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sagemaker 2.214.3

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Open source library for training and deploying models on Amazon SageMaker.

Open source library for training and deploying models on Amazon SageMaker.

Stars: 2032, Watchers: 2032, Forks: 1085, Open Issues: 278

The aws/sagemaker-python-sdk repo was created 6 years ago and the last code push was 30 minutes ago.
The project is very popular with an impressive 2032 github stars!

How to Install sagemaker

You can install sagemaker using pip

pip install sagemaker

or add it to a project with poetry

poetry add sagemaker

Package Details

Author
Amazon Web Services
License
Apache License 2.0
Homepage
https://github.com/aws/sagemaker-python-sdk/
PyPi:
https://pypi.org/project/sagemaker/
GitHub Repo:
https://github.com/aws/sagemaker-python-sdk

Classifiers

No  sagemaker  pypi packages just yet.

Errors

A list of common sagemaker errors.

Code Examples

Here are some sagemaker code examples and snippets.

GitHub Issues

The sagemaker package has 278 open issues on GitHub

  • feat: add PipelineDefinitionConfig to pipelines to toggle custom job …
  • feature: add SageMaker FeatureStore feature processing
  • fix: key prefix preventing jumpstart model repack
  • build(deps): bump apache-airflow from 2.6.0 to 2.6.2 in /requirements/extras
  • fix: Fix unclear error messages for SageMaker Pipelines
  • feat: Add optional monitoring_config_override parameter in suggest_baseline API
  • Use logger and remove print statements
  • feat: SDK defaults add disable profiler to createTrainingJob
  • feature: model registry integration to model cards to support model packages
  • [BUG] Metric definition is not detected
  • Bloomz models having task name as textgeneration1 on JumpStart
  • fix: Fix dependabot alert in transformers package
  • Unable to upgrade to new sagemaker version due to PyYAML conflict
  • Support Lambda - Reduce Size
  • feature: Add segment config for Clarify

See more issues on GitHub

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