PyTorch Not Compiled With CUDA
PyTorch installed without CUDA support prevents GPU training entirely.
PyTorch installed without CUDA support prevents GPU training entirely.
What this failure is
PyTorch Not Compiled With CUDA is a Environment failure seen during ML training runs. PyTorch installed without CUDA support prevents GPU training entirely. Common tags: Pytorch Cpu, Cuda Not Available, Installation, Environment.
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Why it happens (the mechanism)
PyTorch installed from CPU-only package without CUDA variant. 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
- torch.cuda.is_available() returns False with NVIDIA GPUs
- Training fails at import or first .cuda() call
Common symptoms and what they mean
| Symptom | Why it happens |
|---|---|
| torch.cuda.is_available() returns False | PyTorch installed from CPU-only package without CUDA variant |
| RuntimeError: CUDA is not available | PyTorch installed from CPU-only package without CUDA variant |
| nvidia-smi shows GPUs but PyTorch can't access them | PyTorch installed from CPU-only package without CUDA variant |
Which systems are affected
- Fresh PyTorch installs
- CPU-only Docker containers
- Conda without CUDA toolkit
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: torch.cuda.is_available() returns False
- ✓Verified signal present: RuntimeError: CUDA is not available
- ✓Verified signal present: nvidia-smi shows GPUs but PyTorch can't access them
- ✓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
Select the traceback or open the failed terminal, then run Denpex locally to see the initiating rank, collateral failures, exact fix, and verification command without uploading the log.
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.
Compare every cuda error side by sideRelated failures to investigate next
Root cause
- PyTorch installed from CPU-only package without CUDA variant
The fix and how to prevent it
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References
Don't just read the fix, diagnose your run
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