# RuntimeError: cuDNN error: CUDNN_STATUS_NOT_INITIALIZED

- **ID:** `pytorch/cudnn-deterministic-error`
- **Domain:** pytorch
- **Category:** runtime_error
- **Verification:** ai_generated
- **Fix Rate:** 75%

## Root Cause

cuDNN was not properly initialized, often because of an inconsistent CUDA context or because a cuDNN handle was used after the CUDA device was reset.

## Version Compatibility

| Version | Status | Introduced | Deprecated |
|---------|--------|------------|------------|
| torch>=1.10.0 | active | — | — |
| cuDNN>=8.0 | active | — | — |

## Workarounds

1. **Ensure a single CUDA context is used. Avoid creating multiple contexts by calling torch.cuda.init() once at the beginning.** (80% success)
   ```
   Ensure a single CUDA context is used. Avoid creating multiple contexts by calling torch.cuda.init() once at the beginning.
   ```
2. **Set torch.backends.cudnn.deterministic = True and torch.backends.cudnn.benchmark = False to avoid handle conflicts.** (70% success)
   ```
   Set torch.backends.cudnn.deterministic = True and torch.backends.cudnn.benchmark = False to avoid handle conflicts.
   ```

## Dead Ends

- **** — Calling torch.cuda.empty_cache() does not reinitialize cuDNN and may cause further issues. (90% fail)
- **** — Reinstalling PyTorch or cuDNN without addressing the CUDA context issue will not fix the problem. (85% fail)