# Create a service environment in AWS Batch
<a name="create-service-environments"></a>

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
<a name="create-service-environments-prerequisites"></a>

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](IAM_policies.md).

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#### [ 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/](https://console.aws.amazon.com/batch/).

1. In the navigation pane, choose **Environments**.

1. Choose **Create environment**, select **Service environment**.

1. For **Service environment configuration** choose SageMaker AI.

1. For **Name**, enter a unique name for your service environment. Valid characters are a-z, A-Z, 0-9, hyphens (-), and underscores (\$1).

1. For **Max number of instances** enter the maximum number of concurrent training instances

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

1. Choose **Next**.

1. Review the details of the new service environment and choose **Create service environment**.

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#### [ 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
   ```

1. (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
   ```

1. 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.

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