Principle:ContextualAI HALOs Environment Setup

Knowledge Sources	ContextualAI HALOs PyTorch Installation Flash Attention
Domains	Infrastructure, DevOps
Last Updated	2026-02-08 03:00 GMT

Overview

A reproducible environment provisioning strategy that pins all library versions to ensure deterministic training outcomes across machines.

Description

Environment setup for LLM alignment training requires precise version pinning of interdependent libraries. The HALOs framework depends on a stack including PyTorch with CUDA support, Flash Attention for efficient self-attention computation, HuggingFace Transformers for model loading, PEFT for parameter-efficient fine-tuning, Accelerate for distributed training, and vLLM for fast inference during sampling. Mismatched versions between these libraries are a common source of silent failures in ML pipelines, making reproducible environment provisioning a critical first step.

The environment also includes evaluation tooling: AlpacaEval for instruction-following benchmarks and the LM Evaluation Harness for standardized NLP benchmarks. Pre-downloading benchmark datasets and task configurations during setup ensures that evaluation can proceed without network access.

Usage

Use this principle when setting up a new machine or cluster node for HALOs training. It is the prerequisite step for all other workflows: offline SFT alignment, online iterative alignment, reward model training, and model evaluation. The environment should be provisioned once per machine and reused across experiments.

Theoretical Basis

Reproducible environments follow the principle of deterministic dependency resolution: given the same specification, any machine should produce an identical runtime. This is achieved through:

1. Version pinning - Exact version numbers for all packages (e.g., transformers==4.51.3 rather than transformers>=4.0)
2. Isolated environments - Using conda to create a self-contained Python environment that does not interfere with system packages
3. Pre-cached artifacts - Downloading datasets and task configs during setup to avoid runtime network dependencies

Practical Guide

1. Create and activate a conda environment with Python 3.10.14
2. Install PyTorch 2.4.0 with CUDA 12.1 support
3. Install Flash Attention 2.6.3 (requires --no-build-isolation for correct compilation)
4. Install the HuggingFace stack: Transformers 4.51.3, PEFT 0.12.0, Datasets 2.20.0, Accelerate 0.33.0
5. Install vLLM 0.6.3.post1 for fast inference
6. Install evaluation and utility packages: alpaca-eval, wandb, omegaconf, openai, hydra-core 1.3.2
7. Clone and install lm-evaluation-harness for standardized benchmarks
8. Pre-download benchmark task configs and the AlpacaEval dataset

Related Pages

Implemented By

• Implementation:ContextualAI_HALOs_Install_Sh