Principle:Allenai Open instruct Environment Setup
| Knowledge Sources | |
|---|---|
| Domains | MLOps, DevOps, Distributed Systems |
| Last Updated | 2026-02-07 00:00 GMT |
Overview
Environment setup for LLM training is the process of building a reproducible, containerized training environment that bundles code, dependencies, and configuration into a Docker image, then deploying it to a cluster management system for execution.
Description
Training large language models requires a carefully controlled environment with specific versions of CUDA, PyTorch, Flash Attention, DeepSpeed, and numerous other dependencies. Managing these dependencies across different machines and clusters is error-prone. Containerized training environments solve this by:
Docker containers: Package the entire software stack (OS, CUDA drivers, Python packages, training code) into a portable image. This ensures that the same code runs identically on a developer's workstation, a single GPU server, and a multi-node cluster.
Git commit pinning: The Docker image is tagged with the git commit hash of the training code, creating a bidirectional link between the code and the container. This allows any training run to be traced back to the exact code version used.
Cache-from strategy: Docker layer caching (via --cache-from) dramatically speeds up builds by reusing unchanged layers from previous builds. The cache is stored in a registry (e.g., GitHub Container Registry) for cross-machine sharing.
Cluster management: Systems like Beaker (used at AI2) manage GPU allocation, job scheduling, and output storage. The built Docker image is uploaded to Beaker and then used to launch training jobs with specified resource requirements.
Dependency management: Tools like uv provide fast, reproducible Python dependency resolution and installation, ensuring the same package versions are used across builds.
Pre-flight checks: Before building, the script verifies that the working directory is clean (no uncommitted changes), ensuring the Docker image exactly corresponds to a specific git commit.
Usage
Use containerized environment setup whenever running training jobs on shared clusters or when reproducibility across machines is required. It is essential for:
- Multi-node distributed training
- Scheduled or automated training runs
- Reproducing prior experiments
- Sharing training environments across team members
Theoretical Basis
Reproducibility chain:
git commit (code) -> Docker image (environment) -> Training run (results)
Given: git_hash + training_config -> deterministic results
(modulo hardware non-determinism from floating point)
Build caching model:
Docker layers: L1 (base OS) -> L2 (CUDA) -> L3 (pip packages) -> L4 (code)
Cache hit: If L1-L3 unchanged, only L4 is rebuilt.
Build time: Full build ~30 min, cached build ~2 min.
Clean working tree invariant:
PRECONDITION: git status --porcelain == ""
(no staged, unstaged, or untracked changes)
This ensures: Docker image code == git commit code
(no "it works on my machine" differences)
Image naming convention:
image_name = "open-instruct-integration-test-{sanitized_branch}"
description = "Git commit: {git_hash}"
This enables:
- One image per branch (reused across commits)
- Skip rebuild if image already matches current commit