- OS Version: Ubuntu 20.04 LTS
- PyTorch Version: 2.0
- ROCm version: 5.0.2
I installed a fresh copy of Ubuntu 20.04 LTS on my desktop with AMD Radeon RX 5700 XT GPU. Both ROCM and PyTorch installed fine. However, PyTorch is not able to detect GPU. Any pointers here?
$ python -c "import torch; print(torch.__version__)"
2.0.0+cu117
$ apt show rocm-libs -a
Package: rocm-libs
Version: 5.0.2.50002-72
Priority: optional
Section: devel
Maintainer: ROCm Libs Support <[email protected]>
Installed-Size: 13.3 kB
Depends: hipblas, hipfft, hipsolver, hipsparse, miopen-hip, rccl, rocalution, rocblas, rocfft, rocrand, rocsolver, rocsparse, rocm-core, hipblas-dev, hipcub-dev, hipfft-dev, hipsolver-dev, hipsparse-dev, miopen-hip-dev, rccl-dev, rocalution-dev, rocblas-dev, rocfft-dev, rocprim-dev, rocrand-dev, rocsolver-dev, rocsparse-dev, rocthrust-dev
Homepage: https://github.com/RadeonOpenCompute/ROCm
Download-Size: 898 B
APT-Sources: https://repo.radeon.com/rocm/apt/5.0.2 ubuntu/main amd64 Packages
Description: Radeon Open Compute (ROCm) Runtime software stack
$ rocminfo | grep 'Name:'
Name: Intel(R) Core(TM) i3-10100 CPU @ 3.60GHz
Marketing Name: Intel(R) Core(TM) i3-10100 CPU @ 3.60GHz
Vendor Name: CPU
Name: gfx1010
Marketing Name: AMD Radeon RX 5700 XT
Vendor Name: AMD
Name: amdgcn-amd-amdhsa--gfx1010:xnack-
$ python3
Python 3.9.16 (main, Mar 8 2023, 14:00:05)
[GCC 11.2.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import torch
>>> print("GPU available:", torch.cuda.is_available())
GPU available: False
I have also tried Ubuntu 22.04 LTS. But it does not work either.
cu117 means than you install version for NVidia CUDA 11.7 while you need a build for ROCm
Pytorch since version 1.8 available for ROCm from official site (but you need to pay attention to version of ROCm (current is 5.4.2) and version of you GPU(gfx1010)) https://pytorch.org/blog/pytorch-for-amd-rocm-platform-now-available-as-python-package/
AMD doc page for ROCm 5.x can be also helpful if you decide other options like build from sources https://docs.amd.com/bundle/ROCm-Deep-Learning-Guide-v5.0/page/Deep_Learning_Frameworks.html