mirror of
https://github.com/wassname/optuna-dashboard.git
synced 2026-09-06 17:00:30 +08:00
97 lines
2.7 KiB
Python
97 lines
2.7 KiB
Python
import os
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import textwrap
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import time
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from typing import NoReturn
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import optuna
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from optuna.trial import TrialState
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from optuna_dashboard import ChoiceWidget
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from optuna_dashboard import register_objective_form_widgets
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from optuna_dashboard import save_note
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from optuna_dashboard.artifact import get_artifact_path
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from optuna_dashboard.artifact import upload_artifact
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from optuna_dashboard.artifact.file_system import FileSystemBackend
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from PIL import Image
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def suggest_and_generate_image(study: optuna.Study, artifact_backend: FileSystemBackend) -> None:
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# 1. Ask new parameters
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trial = study.ask()
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r = trial.suggest_int("r", 0, 255)
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g = trial.suggest_int("g", 0, 255)
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b = trial.suggest_int("b", 0, 255)
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# 2. Generate image
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image_path = f"tmp/sample-{trial.number}.png"
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image = Image.new("RGB", (320, 240), color=(r, g, b))
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image.save(image_path)
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# 3. Upload Artifact
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artifact_id = upload_artifact(artifact_backend, trial, image_path)
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artifact_path = get_artifact_path(trial, artifact_id)
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# 4. Save Note
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note = textwrap.dedent(
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f"""\
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## Trial {trial.number}
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"""
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)
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save_note(trial, note)
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def start_optimization(artifact_backend: FileSystemBackend) -> NoReturn:
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# 1. Create Study
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study = optuna.create_study(
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study_name="Human-in-the-loop Optimization",
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storage="sqlite:///db.sqlite3",
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sampler=optuna.samplers.TPESampler(constant_liar=True, n_startup_trials=5),
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load_if_exists=True,
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)
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# 2. Set an objective name
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study.set_metric_names(["Looks like sunset color?"])
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# 3. Register ChoiceWidget
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register_objective_form_widgets(
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study,
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widgets=[
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ChoiceWidget(
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choices=["Good 👍", "So-so👌", "Bad 👎"],
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values=[-1, 0, 1],
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description="Please input your score!",
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),
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],
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)
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# 4. Start Human-in-the-loop Optimization
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n_batch = 4
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while True:
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running_trials = study.get_trials(deepcopy=False, states=(TrialState.RUNNING,))
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if len(running_trials) >= n_batch:
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time.sleep(1) # Avoid busy-loop
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continue
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suggest_and_generate_image(study, artifact_backend)
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def main() -> NoReturn:
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tmp_path = os.path.join(os.path.dirname(__file__), "tmp")
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# 1. Create Artifact Store
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artifact_path = os.path.join(os.path.dirname(__file__), "artifact")
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artifact_backend = FileSystemBackend(base_path=artifact_path)
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if not os.path.exists(artifact_path):
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os.mkdir(artifact_path)
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if not os.path.exists(tmp_path):
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os.mkdir(tmp_path)
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# 2. Run optimize loop
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start_optimization(artifact_backend)
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if __name__ == "__main__":
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main()
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