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Update hitl tutorial for artifacts

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keisuke-umezawa
2023-09-10 15:50:37 +09:00
parent 941f926abd
commit 260b760949
2 changed files with 24 additions and 21 deletions
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docs/tutorials/hitl.rst
+14 -13
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@@ -97,7 +97,7 @@ To run `the script <https://github.com/optuna/optuna-dashboard/blob/main/example
.. code-block:: console
$ pip install "optuna>=3.2.0" "optuna-dashboard>=0.10.0" pillow
$ pip install "optuna>=3.3.0" "optuna-dashboard>=0.12.0" pillow
You will use SQLite for the storage backend in this tutorial. Ensure that the following library is installed:
@@ -179,7 +179,9 @@ Let’s walk through the script we used for the optimization.
.. code-block:: python
:linenos:
def suggest_and_generate_image(study: optuna.Study, artifact_backend: FileSystemBackend) -> None:
def suggest_and_generate_image(
study: optuna.Study, artifact_store: FileSystemArtifactStore
) -> None:
# 1. Ask new parameters
trial = study.ask()
r = trial.suggest_int("r", 0, 255)
@@ -192,7 +194,7 @@ Let’s walk through the script we used for the optimization.
image.save(image_path)
# 3. Upload Artifact
artifact_id = upload_artifact(artifact_backend, trial, image_path)
artifact_id = upload_artifact(trial, image_path, artifact_store)
artifact_path = get_artifact_path(trial, artifact_id)
# 4. Save Note
@@ -210,7 +212,7 @@ In the ``suggest_and_generate_image`` function, a new Trial is obtained and new
.. code-block:: python
:linenos:
def start_optimization(artifact_backend: FileSystemBackend) -> NoReturn:
def start_optimization(artifact_store: FileSystemArtifactStore) -> NoReturn:
# 1. Create Study
study = optuna.create_study(
study_name="Human-in-the-loop Optimization",
@@ -218,10 +220,10 @@ In the ``suggest_and_generate_image`` function, a new Trial is obtained and new
sampler=optuna.samplers.TPESampler(constant_liar=True, n_startup_trials=5),
load_if_exists=True,
)
# 2. Set an objective name
study.set_metric_names(["Looks like sunset color?"])
# 3. Register ChoiceWidget
register_objective_form_widgets(
study,
@@ -234,15 +236,14 @@ In the ``suggest_and_generate_image`` function, a new Trial is obtained and new
],
)
# 4. Start Optimization
# 4. Start Human-in-the-loop Optimization
n_batch = 4
while True:
running_trials = study.get_trials(deepcopy=False, states=(TrialState.RUNNING,))
if len(running_trials) >= n_batch:
time.sleep(1) # Avoid busy-loop
continue
suggest_and_generate_image(study, artifact_backend)
suggest_and_generate_image(study, artifact_store)
The function ``start_optimization`` defines our loop for HITL optimization to generate an image resembling a sunset color.
@@ -256,10 +257,10 @@ The function ``start_optimization`` defines our loop for HITL optimization to ge
def main() -> NoReturn:
tmp_path = os.path.join(os.path.dirname(__file__), "tmp")
# 1. Create Artifact Store
artifact_path = os.path.join(os.path.dirname(__file__), "artifact")
artifact_backend = FileSystemBackend(base_path=artifact_path)
artifact_store = FileSystemArtifactStore(artifact_path)
if not os.path.exists(artifact_path):
os.mkdir(artifact_path)
@@ -268,11 +269,11 @@ The function ``start_optimization`` defines our loop for HITL optimization to ge
os.mkdir(tmp_path)
# 2. Run optimize loop
start_optimization(artifact_backend)
start_optimization(artifact_store)
In the ``main`` function, at first, the locations of the Artifact Store is set.
* At #1, the :class:`~optuna_dashboard.FileSystemBackend` is created, which is one of the Artifact Storage options used in the Optuna Dashboard. Artifact Storage is used to store artifacts (data, files, etc.) generated during Optuna trials. For more information, please refer to the API Reference.
* At #1, the `FileSystemArtifactStore <https://optuna.readthedocs.io/en/stable/reference/generated/optuna.artifacts.FileSystemArtifactStore.html>` is created, which is one of the Artifact Store options used in the Optuna. Artifact Store is used to store artifacts (data, files, etc.) generated during Optuna trials. For more information, please refer to the API Reference.
* At #2, `start_optimization()` function, which is described above, is called.
After that, two folders are created, artifact and tmp, and then ``start_optimization`` function is called to start the HITL optimization using Optuna.