config["rank"],
+                   use_mlp=default_config["use_mlp"], mlp=default_config["mlp"], skip=default_config["skip"],
+                   implementation=default_config["implementation"],
+                   stabilizer=default_config["stabilizer"], fno_block_precision=default_config["fno_block_precision"])
+    model = model.to(device)
+
+    # Log parameter count
+    n_params = count_params(model)
+
+
+    print(f'\nn_params: {n_params}')
+    sys.stdout.flush()
+
+
+    # Create the optimizer
+    optimizer = torch.optim.Adam(model.parameters(),
+                                 lr=default_config['opt_learning_rate'],
+                                 weight_decay=default_config['opt_weight_decay'])
+
+    if default_config['opt_scheduler'] == 'ReduceLROnPlateau':
+        scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=default_config['opt_gamma'],
+                                                               patience=default_config['opt_scheduler_patience'],
+                                                               mode='min')
+    elif default_config['opt_scheduler'] == 'CosineAnnealingLR':
+        scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=default_config['opt_scheduler_T_max'])
+    elif default_config['opt_scheduler'] == 'StepLR':
+        scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=default_config['opt_step_size'],
+                                                    gamma=default_config['opt_gamma'])
+    else:
+        raise ValueError(f"Got { default_config['opt_scheduler']= }")
+
+    # Creating the losses
+    l2loss = LpLoss(d=2, p=2)
+    h1loss = H1Loss(d=2)
+    train_loss = h1loss
+    eval_losses = dict(h1=train_loss)
+    # if default_config['opt_training_loss'] == 'l2':
+    #     train_loss = l2loss
+    #     eval_losses = dict(l2=train_loss)
+    # elif default_config['opt_training_loss'] == 'h1':
+    #     train_loss = h1loss
+    #     eval_losses = dict(h1=train_loss)
+    # else:
+    #     raise ValueError(f"Got training_loss={default_config['opt_training_loss']} but expected one of ['l2', 'h1']")
+    # # eval_losses = {'h1': h1loss, 'l2': l2loss}
+
+
+    print('\n### MODEL ###\n', model)
+    print('\n### OPTIMIZER ###\n', optimizer)
+    print('\n### SCHEDULER ###\n', scheduler)
+    print('\n### LOSSES ###')
+    print(f'\n * Train: {train_loss}')
+    print(f'\n * Test: {eval_losses}')
+    print(f'\n### Beginning Training...\n')
+    sys.stdout.flush()
+
+    trainer = Trainer(model, n_epochs=default_config['opt_n_epochs'], device=device,
+                      mg_patching_levels=default_config['patching_levels'],
+                      mg_patching_padding=default_config['patching_padding'],
+                      mg_patching_stitching=default_config['patching_stitching'], wandb_log=False, log_test_interval=1,
+                      log_output=True, use_distributed=False, verbose=True)
+
+    history = trainer.train(train_loader, validation_loader, output_encoder, model, optimizer, scheduler,
+                            regularizer=False, training_loss=train_loss, eval_losses=eval_losses, )
+
+    history["num_parameters"] = n_params
+
+    return model, history
+
+
+
+def bestevaluate(best_config):
+    """we design the model based on the best hyperparameter configuration,
+    we then evaluate this based on the training/validation and testing data"""
+
+    (
+        train_loader,
+        validation_loader,
+        test_loaders,
+        output_encoder,
+    ) = load_darcy_flow_small(
+        n_train=best_config["data_n_train"],
+        batch_size=best_config["data_batch_size"],
+        positional_encoding=best_config["data_positional_encoding"],
+        test_resolutions=[16, 32],
+        n_tests=[100, 50],
+        test_batch_sizes=[32, 32],
+        encode_input=best_config["data_encode_input"],
+        encode_output=best_config["data_encode_output"],
+    )
+    # the build_and_train_model function
+    # gives the training and validation losses
+    best_model, best_history = build_and_train_model(best_config, verbose=1)
+    # next we generate the testing losses
+    trainer = Trainer(
+        best_model,
+        n_epochs=best_config["opt_n_epochs"],
+        device=device,
+        mg_patching_levels=best_config["patching_levels"],
+        mg_patching_padding=best_config["patching_padding"],
+        mg_patching_stitching=best_config["patching_stitching"],
+        wandb_log=best_config["wandb_log"],
+        log_test_interval=best_config["wandb_log_test_interval"],
+        log_output=best_config["wandb_log_output"],
+        use_distributed=best_config["distributed_use_distributed"],
+        verbose=True,
+    )
+    for loader_name, loader in test_loaders.items():
+        l2loss = LpLoss(d=2, p=2)
+        h1loss = H1Loss(d=2)
+        eval_losses = {"h1": h1loss, "l2": l2loss}
+        errors = trainer.evaluate(
+            best_model, eval_losses, loader, output_encoder, log_prefix=loader_name
+        )
+        for loss_name, loss_value in errors.items():
+            best_history[loss_name] = loss_value
+
+    test_samples = test_loaders[32].dataset
+
+    fig = plt.figure(figsize=(7, 7))
+    for index in range(3):
+        data = test_samples[index]
+        # Input x
+        x = data["x"]
+        # Ground-truth
+        y = data["y"]
+        # Model prediction
+        out = best_model(x.unsqueeze(0))
+
+        ax = fig.add_subplot(4, 4, index * 4 + 1)
+        ax.imshow(x[0], cmap="gray")
+        if index == 0:
+            ax.set_title("Input x")
+        plt.xticks([], [])
+        plt.yticks([], [])
+
+        ax = fig.add_subplot(4, 4, index * 4 + 2)
+        ax.imshow(y.squeeze())
+        if index == 0:
+            ax.set_title("Ground-truth y")
+        plt.xticks([], [])
+        plt.yticks([], [])
+
+        ax = fig.add_subplot(4, 4, index * 4 + 3)
+        ax.imshow(out.squeeze().detach().numpy())
+        if index == 0:
+            ax.set_title("Model prediction")
+        plt.xticks([], [])
+        plt.yticks([], [])
+
+        ax = fig.add_subplot(4, 4, index * 4 + 4)
+        err = (np.abs(y.squeeze() - out.detach().numpy())) ** 2
+        im = ax.imshow(err.squeeze(), cmap="plasma", aspect="auto")
+        plt.colorbar(im, label="$|y - \hat{y}|^2$")
+        if index == 0:
+            ax.set_title("Squared Error")
+        plt.xticks([], [])
+        plt.yticks([], [])
+
+    fig.suptitle("Inputs, ground-truth output, prediction and absolute error.", y=0.98)
+    plt.tight_layout()
+    fig.show()
+    return best_history
diff --git a/src/deephyper_benchmark/lib/fnobench/requirements.txt b/src/deephyper_benchmark/lib/fnobench/requirements.txt
new file mode 100644
index 0000000..77a79ef
--- /dev/null
+++ b/src/deephyper_benchmark/lib/fnobench/requirements.txt
@@ -0,0 +1,5 @@
+neuraloperator
+tensorly
+tensorly-torch
+wandb
+zarr
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/hpobench/README.md b/src/deephyper_benchmark/lib/hpobench/README.md
new file mode 100644
index 0000000..0ff9825
--- /dev/null
+++ b/src/deephyper_benchmark/lib/hpobench/README.md
@@ -0,0 +1,28 @@
+# HPOBench
+
+✅ **Benchmark ready to be used.**
+
+This set of benchmarks provides precomputed evaluations of neural networks for hyperparameter optimization. This is particularly useful to compare optimization schemes without training neural networks for real.
+
+## Installation
+
+To install this benchmark the following command can be run:
+
+```console
+python -c "import deephyper_benchmark as dhb; dhb.install('HPOBench/tabular');"
+```
+
+For detailed information see the [NeurIPS paper describing the HPOBench benchmark](https://datasets-benchmarks-proceedings.neurips.cc/paper/2021/hash/93db85ed909c13838ff95ccfa94cebd9-Abstract-round2.html).
+
+## Configuration
+
+Different parameters can be set to configure this benchmark.
+
+- Environment variable `DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME` with boolean value in `[0, 1]` which set if the "recorded training time" of each training epoch should be simulated or not. **Defaults to `0`.**
+- Environment variable `DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME` with real value $> 0$. It represents the proportion of the "recorded training time" which should be used for simulation. For example, `1.0` (default) corresponds to 100% of the training time while `0.1` corresponds to 10% of the training time. **Defaults to `1.0`.**
+- Environment variable `DEEPHYPER_BENCHMARK_TASK` to select the task on which to run the benchmark with values in:
+    - **`navalpropulsion` (Default)**
+    - `parkinsonstelemonitoring`
+    - `proteinstructure`
+    - `slicelocalization`
+- Environment variable `DEEPHYPER_BENCHMARK_MOO` with value `0` or `1` to select if the task should be run with single or multiple objectives. **Defaults to `0` for single-objective**.
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/hpobench/tabular/__init__.py b/src/deephyper_benchmark/lib/hpobench/tabular/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/hpobench/tabular/benchmark.py b/src/deephyper_benchmark/lib/hpobench/tabular/benchmark.py
new file mode 100644
index 0000000..6906ef8
--- /dev/null
+++ b/src/deephyper_benchmark/lib/hpobench/tabular/benchmark.py
@@ -0,0 +1,17 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class HPOBenchTabularNavalPropulsion(Benchmark):
+    version = "0.0.1"
+
+    requires = {
+        "bash-install": {
+            "step": "install",
+            "type": "cmd",
+            "cmd": os.path.join(DIR, "install.sh"),
+        },
+    }
diff --git a/src/deephyper_benchmark/lib/hpobench/tabular/constant_predictor.py b/src/deephyper_benchmark/lib/hpobench/tabular/constant_predictor.py
new file mode 100644
index 0000000..4a42b3b
--- /dev/null
+++ b/src/deephyper_benchmark/lib/hpobench/tabular/constant_predictor.py
@@ -0,0 +1,127 @@
+from typing import Tuple
+
+import numpy as np
+import openml
+from sklearn.dummy import DummyRegressor
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import mean_squared_error
+
+# Reference of HPOBench Paper: https://arxiv.org/pdf/2109.06716.pdf
+# Reference of original paper introducign these benchmarks: https://arxiv.org/pdf/1905.04970.pdf
+# Trying to reproduce as much as possible results/setup from the original paper
+
+map_task_to_openmlid = {
+    # https://archive.ics.uci.edu/dataset/316/condition+based+maintenance+of+naval+propulsion+plants
+    "navalpropulsion": "44969",
+    # https://archive.ics.uci.edu/dataset/189/parkinsons+telemonitoring
+    "parkinsonstelemonitoring": "4531",
+    # https://archive.ics.uci.edu/dataset/265/physicochemical+properties+of+protein+tertiary+structure
+    "proteinstructure": "44963",
+    # https://archive.ics.uci.edu/dataset/206/relative+location+of+ct+slices+on+axial+axis
+    "slicelocalization": "42973",
+}
+
+map_openmlid_to_task = {v: k for k, v in map_task_to_openmlid.items()}
+
+map_task_to_default_target_attribute = {
+    "navalpropulsion": ["gt_compressor_decay_state_coefficient"],
+    "parkinsonstelemonitoring": ["motor_UPDRS", "total_UPDRS"],
+    "proteinstructure": ["RMSD"],
+    "slicelocalization": ["reference"],
+}
+
+
+def load_from_openml(dataset_id: str) -> Tuple[np.ndarray, np.ndarray, dict]:
+    """Load dataset from openml library.
+
+    Args:
+        dataset_id (str): the identifier of the dataset to be loaded from the OpenML database.
+
+    Raises:
+        ValueError: if the dataset is not found.
+
+    Returns:
+        (np.ndarray, np.ndarray, dict): X, y, metadata where X is the input array, y is the target array, and metadata is a dictionary containing additional information about the data.
+    """
+    task_name = map_openmlid_to_task[dataset_id]
+    default_target_attribute = map_task_to_default_target_attribute[task_name]
+
+    dataset = openml.datasets.get_dataset(
+        dataset_id,
+        download_data=True,
+        download_qualities=True,
+        download_features_meta_data=True,
+    )
+
+    X, y, categorical_indicator, _ = dataset.get_data()
+    y = X[default_target_attribute]
+    X = X.drop(default_target_attribute, axis=1)
+    y = y.values
+
+    # Drop constant features
+    constant_features = []
+    continuous_features = []
+    for i in range(X.shape[1]):
+        values, _ = np.unique(X.values[:, i], return_counts=True)
+        if len(values) == 1:
+            constant_features.append(list(X.columns)[i])
+        elif len(values) >= 10:
+            continuous_features.append(list(X.columns)[i])
+
+    X = X.drop(constant_features, axis=1)
+
+    # Split into train and test
+    test_size = int(0.2 * len(X))
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=test_size, random_state=42
+    )
+    X_train, X_valid, y_train, y_valid = train_test_split(
+        X_train, y_train, test_size=test_size, random_state=42
+    )
+
+    # Standard Normalization on features
+    m = np.mean(X_train[continuous_features], axis=0)
+    s = np.std(X_train[continuous_features], axis=0)
+    X_train[continuous_features] = (X_train[continuous_features] - m) / s
+    X_valid[continuous_features] = (X_valid[continuous_features] - m) / s
+    X_test[continuous_features] = (X_test[continuous_features] - m) / s
+
+    # Standard Normalization on targets
+    m = np.mean(y_train, axis=0)
+    s = np.std(y_train, axis=0)
+    y_train = (y_train - m) / s
+    y_valid = (y_valid - m) / s
+    y_test = (y_test - m) / s
+
+    return X_train.values, X_valid.values, X_test.values, y_train, y_valid, y_test
+
+
+if __name__ == "__main__":
+    # Some tests
+    task = "navalpropulsion"
+    # task = "parkinsonstelemonitoring"
+    # task = "proteinstructure"
+    # task = "slicelocalization"
+    print(f"Task: {task}")
+    openmlid = map_task_to_openmlid[task]
+    X_train, X_valid, X_test, y_train, y_valid, y_test = load_from_openml(openmlid)
+
+    print("Num samples:", X_train.shape[0] + X_valid.shape[0] + X_test.shape[0])
+    print("X_train.shape", X_train.shape)
+    print("X_valid.shape", X_valid.shape)
+    print("X_test.shape", X_test.shape)
+    print("y_train.shape", y_train.shape)
+    print("y_valid.shape", y_valid.shape)
+    print("y_test.shape", y_test.shape)
+
+    model = DummyRegressor(strategy="mean")
+    model.fit(X_train, y_train)
+
+    # As the data are normalized these values are expected to be close to 1.0
+    fold = ["train", "valid", "test"]
+    for i, (X, y) in enumerate(
+        zip([X_train, X_valid, X_test], [y_train, y_valid, y_test])
+    ):
+        print(f"Fold: {fold[i]}")
+        y_pred = model.predict(X)
+        print(f"  MSE: {mean_squared_error(y, y_pred)}")
diff --git a/src/deephyper_benchmark/lib/hpobench/tabular/hpo.py b/src/deephyper_benchmark/lib/hpobench/tabular/hpo.py
new file mode 100644
index 0000000..0c662ed
--- /dev/null
+++ b/src/deephyper_benchmark/lib/hpobench/tabular/hpo.py
@@ -0,0 +1,139 @@
+import os
+import time
+
+import numpy as np
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+import hpobench.benchmarks.nas.tabular_benchmarks
+
+from deephyper.evaluator import profile, RunningJob
+from deephyper.hpo import HpProblem
+
+DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME = bool(
+    int(os.environ.get("DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME", 0))
+)
+DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME = float(
+    os.environ.get("DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME", 1.0)
+)
+DEEPHYPER_BENCHMARK_TASK = os.environ.get("DEEPHYPER_BENCHMARK_TASK", "navalpropulsion")
+DEEPHYPER_BENCHMARK_MOO = bool(int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 0)))
+
+
+map_task_to_benchmark = {
+    "navalpropulsion": "NavalPropulsionBenchmark",
+    "parkinsonstelemonitoring": "ParkinsonsTelemonitoringBenchmark",
+    "proteinstructure": "ProteinStructureBenchmark",
+    "slicelocalization": "SliceLocalizationBenchmark",
+}
+data_path = os.path.join(DIR, "build/HPOBench/data/fcnet_tabular_benchmarks/")
+
+
+benchmark_class = getattr(
+    hpobench.benchmarks.nas.tabular_benchmarks,
+    map_task_to_benchmark[DEEPHYPER_BENCHMARK_TASK],
+)
+b = benchmark_class(data_path=data_path)
+config_space = b.get_configuration_space()
+
+problem = HpProblem(config_space=config_space)
+
+
+@profile
+def run(job: RunningJob, optuna_trial=None) -> dict:
+    # otherwise failure
+    config = job.parameters
+
+    seed = int(job.id.split(".")[-1])
+    rng = np.random.RandomState(seed)
+    run_index = int(rng.choice((0, 1, 2, 3)))
+
+    benchmark_class = getattr(
+        hpobench.benchmarks.nas.tabular_benchmarks,
+        map_task_to_benchmark[DEEPHYPER_BENCHMARK_TASK],
+    )
+    b = benchmark_class(data_path=data_path)
+    budget_space = b.get_fidelity_space().get("budget")
+    min_b, max_b = budget_space.lower, budget_space.upper
+
+    # The objective is the coefficient of determination (R^2)
+    # As the target were standardized, the R^2 = 1 - MSE / MSE_baseline with MSE_baseline = 1.0
+    # The objective is maximized in deephyper
+    eval_test = b.objective_function_test(config, fidelity={"budget": max_b})
+    objective_test = 1 - eval_test["function_value"]
+    cost_eval = eval_test["cost"]
+    cost_step = cost_eval / (max_b - min_b + 1)
+
+    eval_val = b.objective_function(
+        config, fidelity={"budget": max_b}, run_index=run_index
+    )
+    objective_val = 1 - eval_val["function_value"]
+
+    other_metadata = {}
+
+    consumed_time = 0  # the cost here corresponds to time
+
+    if optuna_trial:
+        for budget_i in range(min_b, max_b + 1):
+            eval = b.objective_function(
+                config, fidelity={"budget": budget_i}, run_index=run_index
+            )
+
+            consumed_time += cost_step
+            if DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME:
+                time.sleep(cost_step * DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME)
+
+            objective_i = 1 - eval["function_value"]  # maximizing in deephyper
+
+            # Trial report is not support for MOO in Optuna
+            if DEEPHYPER_BENCHMARK_MOO:
+                continue
+
+            optuna_trial.report(objective_i, step=budget_i)
+            if optuna_trial.should_prune():
+                break
+
+        objective = objective_i
+
+    else:
+        for budget_i in range(min_b, max_b + 1):
+            eval = b.objective_function(
+                config, fidelity={"budget": budget_i}, run_index=run_index
+            )
+
+            consumed_time += cost_step
+            if DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME:
+                time.sleep(cost_step * DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME)
+
+            objective_i = 1 - eval["function_value"]  # maximizing in deephyper
+            job.record(budget_i, objective_i)
+            if job.stopped():
+                break
+
+        objective = job.objective
+
+        if hasattr(job, "stopper") and hasattr(job.stopper, "infos_stopped"):
+            other_metadata["infos_stopped"] = job.stopper.infos_stopped
+
+    metadata = {
+        "budget": budget_i,
+        "stopped": budget_i < max_b,
+        "objective_test": objective_test,
+        "objective_val": objective_val,
+    }
+    metadata.update(other_metadata)
+
+    return {
+        "objective": (objective, -consumed_time)
+        if DEEPHYPER_BENCHMARK_MOO
+        else objective,
+        "metadata": metadata,
+    }
+
+
+if __name__ == "__main__":
+    print(problem)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/hpobench/tabular/install.sh b/src/deephyper_benchmark/lib/hpobench/tabular/install.sh
new file mode 100755
index 0000000..1eb5d47
--- /dev/null
+++ b/src/deephyper_benchmark/lib/hpobench/tabular/install.sh
@@ -0,0 +1,39 @@
+#!/bin/bash
+
+set -x
+
+mkdir build/ && cd build
+
+# Clone HPOBench repository
+git clone https://github.com/automl/HPOBench.git
+cd HPOBench/
+
+# Freeze commit used for the benchmark
+git checkout d8b45b1eca9a61c63fe79cdfbe509f77d3f5c779
+
+# Relax the constraint on the Python version required
+sed -i '' 's/>=3.6, <=3.10/>=3.6, <3.12/g' setup.py
+
+# Download data
+mkdir data && cd data/
+
+# For tabular benchmarks
+wget http://ml4aad.org/wp-content/uploads/2019/01/fcnet_tabular_benchmarks.tar.gz
+tar xf fcnet_tabular_benchmarks.tar.gz
+
+# Install other dependencies
+cd ..
+pip install "."
+pip install git+https://github.com/google-research/nasbench.git@master
+
+git clone https://github.com/automl/nas_benchmarks.git
+cd nas_benchmarks
+
+# Freeze commit used for the benchmark
+git checkout 1b09906ba3f522f15766b75643423acccd9db3a5
+
+# Comment out the last line of the file
+sed -i '' '5 s/./#&/' tabular_benchmarks/__init__.py
+
+python setup.py install
+
diff --git a/src/deephyper_benchmark/lib/hpobench/tabular/metrics.py b/src/deephyper_benchmark/lib/hpobench/tabular/metrics.py
new file mode 100644
index 0000000..d135e38
--- /dev/null
+++ b/src/deephyper_benchmark/lib/hpobench/tabular/metrics.py
@@ -0,0 +1,78 @@
+import json
+import os
+
+import numpy as np
+
+from .hpo import b as BENCHMARK
+
+
+class PerformanceEvaluator:
+    """A class defining performance evaluators for the DTLZ problems."""
+
+    def __init__(self):
+        """Read the current problem defn from environment vars."""
+
+        # Retrieve the Best Configuration
+        configs, te, ve = [], [], []
+        for k in BENCHMARK.benchmark.data.keys():
+            configs.append(json.loads(k))
+            te.append(np.min(BENCHMARK.benchmark.data[k]["final_test_error"]))
+            ve.append(np.min(np.mean(BENCHMARK.benchmark.data[k]["valid_mse"], axis=0)))
+
+        idx_opt_ve = np.argmin(ve)
+        idx_opt_te = np.argmin(te)
+
+        # Configuration, Validation Error, Test Error based on the best validation error
+        self.x_min_on_valid = dict(configs[idx_opt_ve])
+        self.y_min_valid_on_valid = ve[idx_opt_ve]
+        self.y_min_test_on_valid = te[idx_opt_ve]
+
+        # Configuration, Validation Error, Test Error based on the best final test error
+        # only available for this benchmark
+        self.x_min_on_test = dict(configs[idx_opt_te])
+        self.y_min_valid_on_test = ve[idx_opt_te]
+        self.y_min_test_on_test = te[idx_opt_te]
+
+    def simple_regret_valid(self, y_valid: np.ndarray) -> np.ndarray:
+        """Compute the regret of the objective (validation RMSE) of a list of ordered solutions.
+
+        Args:
+            y_valid (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return y_valid - self.y_min_valid_on_valid
+
+    def cumul_regret_valid(self, y_valid: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of the objective (validation RMSE) aon n array of ordered solutions.
+
+        Args:
+            y_valid (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret_valid(y_valid))
+
+    def simple_regret_test(self, y_test: np.ndarray) -> np.ndarray:
+        """Compute the regret of the test RMSE of a list of ordered solutions.
+
+        Args:
+            y_test (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return y_test - self.y_min_test_on_test
+
+    def cumul_regret_test(self, y_test: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of the test RMSE aon n array of ordered solutions.
+
+        Args:
+            y_test (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret_test(y_test))
diff --git a/src/deephyper_benchmark/lib/jahsbench/README.md b/src/deephyper_benchmark/lib/jahsbench/README.md
new file mode 100644
index 0000000..26ac239
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/README.md
@@ -0,0 +1,176 @@
+# JAHS Benchmark Suite
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
+
+This module contains a DeepHyper wrapper for
+ [JAHS-Bench-201](https://github.com/automl/jahs_bench_201).
+
+JAHSBench implements a random forest surrogate model, trained on real-world
+performance data for neural networks trained on three standard benchmark
+problems:
+ - ``fashion_mnist`` (**default**)
+ - ``cifar10`` 
+ - ``colorectal_histology``
+
+Using these models as surrogates for the true performance, we can use this
+benchmark problem to study the performance of AutoML techniques on joint
+architecture-hyperparameter search tasks at minimal expense.
+
+The models allow us to tune 2 continuous training hyperparameters
+ - ``LearningRate`` and
+ - ``WeightDecay``,
+
+2 categorical training hyperparameters
+ - ``Activation`` and
+ - ``TrivialAugment``,
+
+and 5 categorical architecture parameters
+ - ``Op{i}`` for ``i=0, ..., 4``.
+
+For DeepHyper's implementation, we have added 9th integer-valued parameter,
+which is the number of epochs trained
+ - ``nepochs``.
+
+When run with the option ``wait=True``, ``JAHSBench`` will wait for an
+amount of time proportional to the ``runtime`` field returned by
+JAHS-Bench-201's surrogates. By default, this is 1% of the true runtime.
+
+The benchmark can be run to tune a single objective (``valid-acc``) or
+three objectives (``valid-acc``, ``latency``, and ``size_MB``).
+*Note that in the original JAHS-Bench-201 benchmark, there are only 2
+objectives and ``size_MB`` is not included as an objective.*
+
+For further information, see:
+
+```
+    @inproceedings{NEURIPS2022_fd78f2f6,
+        author = {Bansal, Archit and Stoll, Danny and Janowski, Maciej and Zela, Arber and Hutter, Frank},
+        booktitle = {Advances in Neural Information Processing Systems},
+        editor = {S. Koyejo and S. Mohamed and A. Agarwal and D. Belgrave and K. Cho and A. Oh},
+        pages = {38788--38802},
+        publisher = {Curran Associates, Inc.},
+        title = {JAHS-Bench-201: A Foundation For Research On Joint Architecture And Hyperparameter Search},
+        url = {https://proceedings.neurips.cc/paper_files/paper/2022/file/fd78f2f65881c1c7ce47e26b040cf48f-Paper-Datasets_and_Benchmarks.pdf},
+        volume = {35},
+        year = {2022}
+    }
+```
+
+## Installation
+
+To install this benchmark the following command can be run:
+```
+python -c "import deephyper_benchmark as dhb; dhb.install('JAHSBench');"
+```
+
+## Configuration
+
+Prior to initialize the problem, set the following environment variables
+to configure the JAHS-Bench problem:
+- ``DEEPHYPER_BENCHMARK_MOO`` to `0` for single objective runs or `1` for
+  multiobjective runs. Defaults to `1`.
+- ``DEEPHYPER_BENCHMARK_JAHS_PROB`` to one of the following:
+  `fashion_mnist` (default), `cifar10`, or `colorectal_histology`. 
+
+## Metadata
+
+In addition to DeepHyper's standard metadata (timestamps), the following metadata
+is produced by JAHS-Bench-201, and recorded by DeepHyper:
+- ``m:size_MB``,
+- ``m:runtime``,
+- ``m:latency``,
+- ``m:FLOPS``,
+- ``m:valid-acc``,
+- ``m:train-acc``, and
+- ``m:test-acc``.
+
+The ``valid-acc`` is used to compute the objective in the single objective
+case.
+Additionally, the negative values of the ``latency`` and ``size_MB`` are
+used in the multiobjective case.
+All metadata is recorded regardless of the case.
+
+## Usage
+
+To use the benchmark follow this example set of instructions:
+
+```python
+
+import deephyper_benchmark as dhb
+
+
+# Load JAHS-bench-201
+dhb.load("JAHSBench")
+
+from deephyper_benchmark.lib.jahsbench import hpo
+
+# Example of running one evaluation of JAHSBench
+from deephyper.evaluator import RunningJob
+config = hpo.problem.jahs_obj.__sample__() # get a default config to test
+res = hpo.run(RunningJob(parameters=config))
+
+```
+
+Note that JAHS-Bench-201 uses XGBoost, which may not be compatible with older
+versions of MacOS.
+Additionally, the surrogate data has been pickled with an older version
+of scikit-learn and newer versions will fail to correctly load the surrogate
+models.
+
+For more information, see the following GitHub issues:
+ - https://github.com/automl/jahs_bench_201/issues/6
+ - https://github.com/automl/jahs_bench_201/issues/18
+
+## Evaluating Results
+
+To evaluate the results, the AutoML team recommends using the validation
+error for single-objective runs or the hypervolume metric over both
+validation error and evaluation latency for multiobjective-runs.
+See their
+[Evaluation Protocol](https://automl.github.io/jahs_bench_201/evaluation_protocol)
+for more details.
+
+**For multiobjective problems:**
+In their original benchmark, no recommended reference point is given,
+as discussed in
+[this GitHub issue](https://github.com/automl/jahs_bench_201/issues/19).
+Since we have already modified the original problem, we have taken the
+liberty of assigning a reference point.
+This reference point was chosen to include all true solutions for the
+``latency`` and ``size_MB`` objectives.
+These values were chosen by observing the true Pareto front for the raw data.
+However, we have purposefully created a lower bound on the "interesting range"
+for the ``valid-acc`` objective, which is higher than it was before.
+In particular, no accuracies less than 95% will be considered when
+computing hypervolume scores for the Fashion MNIST dataset,
+no accuracies less than 90% are considered for the CIFAR-10 dataset,
+and no accuracies less than 93% are considered for the colorectal histology
+dataset.
+When solving any of these problems via DeepHyper, the ``moo_lower_bounds``
+argument should be set accordingly.
+To evaluate hypervolume with these reference points, use our metrics as
+shown below
+
+```python
+
+from deephyper_benchmark.lib.jahsbench import metrics
+evaluator = metrics.PerformanceEvaluator()
+hv = evaluator.hypervolume(res)
+
+```
+
+## Additional Problem Details
+
+Each problem in JAHS-Bench-201 is given by a XGBoost **surrogate** for a
+joint neural network architecture/hyperparameter search problem.
+Surrogates are given for networks trained to solve image classification tasks
+for three different datasets.
+These datasets are listed below and have the following properties:
+
+- ``fashion_mnist`` is based on the Fashion MNIST dataset (https://github.com/zalandoresearch/fashion-mnist).
+  This problem has lower bound of 95% on the "interesting range" of validation accuracies, and the Kendall's tau for the XGBoost model is over 92% for all 3 objectives (92.2% for the ``valid-acc`` objective).
+- ``cifar10`` is based on the Cifar-10 dataset (https://www.cs.toronto.edu/~kriz/cifar.html)
+  This problem has lower bound of 90% on the "interesting range" of validation accuracies, and the Kendall's tau for the XGBoost model is over 89% for all 3 objectives (89% for the ``valid-acc`` objective).
+- ``colorecal-histology`` is based on the colorectal histology dataset (https://zenodo.org/record/53169#.XGZemKwzbmG).
+  This problem has lower bound of 93% on the "interesting range" of validation accuracies, and the Kendall's tau for the XGBoost model is considerably lower than with the other datasets (just 68.2% for the ``valid-acc`` objective), so it may not be an accurate representation of the true problem.
diff --git a/src/deephyper_benchmark/lib/jahsbench/__init__.py b/src/deephyper_benchmark/lib/jahsbench/__init__.py
new file mode 100644
index 0000000..f102a9c
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/__init__.py
@@ -0,0 +1 @@
+__version__ = "0.0.1"
diff --git a/src/deephyper_benchmark/lib/jahsbench/benchmark.py b/src/deephyper_benchmark/lib/jahsbench/benchmark.py
new file mode 100644
index 0000000..2b99dc3
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/benchmark.py
@@ -0,0 +1,22 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class JAHS201Benchmark(Benchmark):
+    version = "0.0.1"
+    
+    requires = {
+        "py-pip-requirements": {
+            "step": "install",
+            "type": "pip",
+            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
+        },
+        "bash-install": {
+            "step": "install",
+            "type": "cmd",
+            "cmd": "cd . && bash " + os.path.join(DIR, "./install.sh"),
+        },
+    }
diff --git a/src/deephyper_benchmark/lib/jahsbench/hpo.py b/src/deephyper_benchmark/lib/jahsbench/hpo.py
new file mode 100644
index 0000000..4e8738a
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/hpo.py
@@ -0,0 +1,57 @@
+import os
+import numpy as np
+import time
+
+from deephyper.evaluator import profile, RunningJob
+from deephyper.hpo import HpProblem
+from . import model
+
+
+# Read in whether to do single- or multi-objectives
+multiobj = int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 1))
+prob_name = os.environ.get("DEEPHYPER_BENCHMARK_JAHS_PROB", "fashion_mnist")
+
+# Create problem
+problem = HpProblem()
+jahs_obj = model.jahs_bench(dataset=prob_name)
+# 2 continuous hyperparameters
+problem.add_hyperparameter((1e-3, 1.0, "log-uniform"), "LearningRate")
+problem.add_hyperparameter((1e-5, 1e-3, "log-uniform"), "WeightDecay")
+# 2 categorical hyperparameters
+problem.add_hyperparameter(["ReLU", "Hardswish", "Mish"], "Activation")
+problem.add_hyperparameter(["on", "off"], "TrivialAugment")
+# 6 categorical architecture design variables
+for i in range(1, 7):
+    problem.add_hyperparameter([0, 1, 2, 3, 4], f"Op{i}")
+# 1 integer hyperparameter number of training epochs (1 to 200)
+problem.add_hyperparameter((1, 200), "nepochs")
+
+
+@profile
+def run(job: RunningJob, sleep=False, sleep_scale=0.01) -> dict:
+    config = job.parameters
+    result = jahs_obj(config)
+
+    if sleep:
+        t_sleep = result["runtime"] * sleep_scale
+        time.sleep(t_sleep)
+
+    dh_data = {}
+    dh_data["metadata"] = result
+    if multiobj:
+        dh_data["objective"] = [
+            result["valid-acc"],
+            -result["latency"],
+            -result["size_MB"],
+        ]
+    else:
+        dh_data["objective"] = result["valid-acc"]
+    return dh_data
+
+
+if __name__ == "__main__":
+    print(problem)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/jahsbench/install.sh b/src/deephyper_benchmark/lib/jahsbench/install.sh
new file mode 100755
index 0000000..76a97b8
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/install.sh
@@ -0,0 +1 @@
+python -m jahs_bench.download --target surrogates
diff --git a/src/deephyper_benchmark/lib/jahsbench/metrics.py b/src/deephyper_benchmark/lib/jahsbench/metrics.py
new file mode 100644
index 0000000..f6040f7
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/metrics.py
@@ -0,0 +1,119 @@
+import numpy as np
+from deephyper.skopt.moo import pareto_front, hypervolume
+
+
+class PerformanceEvaluator:
+    """ A class defining performance evaluators for JAHS Bench 201 problems.
+
+    Contains the following public methods:
+
+     * `__init__()` constructs a new instance by reading the problem
+       definition from environment variables,
+     * `hypervolume(pts)` calculates the total hypervolume dominated by
+       the current point set, w.r.t. the reference point and filtering out
+       solutions that do not dominate the reference point,
+     * `refPt()` calculates a reference point for the current problem, and
+     * `numPts(pts)` calculates the number of solution points that dominate
+       the reference point.
+
+    """
+
+    def __init__(self):
+        """ Read the current JAHS-Bench-201 problem definition and initialize. """
+
+        import os
+
+        multiobj = int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 1))
+        prob_name = os.environ.get("DEEPHYPER_BENCHMARK_JAHS_PROB", "fashion_mnist")
+        self.p_name = prob_name
+        if multiobj:
+            self.nobjs = 3
+        else:
+            self.nobjs = 1
+
+    def hypervolume(self, pts):
+        """ Calculate the hypervolume dominated, w.r.t. the reference point.
+
+        Args:
+            pts (numpy.ndarray): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            float: The total hypervolume dominated by the current solution
+            w.r.t. the reference point, after filtering out points worse than
+            the reference point.
+
+        """
+
+        if self.nobjs < 2:
+            raise ValueError("Cannot calculate hypervolume for 1 objective")
+        if pts.size > 0 and pts[0, 0] > 0:
+            filtered_pts = -pts.copy()
+        else:
+            filtered_pts = pts.copy()
+        rp = self.refPt()
+        for i in range(pts.shape[0]):
+            if np.any(filtered_pts[i, :] > rp):
+                filtered_pts[i, :] = rp
+        return hypervolume(filtered_pts, rp)
+
+    def refPt(self):
+        """ Calculate the reference point for the given problem definition. """
+
+        if self.p_name in ["fashion_mnist"]:
+            rp = np.ones(self.nobjs)
+            rp[0] = -95
+            if self.nobjs > 1:
+                rp[1] = 1.75
+                rp[2] = 0.6
+            return rp
+        elif self.p_name in ["cifar10"]:
+            rp = np.ones(self.nobjs)
+            rp[0] = -90
+            if self.nobjs > 1:
+                rp[1] = 4.0
+                rp[2] = 0.9
+            return rp
+        elif self.p_name in ["colorectal_histology"]:
+            rp = np.ones(self.nobjs)
+            rp[0] = -93
+            if self.nobjs > 1:
+                rp[1] = 4.0
+                rp[2] = 0.4
+            return rp
+        else:
+            raise ValueError(f"{self.p_name} is not a valid problem")
+
+    def numPts(self, pts):
+        """ Calculate the number of solutions that dominate the reference point.
+
+        Args:
+            pts (numpy.ndarra): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            int: The number of fi in pts such that all(fi < self.refPt).
+
+        """
+
+        if np.any(pts < 0):
+            pareto_pts = pareto_front(-pts)
+        else:
+            pareto_pts = pareto_front(pts)
+        return sum([all(fi <= self.refPt()) for fi in pareto_pts])
+
+
+if __name__ == "__main__":
+    """ Driver code to test performance metrics. """
+
+    result = np.array([[90, -1, -2],
+                       [94, -0.1, -0.2],
+                       [96, -0.75, -0.1],
+                       [99.0, -0.5, -200.0]])
+
+    evaluator = PerformanceEvaluator()
+
+    assert abs(evaluator.hypervolume(result) - 0.5) < 1.0e-8
+    assert evaluator.numPts(result) == 1
+    assert np.all(np.abs(evaluator.refPt() - np.array([-95.0, 1.75, 0.6]))
+                  < 1.0e-8)
