Principle:Predibase Lorax Container Environment Setup
| Knowledge Sources | |
|---|---|
| Domains | Infrastructure, MLOps |
| Last Updated | 2026-02-08 02:00 GMT |
Overview
A deployment pattern that packages a GPU-accelerated inference server into a Docker container with NVIDIA runtime support, model weight synchronization, and graceful shutdown handling.
Description
Container Environment Setup addresses the challenge of reproducible deployment for GPU-intensive ML inference workloads. By containerizing the inference server with its dependencies, the environment becomes portable across different host systems. The key challenges addressed are:
- GPU Access: Using the NVIDIA Container Toolkit to pass GPU devices into containers
- Model Weight Management: Synchronizing large model weights between S3 cloud storage and local filesystem cache
- Process Lifecycle: Managing the launcher process within the container, including graceful shutdown that uploads weights back to cache before termination
This principle applies to any ML serving system that needs to run on GPU hardware within a containerized environment.
Usage
Use this principle when deploying a LoRAX inference server in a production or staging environment. It is the prerequisite step before any model loading or inference can occur. Required when:
- Deploying to Kubernetes clusters with GPU nodes
- Running on cloud instances with NVIDIA GPUs
- Setting up reproducible deployment pipelines with Docker
Theoretical Basis
The container environment setup follows a staged initialization pattern:
Pseudo-code:
# Abstract container lifecycle
1. trap_shutdown_signals() # Register graceful shutdown handler
2. sync_model_weights(s3 -> local) # Download/sync model files
3. launch_inference_server() # Start lorax-launcher in background
4. monitor_process_health() # Block until process exits
5. upload_weights_to_cache() # On shutdown, sync back to S3
The pattern ensures that expensive model weights are cached in S3 between container restarts, avoiding repeated downloads from HuggingFace Hub.