Installation¶

DNALLM is a comprehensive, open-source toolkit designed for fine-tuning and inference with DNA Language Models. This guide will help you install DNALLM and its dependencies.

Prerequisites¶

Python 3.10 or higher (Python 3.12 recommended)
Git
CUDA-compatible GPU (optional, for GPU acceleration)
Environment Manager: Choose one of the following:
Python venv (built-in)
Conda/Miniconda (recommended for scientific computing)

Quick Installation with uv (Recommended)¶

DNALLM uses uv for dependency management and packaging.

What is uv is a fast Python package manager that is 10-100x faster than traditional tools like pip.

Method 1: Using venv + uv¶

# Clone repository
git clone https://github.com/zhangtaolab/DNALLM.git
cd DNALLM

# Create virtual environment
python -m venv .venv

# Activate virtual environment
source .venv/bin/activate  # Linux/MacOS
# or
.venv\Scripts\activate     # Windows

# Upgrade pip (recommended)
pip install --upgrade pip

# Install uv in virtual environment
pip install uv

# Install DNALLM with base dependencies
uv pip install -e '.[base]'

# Verify installation
python -c "import dnallm; print('DNALLM installed successfully!')"

Method 2: Using conda + uv¶

# Clone repository
git clone https://github.com/zhangtaolab/DNALLM.git
cd DNALLM

# Create conda environment
conda create -n dnallm python=3.12 -y

# Activate conda environment
conda activate dnallm

# Install uv in conda environment
conda install uv -c conda-forge

# Install DNALLM with base dependencies
uv pip install -e '.[base]'

# Verify installation
python -c "import dnallm; print('DNALLM installed successfully!')"

GPU Support¶

For GPU acceleration, install the appropriate CUDA version:

# For venv users: activate virtual environment
source .venv/bin/activate  # Linux/MacOS
# or
.venv\Scripts\activate     # Windows

# For conda users: activate conda environment
# conda activate dnallm

# CUDA 12.4 (recommended for recent GPUs)
uv pip install -e '.[cuda124]'

# Other supported versions: cpu, cuda121, cuda126, cuda128
uv pip install -e '.[cuda121]'

Native Mamba Support¶

Native Mamba architecture runs significantly faster than transformer-compatible Mamba architecture, but native Mamba depends on Nvidia GPUs.

If you need native Mamba architecture support, after installing DNALLM dependencies, use the following command:

# For venv users: activate virtual environment
source .venv/bin/activate  # Linux/MacOS

# For conda users: activate conda environment
# conda activate dnallm

# Install Mamba support
uv pip install -e '.[mamba]' --no-cache-dir --no-build-isolation

# If encounter network or compile issue, using the special install script for mamba (optional)
sh scripts/install_mamba.sh  # select github proxy

Please ensure your machine can connect to GitHub, otherwise Mamba dependencies may fail to download.

Additional Model Dependencies¶

Specialized Model Dependencies¶

Some models use their own developed model architectures that haven't been integrated into HuggingFace's transformers library yet. Therefore, fine-tuning and inference for these models require pre-installing the corresponding model dependency libraries:

EVO2¶

EVO2 model fine-tuning and inference depends on its own software package or third-party Python library1/library2:

# evo2 requires python version >=3.11
# Install transformer torch engine
uv pip install "transformer-engine[pytorch]==2.3.0" --no-build-isolation --no-cache-dir
# Install evo2
uv pip install evo2
# (Optional) Install flash attention 2
uv pip install "flash_attn<=2.7.4.post1" --no-build-isolation --no-cache-dir
## Note that build transformer-engine and flash-attn package will cost much time.

# add cudnn path to environment
export LD_LIBRARY_PATH=[path_to_DNALLM]/.venv/lib64/python3.11/site-packages/nvidia/cudnn/lib:${LD_LIBRARY_PATH}

EVO-1¶

# Install evo-1 model
uv pip install evo-model
# (Optional) Install flash attention
uv pip install "flash_attn<=2.7.4.post1" --no-build-isolation --no-cache-dir

GPN¶

Project address: https://github.com/songlab-cal/gpn

uv pip install git+https://github.com/songlab-cal/gpn.git

megaDNA¶

Note that megaDNA weights stored at the Hugging Face can be accessed after requesting permission from the author.

Project address: https://github.com/lingxusb/megaDNA

git clone https://github.com/lingxusb/megaDNA
cd megaDNA
uv pip install .

LucaOne¶

Project address: https://github.com/LucaOne/LucaOneTasks

uv pip install lucagplm

Omni-DNA¶

Project address: https://huggingface.co/zehui127/Omni-DNA-20M

uv pip install ai2-olmo

Enformer¶

Project address: https://github.com/lucidrains/enformer-pytorch

uv pip install enformer-pytorch

Borzoi¶

Project address: https://github.com/johahi/borzoi-pytorch

uv pip install borzoi-pytorch

Some models require support from other dependencies. We will continue to add dependencies requirement for different models.

Flash Attention Support¶

Some models support Flash Attention acceleration. If you need to install this dependency, you can refer to the project GitHub for installation. Note that flash-attn versions are tied to different Python versions, PyTorch versions, and CUDA versions. Please first check if there are matching version installation packages in GitHub Releases, otherwise you may encounter HTTP Error 404: Not Found errors.

uv pip install flash-attn --no-build-isolation --no-cache-dir

Compilation Dependencies¶

If compilation is required during installation and compilation errors occur, please first install the dependencies that may be needed. We recommend using conda to install dependencies.

conda install -c conda-forge gxx clang

Verify Installation¶

Check if installation was successful:

# Test basic functionality
python -c "import dnallm; print('DNALLM installed successfully!')"

# Run comprehensive tests
sh tests/test_all.sh