Repository: mad-lab-fau/tpcp (language: python)

Issue:
Optuna search with multi-processing repeats trials
OptunaSearch with n_jobs>1 seems to suggest the same paramters in every process. I thought I tested this...


Discussion / hints:

Fix patch:
diff --git a/CHANGELOG.md b/CHANGELOG.md
index 2bae5b20..c0b99cc5 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -4,6 +4,42 @@ All notable changes to this project will be documented in this file.
 The format is based on [Keep a Changelog](https://keepachangelog.com/en/1.0.0/) (+ the Migration Guide),
 and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0.html).
 
+## [0.20.0] - 2023-07-24
+
+### Changed
+
+- **BREAKING CHANGE**: The way how all Optuna based optimizer work has been changed.
+  Instead of passing a function, that returns a study, you now need to pass a function that returns the parameters of a
+  study.
+  Creating the study is now handled by tpcp internally to avoid issues with multiprocessing.
+  This results in two changes.
+  The parameter name for all optuna pipelines has changed from `create_study` to `get_study_params`.
+  Further, the expected call signature changed, as `get_study_params` now gets a seed as argument.
+  This seed should be used to initialize the random number generator of the sampler and pruner of a study to ensure
+  that each process gets a different seed and sampling process.
+  (https://github.com/mad-lab-fau/tpcp/pull/80)
+  
+  To migrate your code, you need to change the following:
+  
+  OLD:
+
+  ```python
+  def create_study():
+      return optuna.create_study(sampler=RandomSampler(seed=42))
+  
+  OptunaSearch(..., create_study=create_study, ...)
+  ```
+  
+  NEW:
+
+  ```python
+  def get_study_params(seed: int):
+      return dict(sampler=RandomSampler(seed=seed))
+  
+  OptunaSearch(..., get_study_params=get_study_params, random_seed=42, ...)
+  ```
+     
+
 ## [0.19.0] - 2023-07-06
 
 ### Added
diff --git a/examples/parameter_optimization/_04_custom_optuna_optimizer.py b/examples/parameter_optimization/_04_custom_optuna_optimizer.py
index c71ff149..4bdccf36 100644
--- a/examples/parameter_optimization/_04_custom_optuna_optimizer.py
+++ b/examples/parameter_optimization/_04_custom_optuna_optimizer.py
@@ -152,8 +152,8 @@ def f1_score(pipeline: MyPipeline, datapoint: ECGExampleData) -> float:
 # %%
 # The Custom Optimizer
 # --------------------
-# Optimizers in `tpcp` are nothing magical – they are simply algorithms that take a pipeline as input parameter and
-# have an action method called `optimize` that takes in a dataset and then optimizes some parameters of the passed
+# Optimizers in `tpcp` are nothing magical – they are simply pipelines that take a different pipeline as input parameter
+# and have an action method called `optimize` that takes in a dataset and then optimizes some parameters of the passed
 # pipeline using this data.
 #
 # The :class:`~tpcp.optimize.optuna.CustomOptunaOptimize` class already implements most of that for us and simply
@@ -249,16 +249,25 @@ def objective(trial: Trial, pipeline: PipelineT, dataset: DatasetT) -> float:
 # To run the optimization, we need to create a new Optuna study, a custom sampler and the function that defines our
 # search space:
 #
+# Instead of creating the study directly, we create a function that returns the parameters we want to pass to
+# :func:`~optuna.create_study`.
+# This way, the OptunaSearch class can control the study creation and can ensure that we have independent studies
+# for each run.
+# This method gets a random seed as input.
+# We use that to control the random sampler of Optuna.
+# This way, we ensure that a new random seed is used for each process, in case we use multiprocessing.
+# If we passed a fixed seed to the sampler, we would get the same results for each process.
+#
 # We use a simple in-memory study with the direction "maximize", as we want to optimize for the highest f1-score
 # However, we wrap it by a callable to ensure that we get a new and independent study everytime our Optuna optimizer
 # is called.
-from optuna import create_study, samplers
+from optuna import samplers
 
 
-def get_study():
+def get_study_params(seed):
     # We use a simple RandomSampler, but every optuna sampler will work
-    sampler = samplers.RandomSampler(seed=42)
-    return create_study(direction="maximize", sampler=sampler)
+    sampler = samplers.RandomSampler(seed=seed)
+    return dict(sampler=sampler, direction="maximize")
 