diff --git a/src/deephyper_benchmark/lib/jahsbench/model.py b/src/deephyper_benchmark/lib/jahsbench/model.py
new file mode 100644
index 0000000..6f0eef4
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/model.py
@@ -0,0 +1,80 @@
+""" This module contains objective function implementations of the JAHS 201
+benchmark suite, implemented as DeepHyper compatible models.
+
+"""
+
+import numpy as np
+
+class jahs_bench:
+    """ A callable class implementing the JAHS benchmark problems. """
+
+    def __init__(self, dataset="fashion_mnist"):
+        """ Import and configure the jahs-bench module.
+
+        Args:
+            dataset (str): One of "cifar10", "colorectal_histology",
+                or "fashion_mnist" (default)
+
+        """
+
+        from jahs_bench.api import Benchmark
+        import os
+
+        ### JAHS bench settings ###
+        MODEL_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)),
+                                  "jahs_bench_data")
+        # Define the benchmark
+        self.benchmark = Benchmark(
+                task=dataset,
+                save_dir=MODEL_PATH,
+                kind="surrogate",
+                download=False
+            )
+
+    def __sample__(self):
+        """ Randomly sample a JAHS-Bench-201 configuration.
+
+        Returns:
+            dict: A configuration dictionary.
+
+        """
+
+        config = self.benchmark.sample_config()
+        config['nepochs'] = np.random.randint(1, 200)
+        return config
+
+    def __call__(self, x):
+        """ DeepHyper compatible objective function calling jahs-bench.
+
+        Args:
+            x (dict): Configuration dictionary with same keys as jahs-bench.
+
+        Returns:
+            tuple of floats: In order: accuracy (to be maximized), latency
+            (to be minimized).
+
+        """
+
+        # Default config
+        config = {
+            'Optimizer': 'SGD',
+            'N': 5,
+            'W': 16,
+            'Resolution': 1.0,
+        }
+        # Update config using x
+        for key in x.keys():
+            config[key] = x[key]
+        # Special rule for setting "TrivialAugment"
+        if x['TrivialAugment'] == "on":
+            config['TrivialAugment'] = True
+        else:
+            config['TrivialAugment'] = False
+        # Check for nepochs
+        nepochs = 200
+        if 'nepochs' in x.keys():
+            nepochs = x['nepochs']
+        # Evaluate and return
+        fx = np.zeros(2)
+        result = self.benchmark(config, nepochs=nepochs)
+        return result[nepochs]
diff --git a/src/deephyper_benchmark/lib/jahsbench/requirements.txt b/src/deephyper_benchmark/lib/jahsbench/requirements.txt
new file mode 100644
index 0000000..702f41c
--- /dev/null
+++ b/src/deephyper_benchmark/lib/jahsbench/requirements.txt
@@ -0,0 +1,2 @@
+jahs-bench
+xgboost
diff --git a/src/deephyper_benchmark/lib/lcbench/Makefile b/src/deephyper_benchmark/lib/lcbench/Makefile
new file mode 100644
index 0000000..cb13f98
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcbench/Makefile
@@ -0,0 +1,14 @@
+
+COMMIT=8de69dc0e7f7b4baee7129d599e74cc3e09b6d06
+
+build:
+	mkdir -p build
+	wget https://github.com/automl/LCBench/archive/$(COMMIT).zip -O build/LCBench.zip
+	unzip build/LCBench.zip -d build/
+	mv build/LCBench-$(COMMIT) build/LCBench
+	mkdir -p build/LCBench/data
+	wget https://figshare.com/ndownloader/files/21188607 -O build/LCBench/data/data_2k.zip
+	unzip build/LCBench/data/data_2k.zip -d build/LCBench/data/
+
+clean:
+	rm -rf build/
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcbench/README.md b/src/deephyper_benchmark/lib/lcbench/README.md
new file mode 100644
index 0000000..14ff7ae
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcbench/README.md
@@ -0,0 +1,19 @@
+# LCBench
+
+✅ **Benchmark ready to be used.**
+
+This benchmark is only compatible with random search and only interesting to benchmark multi-fidelity algorithms that do not consider hyperparameter optimization. Candidate learners are represented by an id.
+
+## Installation
+
+To install the `LCBench` benchmark, run:
+```console
+python -c "import deephyper_benchmark as dhb; dhb.install('LCBench');"
+```
+
+Run the hyperparameter search
+```
+import deephyper_benchmark as dhb
+dhb.load("LCBench")
+from deephyper_benchmark.lib.lcbench import hpo
+```
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcbench/__init__.py b/src/deephyper_benchmark/lib/lcbench/__init__.py
new file mode 100644
index 0000000..68a63b1
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcbench/__init__.py
@@ -0,0 +1,2 @@
+
+datasets = ['APSFailure', 'Amazon_employee_access', 'Australian', 'Fashion-MNIST', 'KDDCup09_appetency', 'MiniBooNE', 'adult', 'airlines', 'albert', 'bank-marketing', 'blood-transfusion-service-center', 'car', 'christine', 'cnae-9', 'connect-4', 'covertype', 'credit-g', 'dionis', 'fabert', 'helena', 'higgs', 'jannis', 'jasmine', 'jungle_chess_2pcs_raw_endgame_complete', 'kc1', 'kr-vs-kp', 'mfeat-factors', 'nomao', 'numerai28.6', 'phoneme', 'segment', 'shuttle', 'sylvine', 'vehicle', 'volkert']
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcbench/benchmark.py b/src/deephyper_benchmark/lib/lcbench/benchmark.py
new file mode 100644
index 0000000..18ddea6
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcbench/benchmark.py
@@ -0,0 +1,18 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class LCBench(Benchmark):
+    version = "0.0.1"
+
+    requires = {
+        "makefile": {"step": "install", "type": "cmd", "cmd": "make build"},
+        "lcdbench-api": {
+            "step": "load",
+            "type": "pythonpath",
+            "path": f"{DIR}/../build/LCBench/",
+        },
+    }
diff --git a/src/deephyper_benchmark/lib/lcbench/hpo.py b/src/deephyper_benchmark/lib/lcbench/hpo.py
new file mode 100644
index 0000000..9902dc0
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcbench/hpo.py
@@ -0,0 +1,78 @@
+import os
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+from deephyper.evaluator import profile, RunningJob
+from deephyper.hpo import HpProblem
+
+dataset = os.environ.get("DEEPHYPER_BENCHMARK_LCBENCH_DATASET", "APSFailure")
+
+data_path = os.path.join(DIR, "../build/LCBench/data/data_2k.json")
+
+from api import Benchmark as LCBenchBenchmark
+bench = LCBenchBenchmark(data_path, cache=True, cache_dir=os.path.join(DIR, "../build/LCBench/data/"))
+
+num_elements = 2000
+
+problem = HpProblem()
+problem.add_hyperparameter((0, num_elements - 1), "index")
+
+
+@profile
+def run(job: RunningJob, optuna_trial=None) -> dict:
+
+    
+    # otherwise failure
+    idx = job.parameters["index"]
+    y_valid = bench.query(dataset, "Train/val_cross_entropy", idx)[1:51]
+    y_test  = bench.query(dataset, "Train/test_cross_entropy", idx)[1:51]
+
+    min_b, max_b = 1, 50 
+
+    objective_val = -y_valid[-1]
+    objective_test = -y_test[-1]
+
+    other_metadata = {}
+
+    if optuna_trial:
+
+        for budget_i in range(min_b, max_b + 1):
+            objective_i = -y_valid[budget_i-1] # maximizing in deephyper
+            optuna_trial.report(objective_i, step=budget_i)
+            if optuna_trial.should_prune():
+                break
+
+        objective = objective_i
+
+    else:
+
+        for budget_i in range(min_b, max_b + 1):
+            objective_i = -y_valid[budget_i-1]  # maximizing in deephyper
+            job.record(budget_i, objective_i)
+            if job.stopped():
+                break
+
+        objective = job.objective
+
+        if hasattr(job, "stopper") and hasattr(job.stopper, "infos_stopped"):
+            other_metadata["infos_stopped"] = job.stopper.infos_stopped
+
+    metadata = {
+        "budget": budget_i,
+        "stopped": budget_i < max_b,
+        "objective_test": objective_test,
+        "objective_val": objective_val,
+    }
+    metadata.update(other_metadata)
+    return {
+        "objective": objective,
+        "metadata": metadata,
+    }
+
+
+if __name__ == "__main__":
+    print(problem)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/lcdb/README.md b/src/deephyper_benchmark/lib/lcdb/README.md
new file mode 100644
index 0000000..a9d798d
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcdb/README.md
@@ -0,0 +1,4 @@
+# Learning Curve Databased Benchmark
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
diff --git a/src/deephyper_benchmark/lib/lcdb/algorithm_selection/__init__.py b/src/deephyper_benchmark/lib/lcdb/algorithm_selection/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/__init__.py b/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/benchmark.py b/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/benchmark.py
new file mode 100644
index 0000000..377c4c8
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/benchmark.py
@@ -0,0 +1,12 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class LCDBHyperparameterOptimization(Benchmark):
+    """LCDB 2.0
+    """
+
+    version = "0.0.1"
diff --git a/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/hpo.py b/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/hpo.py
new file mode 100644
index 0000000..45100ae
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcdb/hyperparameter_optimization/hpo.py
@@ -0,0 +1,216 @@
+import gzip
+import os
+import time
+
+import numpy as np
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+from deephyper.evaluator import RunningJob, profile
+from deephyper.hpo import HpProblem
+from lcdb.analysis import read_csv_results
+from lcdb.analysis.json import (
+    QueryAnchorValues,
+    QueryMetricValuesFromAnchors,
+    QueryEpochValues,
+    QueryMetricValuesFromEpochs,
+)
+from lcdb.analysis.score import balanced_accuracy_from_confusion_matrix
+from lcdb.utils import import_attr_from_module
+
+# DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME = bool(
+#     int(os.environ.get("DEEPHYPER_BENCHMARK_SIMULATE_RUN_TIME", 0))
+# )
+# DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME = float(
+#     os.environ.get("DEEPHYPER_BENCHMARK_PROP_REAL_RUN_TIME", 1.0)
+# )
+
+# DEEPHYPER_BENCHMARK_FIDELITY = os.environ.get("DEEPHYPER_BENCHMARK_FIDELITY", "anchor")
+# DEEPHYPER_BENCHMARK_WORKFLOW = "lcdb.workflow.sklearn.KNNWorkflow"
+# DEEPHYPER_BENCHMARK_CSV = os.environ.get(
+#     "DEEPHYPER_BENCHMARK_CSV",
+#     "/Users/romainegele/Documents/Research/LCDB/lcdb/publications/2023-neurips/experiments/alcf/polaris/knn/output/lcdb.workflow.sklearn.KNNWorkflow/3/42-42-42/results.csv.gz",
+# )
+DEEPHYPER_BENCHMARK_MAX_FIDELITY = int(
+    os.environ.get("DEEPHYPER_BENCHMARK_MAX_FIDELITY", 100)
+)
+DEEPHYPER_BENCHMARK_FIDELITY = os.environ.get("DEEPHYPER_BENCHMARK_FIDELITY", "epoch")
+DEEPHYPER_BENCHMARK_CSV = os.environ.get(
+    "DEEPHYPER_BENCHMARK_CSV",
+    "/Users/romainegele/Documents/Research/LCDB/lcdb/publications/2023-neurips/experiments/alcf/polaris/densenn/output/lcdb.workflow.keras.DenseNNWorkflow/3/42-42-42/results.csv.gz",
+)
+
+
+# WorkflowClass = import_attr_from_module(DEEPHYPER_BENCHMARK_WORKFLOW)
+# config_space = WorkflowClass.config_space()
+# problem = HpProblem(config_space=config_space)
+problem = HpProblem()
+
+with gzip.GzipFile(DEEPHYPER_BENCHMARK_CSV, "rb") as f:
+    r_df, r_df_failed = read_csv_results(f)
+DEEPHYPER_BENCHMARK_DATA = r_df
+
+if DEEPHYPER_BENCHMARK_FIDELITY == "anchor":
+    query_anchor_values = QueryAnchorValues()
+    query_confusion_matrix_values_on_val = QueryMetricValuesFromAnchors(
+        "confusion_matrix", split_name="val"
+    )
+    query_confusion_matrix_values_on_test = QueryMetricValuesFromAnchors(
+        "confusion_matrix", split_name="test"
+    )
+elif DEEPHYPER_BENCHMARK_FIDELITY == "epoch":
+    # Load the learning "epoch"-curve at last epoch
+    query_anchor_values_ = QueryEpochValues()
+    query_confusion_matrix_values_on_val_ = QueryMetricValuesFromEpochs(
+        "confusion_matrix", split_name="val"
+    )
+    query_confusion_matrix_values_on_test_ = QueryMetricValuesFromEpochs(
+        "confusion_matrix", split_name="test"
+    )
+    query_anchor_values = lambda x: query_anchor_values_(x)[-1]
+    query_confusion_matrix_values_on_val = (
+        lambda x: query_confusion_matrix_values_on_val_(x)[-1]
+    )
+    query_confusion_matrix_values_on_test = (
+        lambda x: query_confusion_matrix_values_on_test_(x)[-1]
+    )
+
+    source = DEEPHYPER_BENCHMARK_DATA["m:json"]
+    values = source.apply(lambda x: query_confusion_matrix_values_on_val(x)).to_list()
+    mask = [len(x) > 0 for x in values]
+    DEEPHYPER_BENCHMARK_DATA = DEEPHYPER_BENCHMARK_DATA[mask]
+
+    source = DEEPHYPER_BENCHMARK_DATA["m:json"]
+    values = source.apply(lambda x: query_confusion_matrix_values_on_test(x)).to_list()
+    mask = [len(x) > 0 for x in values]
+    DEEPHYPER_BENCHMARK_DATA = DEEPHYPER_BENCHMARK_DATA[mask]
+else:
+    raise ValueError(f"Unknown fidelity: {DEEPHYPER_BENCHMARK_FIDELITY}")
+
+problem.add_hyperparameter((0, len(DEEPHYPER_BENCHMARK_DATA) - 1), "eval_id")
+
+
+# TODO: To test LCPFN with accuracy metric
+def query_balanced_accuracy_values_on_val(x):
+    cm = query_confusion_matrix_values_on_val(x)
+    return list(map(balanced_accuracy_from_confusion_matrix, cm))
+
+
+def query_coefficient_of_determiniation_values_on_val(x):
+    cm = query_confusion_matrix_values_on_val(x)
+    num_classes = len(cm[0])
+    balanced_error_rate_baseline = 1 - 1 / num_classes
+    return list(
+        map(
+            lambda x: 1
+            - (1 - balanced_accuracy_from_confusion_matrix(x))
+            / balanced_error_rate_baseline,
+            cm,
+        )
+    )
+
+
+def query_coefficient_of_determiniation_values_on_test(x):
+    cm = query_confusion_matrix_values_on_test(x)
+    num_classes = len(cm[0])
+    balanced_error_rate_baseline = 1 - 1 / num_classes
+    return list(
+        map(
+            lambda x: 1
+            - (1 - balanced_accuracy_from_confusion_matrix(x))
+            / balanced_error_rate_baseline,
+            cm,
+        )
+    )
+
+
+@profile
+def run(job: RunningJob, optuna_trial=None) -> dict:
+    # otherwise failure
+    eval_id = job.parameters["eval_id"]
+
+    source = DEEPHYPER_BENCHMARK_DATA.iloc[eval_id]["m:json"]
+    anchor_values = query_anchor_values(source)
+
+    # TODO: to test with LCPFN
+    objective_values = query_balanced_accuracy_values_on_val(source)
+
+    objective_val_values = query_coefficient_of_determiniation_values_on_val(source)
+    objective_test_values = query_coefficient_of_determiniation_values_on_test(source)
+    length = min(len(objective_val_values), len(objective_test_values))
+    anchor_values = anchor_values[:length]
+    objective_values = objective_values[:length]
+    objective_val_values = objective_val_values[:length]
+    objective_test_values = objective_test_values[:length]
+
+    objective_values = np.array(objective_values)
+    objective_val_values = np.array(objective_val_values)
+    objective_test_values = np.array(objective_test_values)
+    anchor_values = np.array(anchor_values)
+
+    mask = anchor_values <= DEEPHYPER_BENCHMARK_MAX_FIDELITY
+    anchor_values = anchor_values[mask].tolist()
+    objective_values = objective_values[mask].tolist()
+    objective_val_values = objective_val_values[mask].tolist()
+    objective_test_values = objective_test_values[mask].tolist()
+
+    if anchor_values[-1] <= DEEPHYPER_BENCHMARK_MAX_FIDELITY:
+        anchor_values.append(DEEPHYPER_BENCHMARK_MAX_FIDELITY)
+        objective_values.append(objective_values[-1])
+        objective_val_values.append(objective_val_values[-1])
+        objective_test_values.append(objective_test_values[-1])
+
+    min_b, max_b = anchor_values[0], anchor_values[-1]
+
+    other_metadata = {}
+
+    consumed_time = 0  # the cost here corresponds to time
+
+    if optuna_trial:
+        for i, budget_i in enumerate(anchor_values):
+            objective_i = objective_val_values[i]
+
+            optuna_trial.report(objective_i, step=budget_i)
+            if optuna_trial.should_prune():
+                break
+
+        objective = objective_i
+
+    else:
+        for i, budget_i in enumerate(anchor_values):
+            # objective_i = objective_val_values[i]
+            # TODO: to test with LCPFN
+            objective_i = objective_values[i]
+
+            job.record(budget_i, objective_i)
+            if job.stopped():
+                break
+
+        objective = job.objective
+
+        if hasattr(job, "stopper") and hasattr(job.stopper, "infos_stopped"):
+            other_metadata["infos_stopped"] = job.stopper.infos_stopped
+
+    metadata = {
+        "budget": budget_i,
+        "stopped": budget_i < max_b,
+        "objective_test": objective_test_values[-1],
+        "objective_val": objective_val_values[-1],
+    }
+    metadata.update(other_metadata)
+
+    return {
+        "objective": objective,
+        "metadata": metadata,
+    }
+
+
+if __name__ == "__main__":
+    print(problem)
+    # default_config = problem.default_configuration
+    for eval_id in range(10):
+        default_config = dict(eval_id=eval_id)
+        print(f"{default_config=}")
+        result = run(RunningJob(parameters=default_config))
+        print(f"{result=}")
+        print()
diff --git a/src/deephyper_benchmark/lib/lcmbench/README.md b/src/deephyper_benchmark/lib/lcmbench/README.md
new file mode 100644
index 0000000..f65fb4a
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/README.md
@@ -0,0 +1,4 @@
+# LCMBench: Learning Curve Models Benchmark
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
diff --git a/src/deephyper_benchmark/lib/lcmbench/lcdb/README.md b/src/deephyper_benchmark/lib/lcmbench/lcdb/README.md
new file mode 100644
index 0000000..046e4b5
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/lcdb/README.md
@@ -0,0 +1,3 @@
+# LCDB: Learning Curve DataBase
+
+Benchmark based on the LCDB project: https://github.com/fmohr/lcdb
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcmbench/lcdb/__init__.py b/src/deephyper_benchmark/lib/lcmbench/lcdb/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/lcmbench/lcdb/benchmark.py b/src/deephyper_benchmark/lib/lcmbench/lcdb/benchmark.py
new file mode 100644
index 0000000..8067a67
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/lcdb/benchmark.py
@@ -0,0 +1,11 @@
+from deephyper_benchmark import *
+
+
+class LCDBBenchmark(Benchmark):
+
+    version = "0.0.1"
+
+    requires = {
+        "py-lcdb": {"type": "pip", "name": "lcdb"},
+        "py-func-timeout": {"type": "pip", "name": "func-timeout"},
+    }
diff --git a/src/deephyper_benchmark/lib/lcmbench/lcdb/hpo.py b/src/deephyper_benchmark/lib/lcmbench/lcdb/hpo.py
new file mode 100644
index 0000000..280ce6e
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/lcdb/hpo.py
@@ -0,0 +1,128 @@
+import numpy as np
+
+import lcdb
+
+from deephyper.evaluator import profile, RunningJob
+from deephyper.hpo import HpProblem
+
+
+df_meta = lcdb.get_meta_features()
+
+all_openmlid = df_meta["openmlid"].tolist()
+all_name = df_meta["Name"].tolist()
+all_algorithms = [
+    "SVC_linear",
+    "SVC_poly",
+    "SVC_rbf",
+    "SVC_sigmoid",
+    "sklearn.tree.DecisionTreeClassifier",
+    "sklearn.tree.ExtraTreeClassifier",
+    "sklearn.linear_model.LogisticRegression",
+    "sklearn.linear_model.PassiveAggressiveClassifier",
+    "sklearn.linear_model.Perceptron",
+    "sklearn.linear_model.RidgeClassifier",
+    "sklearn.linear_model.SGDClassifier",
+    "sklearn.neural_network.MLPClassifier",
+    # "sklearn.discriminant_analysis.LinearDiscriminantAnalysis",
+    # "sklearn.discriminant_analysis.QuadraticDiscriminantAnalysis",
+    "sklearn.naive_bayes.BernoulliNB",
+    # "sklearn.naive_bayes.MultinomialNB", # often missing for large datasets
+    "sklearn.neighbors.KNeighborsClassifier",
+    "sklearn.ensemble.ExtraTreesClassifier",
+    "sklearn.ensemble.RandomForestClassifier",
+    "sklearn.ensemble.GradientBoostingClassifier",
+]
+
+problem = HpProblem()
+problem.add_hyperparameter(all_algorithms, "model")
+
+
+@profile
+def run(job: RunningJob, optuna_trial=None, task_id=3) -> dict:
+
+    model = job.parameters["model"]
+
+    try:
+        curve = lcdb.get_curve(task_id, model, metric="accuracy")
+    except Exception:
+        if optuna_trial:
+            objective = 0  # accuracy 0
+        else:
+            objective = [[1], [0]]  # accuracy 0 at step 1
+        return {
+            "objective": objective,
+            "metadata": {
+                "budget": budget_i,
+                "stopped": budget_i < anchors[-1],
+                "duration": cum_time,
+            },
+        }
+
+    anchors, _, scores_valid, _ = curve
+    _, times = lcdb.get_train_times(task_id, model)
+
+    # scores_valid = [list(v) for v in scores_valid]
+    # times = [list(t) for t in times]
+
+    anchors = np.array(anchors).tolist()
+    # print(model, type(scores_valid), type(scores_valid[0]), type(scores_valid[0][0]))
+    # scores_valid = np.array(np.array(scores_valid).tolist())
+    # times = np.array(np.array(times).tolist())
+
+    # print("->", np.ndim(scores_valid))
+    # print("->", scores_valid)
+    # print("->", np.shape(scores_valid))
+
+    # if np.ndim(scores_valid) == 2:
+    # scores_valid = np.mean(scores_valid, axis=1)
+    scores_valid = np.array([v[0] for v in scores_valid])
+    times = np.array([t[0] for t in times])
+
+    scores_valid = scores_valid.tolist()
+    times = times.tolist()
+
+    cum_time = 0
+
+    if optuna_trial:
+
+        for i, budget_i in enumerate(anchors):
+            objective_i = scores_valid[i]
+            cum_time += times[i]
+            optuna_trial.report(objective_i, step=budget_i)
+            if optuna_trial.should_prune():
+                break
+
+        return {
+            "objective": objective_i,
+            "metadata": {
+                "budget": budget_i,
+                "stopped": budget_i < anchors[-1],
+                "duration": cum_time,
+            },
+        }
+
+    else:
+
+        for i, budget_i in enumerate(anchors):
+            objective_i = scores_valid[i]
+            cum_time += times[i]
+            job.record(budget_i, objective_i)
+            if job.stopped():
+                break
+
+        return {
+            "objective": job.observations,
+            "metadata": {
+                "budget": budget_i,
+                "stopped": budget_i < anchors[-1],
+                "duration": cum_time,
+            },
+        }
+
+
+if __name__ == "__main__":
+    print(problem)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/lcmbench/loglin2/README.md b/src/deephyper_benchmark/lib/lcmbench/loglin2/README.md
new file mode 100644
index 0000000..e9bd949
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/loglin2/README.md
@@ -0,0 +1,5 @@
+# `LogLin2`
+
+Benchmark for Multi-Fidelity Black-Box Optimization.
+
+$$f_\text{loglin2}(z;\rho) = b(z)^{\rho_1} \cdot \exp{\rho_0}$$
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcmbench/loglin2/__init__.py b/src/deephyper_benchmark/lib/lcmbench/loglin2/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/lcmbench/loglin2/benchmark.py b/src/deephyper_benchmark/lib/lcmbench/loglin2/benchmark.py
new file mode 100644
index 0000000..ca8cd5b
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/loglin2/benchmark.py
@@ -0,0 +1,12 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class LogLin2Benchmark(Benchmark):
+
+    version = "0.0.1"
+
+    requires = {}
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcmbench/loglin2/hpo.py b/src/deephyper_benchmark/lib/lcmbench/loglin2/hpo.py
new file mode 100644
index 0000000..2382caa
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/loglin2/hpo.py
@@ -0,0 +1,71 @@
+import os
+
+import numpy as np
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+from deephyper.evaluator import profile, RunningJob
+from deephyper.hpo import HpProblem
+
+
+problem = HpProblem()
+problem.add_hyperparameter((0.0, 1.0), "rho_0")
+problem.add_hyperparameter((0.0, 1.0/25.0), "rho_1")
+
+
+def f_loglin2(b, rho):
+    return np.power(b, rho[1]) * np.exp(rho[0])
+
+
+@profile
+def run(job: RunningJob, optuna_trial=None) -> dict:
+
+    # otherwise failure
+    min_b, max_b = 1, 100
+
+    rho = [job.parameters["rho_0"], job.parameters["rho_1"]]
+    f = lambda b: f_loglin2(b, rho)
+
+    objective_test = f(max_b)
+
+    if optuna_trial:
+
+        for budget_i in range(min_b, max_b + 1):
+            objective_i = f(budget_i)
+            optuna_trial.report(objective_i, step=budget_i)
+            if optuna_trial.should_prune():
+                break
+
+        return {
+            "objective": objective_i,
+            "metadata": {
+                "budget": budget_i,
+                "stopped": budget_i < max_b,
+                "objective_test": objective_test,
+            },
+        }
+
+    else:
+
+        for budget_i in range(min_b, max_b + 1):
+            objective_i = f(budget_i)
+            job.record(budget_i, objective_i)
+            if job.stopped():
+                break
+
+        return {
+            "objective": job.observations,
+            "metadata": {
+                "budget": budget_i,
+                "stopped": budget_i < max_b,
+                "objective_test": objective_test,
+            },
+        }
+
+
+if __name__ == "__main__":
+    print(problem)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/lcmbench/pow3/README.md b/src/deephyper_benchmark/lib/lcmbench/pow3/README.md
new file mode 100644
index 0000000..01c296a
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/pow3/README.md
@@ -0,0 +1,5 @@
+# `Pow3`: Inverse Power Law with 3 Parameters
+
+Inverse-Power Law benchmark for Multi-Fidelity Black-Box Optimization.
+
+$$f_\text{pow3}(b;\rho) = \rho_0 + \rho_1 \cdot b^{-\rho_2}$$
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcmbench/pow3/__init__.py b/src/deephyper_benchmark/lib/lcmbench/pow3/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/lcmbench/pow3/benchmark.py b/src/deephyper_benchmark/lib/lcmbench/pow3/benchmark.py
new file mode 100644
index 0000000..e740b42
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/pow3/benchmark.py
@@ -0,0 +1,12 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class Pow3Benchmark(Benchmark):
+
+    version = "0.0.1"
+
+    requires = {}
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/lcmbench/pow3/hpo.py b/src/deephyper_benchmark/lib/lcmbench/pow3/hpo.py
new file mode 100644
index 0000000..2f994a3
--- /dev/null
+++ b/src/deephyper_benchmark/lib/lcmbench/pow3/hpo.py
@@ -0,0 +1,81 @@
+import os
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+from deephyper.evaluator import profile, RunningJob
+from deephyper.hpo import HpProblem
+
+
+problem = HpProblem()
+problem.add_hyperparameter((1e-6, 10, "log-uniform"), "alpha")
+problem.add_hyperparameter((1.0, 10.0), "beta")
+problem.add_hyperparameter((0.1, 0.5), "gamma")
+
+# optimum is at
+# C(s=100, alpha=1e-6, beta=1.0, gamma=0.5) -> 0.100001
+
+
+def ipl_curve(s, alpha, beta, gamma):
+    """Inverse Power-Law Model"""
+    return alpha + beta * s**-gamma
+
+
+@profile
+def run(job: RunningJob, optuna_trial=None) -> dict:
+
+    print(f"{job.id=}")
+
+    # otherwise failure
+    min_b, max_b = 1, 1000
+
+    alpha = job.parameters["alpha"]
+    beta = job.parameters["beta"]
+    gamma = job.parameters["gamma"]
+
+    objective_test = -ipl_curve(max_b, alpha, beta, gamma)
+
+    if optuna_trial:
+
+        for budget_i in range(min_b, max_b + 1):
+            objective_i = -ipl_curve(
+                budget_i, alpha, beta, gamma
+            )  # maximisation in deephyper
+            optuna_trial.report(objective_i, step=budget_i)
+            if optuna_trial.should_prune():
+                break
+
+        return {
+            "objective": objective_i,
+            "metadata": {
+                "budget": budget_i,
+                "stopped": budget_i < max_b,
+                "objective_test": objective_test,
+            },
+        }
+
+    else:
+
+        for budget_i in range(min_b, max_b + 1):
+            objective_i = -ipl_curve(
+                budget_i, alpha, beta, gamma
+            )  # maximisation in deephyper
+            job.record(budget_i, objective_i)
+            if job.stopped():
+                break
+
+        return {
+            "objective": job.observations,
+            "metadata": {
+                "budget": budget_i,
+                "stopped": budget_i < max_b,
+                "objective_test": objective_test,
+            },
+        }
+
+
+if __name__ == "__main__":
+    print(problem)
+    default_config = problem.default_configuration
+    print(f"{default_config=}")
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/pinnbench/Burgers/__init__.py b/src/deephyper_benchmark/lib/pinnbench/Burgers/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/pinnbench/Burgers/benchmark.py b/src/deephyper_benchmark/lib/pinnbench/Burgers/benchmark.py
new file mode 100644
index 0000000..cfc92cf
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/Burgers/benchmark.py
@@ -0,0 +1,17 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class PINNDiffusionReactionBenchmark(Benchmark):
+    version = "0.0.1"
+
+    requires = {
+        "py-pip-requirements": {
+            "step": "install",
+            "type": "pip",
+            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
+        },
+    }
diff --git a/src/deephyper_benchmark/lib/pinnbench/Burgers/hpo.py b/src/deephyper_benchmark/lib/pinnbench/Burgers/hpo.py
new file mode 100644
index 0000000..43ab995
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/Burgers/hpo.py
@@ -0,0 +1,111 @@
+import numpy as np
+
+from deephyper.hpo import HpProblem
+from deephyper.search.hps import CBO
+from .model import get_data, PINN, BurgerSupervisor, plotter
+from deephyper.stopper import LCModelStopper
+from deephyper.evaluator import profile, RunningJob
+
+
+@profile
+def run(job: RunningJob) -> dict:
+    # load data
+    nu = 0.01 / np.pi
+    train, val, test = get_data(NT=200, NX=128, X_U=1, X_L=-1, T_max=1, Nu=nu)
+
+    config = job.parameters
+    min_b = 1
+    max_b = 1000
+
+    num_layers = config["num_layers"]
+    hidden_dim = config["hidden_dim"]
+    output_dim = 1
+    epochs = 100
+    lr = config["lr"]
+    alpha = config["alpha"]
+    activation = config["activation"]
+    net = PINN(
+        num_layers=num_layers,
+        hidden_dim=hidden_dim,
+        output_dim=output_dim,
+        act_fn=activation,
+    )
+    sup = BurgerSupervisor(nu, net, epochs, lr, alpha)
+
+    for budget_i in range(min_b, max_b + 1):
+        train_loss, eval = sup.step(train, val)
+        objective_i = -eval  # maximizing in deephyper
+        job.record(budget_i, objective_i)
+        if job.stopped():
+            break
+    objective = job.objective
+
+    # calculate test scores
+    pred_test = sup.net(test["x"][:, 0], test["x"][:, 1])
+    test_loss = np.mean((test["y"] - pred_test) ** 2)
+
+    metadata = {
+        "num_parameters": sup.net.count_params(),
+        "train_loss": train_loss,
+        "val_loss": eval,
+        "test_loss": test_loss,
+        "budget": budget_i,
+        "stopped": budget_i < max_b,
+        "infos_stopped": job.stopper.infos_stopped
+        if hasattr(job, "stopper") and hasattr(job.stopper, "infos_stopped")
+        else None,
+    }
+    return {"objective": objective, "metadata": metadata}
+
+
+# define the search space
+problem = HpProblem()
+problem.add_hyperparameter((5, 20), "num_layers", default_value=5)
+problem.add_hyperparameter((1e-5, 1e-2), "lr", default_value=0.01)
+problem.add_hyperparameter((5, 50), "hidden_dim", default_value=5)
+problem.add_hyperparameter((0.0, 5.0), "alpha", default_value=0.5)
+problem.add_hyperparameter(
+    ["relu", "leaky_relu", "tanh", "elu", "gelu", "sigmoid"],
+    "activation",
+    default_value="tanh",
+)
+
+
+def evaluate(config):
+    """
+    Evaluate an hyperparameter configuration
+    on training/validation and testing data.
+    """
+    nu = 0.01 / np.pi
+    train, val, test = get_data(NT=200, NX=100, X_U=1.0, X_L=-1.0, T_max=1, Nu=nu)
+    num_layers = config["num_layers"]
+    hidden_dim = config["hidden_dim"]
+    output_dim = 1
+    epochs = config["epochs"]
+    lr = config["lr"]
+    alpha = config["alpha"]
+    activation = config["activation"]
+    net = PINN(
+        num_layers=num_layers,
+        hidden_dim=hidden_dim,
+        output_dim=output_dim,
+        act_fn=activation,
+    )
+    sup = BurgerSupervisor(nu=nu, net=net, epochs=epochs, lr=lr, alpha=alpha)
+    val_f_loss = sup.train(train, test)
+
+
+if __name__ == "__main__":
+    import time
+
+    start_time = time.time()
+    stopper = LCModelStopper(max_steps=1000, lc_model="mmf4")
+    scheduler = {
+        "type": "periodic-exp-decay",
+        "periode": 25,
+        "rate": 0.1,
+    }
+
+    default_config = problem.default_configuration
+    result = run(RunningJob(parameters=default_config))
+    print(f"{result=}")
diff --git a/src/deephyper_benchmark/lib/pinnbench/Burgers/model.py b/src/deephyper_benchmark/lib/pinnbench/Burgers/model.py
new file mode 100644
index 0000000..fb2a476
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/Burgers/model.py
@@ -0,0 +1,411 @@
+import numpy as np
+import tensorflow as tf
+import phi.flow as pl
+from scipy.stats import qmc
+
+
+def BurgersSolver(
+    NT: int, NX: int, X_U: float, X_L: float, T_max: float, Nu: float, IN_cond
+):
+    """
+    Burgers equation solver with Phiflow,
+    partially adopted from
+    https://physicsbaseddeeplearning.org/overview-burgers-forw.html
+
+    args:
+        NT: number of points in the t-direction.
+        NX: number of points in the x-direction.
+        X_U: the upper bound of x range.
+        X_L: the lower bound of x range.
+        T_max: the maximum time lenght.
+        Nu: viscosity.
+        IN_cond: the initial condition function.
+    """
+    DX = (X_U - X_L) / NX
+    DT = T_max / NT
+    x_ = np.linspace(X_L, X_U, NX)
+    t_ = np.linspace(0, T_max, NT)
+    xx, tt = np.meshgrid(x_, t_)
+    input_domain = np.stack((xx.flatten(), tt.flatten()), axis=1)
+
+    x_range = np.linspace(X_L + DX / 2.0, X_U - DX / 2.0, NX)
+    INITIAL = IN_cond(x_range)
+    INITIAL = pl.math.tensor(INITIAL, pl.spatial("x"))  # convert to phiflow tensor
+    velocity = pl.CenteredGrid(
+        INITIAL, pl.extrapolation.PERIODIC, x=NX, bounds=pl.Box(x=(X_L, X_U))
+    )
+    vt = pl.advect.semi_lagrangian(velocity, velocity, DT)
+
+    velocities = [velocity]
+    age = 0.0
+    for i in range(NT - 1):
+        v1 = pl.diffuse.explicit(velocities[-1], Nu, DT)
+        v2 = pl.advect.semi_lagrangian(v1, v1, DT)
+        age += DT
+        velocities.append(v2)
+    vels = np.array([v.values.numpy("x,vector") for v in velocities])
+    u = np.squeeze(vels)
+    return x_, t_, u
+
+
+def sample_latin_hypercube(self):
+    bc_engine = qmc.LatinHypercube(d=1)
+    t_d = bc_engine.random(n=50)
+    t_i = np.zeros((50, 1))
+    t_d = np.append(t_i, t_d, axis=0)
+    return
+
+
+def split_data(x_, t_, u, show_points=False):
+    """
+    Split data into train, val, and test.
+        train: input coordinates on the boundaries and its solution (boundary conditions)
+                and the input coordinates of collocation points
+        val: the collocation points (in training) with solution
+        test: the entire domain and its solution.
+    """
+    rd = np.random.RandomState(0)  # set local random seed
+    lhc = qmc.LatinHypercube(d=1, seed=0)
+
+    NUM_IC_PT = 50
+    NUM_BC_PT = 50
+    NUM_COL_PT = 10000
+
+    # x_ic_idx = np.array(rd.uniform(size=NUM_IC_PT) * len(x_), dtype=int)
+    x_ic_idx = np.array(lhc.random(n=NUM_IC_PT) * len(x_), dtype=int).flatten()
+    # print("sampled IC indices are:", x_ic_idx)
+    x_ic = x_[x_ic_idx]
+    input_ic = np.stack((x_ic, np.zeros(len(x_ic))), axis=1)
+    u_ic = u[0, x_ic_idx].reshape(-1, 1)
+
+    # t_bc_idx = np.array(rd.uniform(size=NUM_BC_PT//2) * len(t_), dtype=int)
+    t_bc_idx = np.array(lhc.random(n=NUM_BC_PT) * len(t_), dtype=int).flatten()
+    t_bc = t_[t_bc_idx]
+
+    input_bc_upper = np.stack(
+        [np.repeat(x_[-1], len(t_bc) // 2), t_bc[: NUM_BC_PT // 2]], axis=1
+    )
+    input_bc_lower = np.stack(
+        [np.repeat(x_[0], len(t_bc) // 2), t_bc[NUM_BC_PT // 2 :]], axis=1
+    )
+    input_bc = np.vstack([input_bc_lower, input_bc_upper])
+
+    u_bc_upper = u[t_bc_idx, -1]
+    u_bc_lower = u[t_bc_idx, 0]
+    u_bc = np.hstack([u_bc_lower, u_bc_upper]).reshape(-1, 1)
+
+    t_col_idx = np.array(rd.uniform(size=NUM_COL_PT) * len(t_), dtype=int)
+    x_col_idx = np.array(rd.uniform(size=NUM_COL_PT) * len(x_), dtype=int)
+    input_col = np.stack((x_[x_col_idx], t_[t_col_idx]), axis=1)
+    u_col = u[t_col_idx, x_col_idx].reshape(-1, 1)
+
+    x_u_train = np.vstack([input_ic, input_bc])
+    y_u_train = np.vstack([u_ic, u_bc])
+    idx = np.arange(0, NUM_IC_PT + NUM_BC_PT)
+    rd.shuffle(idx)
+    x_u_train = x_u_train[idx]
+    y_u_train = y_u_train[idx]
+
+    x_f_train = input_col
+
+    # prepare val data
+    rd2 = np.random.RandomState(42)
+    v_t_col_idx = np.array(rd2.uniform(size=NUM_COL_PT) * len(t_), dtype=int)
+    v_x_col_idx = np.array(rd2.uniform(size=NUM_COL_PT) * len(x_), dtype=int)
+    x_val = np.stack((x_[v_x_col_idx], t_[v_t_col_idx]), axis=1)
+    y_val = u[v_t_col_idx, v_x_col_idx].reshape(-1, 1)
+
+    # prepare testing data
+    xx, tt = np.meshgrid(x_, t_)
+    input_domain = np.stack([xx.flatten(), tt.flatten()], axis=1)
+    y_test = u.flatten().reshape(-1, 1)
+
+    train_data = {"x_u": x_u_train, "y_u": y_u_train, "x_f": x_f_train}
+    val_data = {"x": x_f_train, "y": u_col}
+    test_data = {"x": input_domain, "y": y_test}
+
+    if show_points == True:
+        import matplotlib.pyplot as plt
+
+        # plt.scatter(input_ic[:, 1], input_ic[:,0])
+        # plt.scatter(input_bc[:, 1], input_bc[:,0])
+        # plt.scatter(x_f_train[:,1], x_f_train[:,0])
+        plt.scatter(x_val[:, 1], x_val[:, 0])
+        plt.show()
+
+    return train_data, val_data, test_data
+
+
+def get_data(NT, NX, X_U, X_L, T_max, Nu):
+    def init_cond(x):
+        return -np.sin(np.pi * x)
