Developer Environment Setup

This wiki lists the steps for setting up a developer environment to contribute code changes to PyTorch.

Prerequisites

To develop PyTorch you will need:

• Python 3.8 or later (for Linux, Python 3.8.1+ is needed)
• A C++17 compatible compiler, such as clang

We highly recommend installing an Anaconda environment. You will get a high-quality BLAS library (MKL) and you get controlled dependency versions regardless of your Linux distro.

CUDA Support

If you want to compile with CUDA support, install the following (note that CUDA is not supported on macOS)

• NVIDIA CUDA 11.0 or above
• NVIDIA cuDNN v8 or above
• Compiler compatible with CUDA

Note: You could refer to the cuDNN Support Matrix for cuDNN versions with the various supported CUDA, CUDA driver and NVIDIA hardware

ROCm Support

If you want to compile with ROCm support, install

• AMD ROCm 4.0 and above installation
• ROCm is currently supported only for Linux systems.

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Install Dependencies

Common

conda install cmake ninja
# Run this command from the PyTorch directory after cloning the source code using the “Get the PyTorch Source“ section below
pip install -r requirements.txt

On Linux

conda install mkl mkl-include
# CUDA only: Add LAPACK support for the GPU if needed
conda install -c pytorch magma-cuda110  # or the magma-cuda* that matches your CUDA version from https://anaconda.org/pytorch/repo

# (optional) If using torch.compile with inductor/triton, install the matching version of triton
# Run from the pytorch directory after cloning
make triton

On MacOS

# Add this package on intel x86 processor machines only
conda install mkl mkl-include
# Add these packages if torch.distributed is needed
conda install pkg-config libuv

On Windows

conda install mkl mkl-include
# Add these packages if torch.distributed is needed.
# Distributed package support on Windows is a prototype feature and is subject to changes.
conda install -c conda-forge libuv=1.39

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Next Steps

• Fork, clone, and checkout the PyTorch source