PyTorch / CUDA / cuDNN Version Mismatch
Version mismatches between PyTorch, CUDA, and cuDNN cause cryptic errors.
Version mismatches between PyTorch, CUDA, and cuDNN cause cryptic errors.
What this failure is
PyTorch / CUDA / cuDNN Version Mismatch is a Environment failure seen during ML training runs. Version mismatches between PyTorch, CUDA, and cuDNN cause cryptic errors. Common tags: Version Mismatch, Cuda, Cudnn, Pytorch.
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Why it happens (the mechanism)
PyTorch compiled for CUDA 12.1 but system has CUDA 11.8 drivers. CuDNN version doesn't match PyTorch's expected version. Taken together, these mechanisms explain why the failure is reproducible, why it tends to surface on specific workloads or scales, and why generic mitigation attempts often fall short without addressing the underlying cause.
What you'll observe
- PyTorch reports CUDA error at import
- torch.cuda.is_available() returns False
- Training crashes with cuDNN errors
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| RuntimeError: Detected that PyTorch and CUDA are not compatible | PyTorch compiled for CUDA 12.1 but system has CUDA 11.8 drivers |
| CUDA driver version is insufficient for CUDA runtime version | cuDNN version doesn't match PyTorch's expected version |
| cuDNN version mismatch | PyTorch compiled for CUDA 12.1 but system has CUDA 11.8 drivers |
Which systems are affected
- PyTorch installed via pip without CUDA variant
- Container images with mismatched CUDA toolkit
- Multi-node clusters with different driver versions
How to confirm this is the problem
Use this checklist to test the hypothesis against a small reproduction. No single line proves the root cause, so preserve the preceding events and compare one variable at a time.
- ✓Reproduce the failure from a clean checkpoint/seed: the symptom must appear without warm-up state from a previous run.
- ✓Verified signal present: RuntimeError: Detected that PyTorch and CUDA are not compatible
- ✓Verified signal present: CUDA driver version is insufficient for CUDA runtime version
- ✓Verified signal present: cuDNN version mismatch
- ✓A targeted fix from the "How to fix it" section eliminates or substantially reduces the symptom within one validation pass.
The fix and the prevention pattern
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Diagnose this failure in VS Code
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Install the free VS Code extensionCUDA errors in context
CUDA reports errors asynchronously, so the traceback usually points at whatever line synchronised next rather than the one at fault. The hub covers every common CUDA error and how to make it report honestly.
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Root cause
- PyTorch compiled for CUDA 12.1 but system has CUDA 11.8 drivers
- cuDNN version doesn't match PyTorch's expected version
The fix and how to prevent it
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