ECF tensorflow gpu_error ai_generated partial

InternalError: cuDNN execution failed: CUDNN_STATUS_EXECUTION_FAILED

ID: tensorflow/cudnn-status-execution-failed

Also available as: JSON · Markdown · 中文
75%Fix Rate
85%Confidence
1Evidence
2023-08-15First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow 2.10.0 active — — —
cudnn 8.4.1 active — — —
cuda 11.7 active — — —

Root Cause

cuDNN encountered an execution failure, typically due to incompatible tensor shapes or corrupted GPU state.

generic

中文

cuDNN遇到执行失败，通常是由于不兼容的张量形状或损坏的GPU状态。

Official Documentation

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

Workarounds

  1. 80% success Reduce batch size to avoid memory pressure: model.fit(..., batch_size=16) 
    Reduce batch size to avoid memory pressure: model.fit(..., batch_size=16)
  2. 70% success Set TF_GPU_ALLOCATOR=cuda_malloc_async to use async allocator: export TF_GPU_ALLOCATOR=cuda_malloc_async 
    Set TF_GPU_ALLOCATOR=cuda_malloc_async to use async allocator: export TF_GPU_ALLOCATOR=cuda_malloc_async
  3. 75% success Clear GPU memory and reset: tf.keras.backend.clear_session() 
    Clear GPU memory and reset: tf.keras.backend.clear_session()

中文步骤

  1. Reduce batch size to avoid memory pressure: model.fit(..., batch_size=16)
  2. Set TF_GPU_ALLOCATOR=cuda_malloc_async to use async allocator: export TF_GPU_ALLOCATOR=cuda_malloc_async
  3. Clear GPU memory and reset: tf.keras.backend.clear_session()

Dead Ends

Common approaches that don't work:

  1. 60% fail

    Increases batch size thinking more data helps, but often makes shape mismatch worse.

  2. 30% fail

    Restarting kernel may fix transient state but doesn't address underlying shape issue.