SageMaker Studio announces pre-built docker ‘SageMaker Distribution’ for machine learning
Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) that enables Data Scientists and ML practitioners with their end to end machine learning workflow, from preparing data to building, training, tuning, and deploying models. In May 2023, we launched SageMaker Distribution a pre-built docker image which includes the most popular libraries for machine learning as an open-source project at JupyterCon. Today, we are announcing support for SageMaker Distribution in Amazon SageMaker Studio.
Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) that enables Data Scientists and ML practitioners with their end to end machine learning workflow, from preparing data to building, training, tuning, and deploying models. In May 2023, we launched SageMaker Distribution a pre-built docker image which includes the most popular libraries for machine learning as an open-source project at JupyterCon. Today, we are announcing support for SageMaker Distribution in Amazon SageMaker Studio.
SageMaker Distribution enables machine learning practitioners to get started quickly with their ML development. The pre-built docker container comes with 18 popular libraries including deep learning frameworks such as PyTorch, TensorFlow and Keras; popular python packages such as numpy, scikit-learn and pandas; and IDEs such as Jupyter Lab. The versions of these installed libraries and packages are compatible with each other. The SageMaker Distribution image can also be used to run SageMaker training jobs, so customers can now use the same runtime on Studio notebooks and SageMaker training enabling them to seamlessly transition from local experimentation to batch execution.
SageMaker Distribution is now available in all AWS regions where SageMaker Studio is available. You can now get started with SageMaker-distribution by accessing it via ECR gallery or GitHub. To learn more, please refer to the blog post and SageMaker documentation.