Josh Creek
5.3 KiB
Getting this working using an AMD RX 7900 XT or XTX
Install Ubuntu and pre-requisites
Install Ubuntu Desktop 22.04.3 - you can always dual-boot this if you're doing training on your gaming PC. This section of the documentation is based on https://rocm.docs.amd.com/projects/radeon/en/latest/docs/install/install-radeon.html
Install AMD unified driver package repositories and installer script
Download and install the amdgpu-install script on the system.
Enter the following commands to install the installer script for Ubuntu® version 22.04.3:
sudo apt update
wget https://repo.radeon.com/amdgpu-install/23.20.00.48/ubuntu/jammy/amdgpu-install_5.7.00.48.50700-1_all.deb
sudo apt install ./amdgpu-install_5.7.00.48.50700-1_all.deb
Install the Graphics usecase
Run the following command to install open source graphics and ROCm.
amdgpu-install -y --usecase=graphics,rocm
Watch for output warning or errors indicating an unsuccessful driver installation.
NOTE: The -y option installs non-interactively. This step may take several minutes, depending on internet connection and system speed.
Reboot the system.
sudo reboot
Set permissions for Groups to allow access to GPU hardware resources
Once the driver is installed, add any current user to the render and video groups to access GPU resources.
Reboot in order for group changes to take effect.
Add user to render and video groups
Enter the following command to check groups in the system:
groups
Add user to the render and video group using the command:
sudo usermod -a -G render,video $LOGNAME
Reboot the system.
sudo reboot
Post-install verification checks
Run these post-installation checks to verify that the installation is complete:
Verify that the current user is added to the render and video groups.
groups
Expected result:
<username> adm cdrom sudo dip video plugdev render lpadmin lxd sambashare
<username> indicates the current user, and this result will vary in your environment.
Check if amdgpu kernel driver is installed.
dkms status
Expected result:
amdgpu/x.x.x-xxxxxxx.xx.xx, x.x.x-xx-generic, x86_64: installed
Check if the GPU is listed as an agent.
rocminfo
Expected result:
[...]
Agent 2
Name: gfx1100 Uuid: GPU-5ecee39292e80c37 Marketing Name: Radeon RX 7900 XTX Vendor Name: AMD [...] [...]
Check if the GPU is listed.
clinfo
Expected result:
[...] Platform Name: AMD Accelerated Parallel Processing Number of devices: 1 Device Type: CL_DEVICE_TYPE_GPU Vendor ID: 1002h Board name: Radeon RX 7900 XTX [...]
PyTorch via PIP installation method
AMD recommends the PIP install method to create a PyTorch environment when working with ROCm for machine learning development.
Check Pytorch.org for latest PIP install instructions and availability. See Compatibility matrices for support information.
To install PyTorch,
Enter the following command to unpack and begin set up.
sudo apt install python3-pip -y
Enter this command to update the pip wheel.
pip3 install --upgrade pip wheel
Enter this command to install Torch and Torchvision for ROCm AMD GPU support.
wget https://repo.radeon.com/rocm/manylinux/rocm-rel-5.7/torch-2.0.1%2Brocm5.7-cp310-cp310-linux_x86_64.whl
wget https://repo.radeon.com/rocm/manylinux/rocm-rel-5.7/torchvision-0.15.2%2Brocm5.7-cp310-cp310-linux_x86_64.whl
pip3 install --force-reinstall torch-2.0.1+rocm5.7-cp310-cp310-linux_x86_64.whl torchvision-0.15.2+rocm5.7-cp310-cp310-linux_x86_64.whl
This may take several minutes.
Important! AMD recommends proceeding with ROCm WHLs available at repo.radeon.com. The ROCm WHLs available at PyTorch.org are not tested extensively by AMD as the WHLs change regularly when the nightly builds are updated.
Verify PyTorch installation
Confirm if PyTorch is correctly installed.
Verify if Pytorch is installed and detecting the GPU compute device.
python3 -c 'import torch' 2> /dev/null && echo 'Success' || echo 'Failure'
Expected result:
Success
Enter command to test if the GPU is available.
python3 -c 'import torch; print(torch.cuda.is_available())'
Expected result:
True
Enter command to display installed GPU device name.
python3 -c "import torch; print(f'device name [0]:', torch.cuda.get_device_name(0))"
Expected result: Example: device name [0]: Radeon RX 7900 XTX
device name [0]:
Enter command to display component information within the current PyTorch environment.
python3 -m torch.utils.collect_env
Expected result:
PyTorch version
ROCM used to build PyTorch
OS
Is CUDA available
GPU model and configuration
HIP runtime version
MIOpen runtime version
Environment set-up is complete, and the system is ready for use with PyTorch to work with machine learning models, and algorithms.
Update the code
Identify any parts written specifically for NVIDIA GPUs and modify them to be compatible with AMD GPUs.
In PyTorch this typically means replacing .cuda() calls with .to('hip') to leverage ROCm's HIP platform.