e87dba01c6
Flattened out page structure for improved navigability. * Change Table of Contents * Update the install guides for windows and linux * Removed extraneous index pages * GPU architecture pages duplicate entries removed * spack page cleanup --------- Co-authored-by: Sam Wu <samwu103@amd.com> Co-authored-by: Saad Rahim (AMD) <44449863+saadrahim@users.noreply.github.com>
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Troubleshooting
Q: What do I do if I get this error when trying to run PyTorch:
hipErrorNoBinaryForGPU: Unable to find code object for all current devices!
Ans: The error denotes that the installation of PyTorch and/or other dependencies or libraries do not support the current GPU.
Workaround:
To implement a workaround, follow these steps:
-
Confirm that the hardware supports the ROCm stack. Refer to {ref}
linux-supportand {ref}windows-support. -
Determine the gfx target.
rocminfo | grep gfx -
Check if PyTorch is compiled with the correct gfx target.
TORCHDIR=$( dirname $( python3 -c 'import torch; print(torch.__file__)' ) ) roc-obj-ls -v $TORCHDIR/lib/libtorch_hip.so # check for gfx target
Recompile PyTorch with the right gfx target if compiling from the source if
the hardware is not supported. For wheels or Docker installation, contact
ROCm support [^ROCm_issues].
Q: Why am I unable to access Docker or GPU in user accounts?
Ans: Ensure that the user is added to docker, video, and render Linux groups as
described in the ROCm Installation Guide at {ref}linux_group_permissions.
Q: Can I install PyTorch directly on bare metal?
Ans: Bare-metal installation of PyTorch is supported through wheels. Refer to Option 2: Install PyTorch Using Wheels Package. See Installing PyTorch for more information.
Q: How do I profile PyTorch workloads?
Ans: Use the PyTorch Profiler to profile GPU kernels on ROCm.