Installation
Option 1: Install from PyPI (recommended)
This installs gpuma together with its core dependencies. At the moment, installation and tests have only been
validated under Python 3.12; using other Python versions is currently
considered experimental.
GPU support
By default, pip install may pull a CPU-only build of PyTorch
(especially on Windows). To enable GPU acceleration, install PyTorch with
CUDA before installing GPUMA.
Visit pytorch.org/get-started to get the install command matching your platform and CUDA version, e.g.:
Then install GPUMA — pip will see that a CUDA-enabled PyTorch is already present and will not replace it:
Environment setup examples
-
Using a
uvvirtual environment# create and activate a fresh environment (Python 3.12) uv venv .venv --python 3.12 # activate the environment # install PyTorch with CUDA support (pick your CUDA version at https://pytorch.org) uv pip install torch --index-url https://download.pytorch.org/whl/cu124 # install gpuma from PyPI inside the environment uv pip install gpuma -
Using a
condaenvironment# create and activate a fresh environment with Python 3.12 conda create -n gpuma-py312 python=3.12 conda activate gpuma-py312 # install PyTorch with CUDA support (pick your CUDA version at https://pytorch.org) pip install torch --index-url https://download.pytorch.org/whl/cu124 # install gpuma from PyPI inside the environment pip install gpuma
⚠️ Required for UMA models: To access the UMA models on Hugging Face, you must provide a token either via the
HUGGINGFACE_TOKENenvironment variable or via the config (direct token string or path to a file containing the token).
Option 2: Install from source
# clone the repository
git clone https://github.com/niklashoelter/gpuma.git
cd gpuma
# install PyTorch with CUDA support (pick your CUDA version at https://pytorch.org)
pip install torch --index-url https://download.pytorch.org/whl/cu124
# install using (uv) pip
uv pip install .
# or, without uv:
pip install .