Getting Started#
This guide will help you get started with the SageMaker HyperPod CLI and SDK to perform basic operations.
Note
Region Configuration: For commands that accept the --region option, if no region is explicitly provided, the command will use the default region from your AWS credentials configuration.
List Available Clusters#
List all available SageMaker HyperPod clusters in your account:
hyp list-cluster [--region <region>]
from sagemaker.hyperpod import list_clusters
list_clusters(region='aws-region')
Connect to a Cluster#
Configure your local kubectl environment to interact with a specific SageMaker HyperPod cluster and namespace:
hyp set-cluster-context --cluster-name <cluster-name>
from sagemaker.hyperpod import set_cluster_context
set_cluster_context('<my-cluster>')
Get Current Cluster Context#
View information about the currently configured cluster context:
hyp get-cluster-context
from sagemaker.hyperpod import get_cluster_context
get_cluster_context()
Next Steps#
After setting up your environment and connecting to a cluster, you can:
Create and manage PyTorch training jobs
Deploy and manage inference endpoints
Monitor cluster resources and job performance
For more detailed information on specific commands, use the --help flag:
hyp <command> --help