Principle:Openai Evals Environment Setup
| Knowledge Sources | |
|---|---|
| Domains | Evaluation, DevOps |
| Last Updated | 2026-02-14 10:00 GMT |
Overview
A preparatory process that installs the evaluation framework and configures required credentials before any evaluation can be executed.
Description
Environment Setup encompasses installing the evals Python package and configuring the OPENAI_API_KEY environment variable. The evals package provides two CLI entry points (oaieval and oaievalset) and depends on core libraries including openai, pyyaml, blobfile, lz4, pydantic, tqdm, numpy, and requests. Without proper setup, no evaluation workflow can proceed since the CLI tools and registry system require both the installed package and valid API credentials.
Usage
Perform environment setup once per Python environment before running any evaluation. Required whenever setting up a new development machine, CI/CD pipeline, or containerized environment for model evaluation.
Theoretical Basis
Environment setup follows the standard Python packaging model:
- Install the package and its transitive dependencies via pip
- Configure runtime credentials via environment variables
- Verify installation by confirming CLI entry points are available on PATH
The evals package uses pyproject.toml for build configuration with setuptools as the build backend. Entry points are defined as console scripts mapping oaieval to evals.cli.oaieval:main and oaievalset to evals.cli.oaievalset:main.
Practical Guide
Installation
# Install from source (recommended for development)
git clone https://github.com/openai/evals.git
cd evals
pip install -e .
# Or install from PyPI
pip install evals
Configuration
# Set the OpenAI API key
export OPENAI_API_KEY="sk-..."
# Verify installation
oaieval --help
oaievalset --help
Required Dependencies
| Package | Purpose |
|---|---|
| openai | OpenAI API client for model completions |
| pyyaml | YAML registry file parsing |
| blobfile | Cloud storage and remote file access |
| lz4 | LZ4 compression support for data files |
| pydantic | Dataclass validation for specs |
| tqdm | Progress bar display during evaluation |
| numpy | Numerical computation for metrics |
| requests | HTTP requests for remote recording |