Service jobs in AWS Batch
AWS Batch service jobs enable you to submit requests to AWS services through AWS Batch job queues. Currently, AWS Batch supports SageMaker Training jobs as service jobs. Unlike containerized jobs where AWS Batch manages the underlying container execution, service jobs allow AWS Batch to provide job scheduling and queuing capabilities while the target AWS service (such as SageMaker AI) handles the actual job execution.
AWS Batch for SageMaker Training jobs allows data scientists to submit training jobs with priorities to configurable queues, ensuring workloads run without intervention as soon as resources are available. This capability addresses common challenges such as resource coordination, preventing accidental overspending, meeting budget constraints, optimizing costs with reserved instances, and eliminating the need for manual coordination between team members.
Service jobs differ from containerized jobs in several key ways:
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Job submission: Service jobs must be submitted using the SubmitServiceJob API. Service jobs cannot be submitted through the AWS Batch console.
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Job execution: AWS Batch schedules and queues service jobs, but the target AWS service runs the actual job workload.
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Resource identifiers: Service jobs use ARNs that contain "service-job" instead of "job" to distinguish them from containerized jobs.
To get started with AWS Batch service jobs for SageMaker Training, see Getting started with AWS Batch on SageMaker AI.