 
 # %%
@@ -283,10 +292,11 @@ def create_search_space(trial: Trial):
 # Then we can use the familiar :class:`~tpcp.optimize.Optimize` interface to run everything.
 opti = OptunaSearch(
     pipe,
-    get_study,
+    get_study_params,
     create_search_space=create_search_space,
     score_function=f1_score,
     n_trials=10,
+    random_seed=42,
 )
 
 opti = opti.optimize(example_data)
@@ -426,17 +436,18 @@ def single_score_callback(*, step: int, dataset: DatasetT, scores: Tuple[float,
 # %%
 # Running the new Optimizer stays the same (we even reuse the search space).
 # We only need to add an instance of our pruner to the study.
-def get_study() -> Study:
-    sampler = samplers.RandomSampler(seed=42)
-    return create_study(direction="maximize", sampler=sampler, pruner=MinDatapointPerformancePruner(0.3))
+def get_study_params(seed):
+    sampler = samplers.RandomSampler(seed=seed)
+    return dict(direction="maximize", sampler=sampler, pruner=MinDatapointPerformancePruner(0.3))
 
 
 opti_early_stop = OptunaSearchEarlyStopping(
     pipe,
-    get_study,
+    get_study_params,
     create_search_space=create_search_space,
     score_function=f1_score,
     n_trials=10,
+    random_seed=42,
 )
 
 opti_early_stop.optimize(example_data)
diff --git a/examples/parameter_optimization/_05_optuna_search.py b/examples/parameter_optimization/_05_optuna_search.py
index ab8112cd..3daaae1c 100644
--- a/examples/parameter_optimization/_05_optuna_search.py
+++ b/examples/parameter_optimization/_05_optuna_search.py
@@ -65,13 +65,13 @@ def run(self, datapoint: ECGExampleData):
 #
 # .. note:: We use a in-memory study here, if you want to use multiprocessing or ensure that your search can be
 #           continued, use a different study backend.
-from optuna import Trial, create_study, samplers
+from optuna import Trial, samplers
 
 
-def get_study():
+def get_study_params(seed):
     # We use a simple RandomSampler, but every optuna sampler will work
-    sampler = samplers.RandomSampler(seed=42)
-    return create_study(direction="maximize", sampler=sampler)
+    sampler = samplers.RandomSampler(seed=seed)
+    return dict(direction="maximize", sampler=sampler)
 
 
 # %%
@@ -115,7 +115,9 @@ def score(pipeline: MyPipeline, datapoint: ECGExampleData):
 # In this case, we want to maximize the f1 score.
 from tpcp.optimize.optuna import OptunaSearch
 
-opti = OptunaSearch(pipe, get_study, create_search_space, scoring=score, n_trials=10, score_name="f1_score")
+opti = OptunaSearch(
+    pipe, get_study_params, create_search_space, scoring=score, n_trials=10, score_name="f1_score", random_seed=42
+)
 opti = opti.optimize(example_data)
 
 # %%
diff --git a/poetry.lock b/poetry.lock
index 8b6ba70b..f7c170e1 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -143,108 +143,108 @@ uvloop = ["uvloop (>=0.15.2)"]
 
 [[package]]
 name = "certifi"
-version = "2023.5.7"
+version = "2023.7.22"
 description = "Python package for providing Mozilla's CA Bundle."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "certifi-2023.5.7-py3-none-any.whl", hash = "sha256:c6c2e98f5c7869efca1f8916fed228dd91539f9f1b444c314c06eef02980c716"},
-    {file = "certifi-2023.5.7.tar.gz", hash = "sha256:0f0d56dc5a6ad56fd4ba36484d6cc34451e1c6548c61daad8c320169f91eddc7"},
+    {file = "certifi-2023.7.22-py3-none-any.whl", hash = "sha256:92d6037539857d8206b8f6ae472e8b77db8058fec5937a1ef3f54304089edbb9"},
+    {file = "certifi-2023.7.22.tar.gz", hash = "sha256:539cc1d13202e33ca466e88b2807e29f4c13049d6d87031a3c110744495cb082"},
 ]
 
 [[package]]
 name = "charset-normalizer"
-version = "3.1.0"
+version = "3.2.0"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
 optional = false
 python-versions = ">=3.7.0"
 files = [
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 ]
 
 [[package]]
 name = "click"
-version = "8.1.3"
+version = "8.1.6"
 description = "Composable command line interface toolkit"
 optional = false
 python-versions = ">=3.7"
 files = [
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-    {file = "click-8.1.3.tar.gz", hash = "sha256:7682dc8afb30297001674575ea00d1814d808d6a36af415a82bd481d37ba7b8e"},
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 ]
 
 [package.dependencies]
@@ -252,13 +252,13 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [[package]]
 name = "cmaes"
-version = "0.9.1"
+version = "0.10.0"
 description = "Lightweight Covariance Matrix Adaptation Evolution Strategy (CMA-ES) implementation for Python 3."
 optional = true
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
@@ -478,45 +478,45 @@ testing = ["covdefaults (>=2.3)", "coverage (>=7.2.7)", "diff-cover (>=7.5)", "p
 
