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:

• IAM permissions – Permissions to create and manage service environments. For more information, see AWS Batch IAM policies, roles, and permissions.

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.