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(Installation)=

Installation

For installing Trinity-RFT, you have three options: from source (recommended for experienced users), using Docker (recommended for beginners) or via PyPI.

Before you begin, check your system setup:

If you have GPUs and want to use them

Make sure your system meets these requirements:

  • Python: 3.10 – 3.12
  • CUDA: 12.8 or higher
  • GPUs: At least 2 available

If you don’t have GPUs or prefer not to use them

You can use the tinker option instead, which only requires:

  • Python: 3.11 – 3.12
  • GPUs: Not required

From Source (Recommended for experienced users)

This method is best if you plan to customize or contribute to Trinity-RFT.

1. Clone the Repository

git clone https://github.com/modelscope/Trinity-RFT
cd Trinity-RFT

2. Set Up a Virtual Environment

Choose one of the following options:

Using Conda

conda create -n trinity python=3.12
conda activate trinity

pip install -e ".[vllm,flash_attn]"

# If you have no GPU, comment out the line above and uncomment this instead:
# pip install -e ".[tinker]"

# If you encounter issues when installing flash-attn, try:
# pip install flash-attn==2.8.1 --no-build-isolation

pip install -e ".[dev]"  # for development like linting and debugging

Using venv

python3.10 -m venv .venv
source .venv/bin/activate

pip install -e ".[vllm,flash_attn]"

# If you have no GPU, comment out the line above and uncomment this instead:
# pip install -e ".[tinker]"

# If you encounter issues when installing flash-attn, try:
# pip install flash-attn==2.8.1 --no-build-isolation

pip install -e ".[dev]"  # for development like linting and debugging

Using uv

uv is a modern Python package installer.

uv sync --extra vllm --extra dev --extra flash_attn

# If you have no GPU, try to use Tinker instead:
# uv sync --extra tinker --extra dev

Via PyPI

If you just want to use the package without modifying the code:

pip install trinity-rft
pip install flash-attn==2.8.1

Or with uv:

uv pip install trinity-rft
uv pip install flash-attn==2.8.1

Using Docker

You can download the Trinity-RFT Docker image from Github Container Registry or build it locally.

Pull from GitHub Container Registry (Recommended for beginners)

git clone https://github.com/modelscope/Trinity-RFT
cd Trinity-RFT

docker pull ghcr.io/modelscope/trinity-rft:latest

docker run -it \
  --gpus all \
  --shm-size="64g" \
  --rm \
  -v $PWD:/workspace \
  -v <path_to_your_data_and_checkpoints>:/data \
  ghcr.io/modelscope/trinity-rft:latest
This docker image use `uv` to manage python packages, you need to activate the virtual environment using `source /opt/venv/bin/activate` after entering the docker container.
The image has include dependencies such as vllm, flash-attn and Megatron-LM, if you need to download more packages, don't forget to activate the virtual environment and use `uv pip install` to install them.

Build Locally

git clone https://github.com/modelscope/Trinity-RFT
cd Trinity-RFT

# Build the Docker image
## Tip: You can modify the Dockerfile to add mirrors or set API keys
docker build -f scripts/docker/Dockerfile -t trinity-rft:latest .

# Run the container, replacing <path_to_your_data_and_checkpoints> with your actual path
docker run -it \
  --gpus all \
  --shm-size="64g" \
  --rm \
  -v $PWD:/workspace \
  -v <path_to_your_data_and_checkpoints>:/data \
  trinity-rft:latest
For training with **Megatron-LM**, please refer to {ref}`Megatron-LM Backend <Megatron-LM>`.

Troubleshooting

If you encounter installation issues, refer to the FAQ or GitHub Issues.