INSTALL.md

Installation

The easiest way to install prerequisites is via conda.

After installing conda, run the following commands to create a new environment named ocp-models and install dependencies.

Pre-install step

Install conda-merge:

pip install conda-merge

If you're using system pip, then you may want to add the --user flag to avoid using sudo. Check that you can invoke conda-merge by running conda-merge -h.

GPU machines

Instructions are for PyTorch 1.9.0, CUDA 10.2 specifically.

First, check that CUDA is in your PATH and LD_LIBRARY_PATH, e.g.

$ echo $PATH | tr ':' '\n' | grep cuda
/public/apps/cuda/10.2/bin

$ echo $LD_LIBRARY_PATH | tr ':' '\n' | grep cuda
/public/apps/cuda/10.2/lib64

The exact paths may differ on your system.

Then install the dependencies:

conda-merge env.common.yml env.gpu.yml > env.yml
conda env create -f env.yml

Activate the conda environment with conda activate ocp-models.

Install this package with pip install -e ..

Finally, install the pre-commit hooks:

pre-commit install

Ampere GPUs

NVIDIA Ampere cards require a CUDA version >= 11.1 to function properly, modify the lines here to

- cudatoolkit=11.1
- -f https://pytorch-geometric.com/whl/torch-1.9.0+cu111.html

CPU-only machines

Please skip the following if you completed the with-GPU installation from above.

conda-merge env.common.yml env.cpu.yml > env.yml
conda env create -f env.yml
conda activate ocp-models
pip install -e .
pre-commit install

Mac CPU-only machines

Only run the following if installing on a CPU only machine running Mac OS X.

conda env create -f env.common.yml
conda activate ocp-models
MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ pip install torch-cluster torch-scatter torch-sparse torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.9.0+cpu.html
pip install -e .
pre-commit install