Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and reliable training process. The SageMaker Training Toolkit can be easily added to any Docker container, making it compatible with SageMaker for training models. If you use a prebuilt SageMaker Docker image for training, this library may already be included. Write a training script (eg. train.py). Define a container with a Dockerfile that includes the training script and any dependencies.

Features

  • Pass arguments to the entry point using hyperparameters
  • To train a model using the image on SageMaker, push the image to ECR and start a SageMaker training job with the image URI
  • Read additional information using environment variables
  • Get information about the container environment
  • Execute the entry point
  • Create a Docker image and train a model

Project Samples

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License

Apache License V2.0

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Additional Project Details

Programming Language

Python

Related Categories

Python UML Tool, Python Machine Learning Software, Python Data Science Tool

Registered

2022-06-29