list_training_jobs ¶

def list_training_jobs(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    next_token: Optional["capo_sagemaker.types.next_token.NextToken"] = None,
    max_results: Optional["capo_sagemaker.types.max_results.MaxResults"] = None,
    creation_time_after: Optional[
        "capo_sagemaker.types.timestamp.Timestamp"
    ] = None,
    creation_time_before: Optional[
        "capo_sagemaker.types.timestamp.Timestamp"
    ] = None,
    last_modified_time_after: Optional[
        "capo_sagemaker.types.timestamp.Timestamp"
    ] = None,
    last_modified_time_before: Optional[
        "capo_sagemaker.types.timestamp.Timestamp"
    ] = None,
    name_contains: Optional[
        "capo_sagemaker.types.name_contains.NameContains"
    ] = None,
    status_equals: Optional[
        "capo_sagemaker.types.training_job_status.TrainingJobStatus"
    ] = None,
    sort_by: Optional["capo_sagemaker.types.sort_by.SortBy"] = None,
    sort_order: Optional["capo_sagemaker.types.sort_order.SortOrder"] = None,
    warm_pool_status_equals: Optional[
        "capo_sagemaker.types.warm_pool_resource_status.WarmPoolResourceStatus"
    ] = None,
    training_plan_arn_equals: Optional[
        "capo_sagemaker.types.training_plan_arn.TrainingPlanArn"
    ] = None,
) -> "capo_sagemaker.types.list_training_jobs_response.ListTrainingJobsResponse":
    """<p>Lists training jobs.</p> <note> <p>When <code>StatusEquals</code> and <code>MaxResults</code> are set at the same time, the <code>MaxResults</code> number of training jobs are first retrieved ignoring the <code>StatusEquals</code> parameter and then they are filtered by the <code>StatusEquals</code> parameter, which is returned as a response.</p> <p>For example, if <code>ListTrainingJobs</code> is invoked with the following parameters:</p> <p> <code>{ ... MaxResults: 100, StatusEquals: InProgress ... }</code> </p> <p>First, 100 trainings jobs with any status, including those other than <code>InProgress</code>, are selected (sorted according to the creation time, from the most current to the oldest). Next, those with a status of <code>InProgress</code> are returned.</p> <p>You can quickly test the API using the following Amazon Web Services CLI code.</p> <p> <code>aws sagemaker list-training-jobs --max-results 100 --status-equals InProgress</code> </p> </note>

    Args:
        next_token: <p>If the result of the previous <code>ListTrainingJobs</code> request was truncated, the response includes a <code>NextToken</code>. To retrieve the next set of training jobs, use the token in the next request. </p>
        max_results: <p>The maximum number of training jobs to return in the response.</p>
        creation_time_after: <p>A filter that returns only training jobs created after the specified time (timestamp).</p>
        creation_time_before: <p>A filter that returns only training jobs created before the specified time (timestamp).</p>
        last_modified_time_after: <p>A filter that returns only training jobs modified after the specified time (timestamp).</p>
        last_modified_time_before: <p>A filter that returns only training jobs modified before the specified time (timestamp).</p>
        name_contains: <p>A string in the training job name. This filter returns only training jobs whose name contains the specified string.</p>
        status_equals: <p>A filter that retrieves only training jobs with a specific status.</p>
        sort_by: <p>The field to sort results by. The default is <code>CreationTime</code>.</p>
        sort_order: <p>The sort order for results. The default is <code>Ascending</code>.</p>
        warm_pool_status_equals: <p>A filter that retrieves only training jobs with a specific warm pool status.</p>
        training_plan_arn_equals: <p>The Amazon Resource Name (ARN); of the training plan to filter training jobs by. For more information about reserving GPU capacity for your SageMaker training jobs using Amazon SageMaker Training Plan, see <code> <a href="https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_CreateTrainingPlan.html">CreateTrainingPlan</a> </code>.</p>

    Raises:
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.list_training_jobs_request.ListTrainingJobsRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.list_training_jobs_response.ListTrainingJobsResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.list_training_jobs

        output, http_response = (
            capo_sagemaker._operations.sage_maker.list_training_jobs.list_training_jobs(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("ListTrainingJobs")
    )
    input_: capo_sagemaker.types.list_training_jobs_request.ListTrainingJobsRequest = {}
    if next_token is not None:
        input_["next_token"] = next_token
    if max_results is not None:
        input_["max_results"] = max_results
    if creation_time_after is not None:
        input_["creation_time_after"] = creation_time_after
    if creation_time_before is not None:
        input_["creation_time_before"] = creation_time_before
    if last_modified_time_after is not None:
        input_["last_modified_time_after"] = last_modified_time_after
    if last_modified_time_before is not None:
        input_["last_modified_time_before"] = last_modified_time_before
    if name_contains is not None:
        input_["name_contains"] = name_contains
    if status_equals is not None:
        input_["status_equals"] = status_equals
    if sort_by is not None:
        input_["sort_by"] = sort_by
    if sort_order is not None:
        input_["sort_order"] = sort_order
    if warm_pool_status_equals is not None:
        input_["warm_pool_status_equals"] = warm_pool_status_equals
    if training_plan_arn_equals is not None:
        input_["training_plan_arn_equals"] = training_plan_arn_equals

    response = execute_pipeline(
        OperationRequest(input=input_, options=options_),
        handler=_handler,
        interceptors=list(interceptors_),
    )
    response.response.close()
    return response.output