Create a service environment in Amazon Batch - Amazon Batch
Prerequisites
Services or capabilities described in Amazon Web Services documentation might vary by Region. To see the differences applicable to the China Regions, see Getting Started with Amazon Web Services in China (PDF).

Create a service environment in Amazon Batch

Before you can run SageMaker Training jobs in Amazon Batch, you need to create a service environment. You can create a service environment that contains the configuration parameters required for Amazon 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 (Amazon Console)

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

To create a service environment

  1. Open the Amazon 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 (Amazon CLI)

Use the create-service-environment command to create a service environment with the Amazon 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.