PRF tensorflow resource_error ai_generated partial

ResourceExhaustedError: Failed to allocate memory for prefetch queue

ID: tensorflow/resource-exhausted-dataset-prefetch

Also available as: JSON · Markdown · 中文
80%Fix Rate
84%Confidence
1Evidence
2024-02-20First Seen

Version Compatibility

VersionStatusIntroducedDeprecatedNotes
tensorflow 2.14.0 active — — —
tensorflow 2.16.0 active — — —

Root Cause

The tf.data pipeline's prefetch buffer consumes too much memory, often due to large dataset elements or excessive prefetch size.

generic

中文

tf.data管道的预取缓冲区消耗过多内存，通常是由于数据集元素过大或预取大小过大。

Official Documentation

https://www.tensorflow.org/guide/data_performance

Workarounds

  1. 85% success Reduce prefetch buffer size: dataset = dataset.prefetch(buffer_size=tf.data.AUTOTUNE) or set a fixed small value like 1. 
    Reduce prefetch buffer size: dataset = dataset.prefetch(buffer_size=tf.data.AUTOTUNE) or set a fixed small value like 1.
  2. 80% success Use dataset.cache() to store processed elements on disk instead of memory. 
    Use dataset.cache() to store processed elements on disk instead of memory.
  3. 75% success Reduce element size by batching smaller or using lower-resolution images. 
    Reduce element size by batching smaller or using lower-resolution images.

中文步骤

  1. Reduce prefetch buffer size: dataset = dataset.prefetch(buffer_size=tf.data.AUTOTUNE) or set a fixed small value like 1.
  2. Use dataset.cache() to store processed elements on disk instead of memory.
  3. Reduce element size by batching smaller or using lower-resolution images.

Dead Ends

Common approaches that don't work:

  1. 70% fail

    Increasing overall GPU memory limit doesn't target prefetch buffer specifically.

  2. 50% fail

    Using more CPU cores for mapping can increase memory usage.