 [[package]]
 name = "fonttools"
-version = "4.40.0"
+version = "4.41.1"
 description = "Tools to manipulate font files"
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [package.extras]
@@ -630,13 +630,13 @@ files = [
 
 [[package]]
 name = "importlib-metadata"
-version = "6.7.0"
+version = "6.8.0"
 description = "Read metadata from Python packages"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -645,25 +645,25 @@ zipp = ">=0.5"
 [package.extras]
 docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
 perf = ["ipython"]
-testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
+testing = ["flufl.flake8", "importlib-resources (>=1.3)", "packaging", "pyfakefs", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf (>=0.9.2)", "pytest-ruff"]
 
 [[package]]
 name = "importlib-resources"
-version = "5.12.0"
+version = "6.0.0"
 description = "Read resources from Python packages"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
 zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
 
 [package.extras]
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 [[package]]
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@@ -1247,13 +1247,13 @@ tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "pa
 
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 [package.dependencies]
@@ -1939,13 +1939,13 @@ mpmath = ">=0.19"
 
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 [[package]]
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 [package.extras]
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-    {file = "zipp-3.15.0.tar.gz", hash = "sha256:112929ad649da941c23de50f356a2b5570c954b65150642bccdd66bf194d224b"},
+    {file = "zipp-3.16.2-py3-none-any.whl", hash = "sha256:679e51dd4403591b2d6838a48de3d283f3d188412a9782faadf845f298736ba0"},
+    {file = "zipp-3.16.2.tar.gz", hash = "sha256:ebc15946aa78bd63458992fc81ec3b6f7b1e92d51c35e6de1c3804e73b799147"},
 ]
 
 [package.extras]
-docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
-testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
+testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"]
 
 [extras]
 -torch-cpu = ["torch"]
diff --git a/pyproject.toml b/pyproject.toml
index fb0afd8e..f933ba4d 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "tpcp"
-version = "0.19.0"
+version = "0.20.0"
 description = "Pipeline and Dataset helpers for complex algorithm evaluation."
 authors = [
     "Arne Küderle <arne.kuederle@fau.de>",
diff --git a/tpcp/__init__.py b/tpcp/__init__.py
index df6b1c72..bd9b8587 100644
--- a/tpcp/__init__.py
+++ b/tpcp/__init__.py
@@ -23,7 +23,7 @@
 )
 from tpcp._pipeline import OptimizablePipeline, Pipeline
 
-__version__ = "0.19.0"
+__version__ = "0.20.0"
 
 
 __all__ = [
diff --git a/tpcp/optimize/optuna.py b/tpcp/optimize/optuna.py
index 48a3e30a..898483ae 100644
--- a/tpcp/optimize/optuna.py
+++ b/tpcp/optimize/optuna.py
@@ -1,4 +1,5 @@
 """Implementation of methods and classes to wrap the optimization Framework `Optuna`."""
+
 try:
     import optuna
 except ImportError as e:
@@ -10,11 +11,12 @@
 import warnings
 from ast import literal_eval
 from collections import defaultdict
-from typing import Any, Callable, Dict, List, Optional, Sequence, TypeVar, Union
+from typing import Any, Callable, Dict, List, Optional, Sequence, TypedDict, TypeVar, Union
 
 import numpy as np
 from joblib import Parallel
 from optuna import Study
+from optuna.study import StudyDirection
 from optuna.study.study import ObjectiveFuncType
 from optuna.trial import FrozenTrial, Trial
 from typing_extensions import Self
@@ -49,10 +51,21 @@ def _split_trials(n_trials, n_jobs):
         remaining -= 1
 
 
+class StudyParamsDict(TypedDict, total=False):
+    """Type of the dictionary returned by `get_study_params`."""
+
+    study_name: Optional[str]
+    storage: Union[str, optuna.storages.BaseStorage]
+    sampler: Optional[optuna.samplers.BaseSampler]
+    pruner: Optional[optuna.pruners.BasePruner]
+    direction: Optional[Union[str, StudyDirection]]
+    load_if_exists: bool
+    directions: Optional[Sequence[Union[str, StudyDirection]]]
+
+
 class _CustomOptunaOptimize(BaseOptimize[PipelineT, DatasetT]):
     pipeline: PipelineT
-    create_study: Callable[[], Study]
-
+    get_study_params: Callable[[int], StudyParamsDict]
     return_optimized: bool
 
