CfnProcessingJobProps

class aws_cdk.aws_sagemaker.CfnProcessingJobProps(*, app_specification, processing_resources, role_arn, environment=None, experiment_config=None, network_config=None, processing_inputs=None, processing_job_name=None, processing_output_config=None, stopping_condition=None, tags=None)

Bases: object

Properties for defining a CfnProcessingJob.

Parameters:

app_specification (Union[IResolvable, AppSpecificationProperty, Dict[str, Any]]) – Configuration to run a processing job in a specified container image.
processing_resources (Union[IResolvable, ProcessingResourcesProperty, Dict[str, Any]]) – 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.
role_arn (str) – The ARN of the role used to create the processing job.
environment (Union[Mapping[str, str], IResolvable, None]) – Sets the environment variables in the Docker container.
experiment_config (Union[IResolvable, ExperimentConfigProperty, Dict[str, Any], None]) – Associates a SageMaker job as a trial component with an experiment and trial. Specified when you call the CreateProcessingJob API.
network_config (Union[IResolvable, NetworkConfigProperty, Dict[str, Any], None]) – Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.
processing_inputs (Union[IResolvable, Sequence[Union[IResolvable, ProcessingInputsObjectProperty, Dict[str, Any]]], None]) – List of input configurations for the processing job.
processing_job_name (Optional[str]) – The name of the processing job. If you don’t provide a job name, then a unique name is automatically created for the job.
processing_output_config (Union[IResolvable, ProcessingOutputConfigProperty, Dict[str, Any], None]) – Contains information about the output location for the compiled model and the target device that the model runs on. TargetDevice and TargetPlatform are mutually exclusive, so you need to choose one between the two to specify your target device or platform. If you cannot find your device you want to use from the TargetDevice list, use TargetPlatform to describe the platform of your edge device and CompilerOptions if there are specific settings that are required or recommended to use for particular TargetPlatform.
stopping_condition (Union[IResolvable, StoppingConditionProperty, Dict[str, Any], None]) – Configures conditions under which the processing job should be stopped, such as how long the processing job has been running. After the condition is met, the processing job is stopped.
tags (Optional[Sequence[Union[CfnTag, Dict[str, Any]]]]) – An array of key-value pairs. For more information, see Using Cost Allocation Tags in the AWS Billing and Cost Management User Guide .

See:

http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-processingjob.html

ExampleMetadata:

fixture=_generated

Example:

# The code below shows an example of how to instantiate this type.
# The values are placeholders you should change.
from aws_cdk import aws_sagemaker as sagemaker

cfn_processing_job_props = sagemaker.CfnProcessingJobProps(
    app_specification=sagemaker.CfnProcessingJob.AppSpecificationProperty(
        image_uri="imageUri",

        # the properties below are optional
        container_arguments=["containerArguments"],
        container_entrypoint=["containerEntrypoint"]
    ),
    processing_resources=sagemaker.CfnProcessingJob.ProcessingResourcesProperty(
        cluster_config=sagemaker.CfnProcessingJob.ClusterConfigProperty(
            instance_count=123,
            instance_type="instanceType",
            volume_size_in_gb=123,

            # the properties below are optional
            volume_kms_key_id="volumeKmsKeyId"
        )
    ),
    role_arn="roleArn",

    # the properties below are optional
    environment={
        "environment_key": "environment"
    },
    experiment_config=sagemaker.CfnProcessingJob.ExperimentConfigProperty(
        experiment_name="experimentName",
        run_name="runName",
        trial_component_display_name="trialComponentDisplayName",
        trial_name="trialName"
    ),
    network_config=sagemaker.CfnProcessingJob.NetworkConfigProperty(
        enable_inter_container_traffic_encryption=False,
        enable_network_isolation=False,
        vpc_config=sagemaker.CfnProcessingJob.VpcConfigProperty(
            security_group_ids=["securityGroupIds"],
            subnets=["subnets"]
        )
    ),
    processing_inputs=[sagemaker.CfnProcessingJob.ProcessingInputsObjectProperty(
        input_name="inputName",

