| name | torch-npu-pyyaml-preinstall |
| description | Pre-install PyYAML to prevent torch-npu transitive dependency failure |
| tags | ["torch-npu","pyyaml","transitive-dependency","undeclared-requirement","dependency_issue"] |
| category | dependency_issue |
| subtype | undeclared_transitive_dependency |
| confidence | 0.95 |
| occurrence_count | 1 |
Pre-install PyYAML to prevent torch-npu transitive dependency failure
When to Use
- Error involves: import torch # fails with ModuleNotFoundError: No module named 'yaml' when torch-npu is installed but PyYAML is not, import torch_npu # fails at torch_npu/npu/_memory_viz.py line 11
Root Cause
torch-npu 2.5.1's memory visualization submodule (torch_npu/npu/_memory_viz.py) imports yaml unconditionally at module load time. However, PyYAML is not declared in torch-npu's pip metadata (requires field), so a fresh virtual environment with only torch-npu installed will lack it. The error is misleading because the traceback originates from import torch (not import torch_npu), making the actual missing dependency hard to diagnose.
How to Use
-
- Before or alongside installing torch-npu, explicitly install PyYAML:
pip install PyYAML (use domestic mirror if needed: pip install PyYAML -i https://mirrors.aliyun.com/pypi/simple/).
-
- Alternatively, add PyYAML to the project's requirements.txt alongside torch, torch-npu, and torchvision.
-
- After installing torch-npu, run a pre-flight validation:
python -c 'import torch_npu; import torch_npu.npu' — if this exits cleanly without ModuleNotFoundError, the dependency is resolved.
-
- If the error still occurs, verify the yaml module is accessible:
python -c 'import yaml; print(yaml.__version__)'.
Code Examples
File: requirements.txt
Before
torch
torch-npu
torchvision
After
torch
torch-npu
torchvision
PyYAML
Do Not
- Do NOT attempt to patch torch_npu internal files (_memory_viz.py) — the file is overwritten on package upgrade.
- Do NOT confuse this error with a torch installation issue — the traceback starting at 'import torch' is a red herring; the actual failure is in torch_npu backend loading.
- Do NOT use CPU fallback as a workaround — this is a hard dependency failure, not a performance issue.
References
Evidence
- Source runs: e2e-v2-69fe6c6573b0
- Source runs: e2e-v2-c3c4a55c9cd4-exp-20260429211243