+
+    x_, t_, u = BurgersSolver(
+        NT=NT, NX=NX, X_U=X_U, X_L=X_L, T_max=T_max, Nu=Nu, IN_cond=init_cond
+    )
+    train, val, test = split_data(x_, t_, u)
+    return train, val, test
+
+
+class PINN(tf.keras.Model):
+    def __init__(self, num_layers, hidden_dim, output_dim, act_fn):
+        super().__init__()
+        # tf.random.set_seed(0)
+        tf.keras.utils.set_random_seed(0)
+        if act_fn == "relu":
+            self.act_fn = tf.nn.relu
+        elif act_fn == "leaky_relu":
+            self.act_fn = tf.nn.leaky_relu
+        elif act_fn == "elu":
+            self.act_fn = tf.nn.elu
+        elif act_fn == "gelu":
+            self.act_fn = tf.nn.gelu
+        elif act_fn == "tanh":
+            self.act_fn = tf.nn.tanh
+        elif act_fn == "sigmoid":
+            self.act_fn = tf.nn.sigmoid
+        else:
+            raise ValueError("%s is not in the activation function list" % act_fn)
+
+        self.lys = [
+            tf.keras.layers.Dense(
+                units=hidden_dim,
+                activation=self.act_fn,
+                kernel_initializer="glorot_uniform",
+                bias_initializer="zeros",
+            )
+        ]
+        for n in range(num_layers):
+            self.lys.append(
+                tf.keras.layers.Dense(
+                    units=hidden_dim,
+                    activation=self.act_fn,
+                    kernel_initializer="glorot_uniform",
+                    bias_initializer="zeros",
+                )
+            )
+        self.lys.append(
+            tf.keras.layers.Dense(
+                units=output_dim,
+                activation=None,
+                kernel_initializer="glorot_uniform",
+                bias_initializer="zeros",
+            )
+        )
+
+    def call(self, x, t):
+        x = tf.cast(x, tf.float32)
+        t = tf.cast(t, tf.float32)
+
+        x_concat = tf.stack((x, t), axis=1)
+        for layer in self.lys:
+            x_concat = layer(x_concat)
+        return x_concat
+
+
+class BurgerSupervisor:
+    def __init__(self, nu, net, epochs, lr, alpha):
+        self.nu = nu
+        self.net = net  # the net predicting u from x, t pairs.
+        self.epochs = epochs
+
+        self.alpha = alpha
+        self.optimizer = tf.keras.optimizers.Adam(learning_rate=lr)
+
+        self.loss = tf.losses.MeanSquaredError()
+
+    @tf.function
+    def f_net(self, x, t):
+        x = tf.cast(x, tf.float32)
+        t = tf.cast(t, tf.float32)
+        u0 = tf.squeeze(self.net(x, t))
+        u_t = tf.gradients(u0, t)[0]
+        u_x = tf.gradients(u0, x)[0]
+        u_xx = tf.gradients(u_x, x)[0]
+        F = u_t + u0 * u_x - (0.01 / np.pi) * u_xx
+        return F
+
+    def step(self, train, val):
+        x_u_train = tf.cast(train["x_u"], tf.float32)
+        x_f_train = tf.cast(train["x_f"], tf.float32)
+        y_train = tf.cast(train["y_u"], tf.float32)
+
+        x_val = tf.cast(val["x"], tf.float32)
+        y_val = tf.cast(val["y"], tf.float32)
+        x_u = x_u_train[:, 0]
+        t_u = x_u_train[:, 1]
+
+        x_f = x_f_train[:, 0]
+        t_f = x_f_train[:, 1]
+        with tf.GradientTape() as t:
+            u_out = self.net(x_u, t_u)
+            f_out = self.f_net(x_f, t_f)
+            loss_u = self.loss(y_train, u_out)
+            loss_f = self.loss(0, f_out)
+            tot_loss = loss_u + self.alpha * loss_f
+
+        grads = t.gradient(tot_loss, self.net.trainable_weights)
+        self.optimizer.apply_gradients(zip(grads, self.net.trainable_weights))
+        train_loss = loss_u.numpy() + loss_f.numpy()
+        val_pred = self.net(x_val[:, 0], x_val[:, 1])
+        val_u_loss = self.loss(y_val, val_pred).numpy()
+        return train_loss, val_u_loss
+
+    def train(self, train, val):
+        x_u_train = tf.cast(train["x_u"], tf.float32)
+        x_f_train = tf.cast(train["x_f"], tf.float32)
+        y_train = tf.cast(train["y_u"], tf.float32)
+
+        x_val = tf.cast(val["x"], tf.float32)
+        y_val = tf.cast(val["y"], tf.float32)
+        x_u = x_u_train[:, 0]
+        t_u = x_u_train[:, 1]
+
+        x_f = x_f_train[:, 0]
+        t_f = x_f_train[:, 1]
+        for i in range(self.epochs):
+            train_ls = []
+            with tf.GradientTape() as t:
+                u_out = self.net(x_u, t_u)
+                f_out = self.f_net(x_f, t_f)
+                loss_u = self.loss(y_train, u_out)
+                loss_f = self.loss(0, f_out)
+                tot_loss = loss_u + self.alpha * loss_f
+                train_ls.append(tot_loss)
+            grads = t.gradient(tot_loss, self.net.trainable_weights)
+            self.optimizer.apply_gradients(zip(grads, self.net.trainable_weights))
+            obj = loss_u.numpy() + loss_f.numpy()
+            # print('epoch {}, obj is {}'.format(i, obj))
+            # validation loss
+            val_r2 = self.r2(y_val, self.net(x_val[:, 0], x_val[:, 1]))
+            val_f_loss = self.loss(0, self.f_net(x_val[:, 0], x_val[:, 1])).numpy()
+            val_pred = self.net(x_val[:, 0], x_val[:, 1])
+            val_u_loss = self.loss(y_val, val_pred).numpy()
+            # val_f_loss = self.loss(0, self.f_net(x_val[:, 0], x_val[:, 1])).numpy()
+            val_loss = val_u_loss
+            print(
+                "Epoch %d, training loss: (%.4f, %.4f), obj loss %.4f"
+                % (i + 1, loss_u.numpy(), loss_f.numpy(), val_loss)
+            )
+        return val_loss  # return the objective.
+
+    def r2(self, true, pred):
+        true = tf.cast(true, tf.float32)
+        rss = tf.reduce_sum((true - pred) ** 2)
+        tss = tf.reduce_sum((true - tf.reduce_mean(true)) ** 2)
+        return 1 - rss / tss
+
+    def test(self, x_test, y_test):
+        out = self.net(x_test)
+        r2 = self.r2(y_test, out)
+        f_loss = self.f_loss(x_test)
+        return r2, f_loss
+
+
+def plotter(x_, u_, name, NUMX, NUMT, **kwargs):
+    """
+    x_: shape=[num_points, 2]. First dim being x and second t.
+    u_: shape=[num_points,]
+    NUMX: number of points the in x-direction.
+    NUMT: number of points the in t-direction.
+    """
+    import matplotlib.animation as animation
+
+    u_ = u_.reshape(NUMT, NUMX).T
+    xv = x_[:, 0].reshape(NUMT, NUMX)
+    tv = x_[:, 1].reshape(NUMT, NUMX)
+
+    import matplotlib.pyplot as plt
+
+    fig, ax = plt.subplots(2, 2, figsize=(16, 9))
+    ax[0, 0].imshow(
+        u_,
+        interpolation="nearest",
+        cmap="rainbow",
+        extent=[tv.min(), tv.max(), xv.min(), xv.max()],
+        origin="lower",
+        aspect="auto",
+    )
+    ax[0, 0].set_xlabel("t")
+    ax[0, 0].set_ylabel("x")
+
+    (line,) = ax[1, 0].plot(xv[0], u_[:, 0])
+    text = ax[1, 0].text(0.8, 0.9, " ", transform=ax[1, 0].transAxes)
+    ax[1, 0].set_xlabel("x")
+    ax[1, 0].set_ylabel("u")
+
+    u_true = kwargs["u_true"].reshape(NUMT, NUMX).T
+    ax[0, 1].set_title("True", fontsize=24)
+    ax[0, 0].set_title("Predicted", fontsize=24)
+    ax[0, 1].imshow(
+        u_true,
+        interpolation="nearest",
+        cmap="rainbow",
+        extent=[tv.min(), tv.max(), xv.min(), xv.max()],
+        origin="lower",
+        aspect="auto",
+    )
+    ax[0, 1].set_xlabel("t")
+    ax[0, 1].set_ylabel("x")
+
+    (line_true,) = ax[1, 1].plot(xv[0], u_true[:, 0])
+    text_true = ax[1, 1].text(0.8, 0.9, " ", transform=ax[1, 1].transAxes)
+    ax[1, 0].set_xlabel("x")
+    ax[1, 0].set_ylabel("u")
+
+    T = np.linspace(x_[:, 1].min(), x_[:, 1].max(), NUMT)
+
+    def animate(t):
+        line.set_ydata(u_[:, t])
+        line_true.set_ydata(u_true[:, t])
+        text.set_text("t= %.3f" % T[t])
+        text_true.set_text("t= %.3f" % T[t])
+        return line, text, line_true, text_true
+
+    ani = animation.FuncAnimation(fig, animate, frames=u_.shape[1], interval=50)
+    plt.tight_layout()
+    writergif = animation.PillowWriter(fps=60)
+    ani.save("./Burgers_" + name + ".gif", writer=writergif)
+
+
+if __name__ == "__main__":
+    import time
+    import pickle
+
+    start_time = time.time()
+    config = {
+        "num_layers": 17,
+        "hidden_dim": 9,
+        "epochs": 1000,
+        "lr": 0.00580381,
+        "alpha": 0.050742893,
+        "activation": "tanh",
+    }
+
+    nu = 0.01 / np.pi
+    train, val, test = get_data(NT=200, NX=128, X_U=1, X_L=-1, T_max=1, Nu=nu)
+    with open("./data/data.pkl", "rb") as f:
+        data = pickle.load(f)
+
+    train = data["train"]
+    val = data["val"]
+
+    num_layers = config["num_layers"]
+    hidden_dim = config["hidden_dim"]
+    output_dim = 1
+    epochs = config["epochs"]
+    lr = config["lr"]
+    alpha = config["alpha"]
+    activation = config["activation"]
+    net = PINN(
+        num_layers=num_layers,
+        hidden_dim=hidden_dim,
+        output_dim=output_dim,
+        act_fn=activation,
+    )
+    sup = BurgerSupervisor(nu, net, epochs, lr, alpha)
+    val_f_loss = sup.train(train, val)
+    print("--- %s seconds ---" % (time.time() - start_time))
+    print("objective is ", val_f_loss)
+    # pred_test = net(test['x'][:,0], test['x'][:,1]).numpy()
+    # plotter(test['x'], pred_test, 'true_pred_hps-adbo', 128, 200, u_true=test['y'])
diff --git a/src/deephyper_benchmark/lib/pinnbench/Burgers/requirements.txt b/src/deephyper_benchmark/lib/pinnbench/Burgers/requirements.txt
new file mode 100644
index 0000000..ff2206e
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/Burgers/requirements.txt
@@ -0,0 +1,5 @@
+deephyper>=0.5.0
+matplotlib==3.4.2
+numpy==1.23.4
+phiflow==2.2.7
+tensorflow==2.10.0
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/__init__.py b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/__init__.py
new file mode 100644
index 0000000..dc5340c
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/__init__.py
@@ -0,0 +1 @@
+from . import hpo
diff --git a/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/benchmark.py b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/benchmark.py
new file mode 100644
index 0000000..bacef6c
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/benchmark.py
@@ -0,0 +1,28 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class PINNDiffusionReactionBenchmark(Benchmark):
+    version = "0.0.1"
+
+    requires = {
+        "py-pip-requirements": {
+            "step": "install",
+            "type": "pip",
+            "args": "install -r " + os.path.join(DIR, "requirements.txt"),
+        },
+        "bash-install": {
+            "step": "install",
+            "type": "cmd",
+            "cmd": "cd .. && bash " + os.path.join(DIR, "./install.sh"),
+        },
+        "deepxde-backend": {
+            "step": "load",
+            "type": "env",
+            "key": "DDE_BACKEND",
+            "value": "pytorch",
+        },
+    }
diff --git a/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/hpo.py b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/hpo.py
new file mode 100644
index 0000000..7df1633
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/hpo.py
@@ -0,0 +1,206 @@
+import os
+
+import numpy as np
+import torch
+from deephyper.evaluator import RunningJob, profile
+from deephyper.hpo import HpProblem
+from deephyper.stopper.integration.deepxde import DeepXDEStopperCallback
+from deepxde.callbacks import EarlyStopping
+from fvcore.nn import FlopCountAnalysis
+from pdebench.models.pinn.train import run_training
+
+from deephyper_benchmark.integration.torch import count_params
+from deephyper_benchmark.utils.json_utils import array_to_json
+
+from .model import FNN, ACTIVATIONS
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+DEEPHYPER_BENCHMARK_MOO = bool(int(os.environ.get("DEEPHYPER_BENCHMARK_MOO", 0)))
+
+# define the search space
+problem = HpProblem()
+problem.add_hyperparameter((5, 20), "num_layers", default_value=10)
+problem.add_hyperparameter((5, 100), "num_neurons", default_value=10)
+problem.add_hyperparameter((100, 1000), "epochs", default_value=100)
+problem.add_hyperparameter(
+    list(ACTIVATIONS.keys()),
+    "activation",
+    default_value="relu",
+)
+problem.add_hyperparameter([True, False], "skip_co", default_value=False)
+problem.add_hyperparameter((0.0, 0.5), "dropout", default_value=0)
+
+# Regularization hyperparameters
+problem.add_hyperparameter([True, False], "batch_norm", default_value=False)
+problem.add_hyperparameter(["None", "l2"], "regularization", default_value="None")
+problem.add_hyperparameter(
+    (1e-5, 1.0, "log-uniform"), "weight_decay", default_value=0.01
+)
+problem.add_hyperparameter(
+    ["Glorot normal", "Glorot uniform", "He normal", "He uniform"],
+    "kernel_initializer",
+    default_value="Glorot normal",
+)
+
+# Optimization hyperparameters
+problem.add_hyperparameter(["None", "step"], "decay", default_value="None")
+problem.add_hyperparameter((1, 100), "decay_step_size", default_value=5)
+problem.add_hyperparameter((1e-5, 1.0, "log-uniform"), "decay_gamma", default_value=0.1)
+
+problem.add_hyperparameter(
+    ["adam", "sgd", "rmsprop", "adamw"], "optimizer", default_value="adam"
+)
+problem.add_hyperparameter(
+    (1e-5, 1e-1, "log-uniform"), "learning_rate", default_value=0.01
+)
+
+# Loss weights is tuned only if MOO is activated.
+if DEEPHYPER_BENCHMARK_MOO:
+    problem.add_hyperparameter((0.1, 0.9), "loss_weights", default_value=0.5)
+
+
+@profile
+def run(job: RunningJob) -> dict:
+    config = job.parameters.copy()
+    dataset = "2D_diff-react_NA_NA"
+
+    for k, v in config.items():
+        if v == "None":
+            config[k] = None
+
+    if "loss_weights" not in config:
+        lw = 0.5
+    else:
+        lw = config["loss_weights"]
+    config["loss_weights"] = np.array([lw, lw, 1 - lw, 1 - lw, 1 - lw, 1 - lw])
+
+    # https://github.com/lululxvi/deepxde/blob/master/deepxde/optimizers/pytorch/optimizers.py
+    if config["decay"] == "step":
+        config["decay"] = ("step", config["decay_step_size"], config["decay_gamma"])
+
+    # To avoid error: https://github.com/lululxvi/deepxde/blob/master/deepxde/optimizers/pytorch/optimizers.py#L45C4-L45C4
+    if config["optimizer"] == "adamw":
+        config["regularization"] = "l2"
+
+    stopper_callback = DeepXDEStopperCallback(job)
+
+    error_type = None
+    try:
+        (
+            val_loss,
+            test_loss,
+            losshistory,
+            model,
+            duration_batch_inference,
+        ) = run_training(
+            net_class=FNN,
+            scenario="diff-react",
+            epochs=config["epochs"],
+            learning_rate=config["learning_rate"],
+            model_update=500,
+            root_path=os.path.join(
+                DIR, "../build/PDEBench-DH/pdebench/data/" + dataset
+            ),
+            flnm=dataset + ".h5",
+            config=config,
+            seed="0000",
+            callbacks=stopper_callback,
+        )
+    except torch.cuda.OutOfMemoryError:
+        error_type = "F_OOM"
+
+    # Managing failures to learn memory constraints
+    if error_type is not None:
+        metadata = {
+            "num_parameters": None,
+            "num_parameters_train": None,
+            "val_loss": None,
+            "test_rmse": None,
+            "budget": None,
+            "stopped": None,
+            "lc_train_loss": None,
+            "lc_val_loss": None,
+            "flops": None,
+            "duration_batch_inference": None,
+        }
+
+        if DEEPHYPER_BENCHMARK_MOO:
+            objective = [error_type for _ in range(3)]
+        else:
+            objective = error_type
+
+        return {"objective": objective, "metadata": metadata}
+
+    param_count = count_params(model)
+    flops = FlopCountAnalysis(model, inputs=(torch.randn(1, 3))).total()
+
+    train_ls = np.array(losshistory.loss_train).sum(axis=1)
+    val_ls = np.array(losshistory.loss_test).sum(axis=1)
+    steps = np.array(losshistory.steps)
+    lc_train_X = np.stack([steps, train_ls], axis=1)
+    lc_val_X = np.stack([steps, val_ls], axis=1)
+    lc_train_X_json = array_to_json(lc_train_X)
+    lc_val_X_json = array_to_json(lc_val_X)
+
+    if DEEPHYPER_BENCHMARK_MOO:
+        print("Optimizing multiple objectives...")
+
+        if np.isnan(val_loss[:2]).any() or np.isinf(val_loss[:2]).any():
+            objective_0 = "F"
+        else:
+            objective_0 = -(val_loss[:2] / config["loss_weights"][:2]).sum()
+
+        if np.isnan(val_loss[2:]).any() or np.isinf(val_loss[2:]).any():
+            objective_1 = "F"
+        else:
+            objective_1 = -(val_loss[2:] / config["loss_weights"][2:]).sum()
+
+        objective = [
+            objective_0,
+            objective_1,
+            -flops,
+        ]
+    else:
+        objective = (
+            "F"
+            if np.isnan(val_loss).any() or np.isinf(val_loss).any()
+            else -(val_loss / config["loss_weights"]).sum()
+        )
+    metadata = {
+        "num_parameters": param_count["num_parameters"],
+        "num_parameters_train": param_count["num_parameters_train"],
+        "val_loss": array_to_json(val_loss),  # array of 4 elements
+        "test_rmse": float(test_loss[0]),
+        "budget": stopper_callback.budget,
+        "stopped": stopper_callback.stopped,
+        "lc_train_loss": lc_train_X_json,
+        "lc_val_loss": lc_val_X_json,
+        "flops": flops,
+        "duration_batch_inference": duration_batch_inference,  # add the inference time in seconds
+    }
+
+    return {"objective": objective, "metadata": metadata}
+
+
+def evaluate(config):
+    """
+    Evaluate an hyperparameter configuration
+    on training/validation and testing data.
+    """
+    callbacks = EarlyStopping(patience=100_000)
+    DIR = os.path.dirname(os.path.abspath(__file__))
+    dataset = os.environ.get("DEEPHYPER_BENCHMARK_DATASET")
+
+    val_loss, test_loss, losshistory, model = run_training(
+        net_class=FNN,
+        scenario="diff-react",
+        epochs=config["epochs"],
+        learning_rate=config["lr"],
+        model_update=1,
+        root_path=os.path.join(DIR, "../build/PDEBench-DH/pdebench/data/" + dataset),
+        flnm="2D_diff-react_NA_NA.h5",
+        config=config,
+        seed="0000",
+        callbacks=callbacks,
+    )
+    return val_loss, test_loss, losshistory
diff --git a/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/model.py b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/model.py
new file mode 100644
index 0000000..ff9e3d0
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/model.py
@@ -0,0 +1,154 @@
+import torch
+import torch.nn as nn
+
+from deepxde.nn import NN
+from deepxde.nn import initializers
+from deepxde import config
+
+INITIALIZERS = initializers
+
+class Sin(nn.Module):
+    def __init__(self):
+        super(Sin, self).__init__()
+
+    def forward(self, x):
+        return torch.sin(x)
+
+ACTIVATIONS = {
+        # "id": nn.Identity,
+        "elu": nn.ELU,
+        "relu": nn.ReLU,
+        "selu": nn.SELU,
+        "sigmoid": nn.Sigmoid,
+        "silu": nn.SiLU, # same as swish
+        "sin": Sin, 
+        "tanh": nn.Tanh,
+        "hardswish": nn.Hardswish,
+        "leakyrelu": nn.LeakyReLU,
+        "mish": nn.Mish,
+        "softplus": nn.Softplus,
+}
+
+
+class FNN(NN):
+    """Fully-connected neural network."""
+
+    def __init__(
+        self,
+        input_dim: int,
+        output_dim: int,
+        num_layers: int = 5,
+        num_neurons: int = 30,
+        activation: str = "elu",
+        kernel_initializer: str = "Glorot normal",
+        batch_norm: bool = False,
+        skip_co: bool = False,
+        dropout_rate: float = 0.0,
+        regularization: str = None,
+        weight_decay: float = 0.01,
+        **kwargs,
+    ):
+        super(FNN, self).__init__()
+
+        if regularization is None:
+            self.regularizer = None
+        else:
+            self.regularizer = [regularization, weight_decay]
+
+        layer_sizes = [input_dim] + [num_neurons for _ in range(num_layers)]
+
+        initializer = INITIALIZERS.get(kernel_initializer)
+        initializer_zero = INITIALIZERS.get("zeros")
+
+        self.linears = nn.Sequential()
+        for i in range(1, len(layer_sizes)):
+            if skip_co:
+                self.linears.append(
+                    SkipConnection(
+                        in_dim=layer_sizes[i - 1],
+                        out_dim=layer_sizes[i],
+                        batch_norm=batch_norm,
+                        kernel_initializer=kernel_initializer,
+                        activation=activation,
+                    )
+                )
+
+            else:
+                linear_module = nn.Linear(
+                    layer_sizes[i - 1], layer_sizes[i], dtype=config.real(torch)
+                )
+                initializer(linear_module.weight)
+                initializer_zero(linear_module.bias)
+                self.linears.append(linear_module)
+                if batch_norm:
+                    self.linears.append(
+                        nn.BatchNorm1d(layer_sizes[i], dtype=config.real(torch))
+                    )
+                
+                if activation != "id":
+                    self.linears.append(ACTIVATIONS.get(activation)())
+
+            self.linears.append(nn.Dropout(p=dropout_rate))
+
+        self.linears.append(
+            nn.Linear(layer_sizes[-1], output_dim, dtype=config.real(torch))
+        )
+
+    def forward(self, inputs):
+        x = inputs
+        if self._input_transform is not None:
+            x = self._input_transform(x)
+        x = self.linears(x)
+        if self._output_transform is not None:
+            x = self._output_transform(inputs, x)
+        return x
+
+
+class SkipConnection(nn.Module):
+    def __init__(
+        self,
+        in_dim,
+        out_dim,
+        kernel_initializer="Glorot normal",
+        batch_norm=False,
+        activation="elu",
+    ) -> None:
+        super(SkipConnection, self).__init__()
+
+        self.in_dim = in_dim
+        self.out_dim = out_dim
+
+        initializer = INITIALIZERS.get(kernel_initializer)
+        initializer_zero = INITIALIZERS.get("zeros")
+
+        if self.in_dim != self.out_dim:
+            self.map = nn.Linear(in_dim, out_dim)
+            initializer(self.map.weight)
+            initializer_zero(self.map.bias)
+
+        self.block = nn.Sequential()
+        
+        linear_module = nn.Linear(in_dim, out_dim, dtype=config.real(torch))
+        initializer(linear_module.weight)
+        initializer_zero(linear_module.bias)
+        self.block.append(linear_module)
+
+        if batch_norm:
+            self.block.append(nn.BatchNorm1d(out_dim, dtype=config.real(torch)))
+
+        if activation != "id":
+            self.block.append(ACTIVATIONS.get(activation)())
+        
+        if activation != "id":
+            self.act = ACTIVATIONS.get(activation)()
+        else:
+            self.act = None
+
+    def forward(self, x):
+        residual = x
+        if self.in_dim != self.out_dim:
+            residual = self.map(residual)
+        out = self.block(x) + residual
+        if self.act is not None:
+            out = self.act(out)
+        return out
diff --git a/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/requirements.txt b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/requirements.txt
new file mode 100644
index 0000000..3ad0199
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/DiffusionReaction/requirements.txt
@@ -0,0 +1,6 @@
+DeepXDE>=1.8.2
+numpy>=1.24.2
+omegaconf>=2.3.0
+packaging>=23.0
+torch>=2.0.0
+fvcore>=0.1.5
\ No newline at end of file
diff --git a/src/deephyper_benchmark/lib/pinnbench/README.md b/src/deephyper_benchmark/lib/pinnbench/README.md
new file mode 100644
index 0000000..520a485
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/README.md
@@ -0,0 +1,144 @@
+# Physics-informed Neural Networks Benchmark
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
+
+Physics-Informed Neural Networks (PINNs) are a class of machine learning models that combine the strengths of neural networks and physics-based modeling. PINNs are used to solve partial differential equations (PDEs) and other physical problems by learning a solution directly from data.
+
+The basic idea behind PINNs is to use a neural network to approximate the solution to a PDE, while also enforcing the underlying physical laws that govern the problem. This is achieved by incorporating the PDE as a constraint in the neural network training process. More details can be found in the [original work](https://arxiv.org/abs/1711.10561).
+
+This set of benchmarks seeks to incorporate AutoML workflow into the development of PINNs with DeepHyper. The PINN benchmark problems support Hyperparameter Optimization (HPO), Neural Architecture Search (NAS), and Multi-fidelity evaluations. 
+
+The currently available problems are 
+
+<!-- [Burgers Equation](#burgers-equation) -->
+
+- [Diffusion-reaction Equation](#diffusion-reaction-equation) (Dataset Size: 13 GB)
+
+<!-- 
+## Burgers Equation
+### Installation
+To install
+
+```
+python -c "import deephyper_benchmark as dhb; dhb.install('PINNBench/Burgers');"
+```
+-->
+
+
+<!-- PINNs are neural networks are a type of machine learning method that combines deep neural networks with physical equations to solve complex physical problems. In the case of 1-D Burgers Equation, training a PINN, $\hat{u}$, is to minimize a compound loss function. The 1-D Burgers equation has the following form:
+
+$$\frac{\partial u }{\partial t} + u\frac{\partial u}{\partial x} = \nu \frac{\partial^2 u}{\partial x^2}$$
+
+The loss functions has two component,
+$$L_{tot} = L_u + \alpha L_f$$
+
+the solution loss for specified initial and boundary conditions,
+$$L_u = \vert \vert \hat{u} - u \vert \vert^2_2$$
+
+and the loss according to the governing equation (PDE loss) for every other points in the domain,
+$$L_f  = \vert \vert \frac{\partial \hat{u}}{\partial t} + \hat{u}\frac{\partial \hat{u}}{\partial x} - \nu \frac{\partial^2 \hat{u}}{\partial x^2})\vert \vert^2_2.$$ -->
+
+
+<!-- ### Hyperparameter Search
+<!-- ### Hyperparameter Search
+The objective of this benchmark is to optimize a set of hyperparameters for a feedforward neural network, including `num_layers`, `lr` (the learning rate of the optimizer), `hidden_dim` (the number of neurons in the hidden layers), `alpha` (the PDE loss coefficient), and `activation` (the activation function). The optimization is performed by minimizing the negative total loss value from the validation dataset.
+
+To improve the efficiency of the search, the benchmark uses MCModelStopper to predict the training dynamics and perform early stopping when the predicted subsequent iterations are unlikely to lead to a further improvement in model performance. The maximum budget, `max_b`, for this search is 1000 epochs.
+
+```python
+for budget_i in range(min_b, max_b + 1):
+        train_loss, eval = sup.step(train, val)
+        objective_i = -eval  # maximizing in deephyper
+        job.record(budget_i, objective_i)
+        if job.stopped():
+            break
+```
+### Defualt configuration and results
+The default configuration of the PINN.
+
+| | |
+|:--|:--|
+|`num_layers`| 5 |
+|`lr` | 0.01|
+| `hidden_dim`| 5|
+|`alpha`| 5 |
+|`activation`| `tanh`|
+
+```
+result={'objective': -0.06480624, 'metadata': {'timestamp_start': 1680036315.47377, 'timestamp_end': 1680036380.2708638, 'num_parameters': 171, 'train_loss': 0.06834503, 'val_loss': 0.06480624, 'test_loss': 0.066540696, 'budget': 1000, 'stopped': False, 'infos_stopped': None}}
+``` --> 
+
+
+## Diffusion-reaction Equation
+
+This benchmark is based on **modified** [`PDEBench`](https://github.com/pdebench/PDEBench) and [`DeepXDE`](https://github.com/lululxvi/deepxde). 
+
+### Installation
+
+To install the **modified** `PDEBench` and this benchmark, run:
+```
+python -c "import deephyper_benchmark as dhb; dhb.install('PINNBench/DiffusionReaction');"
+```
+
+Run the hyperparameter search
+```
+import os
+os.environ['DEEPHYPER_BENCHMARK_MOO'] = '1' # enable multi-objective optimization
+
+import deephyper_benchmark as dhb
+diff_react = dhb.load("PINNBench/DiffusionReaction")
+
+from deephyper.evaluator import RunningJob
+config = diff_react.hpo.problem.default_configuration # get a default config to test
+res = diff_react.hpo.run(RunningJob(parameters=config))
+```
+
+
+### Configuration
+
+It is necessary to configure `DeepXDE` to use `PyTorch` backend. The instructions can be found [here](https://deepxde.readthedocs.io/en/latest/user/installation.html#working-with-different-backends).
+
+### Metadata
+
+- [x] `num_parameters`: integer value of the number of parameters in the neural network.
+- [x] `num_parameters_train`: integer value of the number of **trainable** parameters of the neural network.
+- [x] `budget`: scalar value (float/int) of the budget consumed by the neural network. Therefore the budget should be defined for each benchmark (e.g., number of epochs in general).
+- [x] `stopped`: boolean value indicating if the evaluation was stopped before consuming the maximum budget.
+- [x] `train_loss`:  scalar value of the training metrics (replace `X` by the metric name, 1 key per metric).
+- [x] `valid_loss`: scalar value of the validation metrics (replace `X` by the metric name, 1 key per metric).
+- [x] `test_rmse`: scalar value of the testing metrics (replace `X` by the metric name, 1 key per metric).
+- [x] `flops`: number of flops of the model such as computed in `fvcore.nn.FlopCountAnalysis(...).total()` (See [documentation](https://detectron2.readthedocs.io/en/latest/modules/fvcore.html#module-fvcore.nn)).
+- [ ] `latency`: TO BE CLARIFIED
+- [x] `lc_train_loss`: recorded learning curves of the trained model, the `bi` variables are the budget value (e.g., epochs/batches), and the `yi` values are the recorded metric. `X` in `train_X` is replaced by the name of the metric such as `train_loss` or `train_accuracy`. The format is `[[b0, y0], [b1, y1], ...]`.
+- [x] `lc_valid_loss`: Same as `lc_train_X` but for validation data.
+- [x] `duration_batch_inference`: average inference time for a single data point.
+
+### Other details
+
+#### Supported datasets
+
+The currently available dataset is `2D_diff-react_NA_NA`. The rest datasets from PDEBench (see [list](https://github.com/iamyixuan/PDEBench-DH/tree/main/pdebench/data_download) )will be supported in the future.
+
+#### Supported hyperparameters
+
+- [x] `num_layers`: number of layers (or other building blocks) in the network.
+- [x] `lr`: learning rate for the optimizer.
+- [x] `num_neurons`: number of neurons per layer.
+- [x] `epochs`: number of maximum epochs for training.
+- [x] `activation`: activation functions.
+- [x] `skip_co`: if using skip connection (residual block).
+- [x] `dropout_rate`: dropout rate.
+- [x] `optimizer`: choices of the optimizer.
+- [x] `weight_decay`: magnitude of L2 regularization.
+- [x] `initialization`: initialization strategy for network weights.
+- [x] `loss_weights`: weights assigned to the PDE loss.
+  
+#### Multi-objective Optimization (MOO)
+
+MOO is supported for the `DiffusionReaction` benchmark. To enable MOO, set environment variable `DEEPHYPER_BENCHMARK_MOO=1`. There are five minimized objectives in this case:
+
+- `objective_0`: Validation PDE loss.
+- `objective_1`: Validation boundary and initial condition solution loss.
+- `objective_2`: Batch inference duration (in seconds).
+- `objective_3`: FLOPS from [FVCORE Package](https://github.com/facebookresearch/fvcore/blob/main/docs/flop_count.md).
diff --git a/src/deephyper_benchmark/lib/pinnbench/install.sh b/src/deephyper_benchmark/lib/pinnbench/install.sh
new file mode 100644
index 0000000..d28fea3
--- /dev/null
+++ b/src/deephyper_benchmark/lib/pinnbench/install.sh
@@ -0,0 +1,13 @@
+#!/bin/bash
+
+
+mkdir build/ && cd build/
+# install modified PDEBench
+git clone https://github.com/iamyixuan/PDEBench-DH.git
+cd PDEBench-DH/
+python -m pip install -e . 
+
+# generate data
+mkdir ./pdebench/data/
+cd ./pdebench/data_gen/
+python gen_diff_react.py
diff --git a/src/deephyper_benchmark/lib/yahpo/README.md b/src/deephyper_benchmark/lib/yahpo/README.md
new file mode 100644
index 0000000..6fbb7c4
--- /dev/null
+++ b/src/deephyper_benchmark/lib/yahpo/README.md
@@ -0,0 +1,13 @@
+# YAHPO Gym - Surrogate Benchmark for Hyperparameter Optimization
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
+
+* [YAHPO Gym - Github](https://github.com/slds-lmu/yahpo_gym)
+
+
+## Installation
+
+```console
+python -c "import deephyper_benchmark as dhb; dhb.install('YAHPO/lcbench');"
+```
diff --git a/src/deephyper_benchmark/lib/yahpo/lcbench/__init__.py b/src/deephyper_benchmark/lib/yahpo/lcbench/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/deephyper_benchmark/lib/yahpo/lcbench/benchmark.py b/src/deephyper_benchmark/lib/yahpo/lcbench/benchmark.py
new file mode 100644
index 0000000..18ec8d7
--- /dev/null
+++ b/src/deephyper_benchmark/lib/yahpo/lcbench/benchmark.py
@@ -0,0 +1,27 @@
+import os
+
+from deephyper_benchmark import *
+
+DIR = os.path.dirname(os.path.abspath(__file__))
+
+
+class YAHPOLCBench(Benchmark):
+    version = "0.0.1"
+
+    data_dir = os.path.join(DIR, "..", "build", "data")
+    requires = {
+        "download-data": {
+            "step": "install",
+            "type": "cmd",
+            "cmd": f"git clone https://github.com/slds-lmu/yahpo_data.git {data_dir}",
+        },
+        "pip-yahpo-gym": {"step": "install", "type": "pip", "args": "install yahpo-gym"},
+    }
+
+    def install(self):
+        super().install()
+
+        from yahpo_gym import local_config
+
+        local_config.init_config()
+        local_config.set_data_path(self.data_dir)
diff --git a/src/deephyper_benchmark/lib/yahpo/lcbench/hpo.py b/src/deephyper_benchmark/lib/yahpo/lcbench/hpo.py
new file mode 100644
index 0000000..539ba7a
--- /dev/null
+++ b/src/deephyper_benchmark/lib/yahpo/lcbench/hpo.py
@@ -0,0 +1,75 @@
+import os
+
+from ConfigSpace import ConfigurationSpace
+from deephyper.evaluator import profile, RunningJob
+from deephyper.hpo import HpProblem
+
+from yahpo_gym import benchmark_set
+import yahpo_gym.benchmarks.lcbench
+
+DEEPHYPER_BENCHMARK_INSTANCE = os.environ.get("DEEPHYPER_BENCHMARK_INSTANCE", "3945")
+
+
+bench = benchmark_set.BenchmarkSet("lcbench")
+bench.set_instance(DEEPHYPER_BENCHMARK_INSTANCE)
+
+config_space = ConfigurationSpace()
+config_space.add_hyperparameters(
+    [
+        hp
+        for hp in bench.config_space.get_hyperparameters()
+        if not (hp.name in ["OpenML_task_id", "epoch"])
+    ]
+)
+problem = HpProblem(config_space=config_space)
+
+
+@profile()
+def run(job: RunningJob) -> dict:
+    config = job.parameters.copy()
+    config["OpenML_task_id"] = DEEPHYPER_BENCHMARK_INSTANCE
+
+    # Min/Max budget (here epochs)
+    min_b, max_b = 2, 51
+
+    # Run the benchmark (batch for better performance)
+    def update_config(config, budget):
+        config = config.copy()
+        config["epoch"] = budget
+        return config
+
+    outputs = [
+        bench.objective_function(update_config(config, budget=b))
+        for b in range(min_b, max_b + 1)
+    ]
+
+    for i, out_i in enumerate(outputs):
+        out_i = out_i[0]
+        budget_i = i + 1
+        objective_i = -out_i["val_cross_entropy"]
+
+        job.record(budget_i, objective_i)
+        if job.stopped():
+            break
+
+    return {
+        "objective": objective_i,
+        "metadata": {
+            "budget": budget_i,
+            "stopped": budget_i < len(outputs),
+            "test_balanced_accuracy": outputs[-1][0]["test_balanced_accuracy"],
+            "val_balanced_accuracy": outputs[-1][0]["val_balanced_accuracy"],
+            "test_cross_entropy": outputs[-1][0]["test_cross_entropy"],
+            "val_cross_entropy": outputs[-1][0]["val_cross_entropy"],
+            "time": outputs[-1][0]["time"],
+        },
+    }
+
+
+if __name__ == "__main__":
+    print(problem)
+    config = bench.config_space.sample_configuration(1).get_dictionary()
+    config.pop("OpenML_task_id")
+    print(config)
+    result = run(RunningJob(id=0, parameters=config))
+    print(result)
diff --git a/src/deephyper_benchmark/lib/yahpo/lcbench/metrics.py b/src/deephyper_benchmark/lib/yahpo/lcbench/metrics.py
new file mode 100644
index 0000000..92d0359
--- /dev/null
+++ b/src/deephyper_benchmark/lib/yahpo/lcbench/metrics.py
@@ -0,0 +1,69 @@
+import json
+import os
+
+import numpy as np
+
+from .hpo import bench as BENCHMARK
+
+
+class PerformanceEvaluator:
+    """A class defining performance evaluators for the YAHPO/lcbench problems."""
+
+    def __init__(self):
+        """Read the current problem defn from environment vars."""
+
+        df = BENCHMARK.target_stats
+
+        self.x_min = None
+
+        self.y_metric = "cross_entropy"
+
+        cond = (df["metric"] == "val_cross_entropy") & (df["statistic"] == "min")
+        self.y_min_valid = df[cond].iloc[0]["value"]
+
+        cond = (df["metric"] == "test_cross_entropy") & (df["statistic"] == "min")
+        self.y_min_test = df[cond].iloc[0]["value"]
+
+    def simple_regret_valid(self, y_valid: np.ndarray) -> np.ndarray:
+        """Compute the regret of the objective (validation RMSE) of a list of ordered solutions.
+
+        Args:
+            y_valid (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return y_valid - self.y_min_valid
+
+    def cumul_regret_valid(self, y_valid: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of the objective (validation RMSE) aon n array of ordered solutions.
+
+        Args:
+            y_valid (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret_valid(y_valid))
+
+    def simple_regret_test(self, y_test: np.ndarray) -> np.ndarray:
+        """Compute the regret of the test RMSE of a list of ordered solutions.
+
+        Args:
+            y_test (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return y_test - self.y_min_test
+
+    def cumul_regret_test(self, y_test: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of the test RMSE aon n array of ordered solutions.
+
+        Args:
+            y_test (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret_test(y_test))

