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