frozenleaves/pytorch
1
0
Fork 0
mirror of https://github.com/pytorch/pytorch.git synced 2025-10-20 21:14:14 +08:00
Code Issues Packages Projects Releases Wiki Activity

Updates to README about CUDA install dir and conda not required (#155458)

Browse Source 
Signed-off-by: Edward Z. Yang <ezyang@meta.com>
Pull Request resolved: https://github.com/pytorch/pytorch/pull/155458
Approved by: https://github.com/malfet
This commit is contained in:
Edward Z. Yang
2025-06-09 21:27:41 -04:00
committed by PyTorch MergeBot
parent f34335bf33
commit 52edfb2cbc
1 changed files with 8 additions and 1 deletions
Download Patch File Download Diff File Expand all files Collapse all files

9
README.md
View File

@ -189,6 +189,11 @@ $ conda activate <CONDA_NAME>
$ call "C:\Program Files\Microsoft Visual Studio\<VERSION>\Community\VC\Auxiliary\Build\vcvarsall.bat" x64
```
A conda environment is not required. You can also do a PyTorch build in a
standard virtual environment, e.g., created with tools like `uv`, provided
your system has installed all the necessary dependencies unavailable as pip
packages (e.g., CUDA, MKL.)
##### NVIDIA CUDA Support
If you want to compile with CUDA support, [select a supported version of CUDA from our support matrix](https://pytorch.org/get-started/locally/), then install the following:
- [NVIDIA CUDA](https://developer.nvidia.com/cuda-downloads)
@ -198,7 +203,9 @@ If you want to compile with CUDA support, [select a supported version of CUDA fr
Note: You could refer to the [cuDNN Support Matrix](https://docs.nvidia.com/deeplearning/cudnn/backend/latest/reference/support-matrix.html) for cuDNN versions with the various supported CUDA, CUDA driver and NVIDIA hardware
If you want to disable CUDA support, export the environment variable `USE_CUDA=0`.
Other potentially useful environment variables may be found in `setup.py`.
Other potentially useful environment variables may be found in `setup.py`. If
CUDA is installed in a non-standard location, set PATH so that the nvcc you
want to use can be found (e.g., `export PATH=/usr/local/cuda-12.8/bin:$PATH`).
If you are building for NVIDIA's Jetson platforms (Jetson Nano, TX1, TX2, AGX Xavier), Instructions to install PyTorch for Jetson Nano are [available here](https://devtalk.nvidia.com/default/topic/1049071/jetson-nano/pytorch-for-jetson-nano/)