     # Optuna Parameters that are directly forwarded to study.optimize
@@ -62,10 +75,12 @@ class _CustomOptunaOptimize(BaseOptimize[PipelineT, DatasetT]):
     gc_after_trial: bool
     show_progress_bar: bool
     n_jobs: int
+    random_seed: Optional[int]
 
     eval_str_paras: Sequence[str]
 
     optimized_pipeline_: PipelineT
+    random_seed_: int
     study_: Study
 
     @property
@@ -139,7 +154,10 @@ def optimize(self, dataset: DatasetT, **_: Any) -> Self:
         self.dataset = dataset
 
         objective = self._create_objective(self.pipeline, dataset=dataset)
-        self.study_ = self.create_study()
+        self.random_seed_ = self.random_seed if self.random_seed is not None else np.random.randint(0, 100)
+
+        study_params = self.get_study_params(self.random_seed_)
+        self.study_ = optuna.create_study(**study_params)
 
         if not isinstance(self.study_._storage, optuna.storages.InMemoryStorage):
             warnings.warn(
@@ -179,19 +197,29 @@ def optimize(self, dataset: DatasetT, **_: Any) -> Self:
 
             # This solution is based on the solution proposed here:
             # https://github.com/optuna/optuna/issues/2862
-            def _multi_process_call_optimize(n_trials: int):
+            def _multi_process_call_optimize(n_trials: int, seed: int):
+                study_params_per_process = self.get_study_params(seed)
+                if (name := study_params_per_process.get("study_name", None)) is not None:
+                    assert (
+                        name == self.study_.study_name
+                    ), "Study name must be the same on every call of `self.get_study_params`"
                 study = optuna.load_study(
+                    # Name and storage come from the existing study, as things will break, if they are not consistent
+                    # across processes
                     study_name=self.study_.study_name,
                     storage=self.study_._storage,
-                    sampler=self.study_.sampler,
-                    pruner=self.study_.pruner,
+                    # Pruner and sampler come from the params, because they have updated random seeds
+                    sampler=study_params_per_process.get("sampler", None),
+                    pruner=study_params_per_process.get("pruner", None),
                 )
                 self._call_optimize_multi_process(study, objective, n_trials)
 
+            # We call the objective function once in the main thread to make sure that the study is created correctly
+            _multi_process_call_optimize(1, self.random_seed_)
             parallel = Parallel(self.n_jobs)
             parallel(
-                delayed(_multi_process_call_optimize)(n_trials=n_trials_i)
-                for n_trials_i in _split_trials(self.n_trials, self.n_jobs)
+                delayed(_multi_process_call_optimize)(n_trials=n_trials_i, seed=self.random_seed_ + i + 1)
+                for (i, n_trials_i) in enumerate(_split_trials(self.n_trials - 1, self.n_jobs))
             )
 
         if self.return_optimized:
@@ -295,13 +323,19 @@ class CustomOptunaOptimize(_CustomOptunaOptimize[PipelineT, DatasetT]):
         A tpcp pipeline with some hyper-parameters that should be optimized.
         This can either be a normal pipeline or an optimizable-pipeline.
         This fully depends on your implementation of the `create_objective` method.
-    create_study
-        A callable that returns an optuna study instance to be used for the optimization.
-        It will be called as part of the `optimize` method without parameters.
-        The resulting study object can be accessed via `self.study_` after the optimization is finished.
-        Creating the study is handled via a callable, instead of providing the study object itself, to make it
-        possible to create individual studies, when CustomOptuna optimize is called by an external wrapper
+    get_study_params
+        A callable that returns a dictionary with the parameters that should be used to create the study (i.e. passed
+        to `optuna.create_study`).
+        Creating the study is handled via a callable, instead of providing the parameters or the study object directly,
+        to make it possible to create individual studies, when CustomOptuna optimize is called by an external wrapper
         (i.e. `cross_validate`).
+        Further, the provided function is called with a single parameter `seed` that can be used to create a samplers
+        and pruners with fixed random seeds.
+        Using the passed seed compared to a fix global seed is important, as otherwise the same sampler/pruner use the
+        same random numbers across multiple processes if `n_jobs > 1` (see more in Notes).
+        Note, that this method should return consistent output when called multiple times with the same seed.
+        Otherwise, unexpected behaviour can occur, where different processes use different samplers/pruners in a
+        multi-processing setting.
     n_trials
         The number of trials.
         If this argument is set to `None`, there is no limitation on the number of trials.
@@ -334,6 +368,10 @@ class CustomOptunaOptimize(_CustomOptunaOptimize[PipelineT, DatasetT]):
         If this is set to -1, all available cores are used.
 
