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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
