The core library is written in PyTorch. Several components have underlying implementation in CUDA for improved performance. A subset of these components have CPU implementations in C++/Pytorch. It is advised to use PyTorch3d with GPU support in order to use all the features.
- Linux or macOS
- Python ≥ 3.6
- PyTorch 1.4
- torchvision that matches the PyTorch installation. You can install them together at pytorch.org to make sure of this.
- gcc & g++ ≥ 4.9
- CUDA 9.2 or 10.0 or 10.1
- fvcore
These can be installed by running:
conda create -n pytorch3d python=3.6
conda activate pytorch3d
conda install -c pytorch pytorch torchvision cudatoolkit=10.0
conda install -c conda-forge -c takatosp1 fvcore
For developing on top of PyTorch3d or contributing, you will need to run the linter and tests. If you want to run any of the notebook tutorials as docs/tutorials you will also need matplotlib.
- scikit-image
- black
- isort
- flake8
- matplotlib
- tdqm
- jupyter
- imageio
These can be installed by running:
# Demos
conda install jupyter
pip install scikit-image matplotlib imageio
# Tests/Linting
pip install black isort flake8
After installing the above dependencies, run one of the following commands:
# Anaconda Cloud
conda install pytorch3d
pip install 'git+https://github.com/facebookresearch/pytorch3d.git'
# (add --user if you don't have permission)
git clone https://github.com/facebookresearch/pytorch3d.git
cd pytorch3d && pip install -e .
To rebuild after installing from a local clone run, rm -rf build/ **/*.so then pip install -e .. You often need to rebuild pytorch3d after reinstalling PyTorch.
Install from local clone on macOS:
MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ pip install -e .