ResourceConfig

class aws_cdk.aws_stepfunctions_tasks.ResourceConfig(*, instance_count, instance_type, volume_size, volume_encryption_key=None)

Bases: object

Specifies the resources, ML compute instances, and ML storage volumes to deploy for model training.

Parameters

instance_count (Union[int, float]) – The number of ML compute instances to use. Default: 1 instance.
instance_type (InstanceType) – ML compute instance type. To provide an instance type from the task input, supply an instance type in the following way where the value in the task input is an EC2 instance type prepended with “ml.”:: new ec2.InstanceType(sfn.JsonPath.stringAt(‘$.path.to.instanceType’)); Default: ec2.InstanceType(ec2.InstanceClass.M4, ec2.InstanceType.XLARGE)
volume_size (Size) – Size of the ML storage volume that you want to provision. Default: 10 GB EBS volume.
volume_encryption_key (Optional[IKey]) – KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job. Default: - Amazon SageMaker uses the default KMS key for Amazon S3 for your role’s account

ExampleMetadata

infused

Example:

tasks.SageMakerCreateTrainingJob(self, "TrainSagemaker",
    training_job_name=sfn.JsonPath.string_at("$.JobName"),
    algorithm_specification=tasks.AlgorithmSpecification(
        algorithm_name="BlazingText",
        training_input_mode=tasks.InputMode.FILE
    ),
    input_data_config=[tasks.Channel(
        channel_name="train",
        data_source=tasks.DataSource(
            s3_data_source=tasks.S3DataSource(
                s3_data_type=tasks.S3DataType.S3_PREFIX,
                s3_location=tasks.S3Location.from_json_expression("$.S3Bucket")
            )
        )
    )],
    output_data_config=tasks.OutputDataConfig(
        s3_output_location=tasks.S3Location.from_bucket(s3.Bucket.from_bucket_name(self, "Bucket", "mybucket"), "myoutputpath")
    ),
    resource_config=tasks.ResourceConfig(
        instance_count=1,
        instance_type=ec2.InstanceType(sfn.JsonPath.string_at("$.InstanceType")),
        volume_size=Size.gibibytes(50)
    ),  # optional: default is 1 instance of EC2 `M4.XLarge` with `10GB` volume
    stopping_condition=tasks.StoppingCondition(
        max_runtime=Duration.hours(2)
    )
)

Attributes

instance_count

The number of ML compute instances to use.

Default: 1 instance.
Return type: Union[int, float]

instance_type

ML compute instance type.

To provide an instance type from the task input, supply an instance type in the following way where the value in the task input is an EC2 instance type prepended with “ml.”:

ec2.InstanceType(sfn.JsonPath.string_at("$.path.to.instanceType"))

Default: ec2.InstanceType(ec2.InstanceClass.M4, ec2.InstanceType.XLARGE)
See: https://docs.aws.amazon.com/sagemaker/latest/APIReference/API_ResourceConfig.html#sagemaker-Type-ResourceConfig-InstanceType
Return type: InstanceType

volume_encryption_key

KMS key that Amazon SageMaker uses to encrypt data on the storage volume attached to the ML compute instance(s) that run the training job.

Default

Amazon SageMaker uses the default KMS key for Amazon S3 for your role’s account

Return type

Optional[IKey]

volume_size

Size of the ML storage volume that you want to provision.

Default: 10 GB EBS volume.
Return type: Size