CUDNN_INIT tensorflow gpu_error ai_generated partial

InternalError: cuDNN initialization failed: CUDNN_STATUS_NOT_INITIALIZED

ID: tensorflow/cudnn-status-not-initialized

Also available as: JSON · Markdown · 中文
75%Fix Rate
85%Confidence
1Evidence
2023-06-15First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow 2.10.0 active — — —
tensorflow 2.11.0 active — — —
tensorflow 2.12.0 active — — —

Root Cause

cuDNN library failed to initialize, often due to incompatible CUDA/cuDNN version or insufficient GPU memory for internal buffers.

generic

中文

cuDNN 库初始化失败，通常是由于 CUDA/cuDNN 版本不兼容或 GPU 内存不足导致内部缓冲区分配失败。

Official Documentation

https://www.tensorflow.org/install/gpu

Workarounds

  1. 85% success Check and align CUDA and cuDNN versions by running `nvidia-smi` and `cat /usr/include/cudnn_version.h | grep CUDNN_MAJOR -A 2`. Then install matching TensorFlow: `pip install tensorflow==2.12.0`. 
    Check and align CUDA and cuDNN versions by running `nvidia-smi` and `cat /usr/include/cudnn_version.h | grep CUDNN_MAJOR -A 2`. Then install matching TensorFlow: `pip install tensorflow==2.12.0`.
  2. 70% success Set environment variable `export TF_CPP_MAX_VLOG_LEVEL=1` before running the script to get detailed cuDNN logs, then adjust library paths accordingly. 
    Set environment variable `export TF_CPP_MAX_VLOG_LEVEL=1` before running the script to get detailed cuDNN logs, then adjust library paths accordingly.

中文步骤

  1. Check and align CUDA and cuDNN versions by running `nvidia-smi` and `cat /usr/include/cudnn_version.h | grep CUDNN_MAJOR -A 2`. Then install matching TensorFlow: `pip install tensorflow==2.12.0`.
  2. Set environment variable `export TF_CPP_MAX_VLOG_LEVEL=1` before running the script to get detailed cuDNN logs, then adjust library paths accordingly.

Dead Ends

Common approaches that don't work:

  1. 90% fail

    The issue is usually a system-level library mismatch, not a Python package problem.

  2. 95% fail

    Memory growth does not help with cuDNN initialization; it only controls dynamic allocation.

  3. 80% fail

    Older versions may have the same or worse cuDNN compatibility issues.