Create a service environment in AWS Batch - AWS Batch
Prerequisites

Create a service environment in AWS Batch

Before you can run SageMaker Training jobs in AWS Batch, you need to create a service environment. You can create a service environment that contains the configuration parameters required for AWS Batch to integrate with SageMaker AI services and submit SageMaker Training jobs on your behalf.

Prerequisites

Before creating a service environment, ensure you have:

Create a service environment (AWS Console)

Use the AWS Batch console to create a service environment through the web interface.

To create a service environment

  1. Open the AWS Batch console at https://console.aws.amazon.com/batch/.

  2. In the navigation pane, choose Environments.

  3. Choose Create environment, select Service environment.

  4. For Service environment configuration choose SageMaker AI.

  5. For Name, enter a unique name for your service environment. Valid characters are a-z, A-Z, 0-9, hyphens (-), and underscores (_).

  6. For Max number of instances enter the maximum number of concurrent training instances

  7. (Optional) Add tags by choosing Add tag and entering key-value pairs.

  8. Choose Next.

  9. Review the details of the new service environment and choose Create service environment.

Create a service environment (AWS CLI)

Use the create-service-environment command to create a service environment with the AWS CLI.

To create a service environment

  1. Create a service environment with the basic required parameters:

    aws batch create-service-environment \
    --service-environment-name my-sagemaker-service-env \
    --service-environment-type SAGEMAKER_TRAINING \
    --capacity-limits capacityUnit=NUM_INSTANCES,maxCapacity=10

  2. (Optional) Create a service environment with tags:

    aws batch create-service-environment \
    --service-environment-name my-sagemaker-service-env \
    --service-environment-type SAGEMAKER_TRAINING \
    --capacity-limits capacityUnit=NUM_INSTANCES,maxCapacity=10 \
    --tags team=data-science,project=ml-training

  3. Verify the service environment was created successfully:

    aws batch describe-service-environments \
    --service-environment my-sagemaker-service-env

The service environment appears in the Environments list with a CREATING state. When creation completes successfully, the state changes to VALID and the service environment is ready to have a service job queue added to it so the service environment can start processing jobs.