Install and verify PyTorch builds for CUDA, ROCm, or CPU backends.
npx skills add https://github.com/amd/Quark --skill quark-torch-install
Please help me install this Agent Skill. Skill: quark-torch-install Source: https://github.com/amd/Quark/tree/main/.claude/skills-impl/l1-atomic/torch/quark-torch-install Command: npx skills add https://github.com/amd/Quark --skill quark-torch-install
This Skill resolves PyTorch installation and compatibility issues before Quark setup, especially when the wrong CPU, CUDA, or ROCm build has been installed and the environment cannot use the intended accelerator.
Ask the assistant to inspect your accelerator and install the correct PyTorch build for it, then verify that torch imports cleanly and the GPU is available.
High-intent search queries and answers about installing and using this skill.
Fix PyTorch CUDA unavailable errors by installing the correct backend-specific PyTorch build for your accelerator. This process detects version conflicts, resolves missing imports, and verifies hardware availability so Quark can run reliably on your GPU.
Matching torchvision and torchaudio versions with PyTorch requires selecting version-matched packages specific to your ROCm backend. This ensures compatibility across the torch ecosystem, preventing import errors and hardware detection failures during accelerator setup.
Install the right PyTorch build for a CPU-only environment by selecting the CPU-specific PyTorch package family. This workflow verifies torch imports cleanly and confirms device availability without attempting to access unavailable CUDA or ROCm GPU hardware.
Importing torch fails after installation when the wrong CPU, CUDA, or ROCm build is installed. Resolve this by applying backend-aware package selection, replacing the mismatched version, and verifying that torch imports cleanly before proceeding.
You need to verify PyTorch hardware availability before setting up Quark to ensure your accelerator backend is correctly configured. This verification-first workflow confirms torch, torchvision, and torchaudio versions and checks GPU detection to prevent runtime failures.
amdCollect OS, Python, GPU, and accelerator facts for Quark installation readiness.
amdQuantize PyTorch transformer models into deployment artifacts using AMD Quark PTQ workflows.
Fudan-SMI-labPin CPU-base PyTorch and install matching torch-npu and torchvision.
amdInstalls AMD Quark and verifies its Python package environment after PyTorch setup.
