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 README.md.

Conda environment

To create a conda environment and install the package from PyPI:

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:

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:

pip install "git+https://github.com/alexjamesing/Deep_LVPM.git#egg=deep-lvpm"

Editable local install

git clone https://github.com/alexjamesing/Deep_LVPM.git
cd Deep_LVPM
pip install -e ".[tutorials,dev]"

Virtualenv

python3 -m venv dlvpm-torch
source dlvpm-torch/bin/activate           # Windows: dlvpm-torch\Scripts\activate
pip install deep-lvpm

Verifying the install

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]".