Installation

Installation#

Prerequisites#

GATAC requires Python ≥ 3.11 and a CUDA-capable GPU with drivers compatible with CUDA 12 or 13.

The following system libraries must be available:

  • CUDA Toolkit 12.x or 13.x

  • cuDNN (optional, for future neural components)

Quick install with uv#

# Clone the repository
git clone https://github.com/Soldatov-Lab/gatac.git
cd GATAC

# Install with CUDA 12 support
uv sync --extra cuda12

# Or with CUDA 13 support
uv sync --extra cuda13

After installation the gatac command will be available:

gatac --help

Install from PyPI (planned)#

A PyPI release is planned for a future version. For now, install directly from the repository as shown above.

Optional: documentation dependencies#

To build the docs locally, install the docs extras:

uv sync --extra docs

Then build with:

cd docs
make html

Verifying your installation#

import gatac as ga
print(ga.__version__)

# Check that GPU is available
import cudf
print(cudf.get_device_info())

Hardware requirements#

Component

Minimum

Recommended

GPU VRAM

8 GB

24–80 GB

RAM

32 GB

128+ GB

Storage

—

NVMe SSD

Note

GATAC can run on CPU-only systems by omitting the cuda12/cuda13 extras and relying on Polars CPU execution, but performance will be significantly reduced and some GPU-only features will be unavailable.

Conda / Mamba#

GATAC is designed around uv for reproducible installs. Conda support may be added in a future release. If you need Conda, you can install the RAPIDS dependencies manually:

conda create -n gatac -c rapidsai -c conda-forge -c nvidia \
    rapids=24.02 python=3.11 cuda-version=12.0
conda activate gatac
pip install -e .