| name | torch-npu-venv-setup-cpu-base |
| description | Set up torch-npu virtual environment with CPU-base torch and +cpu variant torchvision |
| tags | ["torch-npu","venv-setup","dependency-resolution","cpu-base-torch","torchvision-compatibility"] |
| category | dependency_issue |
| subtype | torch_npu_cpu_base_requirement |
| confidence | 0.95 |
| occurrence_count | 4 |
Set up torch-npu virtual environment with CPU-base torch and +cpu variant torchvision
When to Use
- Error involves: pip install -r requirements.txt # where requirements.txt contains 'torch' and 'torch-npu' without version pins, pip install torch torch-npu torchvision # resolves to incompatible versions
Root Cause
The torch-npu package declares a strict dependency on the CPU-only build of PyTorch (e.g., torch==2.5.1+cpu). pip's resolver initially pulls the standard CUDA build of torch from generic PyPI mirrors, causing a version conflict. Additionally, vanilla torchvision expects CUDA-enabled torch and registers operators with the 'CUDA' dispatch key, which conflicts with the CPU-base torch's 'CPU' dispatch key.
How to Use
-
- Probe the system-wide torch-npu version installed by CANN: python -c 'import torch_npu; print(torch_npu.version)'. Note the major.minor version (e.g., 2.5.1).
-
- Create a virtual environment: python -m venv .venv && source .venv/bin/activate.
-
- Install the matching CPU-base torch from PyTorch's official CPU wheel index: pip install torch==+cpu --extra-index-url https://download.pytorch.org/whl/cpu --trusted-host download.pytorch.org.
-
- Install the matching torch-npu from a domestic mirror: pip install torch-npu== -i https://mirrors.aliyun.com/pypi/simple/.
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- Install the matching CPU-variant torchvision: pip install torchvision==<derived_version>+cpu --extra-index-url https://download.pytorch.org/whl/cpu --trusted-host download.pytorch.org. Derive torchvision version as: if torch is 2.5.x, torchvision is 0.20.x; if torch is 2.4.x, torchvision is 0.19.x.
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- Install pyyaml if not already present: pip install pyyaml. (torch_npu requires yaml at import time for its memory visualization module.)
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- Verify the stack: python -c 'import torch; import torch_npu; import torchvision; print(torch.version); print(torch_npu.version); print(torch.npu.is_available())'. Expected: torch shows '+cpu', torch_npu shows the probed version, npu.is_available() returns True.
Do Not
- Do NOT install torch from a generic PyPI mirror first — it will pull the CUDA build and conflict with torch-npu's torch==X.X.X+cpu dependency.
- Do NOT install vanilla torchvision on top of CPU-base torch — the torchvision::nms operator registration will fail with a dispatch key conflict. Always use the +cpu variant.
- Do NOT skip the version probe step — system torch-npu version must match the torch version exactly (e.g., torch-npu 2.5.1 requires torch 2.5.1+cpu, not 2.6.1+cpu).
References
Evidence
- Source runs: e2e-v2-39fff3edbbe2
- Source runs: e2e-v2-39fff3edbbe2-exp-20260429192846
- Source runs: e2e-v2-69fe6c6573b0
- Source runs: e2e-v2-c3c4a55c9cd4
