Using a processing jobs for custom geospatial workloads - Amazon SageMaker

Using a processing jobs for custom geospatial workloads

With Amazon SageMaker Processing, you can use a simplified, managed experience on SageMaker to run your data processing workloads with the purpose-built geospatial container.

The underlying infrastructure for a Amazon SageMaker Processing job is fully managed by SageMaker. During a processing job, cluster resources are provisioned for the duration of your job, and cleaned up when a job completes.


			Running a processing job.

The preceding diagram shows how SageMaker spins up a geospatial processing job. SageMaker takes your geospatial workload script, copies your geospatial data from Amazon Simple Storage Service(Amazon S3), and then pulls the specified geospatial container. The underlying infrastructure for the processing job is fully managed by SageMaker. Cluster resources are provisioned for the duration of your job, and cleaned up when a job completes. The output of the processing job is stored in the bucket you specified.

Path naming constraints

The local paths inside a Processing jobs container must begin with /opt/ml/processing/.

SageMaker geospatial provides a purpose-built container, 081189585635.dkr.ecr.us-west-2.amazonaws.com/sagemaker-geospatial-v1-0:latest that can be speciļ¬ed when running a processing job.