Shows how to use the AWS SDK for .NET to work with Amazon SageMaker.
SageMaker is a fully managed machine learning service.
- 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.
For prerequisites, see the README in the dotnetv3 folder.
- Hello SageMaker (
ListNotebookInstances)
Code excerpts that show you how to call individual service functions.
Code examples that show you how to accomplish a specific task by calling multiple functions within the same service.
For general instructions to run the examples, see the
README in the dotnetv3 folder.
Some projects might include a settings.json file. Before compiling the project, you can change these values to match your own account and resources. Alternatively, add a settings.local.json file with your local settings, which will be loaded automatically when the application runs.
After the example compiles, you can run it from the command line. To do so, navigate to the folder that contains the .csproj file and run the following command:
dotnet run
Alternatively, you can run the example from within your IDE.
This example shows you how to get started using SageMaker.
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.
⚠ Running tests might result in charges to your AWS account.
To find instructions for running these tests, see the README
in the dotnetv3 folder.
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
SPDX-License-Identifier: Apache-2.0