Installation

Install Verifiers and set up your development environment

Requirements

Verifiers requires Python 3.10 or later (up to Python 3.13).

Installation Methods

uv (Recommended)

Install Verifiers using uv, the fast Python package manager:

Install uv

If you don’t have uv installed:

curl -LsSf https://astral.sh/uv/install.sh | sh

Install Verifiers

Core Package

uv add verifiers

Development Installation

For contributing to Verifiers:

git clone https://github.com/PrimeIntellect-ai/verifiers.git
cd verifiers

# CPU-only development
uv sync

# GPU-based trainer development
uv sync --all-extras && uv pip install flash-attn --no-build-isolation

# Install pre-commit hooks
uv run pre-commit install

pip

Install Verifiers using pip:

Core Package

pip install verifiers

For best compatibility, use a virtual environment:

python -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate
pip install verifiers

poetry

Install Verifiers using Poetry:

Core Package

poetry add verifiers

In your pyproject.toml:

[tool.poetry.dependencies]
verifiers = "^0.1.9"

# Or with extras
verifiers = { version = "^0.1.9", extras = ["rl"] }

Optional Features

Verifiers includes several optional feature sets that can be installed as extras:

rl

Reinforcement Learning

Includes PyTorch, transformers, vLLM, and training utilities.

uv add 'verifiers[rl]'

rg

Reasoning Gym

Integration with reasoning-gym environments.

uv add 'verifiers[rg]'

ta

TextArena

Text-based game environments.

uv add 'verifiers[ta]'

browser

Browser Automation

Web browsing environments with Browserbase.

uv add 'verifiers[browser]'

openenv

OpenEnv

OpenEnv environment integration.

uv add 'verifiers[openenv]'

Prime CLI Installation

The Prime CLI provides tools for environment management, evaluation, and training:

# Install Prime CLI
uv tool install prime

# Verify installation
prime --version

# Log in to Prime Intellect
prime login

The Prime CLI is required for:

• Environment initialization and publishing
• Running evaluations
• Managing training jobs
• Accessing the Environments Hub

Verifying Installation

Verify your Verifiers installation:

import verifiers as vf

print(f"Verifiers version: {vf.__version__}")

# Test with a simple environment
from datasets import Dataset

def load_environment():
    dataset = Dataset.from_list([
        {"prompt": [{"role": "user", "content": "Hello!"}]}
    ])
    
    async def dummy_reward(completion) -> float:
        return 1.0
    
    rubric = vf.Rubric(funcs=[dummy_reward])
    return vf.SingleTurnEnv(dataset=dataset, rubric=rubric)

env = load_environment()
print(f"Environment loaded: {type(env).__name__}")

Setting Up a Workspace

After installing Verifiers, set up a development workspace:

1. Navigate to Your Project Directory

   cd ~/dev/my-project
   
2. Run Workspace Setup

   prime lab setup
   

   This creates:

   • Python project structure (if needed)
   • Configuration directory (configs/)
   • Environment directory (environments/)
   • Example configuration files
3. Verify Setup

   ls -la
   # Should show: configs/, environments/, pyproject.toml, etc.
   

Configuration

API Endpoints

Configure API endpoints for model inference in configs/endpoints.toml:

# Prime Inference (default)
[prime]
base_url = "https://api.primeintellect.ai/v1"
api_key_env = "PRIME_API_KEY"

# Custom OpenAI-compatible endpoint
[custom]
base_url = "https://your-api.com/v1"
api_key_env = "YOUR_API_KEY"

# Local vLLM server
[local]
base_url = "http://localhost:8000/v1"

Environment Variables

Set up required environment variables:

# Prime Intellect API key
export PRIME_API_KEY="your-prime-api-key"

# OpenAI API key (if using OpenAI models)
export OPENAI_API_KEY="your-openai-api-key"

# Anthropic API key (if using Claude)
export ANTHROPIC_API_KEY="your-anthropic-api-key"

Store environment variables in a .env file and load them with:

export $(cat .env | xargs)

Updating Verifiers

uv

# Update to latest version
uv add --upgrade verifiers

# Update to specific version
uv add verifiers==0.1.9

pip

# Update to latest version
pip install --upgrade verifiers

# Update to specific version
pip install verifiers==0.1.9

poetry

# Update to latest version
poetry update verifiers

# Update to specific version
poetry add verifiers==0.1.9

Dependencies

Core Dependencies

Verifiers includes these core dependencies:

• anthropic - Anthropic API client
• datasets - Hugging Face datasets
• openai - OpenAI API client
• pydantic - Data validation
• rich - Terminal formatting
• textual - Terminal UI
• prime-sandboxes - Sandboxed code execution
• mcp - Model Context Protocol support

RL Dependencies (Optional)

With verifiers[rl]:

• torch - PyTorch deep learning framework
• transformers - Hugging Face transformers
• vllm - Fast LLM inference
• accelerate - Distributed training
• peft - Parameter-efficient fine-tuning
• wandb - Experiment tracking
• deepspeed - Training optimization
• flash-attn - Optimized attention

GPU Support

For GPU-accelerated training:

1. Install CUDA Toolkit

   Install CUDA 12.1 or later from NVIDIA.

2. Install Verifiers with RL

   uv add 'verifiers[rl]'
   
3. Install Flash Attention (Optional)

   For optimized attention:

   uv pip install flash-attn --no-build-isolation
   
4. Verify GPU Access

   import torch
   print(f"CUDA available: {torch.cuda.is_available()}")
   print(f"GPU count: {torch.cuda.device_count()}")
   

Troubleshooting

Import errors after installation

Ensure you’re in the correct virtual environment:

# Check Python location
which python

# Reinstall if needed
uv add --reinstall verifiers

CUDA/GPU not detected

Check your CUDA installation:

# Check CUDA version
nvcc --version

# Check PyTorch CUDA
python -c "import torch; print(torch.version.cuda)"

Reinstall PyTorch with CUDA support if needed.

Version conflicts

Create a fresh virtual environment:

uv venv --python 3.12 .venv
source .venv/bin/activate
uv add verifiers

Prime CLI not found

Ensure uv tools are in your PATH:

# Add to ~/.bashrc or ~/.zshrc
export PATH="$HOME/.local/bin:$PATH"

# Reload shell
source ~/.bashrc

Next Steps

Quick Start Guide

Create your first environment in minutes

Environment Guide

Learn about datasets, rubrics, and tools

Examples

Browse example environments

API Reference

Explore the complete API