         .. warning:: Read the notes on multiprocessing below before using this feature.
+    random_seed
+        A random seed that is used as base for the seed passed to your implementation `get_study_params`.
+        If None, this is set to a random integer between 0 and 100 (derived using numpy.random.randint).
+        In case of multiprocessing, this seed is used as offset to create different seeds for each process.
     eval_str_paras
         This can be a sequence (tuple/list) of parameter names used by Optuna that should be evaluated using
         `literal_eval` instead of just set as string on the pipeline.
@@ -414,6 +452,9 @@ class CustomOptunaOptimize(_CustomOptunaOptimize[PipelineT, DatasetT]):
     study_
         The study object itself.
         This should usually be identical to `self.study`.
+    random_seed_
+        The actual random seed used for the optimization.
+        This is either the value passed to `random_seed` or a random integer between 0 and 100.
 
     Examples
     --------
@@ -441,14 +482,21 @@ class CustomOptunaOptimize(_CustomOptunaOptimize[PipelineT, DatasetT]):
     The implementation is based on the suggestions made here: https://github.com/optuna/optuna/issues/2862
     However, it depends on internal optuna APIs and might break in future versions.
 
-    To use multiprocessing, the provided `create_study` function must return a study with a persistent backend (
+    To use multiprocessing, the provided `get_study_params` function must return a persistent backend (
     i.e. not the default InMemoryStorage), that can be written to by multiple processes.
     To make sure that your individual runs are independent and you don't leave behind any files, make sure you clean
     up your study after each run. You can use `optuna.delete_study(study_name=opti_instance.study_.study_name,
     storage=opti_instance.study_._storage)` for this.
 
+    Further, if you use samplers, that use a random seed (you likely are), you need to make sure that you pass the
+    `seed` provided to `get_study_params` to the sampler and potentially pruner as well.
+    Otherwise, each sampler/pruner instance might use the same random seed, which will lead to identical trials in
+    each process.
+
     From the implementation perspective, we split the number of trials into `n_jobs` chunks and then spawn one study
     per job.
+    However, we first run the first trial in the main process, to make sure that the study is created correctly.
+    Then we split the remaining trials into chunks and spawn a new process for each chunk.
     This study is a copy of the study from the main process and hence, points to the same database.
     Each process will then complete its chunk of trials and then terminate.
     This is a relatively naive implementation, but it avoids the overhead of spawning a new process for each trial.
@@ -474,21 +522,23 @@ class CustomOptunaOptimize(_CustomOptunaOptimize[PipelineT, DatasetT]):
     def __init__(
         self,
         pipeline: PipelineT,
-        create_study: Callable[[], Study],
+        get_study_params: Callable[[int], StudyParamsDict],
         *,
         n_trials: Optional[int] = None,
         timeout: Optional[float] = None,
         callbacks: Optional[List[Callable[[Study, FrozenTrial], None]]] = None,
         gc_after_trial: bool = False,
         n_jobs: int = 1,
+        random_seed: Optional[int] = None,
         eval_str_paras: Sequence[str] = (),
         show_progress_bar: bool = False,
         return_optimized: bool = True,
     ):
         self.pipeline = pipeline
-        self.create_study = create_study
+        self.get_study_params = get_study_params
 
         self.n_trials = n_trials
+        self.random_seed = random_seed
         self.timeout = timeout
         self.callbacks = callbacks
         self.gc_after_trial = gc_after_trial
@@ -507,7 +557,7 @@ class CustomOptunaOptimizeDc(_CustomOptunaOptimize[PipelineT, DatasetT]):
             """Dataclass version of CustomOptunaOptimize."""
 
             pipeline: PipelineT
-            create_study: Callable[[], Study]
+            get_study_params: Callable[[int], StudyParamsDict]
 
             # Optuna Parameters that are directly forwarded to study.optimize
             n_trials: Optional[int] = None
@@ -517,6 +567,7 @@ class CustomOptunaOptimizeDc(_CustomOptunaOptimize[PipelineT, DatasetT]):
             show_progress_bar: bool = False
 
             n_jobs: int = 1
+            random_seed: Optional[int] = None
             return_optimized: bool = True
 
             eval_str_paras: Sequence[str] = ()
@@ -539,7 +590,7 @@ class CustomOptunaOptimizeAt(_CustomOptunaOptimize[PipelineT, DatasetT]):
             """Attrs version of CustomOptunaOptimize."""
 
             pipeline: PipelineT
-            create_study: Callable[[], Study]
+            get_study_params: Callable[[int], StudyParamsDict]
 
             # Optuna Parameters that are directly forwarded to study.optimize
             n_trials: Optional[int] = None
@@ -548,7 +599,8 @@ class CustomOptunaOptimizeAt(_CustomOptunaOptimize[PipelineT, DatasetT]):
             gc_after_trial: bool = False
             show_progress_bar: bool = False
 