From 0fe2444c5d2dbedb170fc959f2d6feec1ea07b63 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 13:57:29 +0100
Subject: [PATCH 18/43] adding readme to cbbo

---
 .../benchmarks/cbbo/README.md                 | 20 +++++++++++++++++++
 1 file changed, 20 insertions(+)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/README.md

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/README.md b/src/deephyper_benchmark/benchmarks/cbbo/README.md
new file mode 100644
index 0000000..6ecf11d
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/README.md
@@ -0,0 +1,20 @@
+# C-BBO: Continuous Black-Box Optimization
+
+> **Warning**
+> Work in progress, this benchmark is not yet ready.
+
+Set of continuous function benchmarks. The `DEEPHYPER_BENCHMARK_NDIMS` environment variable defines the number of dimensions of the problem.
+
+| Function Name | Number of Dimensions  |                   Comment                   |
+| ------------- | --------------------- | ------------------------------------------- |
+| ackley        | $\infty$ (default 5)  | Many local minima and single global optimum |
+| branin        | 2                     | Three global optimum                        |
+| cossin        | 1                     | Many local minima, good for visualisation.  |
+| easom         | 2                     | Almost flat everywhere                      |
+| griewank      | $\infty$ (default 5)  |                                             |
+| hartmann6D    | 6                     |                                             |
+| levy          | $\infty$ (default 5)  |                                             |
+| michal        | $\infty$ (default 2)  |                                             |
+| rosen         | $\infty$ (default 5)  |                                             |
+| schwefel      | $\infty$ (default 5)  |                                             |
+| shekel        | 4                     | Many local minima with flat areas           |
\ No newline at end of file

