Overview: Run processing jobs using ScriptProcessor and a SageMaker geospatial container - Amazon SageMaker AI
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Overview: Run processing jobs using ScriptProcessor and a SageMaker geospatial container

SageMaker geospatial provides a purpose-built processing container, 081189585635.dkr.ecr.us-west-2.amazonaws.com/sagemaker-geospatial-v1-0:latest. You can use this container when running a job with Amazon SageMaker Processing. When you create an instance of the ScriptProcessor class that is available through the Amazon SageMaker Python SDK for Processing, specify this image_uri.

Note

If you receive a ResourceLimitExceeded error when attempting to start a processing job, you need to request a quota increase. To get started on a Service Quotas quota increase request, see Requesting a quota increase in the Service Quotas User Guide

Prerequisites for using ScriptProcessor

  1. You have created a Python script that specifies your geospatial ML workload.

  2. You have granted the SageMaker AI execution role access to any Amazon S3 buckets that are needed.

  3. Prepare your data for import into the container. Amazon SageMaker Processing jobs support either setting the s3_data_type equal to "ManifestFile" or to "S3Prefix".

The following procedure show you how to create an instance of ScriptProcessor and submit a Amazon SageMaker Processing job using the SageMaker geospatial container.

To create a ScriptProcessor instance and submit a Amazon SageMaker Processing job using a SageMaker geospatial container

  1. Instantiate an instance of the ScriptProcessor class using the SageMaker geospatial image:

    from sagemaker.processing import ScriptProcessor, ProcessingInput, ProcessingOutput
	
sm_session = sagemaker.session.Session()
execution_role_arn = sagemaker.get_execution_role()

# purpose-built geospatial container
image_uri = '081189585635.dkr.ecr.us-west-2.amazonaws.com/sagemaker-geospatial-v1-0:latest'

script_processor = ScriptProcessor(
	command=['python3'],
	image_uri=image_uri,
	role=execution_role_arn,
	instance_count=4,
	instance_type='ml.m5.4xlarge',
	sagemaker_session=sm_session
)

    Replace execution_role_arn with the ARN of the SageMaker AI execution role that has access to the input data stored in Amazon S3 and any other Amazon services that you want to call in your processing job. You can update the instance_count and the instance_type to match the requirements of your processing job.

  2. To start a processing job, use the .run() method:

    # Can be replaced with any S3 compliant string for the name of the folder.
s3_folder = geospatial-data-analysis

# Use .default_bucket() to get the name of the S3 bucket associated with your current SageMaker session
s3_bucket = sm_session.default_bucket()
					
s3_manifest_uri = f's3://{s3_bucket}/{s3_folder}/manifest.json'
s3_prefix_uri =  f's3://{s3_bucket}/{s3_folder}/image-prefix

script_processor.run(
	code='preprocessing.py',
	inputs=[
		ProcessingInput(
			source=s3_manifest_uri | s3_prefix_uri ,
			destination='/opt/ml/processing/input_data/',
			s3_data_type= "ManifestFile" | "S3Prefix",
			s3_data_distribution_type= "ShardedByS3Key" | "FullyReplicated"
		)
	],
	outputs=[
        ProcessingOutput(
            source='/opt/ml/processing/output_data/',
            destination=s3_output_prefix_url
        )
    ]
)

    • Replace preprocessing.py with the name of your own Python data processing script.

    • A processing job supports two methods for formatting your input data. You can either create a manifest file that points to all of the input data for your processing job, or you can use a common prefix on each individual data input. If you created a manifest file set s3_manifest_uri equal to "ManifestFile". If you used a file prefix set s3_manifest_uri equal to "S3Prefix". You specify the path to your data using source.

    • You can distribute your processing job data two ways:

      • Distribute your data to all processing instances by setting s3_data_distribution_type equal to FullyReplicated.

      • Distribute your data in shards based on the Amazon S3 key by setting s3_data_distribution_type equal to ShardedByS3Key. When you use ShardedByS3Key one shard of data is sent to each processing instance.

    You can use a script to process SageMaker geospatial data. That script can be found in Step 3: Writing a script that can calculate the NDVI. To learn more about the .run() API operation, see run in the Amazon SageMaker Python SDK for Processing.

To monitor the progress of your processing job, the ProcessingJobs class supports a describe method. This method returns a response from the DescribeProcessingJob API call. To learn more, see DescribeProcessingJob in the Amazon SageMaker AI API Reference.

The next topic show you how to create an instance of the ScriptProcessor class using the SageMaker geospatial container, and then how to use it to calculate the Normalized Difference Vegetation Index (NDVI) with Sentinel-2 images.