SageMaker / Client / create_cluster

create_cluster#

SageMaker.Client.create_cluster(**kwargs)#

Creates a SageMaker HyperPod cluster. SageMaker HyperPod is a capability of SageMaker for creating and managing persistent clusters for developing large machine learning models, such as large language models (LLMs) and diffusion models. To learn more, see Amazon SageMaker HyperPod in the Amazon SageMaker Developer Guide.

See also: AWS API Documentation

Request Syntax

response = client.create_cluster(
    ClusterName='string',
    InstanceGroups=[
        {
            'InstanceCount': 123,
            'InstanceGroupName': 'string',
            'InstanceType': 'ml.p4d.24xlarge'|'ml.p4de.24xlarge'|'ml.p5.48xlarge'|'ml.trn1.32xlarge'|'ml.trn1n.32xlarge'|'ml.g5.xlarge'|'ml.g5.2xlarge'|'ml.g5.4xlarge'|'ml.g5.8xlarge'|'ml.g5.12xlarge'|'ml.g5.16xlarge'|'ml.g5.24xlarge'|'ml.g5.48xlarge'|'ml.c5.large'|'ml.c5.xlarge'|'ml.c5.2xlarge'|'ml.c5.4xlarge'|'ml.c5.9xlarge'|'ml.c5.12xlarge'|'ml.c5.18xlarge'|'ml.c5.24xlarge'|'ml.c5n.large'|'ml.c5n.2xlarge'|'ml.c5n.4xlarge'|'ml.c5n.9xlarge'|'ml.c5n.18xlarge'|'ml.m5.large'|'ml.m5.xlarge'|'ml.m5.2xlarge'|'ml.m5.4xlarge'|'ml.m5.8xlarge'|'ml.m5.12xlarge'|'ml.m5.16xlarge'|'ml.m5.24xlarge'|'ml.t3.medium'|'ml.t3.large'|'ml.t3.xlarge'|'ml.t3.2xlarge',
            'LifeCycleConfig': {
                'SourceS3Uri': 'string',
                'OnCreate': 'string'
            },
            'ExecutionRole': 'string',
            'ThreadsPerCore': 123
        },
    ],
    VpcConfig={
        'SecurityGroupIds': [
            'string',
        ],
        'Subnets': [
            'string',
        ]
    },
    Tags=[
        {
            'Key': 'string',
            'Value': 'string'
        },
    ]
)
Parameters:

  • ClusterName (string) –

    [REQUIRED]

    The name for the new SageMaker HyperPod cluster.

  • InstanceGroups (list) –

    [REQUIRED]

    The instance groups to be created in the SageMaker HyperPod cluster.

    • (dict) –

      The specifications of an instance group that you need to define.

      • InstanceCount (integer) – [REQUIRED]

        Specifies the number of instances to add to the instance group of a SageMaker HyperPod cluster.

      • InstanceGroupName (string) – [REQUIRED]

        Specifies the name of the instance group.

      • InstanceType (string) – [REQUIRED]

        Specifies the instance type of the instance group.

      • LifeCycleConfig (dict) – [REQUIRED]

        Specifies the LifeCycle configuration for the instance group.

        • SourceS3Uri (string) – [REQUIRED]

          An Amazon S3 bucket path where your lifecycle scripts are stored.

          Warning

          Make sure that the S3 bucket path starts with s3://sagemaker-. The IAM role for SageMaker HyperPod has the managed AmazonSageMakerClusterInstanceRolePolicy attached, which allows access to S3 buckets with the specific prefix sagemaker-.

        • OnCreate (string) – [REQUIRED]

          The file name of the entrypoint script of lifecycle scripts under SourceS3Uri. This entrypoint script runs during cluster creation.

      • ExecutionRole (string) – [REQUIRED]

        Specifies an IAM execution role to be assumed by the instance group.

      • ThreadsPerCore (integer) –

        Specifies the value for Threads per core. For instance types that support multithreading, you can specify 1 for disabling multithreading and 2 for enabling multithreading. For instance types that doesn’t support multithreading, specify 1. For more information, see the reference table of CPU cores and threads per CPU core per instance type in the Amazon Elastic Compute Cloud User Guide.

  • VpcConfig (dict) –

    Specifies an Amazon Virtual Private Cloud (VPC) that your SageMaker jobs, hosted models, and compute resources have access to. You can control access to and from your resources by configuring a VPC. For more information, see Give SageMaker Access to Resources in your Amazon VPC.

    • SecurityGroupIds (list) – [REQUIRED]

      The VPC security group IDs, in the form sg-xxxxxxxx. Specify the security groups for the VPC that is specified in the Subnets field.

      • (string) –

    • Subnets (list) – [REQUIRED]

      The ID of the subnets in the VPC to which you want to connect your training job or model. For information about the availability of specific instance types, see Supported Instance Types and Availability Zones.

      • (string) –

  • Tags (list) –

    Custom tags for managing the SageMaker HyperPod cluster as an Amazon Web Services resource. You can add tags to your cluster in the same way you add them in other Amazon Web Services services that support tagging. To learn more about tagging Amazon Web Services resources in general, see Tagging Amazon Web Services Resources User Guide.

    • (dict) –

      A tag object that consists of a key and an optional value, used to manage metadata for SageMaker Amazon Web Services resources.

      You can add tags to notebook instances, training jobs, hyperparameter tuning jobs, batch transform jobs, models, labeling jobs, work teams, endpoint configurations, and endpoints. For more information on adding tags to SageMaker resources, see AddTags.

      For more information on adding metadata to your Amazon Web Services resources with tagging, see Tagging Amazon Web Services resources. For advice on best practices for managing Amazon Web Services resources with tagging, see Tagging Best Practices: Implement an Effective Amazon Web Services Resource Tagging Strategy.

      • Key (string) – [REQUIRED]

        The tag key. Tag keys must be unique per resource.

      • Value (string) – [REQUIRED]

        The tag value.

Return type:

dict

Returns:

Response Syntax

{
    'ClusterArn': 'string'
}

Response Structure

  • (dict) –

    • ClusterArn (string) –

      The Amazon Resource Name (ARN) of the cluster.

Exceptions