From e7293e275cf1ce5b63e4fc56d1a69a29cda86de9 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 16:50:17 +0100
Subject: [PATCH 19/43] reusable run_function cbbo

---
 .../benchmarks/cbbo/ackley.py                 | 50 +++----------------
 .../benchmarks/cbbo/utils.py                  | 22 ++++++++
 2 files changed, 29 insertions(+), 43 deletions(-)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/utils.py

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/ackley.py b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
index c7d591e..13e9e4c 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
@@ -1,13 +1,14 @@
 """here."""
 
-import numpy as np
-import time
+import functools
 
-from deephyper.evaluator import profile
-from deephyper.evaluator import RunningJob
+import numpy as np
 from deephyper.hpo import HpProblem
+
 from deephyper_benchmark import HPOBenchmark, HPOScorer
 
+from .utils import run_function
+
 
 def ackley(x, a=20, b=0.2, c=2 * np.pi):
     """Ackley function.
@@ -20,22 +21,7 @@ def ackley(x, a=20, b=0.2, c=2 * np.pi):
     term1 = -a * np.exp(-b * np.sqrt(s1 / d))
     term2 = -np.exp(s2 / d)
     y = term1 + term2 + a + np.exp(1)
-    return y
-
-
-@profile
-def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:  # noqa: D103
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -ackley(x)
+    return -y
 
 
 class AckleyScorer(HPOScorer):
@@ -51,28 +37,6 @@ def __init__(
         self.x_max[nparams - nslack :] = np.nan
         self.y_max = 0.0
 
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return self.y_max - y
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
-
 
 class AckleyBenchmark(HPOBenchmark):
     """Ackley benchmark.
@@ -114,7 +78,7 @@ def problem(self):  # noqa: D102
 
     @property
     def run_function(self):  # noqa: D102
-        return run_function
+        return functools.partial(run_function, bb_func=ackley)
 
     @property
     def scorer(self):  # noqa: D102
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/utils.py b/src/deephyper_benchmark/benchmarks/cbbo/utils.py
new file mode 100644
index 0000000..b7c3233
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/utils.py
@@ -0,0 +1,22 @@
+"""here."""
+
+import numpy as np
+import time
+
+from deephyper.evaluator import profile
+from deephyper.evaluator import RunningJob
+
+
+@profile
+def run_function(job: RunningJob, bb_func, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:  # noqa: D103
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return bb_func(x)

From 84553b7a3b8b153982a3f4665149a4d4d3575928 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 16:57:20 +0100
Subject: [PATCH 20/43] updating branin

---
 .../benchmarks/cbbo/branin.py                 | 50 +++----------------
 1 file changed, 7 insertions(+), 43 deletions(-)

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/branin.py b/src/deephyper_benchmark/benchmarks/cbbo/branin.py
index 1717d78..cca3531 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/branin.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/branin.py
@@ -1,13 +1,14 @@
 """here."""
 
