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