submit_service_job ¶

def submit_service_job(
    self,
    *,
    config_overrides: Optional[BatchClientConfig] = None,
    job_name: Optional["capo_batch.types.string.String"] = None,
    job_queue: Optional["capo_batch.types.string.String"] = None,
    retry_strategy: Optional[
        "capo_batch.types.service_job_retry_strategy.ServiceJobRetryStrategy"
    ] = None,
    scheduling_priority: Optional["capo_batch.types.integer.Integer"] = None,
    service_request_payload: Optional["capo_batch.types.string.String"] = None,
    service_job_type: Optional[
        "capo_batch.types.service_job_type.ServiceJobType"
    ] = None,
    share_identifier: Optional["capo_batch.types.string.String"] = None,
    quota_share_name: Optional["capo_batch.types.string.String"] = None,
    preemption_configuration: Optional[
        "capo_batch.types.service_job_preemption_configuration.ServiceJobPreemptionConfiguration"
    ] = None,
    timeout_config: Optional[
        "capo_batch.types.service_job_timeout.ServiceJobTimeout"
    ] = None,
    tags: Optional["capo_batch.types.tagris_tags_map.TagrisTagsMap"] = None,
    client_token: Optional[
        "capo_batch.types.client_request_token.ClientRequestToken"
    ] = None,
) -> "capo_batch.types.submit_service_job_response.SubmitServiceJobResponse":
    """<p>Submits a service job to a specified job queue to run on SageMaker AI. A service job is a unit of work that you submit to Batch for execution on SageMaker AI.</p>

    Args:
        job_name: <p>The name of the service job. It can be up to 128 characters long. It can contain uppercase and lowercase letters, numbers, hyphens (-), and underscores (_).</p>
        job_queue: <p>The job queue into which the service job is submitted. You can specify either the name or the ARN of the queue. The job queue must have the type <code>SAGEMAKER_TRAINING</code>.</p>
        retry_strategy: <p>The retry strategy to use for failed service jobs that are submitted with this service job request. </p>
        scheduling_priority: <p>The scheduling priority of the service job. Valid values are integers between 0 and 9999.</p>
        service_request_payload: <p>The request, in JSON, for the service that the SubmitServiceJob operation is queueing. </p>
        service_job_type: <p>The type of service job. For SageMaker Training jobs, specify <code>SAGEMAKER_TRAINING</code>.</p>
        share_identifier: <p>The share identifier for the service job. Don't specify this parameter if the job queue doesn't have a fair-share scheduling policy. If the job queue has a fair-share scheduling policy, then this parameter must be specified.</p>
        quota_share_name: <p>The quota share for the service job. Don't specify this parameter if the job queue doesn't have a quota share scheduling policy. If the job queue has a quota share scheduling policy, then this parameter must be specified.</p>
        preemption_configuration: <p>Specifies the service job behavior when preempted.</p>
        timeout_config: <p>The timeout configuration for the service job. If none is specified, Batch defers to the default timeout of the underlying service handling the job.</p>
        tags: <p>The tags that you apply to the service job request. Each tag consists of a key and an optional value. For more information, see <a href="https://docs.aws.amazon.com/batch/latest/userguide/using-tags.html">Tagging your Batch resources</a>.</p>
        client_token: <p>A unique identifier for the request. This token is used to ensure idempotency of requests. If this parameter is specified and two submit requests with identical payloads and <code>clientToken</code>s are received, these requests are considered the same request and the second request is rejected.</p>

    Raises:
        capo_batch.errors.client_exception.ClientException: <p>These errors are usually caused by a client action. One example cause is using an action or resource on behalf of a user that doesn't have permissions to use the action or resource. Another cause is specifying an identifier that's not valid.</p>
        capo_batch.errors.server_exception.ServerException: <p>These errors are usually caused by a server issue.</p>
        capo_batch.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_batch.types.submit_service_job_request.SubmitServiceJobRequest]",
    ) -> OperationResponse[
        "capo_batch.types.submit_service_job_response.SubmitServiceJobResponse"
    ]:
        import capo_batch._operations.aws_batch_v20160810.submit_service_job

        output, http_response = (
            capo_batch._operations.aws_batch_v20160810.submit_service_job.submit_service_job(
                req.options, req.input
            )
        )
        return OperationResponse(output=output, response=http_response)

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("SubmitServiceJob")
    )
    input_: capo_batch.types.submit_service_job_request.SubmitServiceJobRequest = {}
    if job_name is not None:
        input_["job_name"] = job_name
    if job_queue is not None:
        input_["job_queue"] = job_queue
    if retry_strategy is not None:
        input_["retry_strategy"] = retry_strategy
    if scheduling_priority is not None:
        input_["scheduling_priority"] = scheduling_priority
    if service_request_payload is not None:
        input_["service_request_payload"] = service_request_payload
    if service_job_type is not None:
        input_["service_job_type"] = service_job_type
    if share_identifier is not None:
        input_["share_identifier"] = share_identifier
    if quota_share_name is not None:
        input_["quota_share_name"] = quota_share_name
    if preemption_configuration is not None:
        input_["preemption_configuration"] = preemption_configuration
    if timeout_config is not None:
        input_["timeout_config"] = timeout_config
    if tags is not None:
        input_["tags"] = tags
    if client_token is None:
        client_token = str(uuid.uuid4())
    input_["client_token"] = client_token

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