Installation ============ Deep LVPM targets **native PyTorch**. The toolbox keeps the familiar ``fit`` / ``evaluate`` / ``predict`` convenience methods, but the implementation uses PyTorch modules, optimizers, tensors, and checkpoints directly. We strongly suggest creating a clean conda environment or virtual environment before installing. The commands below match the instructions in :file:`README.md`. Conda environment ----------------- To create a conda environment and install the package from PyPI: .. code-block:: bash conda create -n dlvpm-torch python=3.11 -y conda activate dlvpm-torch pip install deep-lvpm For NVIDIA CUDA, install the CUDA-enabled PyTorch wheel for your platform first, then install Deep LVPM: .. code-block:: bash pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126 pip install deep-lvpm GitHub install -------------- To install the current default GitHub branch directly: .. code-block:: bash pip install "git+https://github.com/alexjamesing/Deep_LVPM.git#egg=deep-lvpm" Editable local install ---------------------- .. code-block:: bash git clone https://github.com/alexjamesing/Deep_LVPM.git cd Deep_LVPM pip install -e ".[tutorials,dev]" Virtualenv ---------- .. code-block:: bash python3 -m venv dlvpm-torch source dlvpm-torch/bin/activate # Windows: dlvpm-torch\Scripts\activate pip install deep-lvpm Verifying the install --------------------- .. code-block:: bash python -c "import torch, deep_lvpm; print('torch:', torch.__version__); print('cuda:', torch.cuda.is_available())" Additional notes ---------------- * Apple Silicon uses standard PyTorch wheels with MPS support where available. * CUDA-enabled PyTorch wheels should be installed from the PyTorch index for your platform and driver. * PyTorch checkpoints use ``state_dict`` / ``torch.save``. * Optional dependency groups include ``tutorials``, ``coco``, ``survival``, ``docs``, and ``dev``. For example: ``pip install "deep-lvpm[survival]"``.