Create Cluster
sagemaker_create_cluster | R Documentation |
Creates a SageMaker HyperPod cluster¶
Description¶
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.
Usage¶
Arguments¶
ClusterName
[required] The name for the new SageMaker HyperPod cluster.
InstanceGroups
[required] The instance groups to be created in the SageMaker HyperPod cluster.
Tags
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.
Value¶
A list with the following syntax:
Request syntax¶
svc$create_cluster(
ClusterName = "string",
InstanceGroups = list(
list(
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 = list(
SourceS3Uri = "string",
OnCreate = "string"
),
ExecutionRole = "string",
ThreadsPerCore = 123,
InstanceStorageConfigs = list(
list(
EbsVolumeConfig = list(
VolumeSizeInGB = 123
)
)
)
)
),
VpcConfig = list(
SecurityGroupIds = list(
"string"
),
Subnets = list(
"string"
)
),
Tags = list(
list(
Key = "string",
Value = "string"
)
)
)