SubmitServiceJob

Submits a service job to a specified job queue to run on SageMaker AI. A service job is a unit of work that you submit to AWS Batch for execution on SageMaker AI.

Request Syntax

POST /v1/submitservicejob HTTP/1.1
Content-type: application/json

{
   "clientToken": "string",
   "jobName": "string",
   "jobQueue": "string",
   "retryStrategy": { 
      "attempts": number,
      "evaluateOnExit": [ 
         { 
            "action": "string",
            "onStatusReason": "string"
         }
      ]
   },
   "schedulingPriority": number,
   "serviceJobType": "string",
   "serviceRequestPayload": "string",
   "shareIdentifier": "string",
   "tags": { 
      "string" : "string" 
   },
   "timeoutConfig": { 
      "attemptDurationSeconds": number
   }
}

URI Request Parameters

The request does not use any URI parameters.

Request Body

The request accepts the following data in JSON format.

Response Syntax

HTTP/1.1 200
Content-type: application/json

{
   "jobArn": "string",
   "jobId": "string",
   "jobName": "string"
}

Response Elements

If the action is successful, the service sends back an HTTP 200 response.

The following data is returned in JSON format by the service.

Errors

Examples

In the following example or examples, the Authorization header contents ( [authorization-params] ) must be replaced with an AWS Signature Version 4 signature. For more information about creating these signatures, see Signature Version 4 Signing Process in the AWS General Reference.

You only need to learn how to sign HTTP requests if you intend to manually create them. When you use the AWS Command Line Interface (AWS CLI) or one of the AWS SDKs to make requests to AWS, these tools automatically sign the requests for you with the access key that you specify when you configure the tools. When you use these tools, you don't need to learn how to sign requests yourself.

Example

This example submits a SageMaker training job to the specified job queue.

Sample Request

POST /v1/submitservicejob HTTP/1.1
Host: batch.us-east-1.amazonaws.com
Accept-Encoding: identity
Content-Length: [content-length]
Authorization: [authorization-params]
X-Amz-Date: 20250801T083015Z
User-Agent: aws-cli/2.27.33 Python/3.13.4 Darwin/24.3.0

{
    "jobName": "sagemaker-training-job-example",
    "jobQueue": "sagemaker-training-queue",
    "retryStrategy": {
        "attempts": 2,
        "evaluateOnExit": [
            {
                "action": "Retry",
                "onStatusReason": "Received status from SageMaker: AlgorithmError: *"
            },
            {
                "action": "EXIT",
                "onStatusReason": "*"
            }
        ]
    },
    "serviceJobType": "SAGEMAKER_TRAINING",
    "serviceRequestPayload": "{\"TrainingJobName\": \"sagemaker-training-job-example\", \"AlgorithmSpecification\": {\"TrainingImage\": \"123456789012.dkr.ecr.us-east-1.amazonaws.com/pytorch-inference:1.8.0-cpu-py3\", \"TrainingInputMode\": \"File\", \"ContainerEntrypoint\":  [\"sleep\", \"1\"]}, \"RoleArn\":\"arn:aws:iam::123456789012:role/SageMakerExecutionRole\", \"OutputDataConfig\": {\"S3OutputPath\": \"s3://example-bucket/model-output/\"}, \"ResourceConfig\": {\"InstanceType\": \"ml.m5.large\", \"InstanceCount\": 1, \"VolumeSizeInGB\": 1}}",
    "timeoutConfig": {
        "attemptDurationSeconds": 300
    },
    "tags": {
        "tag-name": "value-123"
    }
}

Sample Response

HTTP/1.1 200 OK
Content-Type: application/json
Content-Length: [content-length]
Connection: keep-alive
Date: Fri, 01 Aug 2025 08:30:16 GMT
x-amzn-RequestId: [request-id]
X-Amzn-Trace-Id: [trace-id]
X-Cache: Miss from cloudfront
Via: 1.1 hf65sd33h6j9k2l5m8n1p4q7r0sexample.cloudfront.net (CloudFront)
X-Amz-Cf-Id: jkl5mno8pqr1stu4vwx7yz0123ghijklmnopqrstuvwexample

{
  "jobName": "sagemaker-training-job-example",
  "jobId": "a4d6c728-8ee8-4c65-8e2a-9a5e8f4b7c3d",
  "jobArn": "arn:aws:batch:us-east-1:123456789012:service-job/a4d6c728-8ee8-4c65-8e2a-9a5e8f4b7c3d"
}

See Also

For more information about using this API in one of the language-specific AWS SDKs, see the following: