create_processing_job ¶

def create_processing_job(
    self,
    *,
    config_overrides: Optional[SageMakerClientConfig] = None,
    processing_inputs: Optional[
        "capo_sagemaker.types.processing_inputs.ProcessingInputs"
    ] = None,
    processing_output_config: Optional[
        "capo_sagemaker.types.processing_output_config.ProcessingOutputConfig"
    ] = None,
    processing_job_name: Optional[
        "capo_sagemaker.types.processing_job_name.ProcessingJobName"
    ] = None,
    processing_resources: Optional[
        "capo_sagemaker.types.processing_resources.ProcessingResources"
    ] = None,
    stopping_condition: Optional[
        "capo_sagemaker.types.processing_stopping_condition.ProcessingStoppingCondition"
    ] = None,
    app_specification: Optional[
        "capo_sagemaker.types.app_specification.AppSpecification"
    ] = None,
    environment: Optional[
        "capo_sagemaker.types.processing_environment_map.ProcessingEnvironmentMap"
    ] = None,
    network_config: Optional[
        "capo_sagemaker.types.network_config.NetworkConfig"
    ] = None,
    role_arn: Optional["capo_sagemaker.types.role_arn.RoleArn"] = None,
    tags: Optional["capo_sagemaker.types.tag_list.TagList"] = None,
    experiment_config: Optional[
        "capo_sagemaker.types.experiment_config.ExperimentConfig"
    ] = None,
) -> "capo_sagemaker.types.create_processing_job_response.CreateProcessingJobResponse":
    """<p>Creates a processing job.</p>

    Args:
        processing_inputs: <p>An array of inputs configuring the data to download into the processing container.</p>
        processing_output_config: <p>Output configuration for the processing job.</p>
        processing_job_name: <p> The name of the processing job. The name must be unique within an Amazon Web Services Region in the Amazon Web Services account.</p>
        processing_resources: <p>Identifies the resources, ML compute instances, and ML storage volumes to deploy for a processing job. In distributed training, you specify more than one instance.</p>
        stopping_condition: <p>The time limit for how long the processing job is allowed to run.</p>
        app_specification: <p>Configures the processing job to run a specified Docker container image.</p>
        environment: <p>The environment variables to set in the Docker container. Up to 100 key and values entries in the map are supported.</p> <important> <p>Do not include any security-sensitive information including account access IDs, secrets, or tokens in any environment fields. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request environment variable or plain text fields.</p> </important>
        network_config: <p>Networking options for a processing job, such as whether to allow inbound and outbound network calls to and from processing containers, and the VPC subnets and security groups to use for VPC-enabled processing jobs.</p>
        role_arn: <p>The Amazon Resource Name (ARN) of an IAM role that Amazon SageMaker can assume to perform tasks on your behalf.</p>
        tags: <p>(Optional) An array of key-value pairs. For more information, see <a href="https://docs.aws.amazon.com/awsaccountbilling/latest/aboutv2/cost-alloc-tags.html#allocation-whatURL">Using Cost Allocation Tags</a> in the <i>Amazon Web Services Billing and Cost Management User Guide</i>.</p> <important> <p>Do not include any security-sensitive information including account access IDs, secrets, or tokens in any tags. As part of the shared responsibility model, you are responsible for any potential exposure, unauthorized access, or compromise of your sensitive data if caused by security-sensitive information included in the request tag variable or plain text fields.</p> </important>

    Raises:
        capo_sagemaker.errors.resource_in_use.ResourceInUse: <p>Resource being accessed is in use.</p>
        capo_sagemaker.errors.resource_limit_exceeded.ResourceLimitExceeded: <p> You have exceeded an SageMaker resource limit. For example, you might have too many training jobs created. </p>
        capo_sagemaker.errors.resource_not_found.ResourceNotFound: <p>Resource being access is not found.</p>
        capo_sagemaker.errors.UnknownServiceError: The service returned an error code this client does not model.
    """

    def _handler(
        req: "OperationRequest[capo_sagemaker.types.create_processing_job_request.CreateProcessingJobRequest]",
    ) -> OperationResponse[
        "capo_sagemaker.types.create_processing_job_response.CreateProcessingJobResponse"
    ]:
        import capo_sagemaker._operations.sage_maker.create_processing_job

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

    interceptors_, options_ = self.operation_options(
        config_overrides, OperationLogging("CreateProcessingJob")
    )
    input_: capo_sagemaker.types.create_processing_job_request.CreateProcessingJobRequest = {}
    if processing_inputs is not None:
        input_["processing_inputs"] = processing_inputs
    if processing_output_config is not None:
        input_["processing_output_config"] = processing_output_config
    if processing_job_name is not None:
        input_["processing_job_name"] = processing_job_name
    if processing_resources is not None:
        input_["processing_resources"] = processing_resources
    if stopping_condition is not None:
        input_["stopping_condition"] = stopping_condition
    if app_specification is not None:
        input_["app_specification"] = app_specification
    if environment is not None:
        input_["environment"] = environment
    if network_config is not None:
        input_["network_config"] = network_config
    if role_arn is not None:
        input_["role_arn"] = role_arn
    if tags is not None:
        input_["tags"] = tags
    if experiment_config is not None:
        input_["experiment_config"] = experiment_config

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