-            n_jobs: int = (1,)
+            n_jobs: int = 1
+            random_seed: Optional[int] = None
             return_optimized: bool = True
 
             eval_str_paras: Sequence[str] = ()
@@ -573,13 +625,19 @@ class OptunaSearch(_CustomOptunaOptimize[PipelineT, DatasetT]):
     pipeline
         A tpcp pipeline with some hyper-parameters that should be optimized.
         This should be a normal (i.e. non-optimizable pipeline) when using this class.
-    create_study
-        A callable that returns an optuna study instance to be used for the optimization.
-        It will be called as part of the `optimize` method without parameters.
-        The resulting study object can be accessed via `self.study_` after the optimization is finished.
-        Creating the study is handled via a callable, instead of providing the study object itself, to make it
-        possible to create individual studies, when CustomOptuna optimize is called by an external wrapper
+    get_study_params
+        A callable that returns a dictionary with the parameters that should be used to create the study (i.e. passed
+        to `optuna.create_study`).
+        Creating the study is handled via a callable, instead of providing the parameters or the study object directly,
+        to make it possible to create individual studies, when CustomOptuna optimize is called by an external wrapper
         (i.e. `cross_validate`).
+        Further, the provided fucntion is called with a single parameter `seed` that can be used to create a samplers
+        and pruners with different random seeds.
+        This is important for multi-processing (see more in Notes).
+        Note, that this method should return consistent output when called multiple times with the same seed.
+        Otherwise, unexcepected behaviour can occur, where different processes use different samplers/pruners in a
+        multi-processing setting (`n_jobs > 1`).
+
     create_search_space
         A callable that takes a :class:`~optuna.trial.Trial` object as input and calls `suggest_*` methods on it to
         define the search space.
@@ -618,6 +676,10 @@ class OptunaSearch(_CustomOptunaOptimize[PipelineT, DatasetT]):
 
         .. warning:: Read the notes in :class:`~tpcp.optimize.optuna.CustomOptunaOptimize` on multiprocessing below
                      before using this feature.
+    random_seed
+        A random seed that is used as base for the seed passed to your implementation `get_study_params`.
+        If None, this is set to a random integer between 0 and 100 (derived using numpy.random.randint).
+        In case of multiprocessing, this seed is used as offset to create different seeds for each process.
     eval_str_paras
         This can be a sequence (tuple/list) of parameter names used by Optuna that should be evaluated using
         `literal_eval` instead of just set as string on the pipeline.
@@ -690,6 +752,9 @@ class OptunaSearch(_CustomOptunaOptimize[PipelineT, DatasetT]):
         This should usually be identical to `self.study`.
     multimetric_
         If the scorer returned multiple scores
+    random_seed_
+        The actual random seed used for the optimization.
+        This is either the value passed to `random_seed` or a random integer between 0 and 100.
 
     """
 
@@ -702,7 +767,7 @@ class OptunaSearch(_CustomOptunaOptimize[PipelineT, DatasetT]):
     def __init__(
         self,
         pipeline: PipelineT,
-        create_study: Callable[[], Study],
+        get_study_params: Callable[[int], StudyParamsDict],
         create_search_space: Callable[[Trial], None],
         *,
         scoring: ScorerTypes[PipelineT, DatasetT, T],
@@ -712,6 +777,7 @@ def __init__(
         callbacks: Optional[List[Callable[[Study, FrozenTrial], None]]] = None,
         gc_after_trial: bool = False,
         n_jobs: int = 1,
+        random_seed: Optional[int] = None,
         eval_str_paras: Sequence[str] = (),
         show_progress_bar: bool = False,
         return_optimized: bool = True,
@@ -721,12 +787,13 @@ def __init__(
         self.score_name = score_name
 
         self.pipeline = pipeline
-        self.create_study = create_study
+        self.get_study_params = get_study_params
         self.n_trials = n_trials
         self.timeout = timeout
         self.callbacks = callbacks
         self.gc_after_trial = gc_after_trial
         self.n_jobs = n_jobs
+        self.random_seed = random_seed
         self.eval_str_paras = eval_str_paras
         self.show_progress_bar = show_progress_bar
         self.return_optimized = return_optimized
@@ -740,6 +807,8 @@ def create_objective(self) -> Callable[[Trial, PipelineT, DatasetT], Union[float
 
         scoring = _validate_scorer(self.scoring, self.pipeline)
 
+        # In a multiprocessing case, this function is called once in the main process and then in the worker processes.
+        # The first call in the main process allows us to set the multimetric_ attribute.
         def objective(trial: Trial, pipeline: PipelineT, dataset: DatasetT) -> float:
             # First we need to select parameters for the current trial
             if self.create_search_space is None:


