Creating a custom algorithm container for Amazon SageMaker involves several key steps, from developing your algorithm to packaging it into a Docker container that meets SageMaker's specifications. Here's a high-level overview of the entire process:
1. Develop Your Algorithm
- Design and Code: Begin by developing your machine learning algorithm using your preferred programming language and libraries.
- Test Locally: Ensure your algorithm works as expected in your local environment. This includes performing unit tests and evaluating the model's performance.
2. Prepare Your Docker Environment
- Install Docker: If not already installed, set up Docker on your development machine.
- Familiarize with Docker: Understand the basics of Docker containers, including how to create Dockerfiles and build containers.
3. Containerize Your Algorithm
- Create a Dockerfile: Write a Dockerfile to specify the environment for your algorithm. This includes the base image, any dependencies, and the environment setup.
- Add Your Code: Include your algorithm's code in the container. This can be done by copying the code into the container through the Dockerfile or by using a volume mount during development and testing.
- Define Entry Points: Set the ENTRYPOINT in the Dockerfile to specify how the container should start your algorithm. For SageMaker, it's common to use an entry point script that can handle 'train' and 'serve' commands.
4. Test Your Container Locally
- Run as a Standalone Container: Test your container locally to ensure it behaves as expected. This includes running it in both training and inference modes, if applicable.
- Debug and Iterate: Make any necessary adjustments based on the testing outcomes.
5. Upload Your Container to Amazon ECR
- Create an ECR Repository: Use Amazon Elastic Container Registry (ECR) to store your Docker container image. Create a new repository for your image.
- Authenticate Docker to ECR: Configure Docker to authenticate to your ECR repository.
- Push Your Container Image: Build your Docker image, tag it appropriately, and push it to the ECR repository.
6. Use Your Container with SageMaker
- Configure SageMaker to Use Your Container: When creating a SageMaker training job or deploying a model, specify the ECR image URI of your container.