# Create a SageMaker Training job queue in AWS Batch
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SageMaker Training job queues integrate directly with the SageMaker AI service to provide serverless job scheduling without requiring you to manage underlying compute infrastructure.

## Prerequisites
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Before creating a SageMaker Training job queue, ensure you have:
+ **Service environment** – A service environment that defines capacity limits. For more information, see [Create a service environment in AWS Batch](create-service-environments.md).
+ **IAM permissions** – Permissions to create and manage AWS Batch job queues and service environments. For more information, see [AWS Batch IAM policies, roles, and permissions](IAM_policies.md).

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#### [ Create a SageMaker Training job queue (AWS Batch console) ]

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 **Job queues** and the **Create**.

1. For **Orchestration type**, choose **SageMaker Training**.

1. For **Job queue configuration**:

   1. For **Name**, enter the name of the Job queue.

   1. for **Priority**, enter a value between 0 and 1000. A Job queue with a higher priority is given preference for service environments.

   1. (Optional) For **Scheduling policy Amazon Resource Name (ARN)**, choose an existing scheduling policy.

   1. For **Connected service environments**, select a service environment from the list to associate with the job queue. 

1. (Optional) For **Job state limits**:

   1. For **Misconfiguration**, choose `SERVICE_ENVIRONMENT_MAX_RESOURCE` and enter the **Maximum runnable time (seconds)**.

   1. For **Capacity**, choose `INSUFFICIENT_INSTANCE_CAPACITY` and enter the **Maximum runnable time (seconds)**.

1. Choose **Create job queue**

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#### [ Create a SageMaker Training job queue (AWS CLI) ]

Use the `create-job-queue` command to create a SageMaker Training job queue.

The following example creates a basic SageMaker Training job queue that uses a service environment:

```
aws batch create-job-queue \
  --job-queue-name my-sm-training-fifo-jq \
  --job-queue-type SAGEMAKER_TRAINING \
  --priority 1 \
  --service-environment-order order=1,serviceEnvironment=ExampleServiceEnvironment
```

Replace *ExampleServiceEnvironment* with the name of your service environment.

The command returns output similar to the following:

```
{
  "jobQueueName": "my-sm-training-fifo-jq",
  "jobQueueArn": "arn:aws:batch:region:account:job-queue/my-sm-training-fifo-jq"
}
```

After creating your job queue, verify that it was created successfully and is in a valid state.

Use the `describe-job-queues` command to view details about your job queue:

```
aws batch describe-job-queues --job-queues my-sm-training-fifo-jq
```

The command returns output similar to the following:

```
{
  "jobQueues": [
    {
      "jobQueueName": "my-sm-training-fifo-jq",
      "jobQueueArn": "arn:aws:batch:region:account:job-queue/my-sm-training-fifo-jq",
      "state": "ENABLED",
      "status": "VALID",
      "statusReason": "JobQueue Healthy",
      "priority": 1,
      "computeEnvironmentOrder": [],
      "serviceEnvironmentOrder": [
        {
          "order": 1,
          "serviceEnvironment": "arn:aws:batch:region:account:service-environment/ExampleServiceEnvironment"
        }
      ],
      "jobQueueType": "SAGEMAKER_TRAINING",
      "tags": {},
      "jobStateTimeLimitActions": []
    }
  ]
}
```

Ensure that:
+ The `state` is `ENABLED`
+ The `status` is `VALID`
+ The `statusReason` is `JobQueue Healthy`
+ The `jobQueueType` is `SAGEMAKER_TRAINING`
+ The `serviceEnvironmentOrder` references your service environment

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