-import numpy as np
-import time
+import functools
 
-from deephyper.evaluator import RunningJob
-from deephyper.evaluator import profile
+import numpy as np
 from deephyper.hpo import HpProblem
+
 from deephyper_benchmark import HPOBenchmark, HPOScorer
 
+from .utils import run_function
+
 
 def branin(x):
     """Branin function.
@@ -22,22 +23,7 @@ def branin(x):
     s = 10.0
     t = 1.0 / (8.0 * np.pi)
     y = a * (x[1] - b * x[0] ** 2 + c * x[0] - r) ** 2 + s * (1 - t) * np.cos(x[0]) + s
-    return y
-
-
-@profile
-def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:  # noqa: D103
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -branin(x)
+    return -y
 
 
 class BraninHPOScorer(HPOScorer):
@@ -48,28 +34,6 @@ def __init__(self):
         self.x_max = np.array([[-np.pi, 12.275], [np.pi, 2.275], [9.42478, 2.475]])
         self.y_max = -0.397887
 
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return self.y_max - y
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
-
 
 class BraninBenchmark(HPOBenchmark):
     """Branin benchmark."""
@@ -83,7 +47,7 @@ def problem(self):  # noqa: D102
 
     @property
     def run_function(self):  # noqa: D102
-        return run_function
+        return functools.partial(run_function, bb_func=branin)
 
     @property
     def scorer(self):  # noqa: D102

From 12c9a2cb8f316392deb6d5220dcd1dfd61ff3f4e Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 16:58:30 +0100
Subject: [PATCH 21/43] adding easom

---
 .../benchmarks/cbbo/easom.py                  | 52 +++++++++++++++++++
 1 file changed, 52 insertions(+)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/easom.py

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/easom.py b/src/deephyper_benchmark/benchmarks/cbbo/easom.py
new file mode 100644
index 0000000..1b86480
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/easom.py
@@ -0,0 +1,52 @@
+"""here."""
+
+import functools
+
+import numpy as np
+from deephyper.hpo import HpProblem
+
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+from .utils import run_function
+
+
+def easom(x):
+    """Easom function.
+
+    Description of the function: https://www.sfu.ca/~ssurjano/easom.html
+    """
+    assert len(x) == 2
+    y = -np.cos(x[0]) * np.cos(x[1]) * np.exp(-((x[0] - np.pi) ** 2 + (x[1] - np.pi) ** 2))
+    return -y
+
+
+class EasomScorer(HPOScorer):
+    """A class defining performance evaluators for the Easom problem."""
+
+    def __init__(self):
+        self.nparams = 2
+        self.x_max = np.array([np.pi, np.pi])
+        self.y_max = 1.0
+
+
+class EasomBenchmark(HPOBenchmark):
+    """Easom benchmark."""
+
+    def __init__(self) -> None:
+        self.nparams = 2
+
+    @property
+    def problem(self):  # noqa: D102
+        domain = (-100.0, 100.0)
+        problem = HpProblem()
+        for i in range(self.nparams):
+            problem.add_hyperparameter(domain, f"x{i}")
+        return problem
+
+    @property
+    def run_function(self):  # noqa: D102
+        return functools.partial(run_function, bb_func=easom)
+
+    @property
+    def scorer(self):  # noqa: D102
+        return EasomScorer()

From 0a079a1b0c5525c85dd48b06f875cf580db0b8e5 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 17:11:55 +0100
Subject: [PATCH 22/43] adding griewank

---
 .../benchmarks/cbbo/ackley.py                 | 14 ++--
 .../benchmarks/cbbo/griewank.py               | 76 +++++++++++++++++++
 2 files changed, 83 insertions(+), 7 deletions(-)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/griewank.py

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/ackley.py b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
index 13e9e4c..3205ce0 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
@@ -31,9 +31,12 @@ def __init__(
         self,
         nparams,
         nslack,
+        offset=0.0,
     ):
         self.nparams = nparams
-        self.x_max = np.full(self.nparams, fill_value=0.0)
+        self.nslack = nslack
+        self.offset = offset
+        self.x_max = np.full(self.nparams, fill_value=offset)
         self.x_max[nparams - nslack :] = np.nan
         self.y_max = 0.0
 
@@ -49,20 +52,17 @@ class AckleyBenchmark(HPOBenchmark):
 
     def __init__(self, nparams: int = 5, offset: int = -4.0, nslack: int = 0) -> None:
         self.nparams = nparams
+        self.nslack = nslack
         assert offset <= 32.768 and offset >= -32.768, (
             "offset must be in [-32.768, 32.768] to keep the same maximum value."
         )
         self.offset = offset
-        self.nslack = nslack
 
     @property
     def problem(self):  # noqa: D102
         # The original range is simetric (-32.768, 32.768) but we make it less simetric to avoid
         # Grid sampling or QMC sampling to directly hit the optimum...
-        domain = (
-            -32.768 + self.offset,
-            32.768 + self.offset,
-        )
+        domain = (-32.768 + self.offset, 32.768 + self.offset)
         problem = HpProblem()
         for i in range(self.nparams - self.nslack):
             problem.add_hyperparameter(domain, f"x{i}")
@@ -82,4 +82,4 @@ def run_function(self):  # noqa: D102
 
     @property
     def scorer(self):  # noqa: D102
-        return AckleyScorer(self.nparams, self.nslack)
+        return AckleyScorer(self.nparams, self.nslack, self.offset)
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/griewank.py b/src/deephyper_benchmark/benchmarks/cbbo/griewank.py
new file mode 100644
index 0000000..a49fc33
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/griewank.py
@@ -0,0 +1,76 @@
+"""here."""
+
+import functools
+
+import numpy as np
+from deephyper.hpo import HpProblem
+
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+from .utils import run_function
+
+
+def griewank(x, fr=4000):  # noqa: D103
+    n = len(x)
+    j = np.arange(1.0, n + 1)
+    s = np.sum(x**2)
+    p = np.prod(np.cos(x / np.sqrt(j)))
+    y = s / fr - p + 1
+    return -y
+
+
+class GriewankScorer(HPOScorer):
+    """A class defining performance evaluators for the Griewank problem."""
+
+    def __init__(
+        self,
+        nparams,
+        nslack,
+        offset=0.0,
+    ):
+        self.nparams = nparams
+        self.nslack = nslack
+        self.offset = offset
+        self.x_max = np.full(self.nparams, fill_value=offset)
+        self.x_max[nparams - nslack :] = np.nan
+        self.y_max = 0.0
+
+
+class GriewankBenchmark(HPOBenchmark):
+    """Griewank benchmark.
+
+    Args:
+        nparams (int, optional): the number of parameters in the problem.
+        offset (int, optional): the offset in the space of parameters.
+        nslack (int, optional): the number of additional slack parameters in the problem.
+    """
+
+    def __init__(self, nparams: int = 5, offset: int = -4.0, nslack: int = 0) -> None:
+        self.nparams = nparams
+        assert offset <= 600.0 and offset >= -600.0, (
+            "offset must be in [-600.0, 600.0] to keep the same maximum value."
+        )
+        self.offset = offset
+        self.nslack = nslack
+
+    @property
+    def problem(self):  # noqa: D102
+        domain = (-600.0 + self.offset, 600.0 + self.offset)
+        problem = HpProblem()
+        for i in range(self.nparams - self.nslack):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+        for i in range(
+            self.nparams - self.nslack,
+            self.nparams,
+        ):
+            problem.add_hyperparameter(domain, f"z{i}")
+        return problem
+
+    @property
+    def run_function(self):  # noqa: D102
+        return functools.partial(run_function, bb_func=griewank)
+
+    @property
+    def scorer(self):  # noqa: D102
+        return GriewankScorer(self.nparams, self.nslack, self.offset)

From 2c98c1fdefdb82ecf9dbb114f5296cde8720caed Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 17:12:40 +0100
Subject: [PATCH 23/43] moving cum_regret, simple_regret to HPOScorer

---
 src/deephyper_benchmark/_scorer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/deephyper_benchmark/_scorer.py b/src/deephyper_benchmark/_scorer.py
index cd0e695..5cd0ead 100644
--- a/src/deephyper_benchmark/_scorer.py
+++ b/src/deephyper_benchmark/_scorer.py
@@ -7,7 +7,6 @@ class Scorer(abc.ABC):
 
 
 class HPOScorer(Scorer):
-    @abc.abstractmethod
     def simple_regret(self, y: np.ndarray) -> np.ndarray:
         """Compute the regret of a list of given solution.
 
@@ -17,8 +16,8 @@ def simple_regret(self, y: np.ndarray) -> np.ndarray:
         Returns:
             np.ndarray: An array of regret values.
         """
+        return self.y_max - y
 
-    @abc.abstractmethod
     def cumul_regret(self, y: np.ndarray) -> np.ndarray:
         """Compute the cumulative regret of an array of ordered given solution.
 
@@ -28,3 +27,4 @@ def cumul_regret(self, y: np.ndarray) -> np.ndarray:
         Returns:
             np.ndarray: An array of cumulative regret values.
         """
+        return np.cumsum(self.simple_regret(y))

From f365d8a90eb42f80c6eb72c0144b368936e470d7 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 17:16:06 +0100
Subject: [PATCH 24/43] adding hartmann6D

---
 .../benchmarks/cbbo/hartmann6D.py             | 76 +++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py b/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py
new file mode 100644
index 0000000..98b7604
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py
@@ -0,0 +1,76 @@
+"""here."""
+
+import functools
+
+import numpy as np
+from deephyper.hpo import HpProblem
+
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+from .utils import run_function
+
+
+def hartmann6D(x):  # noqa: D103
+    alpha = np.array([1.0, 1.2, 3.0, 3.2])
+    A = np.array(
+        [
+            [10, 3, 17, 3.5, 1.7, 8],
+            [0.05, 10, 17, 0.1, 8, 14],
+            [3, 3.5, 1.7, 10, 17, 8],
+            [17, 8, 0.05, 10, 0.1, 14],
+        ]
+    )
+    P = 1e-4 * np.array(
+        [
+            [1312, 1696, 5569, 124, 8283, 5886],
+            [2329, 4135, 8307, 3736, 1004, 9991],
+            [2348, 1451, 3522, 2883, 3047, 6650],
+            [4047, 8828, 8732, 5743, 1091, 381],
+        ]
+    )
+    X = np.array([x for _ in range(4)])
+    inner = np.sum(np.multiply(A, np.square(X - P)), axis=1)
+    outer = np.sum(alpha * np.exp(-inner))
+    y = -(2.58 + outer) / 1.94
+    return -y
+
+
+class Hartmann6DScorer(HPOScorer):
+    """A class defining performance evaluators for the Hartmann6D problem."""
+
+    def __init__(self):
+        self.nparams = 6
+        self.x_max = np.array(
+            [
+                0.20169,
+                0.150011,
+                0.476874,
+                0.275332,
+                0.311652,
+                0.6573,
+            ]
+        )
+        self.y_max = 3.32237
+
+
+class Hartmann6DBenchmark(HPOBenchmark):
+    """Hartmann6D benchmark."""
+
+    def __init__(self) -> None:
+        self.nparams = 6
+
+    @property
+    def problem(self):  # noqa: D102
+        domain = (0.0, 1.0)
+        problem = HpProblem()
+        for i in range(self.nparams - self.nslack):
+            problem.add_hyperparameter(domain, f"x{i}")
+        return problem
+
+    @property
+    def run_function(self):  # noqa: D102
+        return functools.partial(run_function, bb_func=hartmann6D)
+
+    @property
+    def scorer(self):  # noqa: D102
+        return Hartmann6DScorer()

From 716acde8737fe8a32cb0177df7f1319b824c5274 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 17:17:44 +0100
Subject: [PATCH 25/43] adding benchmarks to top-level __init__

---
 src/deephyper_benchmark/benchmarks/cbbo/__init__.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/__init__.py b/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
index 42d103a..26fa444 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
@@ -2,5 +2,14 @@
 
 from .ackley import AckleyBenchmark
 from .branin import BraninBenchmark
+from .easom import EasomBenchmark
+from .griewank import GriewankBenchmark
+from .hartmann6D import Hartmann6DBenchmark
 
-__all__ = ["AckleyBenchmark", "BraninBenchmark"]
+__all__ = [
+    "AckleyBenchmark",
+    "BraninBenchmark",
+    "EasomBenchmark",
+    "GriewankBenchmark",
+    "Hartmann6DBenchmark",
+]

From 8073701c1cbd3c0c3be41cb196474183128bf5ca Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 17:23:11 +0100
Subject: [PATCH 26/43] added tests

---
 .../benchmarks/cbbo/hartmann6D.py             |  2 +-
 tests/test_c_bbo.py                           | 12 ----------
 tests/test_cbbo.py                            | 24 +++++++++++++++++++
 3 files changed, 25 insertions(+), 13 deletions(-)
 delete mode 100644 tests/test_c_bbo.py
 create mode 100644 tests/test_cbbo.py

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py b/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py
index 98b7604..cab922b 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py
@@ -63,7 +63,7 @@ def __init__(self) -> None:
     def problem(self):  # noqa: D102
         domain = (0.0, 1.0)
         problem = HpProblem()
-        for i in range(self.nparams - self.nslack):
+        for i in range(self.nparams):
             problem.add_hyperparameter(domain, f"x{i}")
         return problem
 
diff --git a/tests/test_c_bbo.py b/tests/test_c_bbo.py
deleted file mode 100644
index 543f8f8..0000000
--- a/tests/test_c_bbo.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import deephyper_benchmark.lib.c_bbo.ackley as bench
-
-
-def test_benchmark():
-
-    bench.hpo.problem
-    bench.hpo.run_function
-    bench.hpo.scorer
-
-
-if __name__ == "__main__":
-    test_benchmark()
diff --git a/tests/test_cbbo.py b/tests/test_cbbo.py
new file mode 100644
index 0000000..d4d7dde
--- /dev/null
+++ b/tests/test_cbbo.py
@@ -0,0 +1,24 @@
+"""here."""
+
+from deephyper.hpo import RandomSearch
+import deephyper_benchmark.benchmarks.cbbo as bench
+
+
+def test_cbbo_benchmarks():
+    """Test cbbo benchmarks."""
+
+    benchmarks = [
+        bench.AckleyBenchmark(),
+        bench.BraninBenchmark(),
+        bench.EasomBenchmark(),
+        bench.GriewankBenchmark(),
+        bench.Hartmann6DBenchmark(),
+    ]
+
+    max_evals = 25
+    for b in benchmarks:
+        search = RandomSearch(b.problem, b.run_function)
+        results = search.search(max_evals)
+        assert len(results) == max_evals
+        cumul_regret = b.scorer.cumul_regret(results.objective)
+        assert len(cumul_regret) == max_evals

From cfca6c82d4dfafeac0decd2df3f567e5bed51bde Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 16:50:17 +0100
Subject: [PATCH 27/43] reusable run_function cbbo

---
 .../benchmarks/cbbo/ackley.py                 | 50 +++----------------
 .../benchmarks/cbbo/utils.py                  | 22 ++++++++
 2 files changed, 29 insertions(+), 43 deletions(-)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/utils.py

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/ackley.py b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
index c7d591e..13e9e4c 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
@@ -1,13 +1,14 @@
 """here."""
 
-import numpy as np
-import time
+import functools
 
-from deephyper.evaluator import profile
-from deephyper.evaluator import RunningJob
+import numpy as np
 from deephyper.hpo import HpProblem
+
 from deephyper_benchmark import HPOBenchmark, HPOScorer
 
+from .utils import run_function
+
 
 def ackley(x, a=20, b=0.2, c=2 * np.pi):
     """Ackley function.
@@ -20,22 +21,7 @@ def ackley(x, a=20, b=0.2, c=2 * np.pi):
     term1 = -a * np.exp(-b * np.sqrt(s1 / d))
     term2 = -np.exp(s2 / d)
     y = term1 + term2 + a + np.exp(1)
-    return y
-
-
-@profile
-def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:  # noqa: D103
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -ackley(x)
+    return -y
 
 
 class AckleyScorer(HPOScorer):
@@ -51,28 +37,6 @@ def __init__(
         self.x_max[nparams - nslack :] = np.nan
         self.y_max = 0.0
 
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return self.y_max - y
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
-
 
 class AckleyBenchmark(HPOBenchmark):
     """Ackley benchmark.
@@ -114,7 +78,7 @@ def problem(self):  # noqa: D102
 
     @property
     def run_function(self):  # noqa: D102
-        return run_function
+        return functools.partial(run_function, bb_func=ackley)
 
     @property
     def scorer(self):  # noqa: D102
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/utils.py b/src/deephyper_benchmark/benchmarks/cbbo/utils.py
new file mode 100644
index 0000000..b7c3233
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/utils.py
@@ -0,0 +1,22 @@
+"""here."""
+
+import numpy as np
+import time
+
+from deephyper.evaluator import profile
+from deephyper.evaluator import RunningJob
+
+
+@profile
+def run_function(job: RunningJob, bb_func, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:  # noqa: D103
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    return bb_func(x)

From 93b80ecfa5f0ae93cf764c4566d7e5c63c37c6d3 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 16:57:20 +0100
Subject: [PATCH 28/43] updating branin

---
 .../benchmarks/cbbo/branin.py                 | 50 +++----------------
 1 file changed, 7 insertions(+), 43 deletions(-)

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/branin.py b/src/deephyper_benchmark/benchmarks/cbbo/branin.py
index 1717d78..cca3531 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/branin.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/branin.py
@@ -1,13 +1,14 @@
 """here."""
 
-import numpy as np
-import time
+import functools
 
-from deephyper.evaluator import RunningJob
-from deephyper.evaluator import profile
+import numpy as np
 from deephyper.hpo import HpProblem
+
 from deephyper_benchmark import HPOBenchmark, HPOScorer
 
+from .utils import run_function
+
 
 def branin(x):
     """Branin function.
@@ -22,22 +23,7 @@ def branin(x):
     s = 10.0
     t = 1.0 / (8.0 * np.pi)
     y = a * (x[1] - b * x[0] ** 2 + c * x[0] - r) ** 2 + s * (1 - t) * np.cos(x[0]) + s
-    return y
-
-
-@profile
-def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20) -> dict:  # noqa: D103
-    config = job.parameters
-
-    if sleep:
-        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
-        t_sleep = max(t_sleep, 0)
-        time.sleep(t_sleep)
-
-    x = np.array([config[k] for k in config if "x" in k])
-    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
-
-    return -branin(x)
+    return -y
 
 
 class BraninHPOScorer(HPOScorer):
@@ -48,28 +34,6 @@ def __init__(self):
         self.x_max = np.array([[-np.pi, 12.275], [np.pi, 2.275], [9.42478, 2.475]])
         self.y_max = -0.397887
 
-    def simple_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        return self.y_max - y
-
-    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
-        """Compute the cumulative regret of an array of ordered given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of cumulative regret values.
-        """
-        return np.cumsum(self.simple_regret(y))
-
 
 class BraninBenchmark(HPOBenchmark):
     """Branin benchmark."""
@@ -83,7 +47,7 @@ def problem(self):  # noqa: D102
 
     @property
     def run_function(self):  # noqa: D102
-        return run_function
+        return functools.partial(run_function, bb_func=branin)
 
     @property
     def scorer(self):  # noqa: D102

From 4314806f5dd436a648088f7e5b728bd93c37f26e Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 16:58:30 +0100
Subject: [PATCH 29/43] adding easom

---
 .../benchmarks/cbbo/easom.py                  | 52 +++++++++++++++++++
 1 file changed, 52 insertions(+)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/easom.py

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/easom.py b/src/deephyper_benchmark/benchmarks/cbbo/easom.py
new file mode 100644
index 0000000..1b86480
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/easom.py
@@ -0,0 +1,52 @@
+"""here."""
+
+import functools
+
+import numpy as np
+from deephyper.hpo import HpProblem
+
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+from .utils import run_function
+
+
+def easom(x):
+    """Easom function.
+
+    Description of the function: https://www.sfu.ca/~ssurjano/easom.html
+    """
+    assert len(x) == 2
+    y = -np.cos(x[0]) * np.cos(x[1]) * np.exp(-((x[0] - np.pi) ** 2 + (x[1] - np.pi) ** 2))
+    return -y
+
+
+class EasomScorer(HPOScorer):
+    """A class defining performance evaluators for the Easom problem."""
+
+    def __init__(self):
+        self.nparams = 2
+        self.x_max = np.array([np.pi, np.pi])
+        self.y_max = 1.0
+
+
+class EasomBenchmark(HPOBenchmark):
+    """Easom benchmark."""
+
+    def __init__(self) -> None:
+        self.nparams = 2
+
+    @property
+    def problem(self):  # noqa: D102
+        domain = (-100.0, 100.0)
+        problem = HpProblem()
+        for i in range(self.nparams):
+            problem.add_hyperparameter(domain, f"x{i}")
+        return problem
+
+    @property
+    def run_function(self):  # noqa: D102
+        return functools.partial(run_function, bb_func=easom)
+
+    @property
+    def scorer(self):  # noqa: D102
+        return EasomScorer()

From e074e3072641d33dbae1b5aef698b1148cbb66f8 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 17:11:55 +0100
Subject: [PATCH 30/43] adding griewank

---
 .../benchmarks/cbbo/ackley.py                 | 14 ++--
 .../benchmarks/cbbo/griewank.py               | 76 +++++++++++++++++++
 2 files changed, 83 insertions(+), 7 deletions(-)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/griewank.py

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/ackley.py b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
index 13e9e4c..3205ce0 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
@@ -31,9 +31,12 @@ def __init__(
         self,
         nparams,
         nslack,
+        offset=0.0,
     ):
         self.nparams = nparams
-        self.x_max = np.full(self.nparams, fill_value=0.0)
+        self.nslack = nslack
+        self.offset = offset
+        self.x_max = np.full(self.nparams, fill_value=offset)
         self.x_max[nparams - nslack :] = np.nan
         self.y_max = 0.0
 
@@ -49,20 +52,17 @@ class AckleyBenchmark(HPOBenchmark):
 
     def __init__(self, nparams: int = 5, offset: int = -4.0, nslack: int = 0) -> None:
         self.nparams = nparams
+        self.nslack = nslack
         assert offset <= 32.768 and offset >= -32.768, (
             "offset must be in [-32.768, 32.768] to keep the same maximum value."
         )
         self.offset = offset
-        self.nslack = nslack
 
     @property
     def problem(self):  # noqa: D102
         # The original range is simetric (-32.768, 32.768) but we make it less simetric to avoid
         # Grid sampling or QMC sampling to directly hit the optimum...
-        domain = (
-            -32.768 + self.offset,
-            32.768 + self.offset,
-        )
+        domain = (-32.768 + self.offset, 32.768 + self.offset)
         problem = HpProblem()
         for i in range(self.nparams - self.nslack):
             problem.add_hyperparameter(domain, f"x{i}")
@@ -82,4 +82,4 @@ def run_function(self):  # noqa: D102
 
     @property
     def scorer(self):  # noqa: D102
-        return AckleyScorer(self.nparams, self.nslack)
+        return AckleyScorer(self.nparams, self.nslack, self.offset)
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/griewank.py b/src/deephyper_benchmark/benchmarks/cbbo/griewank.py
new file mode 100644
index 0000000..a49fc33
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/griewank.py
@@ -0,0 +1,76 @@
+"""here."""
+
+import functools
+
+import numpy as np
+from deephyper.hpo import HpProblem
+
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+from .utils import run_function
+
+
+def griewank(x, fr=4000):  # noqa: D103
+    n = len(x)
+    j = np.arange(1.0, n + 1)
+    s = np.sum(x**2)
+    p = np.prod(np.cos(x / np.sqrt(j)))
+    y = s / fr - p + 1
+    return -y
+
+
+class GriewankScorer(HPOScorer):
+    """A class defining performance evaluators for the Griewank problem."""
+
+    def __init__(
+        self,
+        nparams,
+        nslack,
+        offset=0.0,
+    ):
+        self.nparams = nparams
+        self.nslack = nslack
+        self.offset = offset
+        self.x_max = np.full(self.nparams, fill_value=offset)
+        self.x_max[nparams - nslack :] = np.nan
+        self.y_max = 0.0
+
+
+class GriewankBenchmark(HPOBenchmark):
+    """Griewank benchmark.
+
+    Args:
+        nparams (int, optional): the number of parameters in the problem.
+        offset (int, optional): the offset in the space of parameters.
+        nslack (int, optional): the number of additional slack parameters in the problem.
+    """
+
+    def __init__(self, nparams: int = 5, offset: int = -4.0, nslack: int = 0) -> None:
+        self.nparams = nparams
+        assert offset <= 600.0 and offset >= -600.0, (
+            "offset must be in [-600.0, 600.0] to keep the same maximum value."
+        )
+        self.offset = offset
+        self.nslack = nslack
+
+    @property
+    def problem(self):  # noqa: D102
+        domain = (-600.0 + self.offset, 600.0 + self.offset)
+        problem = HpProblem()
+        for i in range(self.nparams - self.nslack):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+        for i in range(
+            self.nparams - self.nslack,
+            self.nparams,
+        ):
+            problem.add_hyperparameter(domain, f"z{i}")
+        return problem
+
+    @property
+    def run_function(self):  # noqa: D102
+        return functools.partial(run_function, bb_func=griewank)
+
+    @property
+    def scorer(self):  # noqa: D102
+        return GriewankScorer(self.nparams, self.nslack, self.offset)

From 12294b5760c1ef6b440b32a048cda5df4e490126 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 17:12:40 +0100
Subject: [PATCH 31/43] moving cum_regret, simple_regret to HPOScorer

---
 src/deephyper_benchmark/_scorer.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/deephyper_benchmark/_scorer.py b/src/deephyper_benchmark/_scorer.py
index cd0e695..5cd0ead 100644
--- a/src/deephyper_benchmark/_scorer.py
+++ b/src/deephyper_benchmark/_scorer.py
@@ -7,7 +7,6 @@ class Scorer(abc.ABC):
 
 
 class HPOScorer(Scorer):
-    @abc.abstractmethod
     def simple_regret(self, y: np.ndarray) -> np.ndarray:
         """Compute the regret of a list of given solution.
 
@@ -17,8 +16,8 @@ def simple_regret(self, y: np.ndarray) -> np.ndarray:
         Returns:
             np.ndarray: An array of regret values.
         """
+        return self.y_max - y
 
-    @abc.abstractmethod
     def cumul_regret(self, y: np.ndarray) -> np.ndarray:
         """Compute the cumulative regret of an array of ordered given solution.
 
