How Amazon SageMaker Uses Containers
Amazon SageMaker offers flexible container-based deployment options for both training and inference. Developers can use separate Docker images for training and inference code, or combine them into a single image.
Container Structure and Components
The standard SageMaker container structure includes:
Key Components:
Training Container
- Handles model training process
- Reads data from S3
- Outputs model artifacts
- Contains training algorithms and dependencies
Inference Container
- Serves real-time predictions
- Uses Flask for API endpoints
- Includes nginx for load balancing
- Contains model serving code
Container Workflow
Development Phase
- Developers can create custom containers or use pre-built ones
- Containers must implement specific SageMaker interfaces
- Support for both training and inference or separate containers
Deployment Phase
- Containers are registered with Amazon ECR
- SageMaker manages container lifecycle
- Automatic scaling based on demand
Additional Insights:
- Consider using multi-stage Docker builds to optimize container size
- Implement proper error handling in container scripts
- Monitor container metrics using CloudWatch
- Use container health checks for improved reliability