Skip to content

INSTALL_MUSA.md

Latest commit

 

History

History
50 lines (39 loc) · 1.56 KB

INSTALL_MUSA.md

File metadata and controls

50 lines (39 loc) · 1.56 KB

Installation

Requirements

Core library

The core library is written in PyTorch. Several components have underlying implementation in MUSA 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
  • Python
  • torch_musa >= 2.1.0
  • PyTorch that matches the torch_musa installation, for torch_musa==2.1.0, PyTorch should be 2.5.0
  • torchvision that matches the PyTorch installation. You can install them together as explained at pytorch.org to make sure of this.
  • gcc & g++ ≥ 4.9
  • ioPath
  • MUSA SDK >= 4.2.0

Some prerequisites are listed in torch_musa, make sure one have torch_musa installed before installing PyTorch3D.

To build and install PyTorch3D, run:

python setup.py install

Tests/Linting and Demos

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 or the examples in docs/examples you will also need matplotlib and OpenCV.

  • scikit-image
  • black
  • usort
  • flake8
  • matplotlib
  • tdqm
  • jupyter
  • imageio
  • fvcore
  • plotly
  • opencv-python

These can be installed by running:

# Demos and examples
conda install jupyter
pip install scikit-image matplotlib imageio plotly opencv-python

# Tests/Linting
conda install -c fvcore -c conda-forge fvcore
pip install black usort flake8 flake8-bugbear flake8-comprehensions