@@ -28,3 +27,4 @@ def cumul_regret(self, y: np.ndarray) -> np.ndarray:
         Returns:
             np.ndarray: An array of cumulative regret values.
         """
+        return np.cumsum(self.simple_regret(y))

From bce0c804380e11e583284af398184b03dadd946a Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 17:16:06 +0100
Subject: [PATCH 32/43] adding hartmann6D

---
 .../benchmarks/cbbo/hartmann6D.py             | 76 +++++++++++++++++++
 1 file changed, 76 insertions(+)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py b/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py
new file mode 100644
index 0000000..98b7604
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py
@@ -0,0 +1,76 @@
+"""here."""
+
+import functools
+
+import numpy as np
+from deephyper.hpo import HpProblem
+
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+from .utils import run_function
+
+
+def hartmann6D(x):  # noqa: D103
+    alpha = np.array([1.0, 1.2, 3.0, 3.2])
+    A = np.array(
+        [
+            [10, 3, 17, 3.5, 1.7, 8],
+            [0.05, 10, 17, 0.1, 8, 14],
+            [3, 3.5, 1.7, 10, 17, 8],
+            [17, 8, 0.05, 10, 0.1, 14],
+        ]
+    )
+    P = 1e-4 * np.array(
+        [
+            [1312, 1696, 5569, 124, 8283, 5886],
+            [2329, 4135, 8307, 3736, 1004, 9991],
+            [2348, 1451, 3522, 2883, 3047, 6650],
+            [4047, 8828, 8732, 5743, 1091, 381],
+        ]
+    )
+    X = np.array([x for _ in range(4)])
+    inner = np.sum(np.multiply(A, np.square(X - P)), axis=1)
+    outer = np.sum(alpha * np.exp(-inner))
+    y = -(2.58 + outer) / 1.94
+    return -y
+
+
+class Hartmann6DScorer(HPOScorer):
+    """A class defining performance evaluators for the Hartmann6D problem."""
+
+    def __init__(self):
+        self.nparams = 6
+        self.x_max = np.array(
+            [
+                0.20169,
+                0.150011,
+                0.476874,
+                0.275332,
+                0.311652,
+                0.6573,
+            ]
+        )
+        self.y_max = 3.32237
+
+
+class Hartmann6DBenchmark(HPOBenchmark):
+    """Hartmann6D benchmark."""
+
+    def __init__(self) -> None:
+        self.nparams = 6
+
+    @property
+    def problem(self):  # noqa: D102
+        domain = (0.0, 1.0)
+        problem = HpProblem()
+        for i in range(self.nparams - self.nslack):
+            problem.add_hyperparameter(domain, f"x{i}")
+        return problem
+
+    @property
+    def run_function(self):  # noqa: D102
+        return functools.partial(run_function, bb_func=hartmann6D)
+
+    @property
+    def scorer(self):  # noqa: D102
+        return Hartmann6DScorer()

From eaaad23529b0c0b44472ff9534284096477b4675 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 17:17:44 +0100
Subject: [PATCH 33/43] adding benchmarks to top-level __init__

---
 src/deephyper_benchmark/benchmarks/cbbo/__init__.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/__init__.py b/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
index 42d103a..26fa444 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
@@ -2,5 +2,14 @@
 
 from .ackley import AckleyBenchmark
 from .branin import BraninBenchmark
+from .easom import EasomBenchmark
+from .griewank import GriewankBenchmark
+from .hartmann6D import Hartmann6DBenchmark
 
-__all__ = ["AckleyBenchmark", "BraninBenchmark"]
+__all__ = [
+    "AckleyBenchmark",
+    "BraninBenchmark",
+    "EasomBenchmark",
+    "GriewankBenchmark",
+    "Hartmann6DBenchmark",
+]

From a691a7226eee079d44bf293e72025633e4a59204 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 17:23:11 +0100
Subject: [PATCH 34/43] added tests

---
 .../benchmarks/cbbo/hartmann6D.py             |  2 +-
 tests/test_c_bbo.py                           | 12 ----------
 tests/test_cbbo.py                            | 24 +++++++++++++++++++
 3 files changed, 25 insertions(+), 13 deletions(-)
 delete mode 100644 tests/test_c_bbo.py
 create mode 100644 tests/test_cbbo.py

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py b/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py
index 98b7604..cab922b 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py
@@ -63,7 +63,7 @@ def __init__(self) -> None:
     def problem(self):  # noqa: D102
         domain = (0.0, 1.0)
         problem = HpProblem()
-        for i in range(self.nparams - self.nslack):
+        for i in range(self.nparams):
             problem.add_hyperparameter(domain, f"x{i}")
         return problem
 
diff --git a/tests/test_c_bbo.py b/tests/test_c_bbo.py
deleted file mode 100644
index 543f8f8..0000000
--- a/tests/test_c_bbo.py
+++ /dev/null
@@ -1,12 +0,0 @@
-import deephyper_benchmark.lib.c_bbo.ackley as bench
-
-
-def test_benchmark():
-
-    bench.hpo.problem
-    bench.hpo.run_function
-    bench.hpo.scorer
-
-
-if __name__ == "__main__":
-    test_benchmark()
diff --git a/tests/test_cbbo.py b/tests/test_cbbo.py
new file mode 100644
index 0000000..d4d7dde
--- /dev/null
+++ b/tests/test_cbbo.py
@@ -0,0 +1,24 @@
+"""here."""
+
+from deephyper.hpo import RandomSearch
+import deephyper_benchmark.benchmarks.cbbo as bench
+
+
+def test_cbbo_benchmarks():
+    """Test cbbo benchmarks."""
+
+    benchmarks = [
+        bench.AckleyBenchmark(),
+        bench.BraninBenchmark(),
+        bench.EasomBenchmark(),
+        bench.GriewankBenchmark(),
+        bench.Hartmann6DBenchmark(),
+    ]
+
+    max_evals = 25
+    for b in benchmarks:
+        search = RandomSearch(b.problem, b.run_function)
+        results = search.search(max_evals)
+        assert len(results) == max_evals
+        cumul_regret = b.scorer.cumul_regret(results.objective)
+        assert len(cumul_regret) == max_evals

From 4ede96546e049253affd623a4e9065ea76a0d32e Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 18:42:35 +0100
Subject: [PATCH 35/43] adding pydantic validation for dtlz scorer

---
 src/deephyper_benchmark/__init__.py           | 10 ++-
 src/deephyper_benchmark/_scorer.py            | 51 ++++++++++++++-
 .../benchmarks/dtlz/dtlz.py                   | 63 ++++---------------
 3 files changed, 71 insertions(+), 53 deletions(-)

diff --git a/src/deephyper_benchmark/__init__.py b/src/deephyper_benchmark/__init__.py
index c8df6c3..0c5a6ea 100644
--- a/src/deephyper_benchmark/__init__.py
+++ b/src/deephyper_benchmark/__init__.py
@@ -1,7 +1,13 @@
 """Public interface for deephyper_benchmark package."""
 
 from ._benchmark import Benchmark, HPOBenchmark
-from ._scorer import Scorer, HPOScorer
+from ._scorer import Scorer, HPOScorer, MultiObjHPOScorer
 
 
-__all__ = ["Benchmark", "HPOBenchmark", "Scorer", "HPOScorer"]
+__all__ = [
+    "Benchmark",
+    "HPOBenchmark",
+    "Scorer",
+    "HPOScorer",
+    "MultiObjHPOScorer",
+]
diff --git a/src/deephyper_benchmark/_scorer.py b/src/deephyper_benchmark/_scorer.py
index 5cd0ead..acdc1f8 100644
--- a/src/deephyper_benchmark/_scorer.py
+++ b/src/deephyper_benchmark/_scorer.py
@@ -1,8 +1,11 @@
 import abc
+
 import numpy as np
+from deephyper.skopt.moo import hypervolume, pareto_front
+from pydantic import BaseModel
 
 
-class Scorer(abc.ABC):
+class Scorer(BaseModel, abc.ABC):
     pass
 
 
@@ -28,3 +31,49 @@ def cumul_regret(self, y: np.ndarray) -> np.ndarray:
             np.ndarray: An array of cumulative regret values.
         """
         return np.cumsum(self.simple_regret(y))
+
+
+class MultiObjHPOScorer(Scorer):
+    nobj: int
+
+    def hypervolume_score(self, pts: np.ndarray):
+        """Calculate the hypervolume dominated by soln, wrt the Nadir point.
+
+        Args:
+            pts (np.ndarray): A 2d array of objective values.
+                Each row is an objective value in the solution set.
+
+        Returns:
+            float: The total hypervolume dominated by the current solution,
+            filtering out points worse than the Nadir point and using the
+            Nadir point as the reference.
+
+        """
+        if np.any(pts < 0):
+            filtered_pts = -pts.copy()
+        else:
+            filtered_pts = pts.copy()
+        nadir = self.nadir_point
+        for i in range(pts.shape[0]):
+            if np.any(filtered_pts[i, :] > nadir):
+                filtered_pts[i, :] = nadir
+        return hypervolume(filtered_pts, nadir)
+
+    @property
+    @abc.abstractmethod
+    def nadir_point(self):
+        pass
+
+    def hypervolume(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        scores = np.zeros(y.shape[0])
+        for i in range(1, y.shape[0] + 1):
+            scores[i - 1] = self.hypervolume_score(y[:i])
+        return scores
diff --git a/src/deephyper_benchmark/benchmarks/dtlz/dtlz.py b/src/deephyper_benchmark/benchmarks/dtlz/dtlz.py
index 3f86c3f..8204013 100644
--- a/src/deephyper_benchmark/benchmarks/dtlz/dtlz.py
+++ b/src/deephyper_benchmark/benchmarks/dtlz/dtlz.py
@@ -9,7 +9,7 @@
 from deephyper.hpo import HpProblem
 from deephyper.skopt.moo import hypervolume, pareto_front
 
-from deephyper_benchmark import HPOBenchmark, Scorer
+from deephyper_benchmark import HPOBenchmark, MultiObjHPOScorer
 
 from . import model as dtlz
 
@@ -42,7 +42,7 @@ def run_function(  # noqa: D103
     return ff
 
 
-class DTLZScorer(Scorer):
+class DTLZScorer(MultiObjHPOScorer):
     """A class defining performance evaluators for the DTLZ problems.
 
     Contains the following public methods:
@@ -58,35 +58,14 @@ class DTLZScorer(Scorer):
 
     """
 
+    prob_id: int
+
     def __init__(self, prob_id: int, nobj: int):
         """Read the current DTLZ problem defn from environment vars."""
-        self.prob_id = prob_id
-        self.nobj = nobj
-
-    def hypervolume(self, pts):
-        """Calculate the hypervolume dominated by soln, wrt the Nadir point.
-
-        Args:
-            pts (numpy.ndarray): A 2d array of objective values.
-                Each row is an objective value in the solution set.
+        super().__init__(prob_id=prob_id, nobj=nobj)
 
-        Returns:
-            float: The total hypervolume dominated by the current solution,
-            filtering out points worse than the Nadir point and using the
-            Nadir point as the reference.
-
-        """
-        if np.any(pts < 0):
-            filtered_pts = -pts.copy()
-        else:
-            filtered_pts = pts.copy()
-        nadir = self.nadirPt()
-        for i in range(pts.shape[0]):
-            if np.any(filtered_pts[i, :] > nadir):
-                filtered_pts[i, :] = nadir
-        return hypervolume(filtered_pts, nadir)
-
-    def nadirPt(self):
+    @property
+    def nadir_point(self):
         """Calculate the Nadir point for the given problem definition."""
         if self.prob_id == 1:
             return np.ones(self.nobj) * 0.5
@@ -99,7 +78,7 @@ def nadirPt(self):
         else:
             raise ValueError(f"DTLZ{self.prob_id} is not a valid problem")
 
-    def numPts(self, pts):
+    def num_points_dominate_nadir(self, pts):
         """Calculate the number of solutions that dominate the Nadir point.
 
         Args:
@@ -114,9 +93,9 @@ def numPts(self, pts):
             pareto_pts = pareto_front(-pts)
         else:
             pareto_pts = pareto_front(pts)
-        return sum([all(fi <= self.nadirPt()) for fi in pareto_pts])
+        return sum([all(fi <= self.nadir_point) for fi in pareto_pts])
 
-    def gdPlus(self, pts):
+    def gdplus_score(self, pts):
         """Calculate the p=1 generational distance for a given solution set.
 
         Args:
@@ -194,22 +173,7 @@ def _dtlz7Dist(self, pts):
             pts_proj.append(gx * hx)
         return np.array([np.abs(np.maximum(fi[-1] - fj, 0)) for fi, fj in zip(pts, pts_proj)])
 
-    def hypervolume_iter(self, y: np.ndarray) -> np.ndarray:
-        """Compute the regret of a list of given solution.
-
-        Args:
-            y (np.ndarray): An array of solutions.
-
-        Returns:
-            np.ndarray: An array of regret values.
-        """
-        scores = []
-        for i in range(1, y.shape[0] + 1):
-            scores.append(self.hypervolume(y[:i]))
-        scores = np.asarray(scores)
-        return scores
-
-    def gdPlus_iter(self, y: np.ndarray) -> np.ndarray:
+    def gdplus(self, y: np.ndarray) -> np.ndarray:
         """Compute the regret of a list of given solution.
 
         Args:
@@ -220,7 +184,7 @@ def gdPlus_iter(self, y: np.ndarray) -> np.ndarray:
         """
         scores = []
         for i in range(1, y.shape[0] + 1):
-            scores.append(self.gdPlus(y[:i]))
+            scores.append(self.gdplus_score(y[:i]))
         scores = np.asarray(scores)
         return scores
 
@@ -266,9 +230,8 @@ def __init__(
 
     @property
     def problem(self):  # noqa: D102
-        domain = (0.0, 1.0)
-        # Create problem
         problem = HpProblem()
+        domain = (0.0, 1.0)
         for i in range(self.nparams):
             problem.add_hyperparameter(domain, f"x{i}")
         return problem

From 94bd22012c6a1079f7001b021f6ddd14707f739d Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 18:45:08 +0100
Subject: [PATCH 36/43] add default attribute in HPOScorer

---
 src/deephyper_benchmark/_scorer.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/src/deephyper_benchmark/_scorer.py b/src/deephyper_benchmark/_scorer.py
index e4e59a7..5fda423 100644
--- a/src/deephyper_benchmark/_scorer.py
+++ b/src/deephyper_benchmark/_scorer.py
@@ -10,8 +10,8 @@ class Scorer(BaseModel, abc.ABC):
 
 
 class HPOScorer(Scorer):
-
     y_max: float
+
     def simple_regret(self, y: np.ndarray) -> np.ndarray:
         """Compute the regret of a list of given solution.
 

From 29ca8ddcced44f1133115e30cc0b6a4d935d8371 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 18:57:20 +0100
Subject: [PATCH 37/43] rolling back, removed pydantic validation

---
 src/deephyper_benchmark/_scorer.py                | 10 ++++++----
 src/deephyper_benchmark/benchmarks/cbbo/ackley.py |  4 ++--
 src/deephyper_benchmark/benchmarks/dtlz/dtlz.py   |  7 +++----
 3 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/src/deephyper_benchmark/_scorer.py b/src/deephyper_benchmark/_scorer.py
index 5fda423..acea947 100644
--- a/src/deephyper_benchmark/_scorer.py
+++ b/src/deephyper_benchmark/_scorer.py
@@ -2,15 +2,16 @@
 
 import numpy as np
 from deephyper.skopt.moo import hypervolume
-from pydantic import BaseModel
 
 
-class Scorer(BaseModel, abc.ABC):
+class Scorer(abc.ABC):
     pass
 
 
 class HPOScorer(Scorer):
-    y_max: float
+    def __init__(self, nparams: int, y_max: float):
+        self.nparams = nparams
+        self.y_max = y_max
 
     def simple_regret(self, y: np.ndarray) -> np.ndarray:
         """Compute the regret of a list of given solution.
@@ -36,7 +37,8 @@ def cumul_regret(self, y: np.ndarray) -> np.ndarray:
 
 
 class MultiObjHPOScorer(Scorer):
-    nobj: int
+    def __init__(self, nobj: int):
+        self.nobj = nobj
 
     def hypervolume_score(self, pts: np.ndarray):
         """Calculate the hypervolume dominated by soln, wrt the Nadir point.
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/ackley.py b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
index 3205ce0..c234353 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
@@ -4,6 +4,7 @@
 
 import numpy as np
 from deephyper.hpo import HpProblem
+from typing import List
 
 from deephyper_benchmark import HPOBenchmark, HPOScorer
 
@@ -36,8 +37,7 @@ def __init__(
         self.nparams = nparams
         self.nslack = nslack
         self.offset = offset
-        self.x_max = np.full(self.nparams, fill_value=offset)
-        self.x_max[nparams - nslack :] = np.nan
+        self.x_max = [offset for _ in range(self.nparams)]
         self.y_max = 0.0
 
 
diff --git a/src/deephyper_benchmark/benchmarks/dtlz/dtlz.py b/src/deephyper_benchmark/benchmarks/dtlz/dtlz.py
index 8204013..08f4f21 100644
--- a/src/deephyper_benchmark/benchmarks/dtlz/dtlz.py
+++ b/src/deephyper_benchmark/benchmarks/dtlz/dtlz.py
@@ -7,7 +7,7 @@
 import numpy as np
 from deephyper.evaluator import RunningJob, profile
 from deephyper.hpo import HpProblem
-from deephyper.skopt.moo import hypervolume, pareto_front
+from deephyper.skopt.moo import pareto_front
 
 from deephyper_benchmark import HPOBenchmark, MultiObjHPOScorer
 
@@ -58,11 +58,10 @@ class DTLZScorer(MultiObjHPOScorer):
 
     """
 
-    prob_id: int
-
     def __init__(self, prob_id: int, nobj: int):
         """Read the current DTLZ problem defn from environment vars."""
-        super().__init__(prob_id=prob_id, nobj=nobj)
+        super().__init__(nobj)
+        self.prob_id = prob_id
 
     @property
     def nadir_point(self):

From bfdba6528b924a04c7ae331752f2a5f57762c992 Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Mon, 24 Mar 2025 19:03:42 +0100
Subject: [PATCH 38/43] adding tests for dtlz

---
 tests/test_cbbo.py |  4 ++--
 tests/test_dtlz.py | 21 +++++++++++++++++++++
 2 files changed, 23 insertions(+), 2 deletions(-)
 create mode 100644 tests/test_dtlz.py

diff --git a/tests/test_cbbo.py b/tests/test_cbbo.py
index d4d7dde..7be8073 100644
--- a/tests/test_cbbo.py
+++ b/tests/test_cbbo.py
@@ -4,7 +4,7 @@
 import deephyper_benchmark.benchmarks.cbbo as bench
 
 
-def test_cbbo_benchmarks():
+def test_cbbo_benchmarks(tmp_path="."):
     """Test cbbo benchmarks."""
 
     benchmarks = [
@@ -17,7 +17,7 @@ def test_cbbo_benchmarks():
 
     max_evals = 25
     for b in benchmarks:
-        search = RandomSearch(b.problem, b.run_function)
+        search = RandomSearch(b.problem, b.run_function, log_dir=tmp_path)
         results = search.search(max_evals)
         assert len(results) == max_evals
         cumul_regret = b.scorer.cumul_regret(results.objective)
diff --git a/tests/test_dtlz.py b/tests/test_dtlz.py
new file mode 100644
index 0000000..dece560
--- /dev/null
+++ b/tests/test_dtlz.py
@@ -0,0 +1,21 @@
+"""here."""
+
+from deephyper.hpo import RandomSearch
+import deephyper_benchmark.benchmarks.dtlz as bench
+
+
+def test_dtlz_benchmarks(tmp_path="."):
+    """Test cbbo benchmarks."""
+
+    max_evals = 25
+    nobj = 2
+
+    for prob_id in range(1, 8):
+        b = bench.DTLZBenchmark(prob_id=prob_id, nobj=nobj)
+        search = RandomSearch(b.problem, b.run_function, log_dir=tmp_path)
+        results = search.search(max_evals)
+        assert len(results) == max_evals
+        cols = [f"objective_{i}" for i in range(nobj)]
+        assert all(c in results.columns for c in cols)
+        hvi = b.scorer.hypervolume(results[cols].values)
+        assert len(hvi) == max_evals

From ae74a70dc69e5a5ea3aff08a5046e36e2b2ddc87 Mon Sep 17 00:00:00 2001
From: Gavin Wiggins <6828967+wigging@users.noreply.github.com>
Date: Mon, 24 Mar 2025 14:45:32 -0400
Subject: [PATCH 39/43] Add pyright settings

---
 pyproject.toml | 21 ++++++++-------------
 1 file changed, 8 insertions(+), 13 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index f692c9b..62d8017 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,6 @@ build-backend = "hatchling.build"
 name = "deephyper-benchmark"
 version = "0.0.2"
 dependencies = [
-    # "deephyper",
     "deephyper>=0.9.3",
 ]
 requires-python = ">=3.10"
@@ -29,12 +28,6 @@ classifiers = [
     "Programming Language :: Python :: 3.13",
 ]
 
-[project.optional-dependencies]
-
-
-[project.scripts]
-
-
 [project.urls]
 Documentation = "http://deephyper.readthedocs.io"
 Changes = "https://github.com/deephyper/deephyper/releases"
@@ -42,10 +35,6 @@ Forum = "https://github.com/deephyper/deephyper/discussions"
 GitHub = "https://github.com/deephyper/deephyper"
 Issues = "https://github.com/deephyper/deephyper/issues"
 
-[tool.pytest.ini_options]
-norecursedirs = ".git"
-
-
 [tool.coverage.paths]
 source = ["src/", "*/site-packages"]
 
@@ -56,9 +45,15 @@ source = ["deephyper"]
 [tool.coverage.report]
 exclude_lines = ["if __name__ == '__main__':"]
 
+[tool.pyright]
+pythonVersion = "3.13"
+venvPath = "."
+venv = ".venv"
+
+[tool.pytest.ini_options]
+norecursedirs = ".git"
+
 [tool.ruff]
-exclude = [
-]
 line-length = 100
 
 [tool.ruff.lint]

From 4ec279750e7ac497774580c3df156c6b549c6972 Mon Sep 17 00:00:00 2001
From: Gavin Wiggins <6828967+wigging@users.noreply.github.com>
Date: Mon, 24 Mar 2025 16:08:35 -0400
Subject: [PATCH 40/43] Add levy and michal modules

---
 .../benchmarks/cbbo/__init__.py               |  6 ++-
 .../benchmarks/cbbo/levy.py                   | 51 +++++++++++++++++++
 .../benchmarks/cbbo/michal.py                 | 47 +++++++++++++++++
 3 files changed, 103 insertions(+), 1 deletion(-)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/levy.py
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/michal.py

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/__init__.py b/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
index 26fa444..3ccd07d 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
@@ -1,10 +1,12 @@
-"""here."""
+"""Subpackage for C-BBO benchmarks."""
 
 from .ackley import AckleyBenchmark
 from .branin import BraninBenchmark
 from .easom import EasomBenchmark
 from .griewank import GriewankBenchmark
 from .hartmann6D import Hartmann6DBenchmark
+from .levy import LevyBenchmark
+from .michal import MichalBenchmark
 
 __all__ = [
     "AckleyBenchmark",
@@ -12,4 +14,6 @@
     "EasomBenchmark",
     "GriewankBenchmark",
     "Hartmann6DBenchmark",
+    "LevyBenchmark",
+    "MichalBenchmark",
 ]
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/levy.py b/src/deephyper_benchmark/benchmarks/cbbo/levy.py
new file mode 100644
index 0000000..7346708
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/levy.py
@@ -0,0 +1,51 @@
+"""Module for Levy benchmark."""
+
+import functools
+import numpy as np
+from deephyper.hpo import HpProblem
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+
+def levy(x):
+    """Levy benchmark function."""
+    z = 1 + (x - 1) / 4
+    func = (
+        np.sin(np.pi * z[0]) ** 2
+        + np.sum((z[:-1] - 1) ** 2 * (1 + 10 * np.sin(np.pi * z[:-1] + 1) ** 2))
+        + (z[-1] - 1) ** 2 * (1 + np.sin(2 * np.pi * z[-1]) ** 2)
+    )
+    return func
+
+
+class LevyScorer(HPOScorer):
+    """A class defining performance evaluators for the Levy problem."""
+
+    def __init__(self, nparams: int = 5):
+        self.nparams = nparams
+
+
+class LevyBenchmark(HPOBenchmark):
+    """Levy benchmark."""
+
+    def __init__(self, ndims=5):
+        """Create a Levy benchmark."""
+        self.ndims = ndims
+
+    @property
+    def problem(self):
+        """Define the hyperparameter problem."""
+        domain = (-10.0, 10.0)
+        problem = HpProblem()
+
+        for i in range(self.ndims):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+    @property
+    def run_function(self):
+        """Provide the run function for the hyperparameter benchmark."""
+        return functools.partial(run_function, bb_func=levy)
+
+    @property
+    def scorer(self):
+        """Provide the scorer for the hyperparameter benchmark."""
+        return LevyScorer(self.ndims)
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/michal.py b/src/deephyper_benchmark/benchmarks/cbbo/michal.py
new file mode 100644
index 0000000..5c80a58
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/michal.py
@@ -0,0 +1,47 @@
+"""Module for Michal benchmark."""
+
+import functools
+import numpy as np
+from deephyper.hpo import HpProblem
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+
+def michal(x, m=10):
+    """Michal benchmark function."""
+    ix2 = np.arange(1, len(x) + 1) * x**2
+    y = -np.sum(np.sin(x) * np.power(np.sin(ix2 / np.pi), 2 * m))
+    return y
+
+
+class MichalScorer(HPOScorer):
+    """Define performance evaluators for the Michal problem."""
+
+    def __init__(self, nparams=2):
+        self.nparams = nparams
+
+
+class MichalBenchmark(HPOBenchmark):
+    """Michal benchmark."""
+
+    def __init__(self, nparams=2):
+        """Create a Michal benchmark."""
+        self.nparams = nparams
+
+    @property
+    def problem(self):
+        """Define the hyperparameter problem."""
+        domain = (0, np.pi)
+        problem = HpProblem()
+
+        for i in range(self.nparams):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+    @property
+    def run_function(self):
+        """Provide the run function for the hyperparameter benchmark."""
+        return functools.partial(run_function, bb_func=michal)
+
+    @property
+    def scorer(self):
+        """Provide the scorer for the hyperparameter benchmark."""
+        return MichalScorer(self.nparams)

