SageMaker code examples for the SDK for Java 2.x

Overview

Shows how to use the AWS SDK for Java 2.x to work with Amazon SageMaker.

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SageMaker is a fully managed machine learning service.

⚠ Important

• Running this code might result in charges to your AWS account. For more details, see AWS Pricing and Free Tier.
• Running the tests might result in charges to your AWS account.
• We recommend that you grant your code least privilege. At most, grant only the minimum permissions required to perform the task. For more information, see Grant least privilege.
• This code is not tested in every AWS Region. For more information, see AWS Regional Services.

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Code examples

Prerequisites

For prerequisites, see the README in the javav2 folder.

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Get started

• Hello SageMaker (ListNotebookInstances)

Single actions

Code excerpts that show you how to call individual service functions.

• CreatePipeline
• DeletePipeline
• DescribePipelineExecution
• StartPipelineExecution

Scenarios

Code examples that show you how to accomplish a specific task by calling multiple functions within the same service.

• Get started with geospatial jobs and pipelines

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Run the examples

Instructions

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Hello SageMaker

This example shows you how to get started using SageMaker.

Get started with geospatial jobs and pipelines

This example shows you how to do the following:

• Set up resources for a pipeline.
• Set up a pipeline that executes a geospatial job.
• Start a pipeline execution.
• Monitor the status of the execution.
• View the output of the pipeline.
• Clean up resources.

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Tests

⚠ Running tests might result in charges to your AWS account.

To find instructions for running these tests, see the README in the javav2 folder.

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Properties file

Before running the SageMaker JUnit tests, you must define values in the config.properties file located in the resources folder. This file contains values that are required to run the JUnit tests. For example, you define a model name used in the tests. If you do not define all values, the JUnit tests fail.

Define these values to successfully run the JUnit tests:

• image - The registry path of the Docker image that contains the training algorithm.
• modelDataUrl - The Amazon S3 path where the model artifacts, which result from model training, are stored.
• executionRoleArn - The Amazon Resource Name (ARN) of the IAM role that SageMaker uses.
• modelName - The model name used in various tests.
• s3UriData - The Amazon S3 path where the model data is stored and used in the CreateTrainingJob test.
• s3Uri - The Amazon S3 path where you want SageMaker to store checkpoints.
• trainingJobName - The name of the training job.
• roleArn - The ARN of the IAM role that SageMaker uses.
• s3OutputPath - The output path located in an Amazon S3 bucket (i.e., s3://trainbucket/sagemaker).
• channelName - The channel name.
• trainingImage - The training image.

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Additional resources

• SageMaker Developer Guide
• SageMaker API Reference
• SDK for Java 2.x SageMaker reference

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SPDX-License-Identifier: Apache-2.0