CUDNN_STATUS_NOT_INITIALIZED pytorch runtime_error ai_generated true

运行时错误：cuDNN 错误：调用 cudnnCreate 时 CUDNN_STATUS_NOT_INITIALIZED

RuntimeError: cuDNN error: CUDNN_STATUS_NOT_INITIALIZED when calling cudnnCreate

ID: pytorch/cudnn-status-not-initialized

其他格式: JSON · Markdown 中文 · English
80%修复率
87%置信度
1证据数
2023-01-20首次发现

版本兼容性

版本状态引入弃用备注
pytorch>=1.10 active — — —
cuda>=11.0 active — — —
cudnn>=8.0 active — — —

根因分析

cuDNN 库初始化失败，通常由于 CUDA/cuDNN 版本不兼容、缺少库文件或安装损坏。

English

cuDNN library failed to initialize, often due to incompatible CUDA/cuDNN versions, missing library files, or corrupted installation.

generic

官方文档

https://docs.nvidia.com/deeplearning/cudnn/install-guide/index.html

解决方案

  1. Verify CUDA and cuDNN versions match PyTorch requirements: run `python -c "import torch; print(torch.version.cuda); print(torch.backends.cudnn.version())"` and compare with PyTorch documentation.
  2. Reinstall cuDNN by downloading the correct version from NVIDIA and copying library files to the CUDA toolkit directory (e.g., /usr/local/cuda/lib64).
  3. Use a PyTorch Docker image with pre-matched CUDA/cuDNN versions: `docker pull pytorch/pytorch:2.0.1-cuda11.7-cudnn8-devel`

无效尝试

常见但无效的做法:

  1. Reinstalling PyTorch without changing CUDA version 85% 失败

    If the underlying CUDA/cuDNN mismatch persists, reinstalling PyTorch alone does not resolve it.

  2. Setting environment variable CUDNN_LOGINFO_DBG=1 90% 失败

    This only enables logging, does not fix the initialization issue.

  3. Downgrading PyTorch to an older version 70% 失败

    May temporarily work but is not a proper fix; the root cause is version compatibility.