Service job payloads in AWS Batch - AWS Batch
Example service job payload

Service job payloads in AWS Batch

When you submit service jobs using SubmitServiceJob, you provide two key parameters that define the job: serviceJobType, and serviceRequestPayload.

  • The serviceJobType specifies which AWS service will execute the job. For SageMaker Training jobs, this value is SAGEMAKER_TRAINING.

  • The serviceRequestPayload is a JSON-encoded string that contains the complete request that would normally be sent directly to the target service. For SageMaker Training jobs, this payload contains the same parameters you would use with the SageMaker AI CreateTrainingJob API.

For a complete list of all available parameters and their descriptions, see the SageMaker AI CreateTrainingJob API reference. All parameters supported by CreateTrainingJob can be included in your service job payload.

For examples of more training job configurations, see APIs, CLI, and SDKs in the SageMaker AI Developer Guide.

We recommend using the PySDK for service job creation because PySDK has helper classes and utilities. For an example of using PySDK, see SageMaker AI examples on GitHub.

Example service job payload

The following example shows a simple service job payload for a SageMaker Training job that runs a "hello world" training script:

This payload would be passed as a JSON string to the serviceRequestPayload parameter when calling SubmitServiceJob.

{
  "TrainingJobName": "my-simple-training-job",
  "RoleArn": "arn:aws:iam::123456789012:role/SageMakerExecutionRole",
  "AlgorithmSpecification": {
    "TrainingInputMode": "File",
    "TrainingImage": "763104351884.dkr.ecr.us-west-2.amazonaws.com/pytorch-training:2.0.0-cpu-py310",
    "ContainerEntrypoint": [
      "echo",
      "hello world"
    ]
  },
  "ResourceConfig": {
    "InstanceType": "ml.c5.xlarge",
    "InstanceCount": 1,
    "VolumeSizeInGB": 1
  },
  "OutputDataConfig": {
    "S3OutputPath": "s3://your-output-bucket/output"
  },
  "StoppingCondition": {
    "MaxRuntimeInSeconds": 30
  }
}