Test patch:
diff --git a/tests/test_pipelines/test_optuna_optimize.py b/tests/test_pipelines/test_optuna_optimize.py
index 441fe352..aa44fc15 100644
--- a/tests/test_pipelines/test_optuna_optimize.py
+++ b/tests/test_pipelines/test_optuna_optimize.py
@@ -1,10 +1,11 @@
+import tempfile
 from typing import Callable, List, Optional, Sequence, Union
 from unittest.mock import Mock, patch
 
 import numpy as np
 import pytest
-from optuna import Study, Trial, create_study
-from optuna.samplers import GridSampler, RandomSampler
+from optuna import Study, Trial
+from optuna.samplers import GridSampler, RandomSampler, TPESampler
 from optuna.trial import FrozenTrial
 
 from tests.mixins.test_algorithm_mixin import TestAlgorithmMixin
@@ -18,7 +19,7 @@
 from tpcp import make_optimize_safe
 from tpcp._dataset import DatasetT
 from tpcp._pipeline import OptimizablePipeline, PipelineT
-from tpcp.optimize.optuna import CustomOptunaOptimize, OptunaSearch
+from tpcp.optimize.optuna import CustomOptunaOptimize, OptunaSearch, StudyParamsDict
 from tpcp.validate import Scorer
 
 
@@ -26,7 +27,7 @@ class DummyOptunaOptimizer(CustomOptunaOptimize[PipelineT, DatasetT]):
     def __init__(
         self,
         pipeline: PipelineT,
-        create_study: Callable[[], Study],
+        get_study_params: Callable[[int], StudyParamsDict],
         scoring: Callable,
         create_search_space: Callable,
         *,
@@ -37,6 +38,7 @@ def __init__(
         show_progress_bar: bool = False,
         return_optimized: bool = True,
         n_jobs: int = 1,
+        random_seed: Optional[int] = None,
         eval_str_paras: Sequence[str] = (),
         mock_objective=None,
     ) -> None:
@@ -45,7 +47,7 @@ def __init__(
         self.mock_objective = mock_objective
         super().__init__(
             pipeline,
-            create_study,
+            get_study_params,
             n_trials=n_trials,
             timeout=timeout,
             callbacks=callbacks,
@@ -54,6 +56,7 @@ def __init__(
             return_optimized=return_optimized,
             n_jobs=n_jobs,
             eval_str_paras=eval_str_paras,
+            random_seed=random_seed,
         )
 
     def create_objective(self) -> Callable[[Trial, PipelineT, DatasetT], Union[float, Sequence[float]]]:
@@ -76,9 +79,9 @@ def dummy_search_space(trial: Trial):
     trial.suggest_categorical("para_1", [1])
 
 
-def _get_study():
+def _get_study_params(seed):
     # We define it globally so that we can pickle it
-    return create_study(sampler=RandomSampler(42))
+    return {"sampler": RandomSampler(seed)}
 
 
 class TestMetaFunctionalityOptuna(TestAlgorithmMixin):
@@ -88,10 +91,9 @@ class TestMetaFunctionalityOptuna(TestAlgorithmMixin):
 
     @pytest.fixture()
     def after_action_instance(self) -> DummyOptunaOptimizer:
-
         gs = DummyOptunaOptimizer(
             DummyOptimizablePipeline(),
-            _get_study,
+            _get_study_params,
             scoring=dummy_single_score_func,
             create_search_space=dummy_search_space,
             n_trials=1,
@@ -108,7 +110,7 @@ def test_invalid_study_stop(self):
         with pytest.raises(ValueError):
             DummyOptunaOptimizer(
                 DummyOptimizablePipeline(),
-                _get_study,
+                _get_study_params,
                 scoring=dummy_single_score_func,
                 create_search_space=dummy_search_space,
                 n_trials=None,  # These should not be both None
@@ -124,7 +126,7 @@ def test_objective_called(self):
 
         DummyOptunaOptimizer(
             pipe,
-            _get_study,
+            _get_study_params,
             scoring=dummy_single_score_func,
             create_search_space=dummy_search_space,
             n_trials=n_trials,
@@ -146,7 +148,7 @@ def test_return_optimized(self, return_optimize):
 
         opti = DummyOptunaOptimizer(
             DummyOptimizablePipeline(),
-            _get_study,
+            _get_study_params,
             scoring=dummy_single_score_func,
             create_search_space=dummy_search_space,
             n_trials=1,
@@ -168,7 +170,7 @@ def test_return_optimized_calls_optimize(self):
 