        # the properties below are optional
        app_managed=False,
        dataset_definition=sagemaker.CfnProcessingJob.DatasetDefinitionProperty(
            athena_dataset_definition=sagemaker.CfnProcessingJob.AthenaDatasetDefinitionProperty(
                catalog="catalog",
                database="database",
                output_format="outputFormat",
                output_s3_uri="outputS3Uri",
                query_string="queryString",

                # the properties below are optional
                kms_key_id="kmsKeyId",
                output_compression="outputCompression",
                work_group="workGroup"
            ),
            data_distribution_type="dataDistributionType",
            input_mode="inputMode",
            local_path="localPath",
            redshift_dataset_definition=sagemaker.CfnProcessingJob.RedshiftDatasetDefinitionProperty(
                cluster_id="clusterId",
                cluster_role_arn="clusterRoleArn",
                database="database",
                db_user="dbUser",
                output_format="outputFormat",
                output_s3_uri="outputS3Uri",
                query_string="queryString",

                # the properties below are optional
                kms_key_id="kmsKeyId",
                output_compression="outputCompression"
            )
        ),
        s3_input=sagemaker.CfnProcessingJob.S3InputProperty(
            s3_data_type="s3DataType",
            s3_uri="s3Uri",

            # the properties below are optional
            local_path="localPath",
            s3_compression_type="s3CompressionType",
            s3_data_distribution_type="s3DataDistributionType",
            s3_input_mode="s3InputMode"
        )
    )],
    processing_job_name="processingJobName",
    processing_output_config=sagemaker.CfnProcessingJob.ProcessingOutputConfigProperty(
        outputs=[sagemaker.CfnProcessingJob.ProcessingOutputsObjectProperty(
            output_name="outputName",

            # the properties below are optional
            app_managed=False,
            feature_store_output=sagemaker.CfnProcessingJob.FeatureStoreOutputProperty(
                feature_group_name="featureGroupName"
            ),
            s3_output=sagemaker.CfnProcessingJob.S3OutputProperty(
                s3_upload_mode="s3UploadMode",
                s3_uri="s3Uri",

                # the properties below are optional
                local_path="localPath"
            )
        )],

        # the properties below are optional
        kms_key_id="kmsKeyId"
    ),
    stopping_condition=sagemaker.CfnProcessingJob.StoppingConditionProperty(
        max_runtime_in_seconds=123
    ),
    tags=[CfnTag(
        key="key",
        value="value"
    )]
)

Attributes

app_specification

Configuration to run a processing job in a specified container image.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-processingjob.html#cfn-sagemaker-processingjob-appspecification

environment

Sets the environment variables in the Docker container.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-processingjob.html#cfn-sagemaker-processingjob-environment

experiment_config

Associates a SageMaker job as a trial component with an experiment and trial.

Specified when you call the CreateProcessingJob API.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-processingjob.html#cfn-sagemaker-processingjob-experimentconfig

network_config

Networking options for a job, such as network traffic encryption between containers, whether to allow inbound and outbound network calls to and from containers, and the VPC subnets and security groups to use for VPC-enabled jobs.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-processingjob.html#cfn-sagemaker-processingjob-networkconfig

processing_inputs

List of input configurations for the processing job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-processingjob.html#cfn-sagemaker-processingjob-processinginputs

processing_job_name

The name of the processing job.

If you don’t provide a job name, then a unique name is automatically created for the job.

See:: http://docs.aws.amazon.com/AWSCloudFormation/latest/UserGuide/aws-resource-sagemaker-processingjob.html#cfn-sagemaker-processingjob-processingjobname

processing_output_config

Contains information about the output location for the compiled model and the target device that the model runs on.

TargetDevice and TargetPlatform are mutually exclusive, so you need to choose one between the two to specify your target device or platform. If you cannot find your device you want to use from the TargetDevice list, use TargetPlatform to describe the platform of your edge device and CompilerOptions if there are specific settings that are required or recommended to use for particular TargetPlatform.