From 61d36f2794e5179922b026be9c6be102e6242352 Mon Sep 17 00:00:00 2001
From: Gavin Wiggins <6828967+wigging@users.noreply.github.com>
Date: Mon, 24 Mar 2025 16:41:53 -0400
Subject: [PATCH 41/43] Add modules for rosen, schwefel, shekel benchmarks

---
 .../benchmarks/cbbo/__init__.py               |  8 ++-
 .../benchmarks/cbbo/rosen.py                  | 41 +++++++++++++
 .../benchmarks/cbbo/schwefel.py               | 48 +++++++++++++++
 .../benchmarks/cbbo/shekel.py                 | 60 +++++++++++++++++++
 4 files changed, 156 insertions(+), 1 deletion(-)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/rosen.py
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/schwefel.py
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/shekel.py

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/__init__.py b/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
index 3ccd07d..c85debc 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/__init__.py
@@ -1,4 +1,4 @@
-"""Subpackage for C-BBO benchmarks."""
+"""Subpackage for the C-BBO benchmarks."""
 
 from .ackley import AckleyBenchmark
 from .branin import BraninBenchmark
@@ -7,6 +7,9 @@
 from .hartmann6D import Hartmann6DBenchmark
 from .levy import LevyBenchmark
 from .michal import MichalBenchmark
+from .rosen import RosenBenchmark
+from .schwefel import SchwefelBenchmark
+from .shekel import ShekelBenchmark
 
 __all__ = [
     "AckleyBenchmark",
@@ -16,4 +19,7 @@
     "Hartmann6DBenchmark",
     "LevyBenchmark",
     "MichalBenchmark",
+    "RosenBenchmark",
+    "SchwefelBenchmark",
+    "ShekelBenchmark",
 ]
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/rosen.py b/src/deephyper_benchmark/benchmarks/cbbo/rosen.py
new file mode 100644
index 0000000..a4fe9d4
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/rosen.py
@@ -0,0 +1,41 @@
+"""Module for Rosen benchmark."""
+
+import functools
+from deephyper.hpo import HpProblem
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+from scipy.optimize import rosen
+from .utils import run_function
+
+
+class RosenScorer(HPOScorer):
+    """Define performance evaluators for the Rosen problem."""
+
+    def __init__(self, nparams=5):
+        self.nparams = nparams
+
+
+class RosenBenchmark(HPOBenchmark):
+    """Rosen benchmark."""
+
+    def __init__(self, nparams=5):
+        """Create a Rosen benchmark."""
+        self.nparams = nparams
+
+    @property
+    def problem(self):
+        """Define the hyperparameter problem."""
+        domain = (-5.0, 10.0)
+        problem = HpProblem()
+
+        for i in range(self.nparams):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+    @property
+    def run_function(self):
+        """Provide the run function for the hyperparameter benchmark."""
+        return functools.partial(run_function, bb_func=rosen)
+
+    @property
+    def scorer(self):
+        """Provide the scorer for the hyperparameter benchmark."""
+        return RosenScorer(self.nparams)
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/schwefel.py b/src/deephyper_benchmark/benchmarks/cbbo/schwefel.py
new file mode 100644
index 0000000..ad4469f
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/schwefel.py
@@ -0,0 +1,48 @@
+"""Module for Schwefel benchmark."""
+
+import functools
+import numpy as np
+from deephyper.hpo import HpProblem
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+from .utils import run_function
+
+
+def schwefel(x):
+    """Schwefel benchmark function."""
+    n = len(x)
+    y = 418.9829 * n - sum(x * np.sin(np.sqrt(np.abs(x))))
+    return y
+
+
+class SchwefelScorer(HPOScorer):
+    """Define performance evaluators for the Schwefel problem."""
+
+    def __init__(self, nparams=5):
+        self.nparams = nparams
+
+
+class SchwefelBenchmark(HPOBenchmark):
+    """Schwefel benchmark."""
+
+    def __init__(self, nparams=5):
+        """Create a Schwefel benchmark."""
+        self.nparams = nparams
+
+    @property
+    def problem(self):
+        """Define the hyperparameter problem."""
+        domain = (-500.0, 500.0)
+        problem = HpProblem()
+
+        for i in range(self.nparams):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+    @property
+    def run_function(self):
+        """Provide the run function for the hyperparameter benchmark."""
+        return functools.partial(run_function, bb_func=schwefel)
+
+    @property
+    def scorer(self):
+        """Provide the scorer for the hyperparameter benchmark."""
+        return SchwefelScorer(self.nparams)
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/shekel.py b/src/deephyper_benchmark/benchmarks/cbbo/shekel.py
new file mode 100644
index 0000000..7268450
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/shekel.py
@@ -0,0 +1,60 @@
+"""Module for Shekel benchmark."""
+
+import functools
+import numpy as np
+from deephyper.hpo import HpProblem
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+from .utils import run_function
+
+
+def shekel(x):
+    """Shekel benchmark function."""
+    m = 10
+    beta = 0.1 * np.array([1, 2, 2, 4, 4, 6, 3, 7, 5, 5]).T
+
+    C = np.array(
+        [
+            [4, 1, 8, 6, 3, 2, 5, 8, 6, 7],
+            [4, 1, 8, 6, 7, 9, 3, 1, 2, 3.6],
+            [4, 1, 8, 6, 3, 2, 5, 8, 6, 7],
+            [4, 1, 8, 6, 7, 9, 3, 1, 2, 3.6],
+        ]
+    )
+
+    y = -sum([1 / (np.sum((x - C[:, i]) ** 2) + beta[i]) for i in range(m)])
+
+    return y
+
+
+class ShekelScorer(HPOScorer):
+    """Define performance evaluators for the Shekel problem."""
+
+    def __init__(self, nparams=4):
+        self.nparams = nparams
+
+
+class ShekelBenchmark(HPOBenchmark):
+    """Shekel benchmark."""
+
+    def __init__(self, nparams=4):
+        """Create a Shekel benchmark."""
+        self.nparams = nparams
+
+    @property
+    def problem(self):
+        """Define the hyperparameter problem."""
+        domain = (0.0, 10.0)
+        problem = HpProblem()
+
+        for i in range(self.nparams):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+    @property
+    def run_function(self):
+        """Provide the run function for the hyperparameter benchmark."""
+        return functools.partial(run_function, bb_func=shekel)
+
+    @property
+    def scorer(self):
+        """Provide the scorer for the hyperparameter benchmark."""
+        return ShekelScorer(self.nparams)

From b461e76cf0ea9dc88045d8202ea6b2204e8a6276 Mon Sep 17 00:00:00 2001
From: Gavin Wiggins <6828967+wigging@users.noreply.github.com>
Date: Mon, 24 Mar 2025 16:43:18 -0400
Subject: [PATCH 42/43] Import run_function from utils

---
 src/deephyper_benchmark/benchmarks/cbbo/levy.py   | 9 +++++----
 src/deephyper_benchmark/benchmarks/cbbo/michal.py | 1 +
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/levy.py b/src/deephyper_benchmark/benchmarks/cbbo/levy.py
index 7346708..f4e838b 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/levy.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/levy.py
@@ -4,6 +4,7 @@
 import numpy as np
 from deephyper.hpo import HpProblem
 from deephyper_benchmark import HPOBenchmark, HPOScorer
+from .utils import run_function
 
 
 def levy(x):
@@ -27,9 +28,9 @@ def __init__(self, nparams: int = 5):
 class LevyBenchmark(HPOBenchmark):
     """Levy benchmark."""
 
-    def __init__(self, ndims=5):
+    def __init__(self, nparams=5):
         """Create a Levy benchmark."""
-        self.ndims = ndims
+        self.nparams = nparams
 
     @property
     def problem(self):
@@ -37,7 +38,7 @@ def problem(self):
         domain = (-10.0, 10.0)
         problem = HpProblem()
 
-        for i in range(self.ndims):
+        for i in range(self.nparams):
             problem.add_hyperparameter(domain, f"x{i}")
 
     @property
@@ -48,4 +49,4 @@ def run_function(self):
     @property
     def scorer(self):
         """Provide the scorer for the hyperparameter benchmark."""
-        return LevyScorer(self.ndims)
+        return LevyScorer(self.nparams)
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/michal.py b/src/deephyper_benchmark/benchmarks/cbbo/michal.py
index 5c80a58..4d7253c 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/michal.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/michal.py
@@ -4,6 +4,7 @@
 import numpy as np
 from deephyper.hpo import HpProblem
 from deephyper_benchmark import HPOBenchmark, HPOScorer
+from .utils import run_function
 
 
 def michal(x, m=10):

From 8aa7696d337e7c6a5e7f04a72ca51a811ed0a8ee Mon Sep 17 00:00:00 2001
From: Deathn0t <romainegele@gmail.com>
Date: Tue, 25 Mar 2025 08:50:17 +0100
Subject: [PATCH 43/43] fixed cbbo benchmark functions

---
 .../benchmarks/cbbo/ackley.py                 |   3 +-
 .../benchmarks/cbbo/cossin/hpo_benchmark.py   | 115 ++++++++++++++++++
 .../benchmarks/cbbo/griewank.py               |   7 +-
 .../benchmarks/cbbo/hartmann6D.py             |   6 +-
 .../benchmarks/cbbo/levy.py                   |  16 ++-
 .../benchmarks/cbbo/michal.py                 |  30 ++++-
 .../benchmarks/cbbo/rosen.py                  |  23 +++-
 .../benchmarks/cbbo/schwefel.py               |  13 +-
 .../benchmarks/cbbo/shekel.py                 |  19 ++-
 9 files changed, 202 insertions(+), 30 deletions(-)
 create mode 100644 src/deephyper_benchmark/benchmarks/cbbo/cossin/hpo_benchmark.py

diff --git a/src/deephyper_benchmark/benchmarks/cbbo/ackley.py b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
index c234353..0d9654d 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/ackley.py
@@ -4,7 +4,6 @@
 
 import numpy as np
 from deephyper.hpo import HpProblem
-from typing import List
 
 from deephyper_benchmark import HPOBenchmark, HPOScorer
 
@@ -37,7 +36,7 @@ def __init__(
         self.nparams = nparams
         self.nslack = nslack
         self.offset = offset
-        self.x_max = [offset for _ in range(self.nparams)]
+        self.x_max = np.asarray([offset for _ in range(self.nparams)])
         self.y_max = 0.0
 
 
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/cossin/hpo_benchmark.py b/src/deephyper_benchmark/benchmarks/cbbo/cossin/hpo_benchmark.py
new file mode 100644
index 0000000..b4d0487
--- /dev/null
+++ b/src/deephyper_benchmark/benchmarks/cbbo/cossin/hpo_benchmark.py
@@ -0,0 +1,115 @@
+"""Module defining the problem and run-function of the benchmark."""
+
+import os
+import time
+
+import numpy as np
+
+from deephyper.hpo import HpProblem
+from deephyper.evaluator import profile, RunningJob
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
+__all__ = ["benchmark"]
+
+
+def cossin(x):
+    x = np.sum(x)
+    y = np.cos(5 * x / 10) + 2 * np.sin(x / 10) + x / 100
+    return y
+
+
+@profile
+def run_function(job: RunningJob, sleep=False, sleep_mean=60, sleep_noise=20, y_noise=0.0) -> dict:
+    config = job.parameters
+
+    if sleep:
+        t_sleep = np.random.normal(loc=sleep_mean, scale=sleep_noise)
+        t_sleep = max(t_sleep, 0)
+        time.sleep(t_sleep)
+
+    x = np.array([config[k] for k in config if "x" in k])
+    x = np.asarray_chkfinite(x)  # ValueError if any NaN or Inf
+
+    y = cossin(x)
+
+    if y_noise > 0.0:
+        rng = np.random.RandomState(int(job.id.split(".")[-1]))
+        eps = rng.normal(loc=0.0, scale=y_noise)
+        y += eps
+
+    return y
+
+
+class CosSinHPOScorer(HPOScorer):
+    """A class defining performance evaluators for the CosSin problem."""
+
+    def __init__(
+        self,
+        p_num,
+        p_num_slack,
+    ):
+        self.p_num = p_num
+        self.x_min = np.full((self.p_num,), 18.6738)
+        self.x_min[self.p_num - p_num_slack :] = np.nan
+        self.y_min = 3.1
+
+    def simple_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the regret of a list of given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of regret values.
+        """
+        return self.y_min - y
+
+    def cumul_regret(self, y: np.ndarray) -> np.ndarray:
+        """Compute the cumulative regret of an array of ordered given solution.
+
+        Args:
+            y (np.ndarray): An array of solutions.
+
+        Returns:
+            np.ndarray: An array of cumulative regret values.
+        """
+        return np.cumsum(self.simple_regret(y))
+
+
+class CosSinHPOBenchmark(HPOBenchmark):
+    def refresh_settings(self):
+        self.DEEPHYPER_BENCHMARK_NDIMS = int(os.environ.get("DEEPHYPER_BENCHMARK_NDIMS", 1))
+        self.DEEPHYPER_BENCHMARK_OFFSET = float(os.environ.get("DEEPHYPER_BENCHMARK_OFFSET", 0.0))
+        self.DEEPHYPER_BENCHMARK_NDIMS_SLACK = int(
+            os.environ.get("DEEPHYPER_BENCHMARK_NDIMS_SLACK", 0)
+        )
+
+    @property
+    def problem(self):
+        domain = (
+            -50.0 - self.DEEPHYPER_BENCHMARK_OFFSET,
+            50.0 - self.DEEPHYPER_BENCHMARK_OFFSET,
+        )
+        problem = HpProblem()
+        for i in range(self.DEEPHYPER_BENCHMARK_NDIMS - self.DEEPHYPER_BENCHMARK_NDIMS_SLACK):
+            problem.add_hyperparameter(domain, f"x{i}")
+
+        # Add slack/dummy dimensions (useful to test predicors which are sensitive
+        # to unimportant features)
+        for i in range(
+            self.DEEPHYPER_BENCHMARK_NDIMS - self.DEEPHYPER_BENCHMARK_NDIMS_SLACK,
+            self.DEEPHYPER_BENCHMARK_NDIMS,
+        ):
+            problem.add_hyperparameter(domain, f"z{i}")
+        return problem
+
+    @property
+    def run_function(self):
+        return run_function
+
+    @property
+    def scorer(self):
+        return CosSinHPOScorer(self.DEEPHYPER_BENCHMARK_NDIMS, self.DEEPHYPER_BENCHMARK_NDIMS_SLACK)
+
+
+benchmark = CosSinHPOBenchmark()
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/griewank.py b/src/deephyper_benchmark/benchmarks/cbbo/griewank.py
index a49fc33..62ed818 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/griewank.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/griewank.py
@@ -11,6 +11,10 @@
 
 
 def griewank(x, fr=4000):  # noqa: D103
+    """Griewank function benchmark.
+
+    Description of the function: https://www.sfu.ca/~ssurjano/griewank.html
+    """
     n = len(x)
     j = np.arange(1.0, n + 1)
     s = np.sum(x**2)
@@ -31,8 +35,7 @@ def __init__(
         self.nparams = nparams
         self.nslack = nslack
         self.offset = offset
-        self.x_max = np.full(self.nparams, fill_value=offset)
-        self.x_max[nparams - nslack :] = np.nan
+        self.x_max = np.asarray([offset for _ in range(self.nparams)])
         self.y_max = 0.0
 
 
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py b/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py
index cab922b..baa716e 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/hartmann6D.py
@@ -11,6 +11,10 @@
 
 
 def hartmann6D(x):  # noqa: D103
+    """Hartmann6D function benchmark.
+
+    Description of the function: https://www.sfu.ca/~ssurjano/hart6.html
+    """
     alpha = np.array([1.0, 1.2, 3.0, 3.2])
     A = np.array(
         [
@@ -40,7 +44,7 @@ class Hartmann6DScorer(HPOScorer):
 
     def __init__(self):
         self.nparams = 6
-        self.x_max = np.array(
+        self.x_max = np.asarray(
             [
                 0.20169,
                 0.150011,
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/levy.py b/src/deephyper_benchmark/benchmarks/cbbo/levy.py
index f4e838b..27102f9 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/levy.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/levy.py
@@ -1,21 +1,27 @@
-"""Module for Levy benchmark."""
+"""Module for Levy benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/levy.html
+"""
 
 import functools
+
 import numpy as np
 from deephyper.hpo import HpProblem
+
 from deephyper_benchmark import HPOBenchmark, HPOScorer
+
 from .utils import run_function
 
 
 def levy(x):
     """Levy benchmark function."""
     z = 1 + (x - 1) / 4
-    func = (
+    y = (
         np.sin(np.pi * z[0]) ** 2
         + np.sum((z[:-1] - 1) ** 2 * (1 + 10 * np.sin(np.pi * z[:-1] + 1) ** 2))
         + (z[-1] - 1) ** 2 * (1 + np.sin(2 * np.pi * z[-1]) ** 2)
     )
-    return func
+    return -y
 
 
 class LevyScorer(HPOScorer):
@@ -23,6 +29,8 @@ class LevyScorer(HPOScorer):
 
     def __init__(self, nparams: int = 5):
         self.nparams = nparams
+        self.x_max = np.ones(self.nparams)
+        self.y_max = 0.0
 
 
 class LevyBenchmark(HPOBenchmark):
@@ -37,9 +45,9 @@ def problem(self):
         """Define the hyperparameter problem."""
         domain = (-10.0, 10.0)
         problem = HpProblem()
-
         for i in range(self.nparams):
             problem.add_hyperparameter(domain, f"x{i}")
+        return problem
 
     @property
     def run_function(self):
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/michal.py b/src/deephyper_benchmark/benchmarks/cbbo/michal.py
index 4d7253c..af1a4c9 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/michal.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/michal.py
@@ -1,41 +1,59 @@
-"""Module for Michal benchmark."""
+"""Module for Michal benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/michal.html
+"""
 
 import functools
+
 import numpy as np
 from deephyper.hpo import HpProblem
+
 from deephyper_benchmark import HPOBenchmark, HPOScorer
+
 from .utils import run_function
 
 
 def michal(x, m=10):
-    """Michal benchmark function."""
+    """Michalewicz function benchmark."""
     ix2 = np.arange(1, len(x) + 1) * x**2
     y = -np.sum(np.sin(x) * np.power(np.sin(ix2 / np.pi), 2 * m))
-    return y
+    return -y
 
 
 class MichalScorer(HPOScorer):
     """Define performance evaluators for the Michal problem."""
 
     def __init__(self, nparams=2):
+        assert nparams in [2, 5, 10], (
+            "nparams should be in [2, 5, 10] otherwise the solution is unknown."
+        )
         self.nparams = nparams
+        if self.nparams == 2:
+            self.x_max = np.array([2.20, 1.57])
+            self.y_max = 1.8013
+        elif self.nparams == 5:
+            self.x_max = None
+            self.y_max = 4.687658
+        elif self.nparams == 10:
+            self.x_max = None
+            self.y_max = 9.66015
 
 
 class MichalBenchmark(HPOBenchmark):
     """Michal benchmark."""
 
-    def __init__(self, nparams=2):
+    def __init__(self, nparams: int = 5):
         """Create a Michal benchmark."""
         self.nparams = nparams
 
     @property
     def problem(self):
         """Define the hyperparameter problem."""
-        domain = (0, np.pi)
+        domain = (0.0, np.pi)
         problem = HpProblem()
-
         for i in range(self.nparams):
             problem.add_hyperparameter(domain, f"x{i}")
+        return problem
 
     @property
     def run_function(self):
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/rosen.py b/src/deephyper_benchmark/benchmarks/cbbo/rosen.py
index a4fe9d4..e5cbda1 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/rosen.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/rosen.py
@@ -1,17 +1,30 @@
-"""Module for Rosen benchmark."""
+"""Module for Rosen benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/rosen.html
+"""
 
 import functools
+
+import numpy as np
 from deephyper.hpo import HpProblem
-from deephyper_benchmark import HPOBenchmark, HPOScorer
 from scipy.optimize import rosen
+
+from deephyper_benchmark import HPOBenchmark, HPOScorer
+
 from .utils import run_function
 
 
+def rosen_(x):  # noqa: D103
+    return -rosen(x)
+
+
 class RosenScorer(HPOScorer):
     """Define performance evaluators for the Rosen problem."""
 
-    def __init__(self, nparams=5):
+    def __init__(self, nparams: int = 5):
         self.nparams = nparams
+        self.x_max = np.ones(self.nparams)
+        self.y_max = 0.0
 
 
 class RosenBenchmark(HPOBenchmark):
@@ -26,14 +39,14 @@ def problem(self):
         """Define the hyperparameter problem."""
         domain = (-5.0, 10.0)
         problem = HpProblem()
-
         for i in range(self.nparams):
             problem.add_hyperparameter(domain, f"x{i}")
+        return problem
 
     @property
     def run_function(self):
         """Provide the run function for the hyperparameter benchmark."""
-        return functools.partial(run_function, bb_func=rosen)
+        return functools.partial(run_function, bb_func=rosen_)
 
     @property
     def scorer(self):
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/schwefel.py b/src/deephyper_benchmark/benchmarks/cbbo/schwefel.py
index ad4469f..c0de78d 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/schwefel.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/schwefel.py
@@ -1,4 +1,7 @@
-"""Module for Schwefel benchmark."""
+"""Module for Schwefel benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/schwef.html
+"""
 
 import functools
 import numpy as np
@@ -11,7 +14,7 @@ def schwefel(x):
     """Schwefel benchmark function."""
     n = len(x)
     y = 418.9829 * n - sum(x * np.sin(np.sqrt(np.abs(x))))
-    return y
+    return -y
 
 
 class SchwefelScorer(HPOScorer):
@@ -19,12 +22,14 @@ class SchwefelScorer(HPOScorer):
 
     def __init__(self, nparams=5):
         self.nparams = nparams
+        self.x_max = np.full(self.nparams, fill_value=420.9687)
+        self.y_max = 0.0
 
 
 class SchwefelBenchmark(HPOBenchmark):
     """Schwefel benchmark."""
 
-    def __init__(self, nparams=5):
+    def __init__(self, nparams: int = 5):
         """Create a Schwefel benchmark."""
         self.nparams = nparams
 
@@ -33,9 +38,9 @@ def problem(self):
         """Define the hyperparameter problem."""
         domain = (-500.0, 500.0)
         problem = HpProblem()
-
         for i in range(self.nparams):
             problem.add_hyperparameter(domain, f"x{i}")
+        return problem
 
     @property
     def run_function(self):
diff --git a/src/deephyper_benchmark/benchmarks/cbbo/shekel.py b/src/deephyper_benchmark/benchmarks/cbbo/shekel.py
index 7268450..e8fc92a 100644
--- a/src/deephyper_benchmark/benchmarks/cbbo/shekel.py
+++ b/src/deephyper_benchmark/benchmarks/cbbo/shekel.py
@@ -1,9 +1,15 @@
-"""Module for Shekel benchmark."""
+"""Module for Shekel benchmark.
+
+Description of the function: https://www.sfu.ca/~ssurjano/shekel.html
+"""
 
 import functools
+
 import numpy as np
 from deephyper.hpo import HpProblem
+
 from deephyper_benchmark import HPOBenchmark, HPOScorer
+
 from .utils import run_function
 
 
@@ -22,8 +28,7 @@ def shekel(x):
     )
 
     y = -sum([1 / (np.sum((x - C[:, i]) ** 2) + beta[i]) for i in range(m)])
-
-    return y
+    return -y
 
 
 class ShekelScorer(HPOScorer):
@@ -31,23 +36,25 @@ class ShekelScorer(HPOScorer):
 
     def __init__(self, nparams=4):
         self.nparams = nparams
+        self.x_max = np.full(self.nparams, 4)
+        self.y_max = 10.5364
 
 
 class ShekelBenchmark(HPOBenchmark):
     """Shekel benchmark."""
 
-    def __init__(self, nparams=4):
+    def __init__(self):
         """Create a Shekel benchmark."""
-        self.nparams = nparams
+        self.nparams = 4
 
     @property
     def problem(self):
         """Define the hyperparameter problem."""
         domain = (0.0, 10.0)
         problem = HpProblem()
-
         for i in range(self.nparams):
             problem.add_hyperparameter(domain, f"x{i}")
+        return problem
 
     @property
     def run_function(self):