             DummyOptunaOptimizer(
                 DummyOptimizablePipeline(),
-                _get_study,
+                _get_study_params,
                 scoring=dummy_single_score_func,
                 create_search_space=dummy_search_space,
                 n_trials=1,
@@ -195,9 +197,11 @@ def scoring(pipe, _):
 
         opti = DummyOptunaOptimizer(
             pipe,
-            lambda: create_study(
-                sampler=GridSampler({"para_1": list(scores.keys())}), direction="maximize", storage=storage
-            ),
+            lambda _: {
+                "sampler": GridSampler({"para_1": list(scores.keys())}),
+                "direction": "maximize",
+                "storage": storage,
+            },
             scoring=scoring,
             create_search_space=create_search_space,
             # In case of the multiprocessing backend, we need to set n_trials to a value > len(scores), as we can not
@@ -232,7 +236,7 @@ def search_space(trial):
 
         optuna_search = DummyOptunaOptimizer(
             DummyOptimizablePipeline(),
-            _get_study,
+            _get_study_params,
             create_search_space=search_space,
             scoring=dummy_single_score_func,
             n_trials=1,
@@ -254,10 +258,9 @@ class TestMetaFunctionalityOptunaSearch(TestAlgorithmMixin):
 
     @pytest.fixture()
     def after_action_instance(self) -> OptunaSearch:
-
         gs = OptunaSearch(
             DummyOptimizablePipeline(),
-            _get_study,
+            _get_study_params,
             dummy_search_space,
             scoring=dummy_single_score_func,
             n_trials=1,
@@ -273,7 +276,7 @@ class TestOptunaSearch:
     def test_single_score(self):
         optuna_search = OptunaSearch(
             DummyOptimizablePipeline(),
-            _get_study,
+            _get_study_params,
             dummy_search_space,
             scoring=dummy_single_score_func,
             n_trials=1,
@@ -297,7 +300,7 @@ def test_single_score(self):
     def test_search_result_columns_multi_score(self, score_name):
         optuna_search = OptunaSearch(
             DummyOptimizablePipeline(),
-            _get_study,
+            _get_study_params,
             dummy_search_space,
             scoring=dummy_multi_score_func,
             score_name=score_name,
@@ -330,7 +333,7 @@ def test_multi_metric_wrong_score_name(self, score_name):
         with pytest.raises(ValueError):
             OptunaSearch(
                 DummyOptimizablePipeline(),
-                _get_study,
+                _get_study_params,
                 dummy_search_space,
                 scoring=dummy_multi_score_func,
                 score_name=score_name,
@@ -341,7 +344,7 @@ def test_warns_single_score_score_name(self):
         with pytest.warns(UserWarning):
             OptunaSearch(
                 DummyOptimizablePipeline(),
-                _get_study,
+                _get_study_params,
                 dummy_search_space,
                 scoring=dummy_single_score_func,
                 score_name="score_1",
@@ -358,7 +361,7 @@ def search_space(trial):
 
         optuna_search = OptunaSearch(
             DummyOptimizablePipeline(),
-            _get_study,
+            _get_study_params,
             search_space,
             scoring=dummy_single_score_func,
             n_trials=1,
@@ -372,3 +375,40 @@ def search_space(trial):
 
         assert optuna_search.best_params_["para_1"] == "('a', 'b')"
         assert optuna_search.best_params_["para_2"] == ("a", "b")
+
+    @pytest.mark.parametrize("ignore_seed", (True, False, 42))
+    def test_multiprocessing_does_not_repeat_trials(self, ignore_seed):
+        # Note, we expect this test to path independent of the seed.
+        # In both cases, a new instance of TPE sampler is created internally for each process.
+        # If None is passed for the seed variable, it used the current numpy to create a new seed.
+        # However, we expect this to fail, if we set a fixed seed that is not None.
+        with tempfile.TemporaryDirectory() as tmp_dir:
+
+            def get_study_params(seed):
+                seed = (None if ignore_seed else seed) if isinstance(ignore_seed, bool) else ignore_seed
+
+                return {
+                    "direction": "maximize",
+                    "storage": f"sqlite:///{tmp_dir}/optuna.db",
+                    "sampler": TPESampler(seed=seed),
+                }
+
+            def search_space(trial):
+                trial.suggest_float("para_1", 0, 10)
+
+            optuna_search = OptunaSearch(
+                DummyOptimizablePipeline(),
+                get_study_params,
+                search_space,
+                scoring=dummy_single_score_func,
+                n_trials=3,
+                n_jobs=3,
+            )
+
+            optuna_search.optimize(DummyDataset())
+
+            if isinstance(ignore_seed, bool):
+                assert len({v["para_1"] for v in optuna_search.search_results_["params"]}) == 3
+            else:
+                # This is bad and happens if users set a fixed seed.
+                assert len({v["para_1"] for v in optuna_search.search_results_["params"]}) == 1