Installation¶
The project supports both uv and pip.
A pinned uv.lock is committed to the repository, so uv sync
produces a reproducible install across machines.
Basic Install¶
With uv (recommended)¶
uv sync creates a .venv/ in the repo, installs the project in
editable mode, and resolves all dependencies from uv.lock. Activate
the environment with source .venv/bin/activate, or prefix commands
with uv run (for example, uv run python -m deepdrivewe ...).
With pip¶
git clone git@github.com:ramanathanlab/deepdrivewe-academy.git
cd deepdrivewe-academy
pip install -e .
Full Install with MD Dependencies¶
For running molecular dynamics simulations with OpenMM and AmberTools, use conda to install the simulation backends first, then install the Python package with either uv or pip:
git clone git@github.com:ramanathanlab/deepdrivewe-academy.git
cd deepdrivewe-academy
conda create -n deepdrivewe python=3.11 -y
conda activate deepdrivewe
conda install -c conda-forge openmm=8.1
conda install omnia::ambertools -y
pip install -e . # or: uv pip install -e .
Deep Learning Models¶
To use the built-in AI models (convolutional VAE, adversarial autoencoder), install the correct version of PyTorch for your system and GPU drivers:
Note
The mdlearn dependency may require an earlier version of PyTorch.
If you encounter compatibility issues, try:
Development Setup¶
For contributing or running the test suite:
# uv (recommended)
uv sync --extra dev --extra docs
uv run pre-commit install
# pip
python -m venv venv
source venv/bin/activate
pip install -U pip setuptools wheel
pip install -e '.[dev,docs]'
pre-commit install
Verify the setup:
Documentation Build¶
To build the documentation locally:
# uv
uv sync --extra docs
uv run properdocs serve
# pip
pip install -e '.[dev,docs]'
properdocs serve
Then open http://localhost:8000 in your browser. For a production build with strict checking:
See the Contributing guide for more details.