Installation¶
Jaeger can be installed via one-liner script, Bioconda, PyPI, git, or Apptainer/Singularity. Choose the method that best fits your environment.
Quick reference¶
Method |
Best for |
GPU support |
Command |
|---|---|---|---|
Quick setup / auto-detection |
Yes |
|
|
HPC / reproducible environments |
Yes |
|
|
Development / latest features |
Yes |
|
|
Contributors / bleeding edge |
Yes |
|
|
Containerized HPC workflows |
Yes |
|
One-liner (recommended)¶
The easiest way to install Jaeger. Auto-detects your platform and installs the correct variant.
curl -sSL https://raw.githubusercontent.com/MGXlab/Jaeger/main/install.sh | bash
The script will:
Detect whether you have an NVIDIA GPU, CPU-only, or Apple Silicon
Create a
jaegerconda environment with Python 3.11–3.12Install the correct package variant (
[gpu],[cpu], or[darwin-arm])Run
jaeger healthto verify the installation
Bioconda¶
The simplest way to install Jaeger on most systems. GPU support requires the CUDA Toolkit and cuDNN to be accessible to conda.
# Add required channels (once)
conda config --add channels defaults
conda config --add channels bioconda
conda config --add channels conda-forge
conda config --set channel_priority strict
# Create environment and install
mamba create -n jaeger -c nvidia -c conda-forge cuda-nvcc "python>=3.11,<3.13" pip jaeger-bio
# Activate
conda activate jaeger
# Verify installation
jaeger health
PyPI¶
Recommended for users who want the latest stable release or need to install into an existing Python environment.
# Create a conda environment
mamba create -n jaeger -c nvidia -c conda-forge cuda-nvcc "python>=3.11,<3.13" pip
conda activate jaeger
# Or use venv
python3 -m venv jaeger
source jaeger/bin/activate
# Install with GPU support
pip install jaeger-bio[gpu]
# Or CPU-only
pip install jaeger-bio[cpu]
# Or on Apple Silicon (Mac ARM)
pip install jaeger-bio[darwin-arm]
# Verify installation
jaeger health
Git¶
For developers, contributors, or anyone who needs the very latest code from the main branch.
# Clone the repository
git clone https://github.com/MGXlab/Jaeger.git
cd Jaeger
# Create a conda environment
mamba create -n jaeger -c nvidia -c conda-forge cuda-nvcc "python>=3.11,<3.13" pip
conda activate jaeger
# Or use venv
python3 -m venv jaeger
source jaeger/bin/activate
# Install in editable mode
pip install -e ".[gpu]" # GPU
pip install -e ".[cpu]" # CPU
pip install -e ".[darwin-arm]" # Mac ARM
Apptainer¶
For HPC clusters or environments where you prefer a self-contained container.
# Download container definition and config
wget -O jaeger_singularity.def https://raw.githubusercontent.com/Yasas1994/Jaeger/main/singularity/jaeger_singularity.def
wget -O config.json https://raw.githubusercontent.com/Yasas1994/Jaeger/main/src/jaeger/data/config.json
# Build the container
apptainer build jaeger.sif singularity/jaeger_singularity.def
# Test the container
apptainer run --nv jaeger.sif jaeger --help
apptainer run --nv jaeger.sif jaeger health
# List available models
apptainer run --nv jaeger.sif jaeger download --list
# Download a model
apptainer run --nv jaeger.sif jaeger download \
--model_name jaeger_57341_1.5M_fragment \
--path /path/to/save/model \
--config /path/to/config.json
# Run prediction
apptainer run --nv jaeger.sif jaeger predict \
--model jaeger_57341_1.5M_fragment \
--config /path/to/config.json \
-i /path/to/input.fasta \
-o /path/to/save/results
Troubleshooting¶
Jaeger fails to detect the GPU¶
Check CUDA modules (HPC only):
module avail module load cuda/12.0.0
Verify NVIDIA drivers:
nvidia-smi
Manually install CUDA toolkit (if the above fails):
# Create environment conda create -n jaeger -c conda-forge -c bioconda -c defaults "python>=3.11,<3.13" pip # Install CUDA and cuDNN conda install -n jaeger -c "nvidia/label/cuda-11.8.0" cudatoolkit=11 conda install -n jaeger -c conda-forge cudnn # Install Jaeger conda install -n jaeger -c conda-forge -c bioconda jaeger-bio conda activate jaeger
See the TensorFlow installation guide for more details on GPU setup.