建立及管理實驗執行作業

透過集合功能整理內容 你可以依據偏好儲存及分類內容。

from typing import Optional, Union

from google.cloud import aiplatform


def create_experiment_run_sample(
    experiment_name: str,
    run_name: str,
    experiment_run_tensorboard: Optional[Union[str, aiplatform.Tensorboard]],
    project: str,
    location: str,
):
    aiplatform.init(experiment=experiment_name, project=project, location=location)

    aiplatform.start_run(run=run_name, tensorboard=experiment_run_tensorboard)

from google.cloud import aiplatform


def end_experiment_run_sample(
    experiment_name: str,
    run_name: str,
    project: str,
    location: str,
):
    aiplatform.init(experiment=experiment_name, project=project, location=location)

    aiplatform.start_run(run=run_name, resume=True)

    aiplatform.end_run()

from google.cloud import aiplatform


def resume_experiment_run_sample(
    experiment_name: str,
    run_name: str,
    project: str,
    location: str,
):
    aiplatform.init(experiment=experiment_name, project=project, location=location)

    aiplatform.start_run(run=run_name, resume=True)

from typing import Union

from google.cloud import aiplatform


def delete_experiment_run_sample(
    run_name: str,
    experiment: Union[str, aiplatform.Experiment],
    project: str,
    location: str,
    delete_backing_tensorboard_run: bool = False,
):
    experiment_run = aiplatform.ExperimentRun(
        run_name=run_name, experiment=experiment, project=project, location=location
    )

    experiment_run.delete(delete_backing_tensorboard_run=delete_backing_tensorboard_run)

from typing import Union

from google.cloud import aiplatform


def update_experiment_run_state_sample(
    run_name: str,
    experiment: Union[str, aiplatform.Experiment],
    project: str,
    location: str,
    state: aiplatform.gapic.Execution.State,
) -> None:
    experiment_run = aiplatform.ExperimentRun(
        run_name=run_name,
        experiment=experiment,
        project=project,
        location=location,
    )

    experiment_run.update_state(state)