Installation#

See also

Environment

Below, we outline how one may set up a virtual environment for proteinworkshop. Note that these installation instructions currently target Linux-like systems with NVIDIA CUDA support. Note that Windows and macOS are currently not officially supported.

From PyPI#

proteinworkshop is available for install from PyPI. This enables training of specific configurations via the CLI or using individual components from the benchmark, such as datasets, featurisers, or transforms, as drop-ins to other projects. Make sure to install PyTorch (specifically version 2.1.2 or newer) using its official pip installation instructions, with CUDA support as desired.

# install `proteinworkshop` from PyPI
pip install proteinworkshop

# install PyTorch Geometric using the (now-installed) CLI
workshop install pyg

# set a custom data directory for file downloads; otherwise, all data will be downloaded to `site-packages`
export DATA_PATH="where/you/want/data/" # e.g., `export DATA_PATH="proteinworkshop/data"`

However, for full exploration we recommend cloning the repository and building from source.

Building from source#

With a local virtual environment activated (e.g., one created with conda create -n proteinworkshop python=3.10):

  1. Clone and install the project

    git clone https://github.com/a-r-j/ProteinWorkshop
cd ProteinWorkshop
pip install -e .

  2. Install PyTorch (specifically version 2.1.2 or newer) using its official pip installation instructions, with CUDA support as desired

    # e.g., to install PyTorch with CUDA 11.8 support on Linux:
pip install torch==2.1.2+cu118 torchvision==0.16.2+cu118 torchaudio==2.1.2+cu118 --index-url https://download.pytorch.org/whl/cu118

  3. Then use the newly-installed proteinworkshop CLI to install PyTorch Geometric

    workshop install pyg