Installation#

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

Before installing, ensure your system meets the following requirements:

  • Python: Version 3.10 to 3.12 (inclusive)

  • CUDA: Version 12.4 to 12.8 (inclusive)

  • GPUs: At least 2 GPUs



Via PyPI#

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

pip install trinity-rft==0.3.0
pip install flash-attn==2.8.1

Or with uv:

uv pip install trinity-rft==0.3.0
uv pip install flash-attn==2.8.1

Using Docker#

We provide a Docker setup for hassle-free environment configuration.

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

Note

For training with Megatron-LM, please refer to Megatron-LM Backend.


Troubleshooting#

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