REI tensorflow resource_error ai_generated true

ResourceExhaustedError: Failed to get next element from iterator: Out of memory while reading data

ID: tensorflow/resource-exhausted-iterator-get-next

Also available as: JSON · Markdown · 中文

82%Fix Rate

86%Confidence

1Evidence

2024-06-18First Seen

Version Compatibility

Version	Status	Introduced	Deprecated	Notes
tensorflow 2.13.0	active	—	—	—
tensorflow 2.14.0	active	—	—	—

Root Cause

Data pipeline prefetching consumes too much memory, often due to large batch size, large dataset elements, or infinite prefetch.

generic

中文

数据管道预取消耗过多内存，通常由于批大小过大、数据集元素过大或无限预取导致。

Official Documentation

https://www.tensorflow.org/api_docs/python/tf/data/Dataset#prefetch

Workarounds

85% success Limit the prefetch buffer size using tf.data.Dataset.prefetch with a specific buffer size: dataset = dataset.batch(32).prefetch(buffer_size=tf.data.AUTOTUNE) # Or manually set buffer_size to a small number like 1 or 2 # Alternatively, use .prefetch(1) to limit to one batch
```
Limit the prefetch buffer size using tf.data.Dataset.prefetch with a specific buffer size:
dataset = dataset.batch(32).prefetch(buffer_size=tf.data.AUTOTUNE)
# Or manually set buffer_size to a small number like 1 or 2
# Alternatively, use .prefetch(1) to limit to one batch
```
80% success Reduce batch size to lower memory usage per element: dataset = dataset.batch(16).prefetch(tf.data.AUTOTUNE) # Or use smaller batch size like 8
```
Reduce batch size to lower memory usage per element:
dataset = dataset.batch(16).prefetch(tf.data.AUTOTUNE)
# Or use smaller batch size like 8
```
75% success Use tf.data.Dataset.cache to avoid re-reading large files, combined with controlled prefetch: dataset = dataset.cache().batch(32).prefetch(1)
```
Use tf.data.Dataset.cache to avoid re-reading large files, combined with controlled prefetch:
dataset = dataset.cache().batch(32).prefetch(1)
```

中文步骤

Limit the prefetch buffer size using tf.data.Dataset.prefetch with a specific buffer size:
dataset = dataset.batch(32).prefetch(buffer_size=tf.data.AUTOTUNE)
# Or manually set buffer_size to a small number like 1 or 2
# Alternatively, use .prefetch(1) to limit to one batch

Reduce batch size to lower memory usage per element:
dataset = dataset.batch(16).prefetch(tf.data.AUTOTUNE)
# Or use smaller batch size like 8

Use tf.data.Dataset.cache to avoid re-reading large files, combined with controlled prefetch:
dataset = dataset.cache().batch(32).prefetch(1)

Dead Ends

Common approaches that don't work:

70% fail
Memory is consumed by prefetch buffer, not worker count.
90% fail
The error is about TensorFlow's internal buffer limits, not system memory capacity.