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
From Source (Recommended)#
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=3.10
conda activate trinity
pip install -e ".[dev]"
pip install -e ".[flash_attn]"
# if you encounter issues when installing flash-attn, try:
# pip install flash-attn==2.8.1 --no-build-isolation
Using venv#
python3.10 -m venv .venv
source .venv/bin/activate
pip install -e ".[dev]"
pip install -e ".[flash_attn]"
# if you encounter issues when installing flash-attn, try:
# pip install flash-attn==2.8.1 --no-build-isolation
Using uv#
uv is a modern Python package installer.
uv sync --extra dev --extra flash_attn
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.