Error4 reports
Fix ResourceExhaustedError
in TensorFlow
✅ Solution
ResourceExhaustedError in TensorFlow usually means your operation tried to allocate more memory (typically GPU) than available. Reduce the batch size, image/sequence dimensions, model size, or number of parallel threads/processes to decrease memory usage. Consider using tf.data API for efficient memory management and data streaming; also, check for memory leaks or inefficient tensor operations.
Related Issues
Real GitHub issues where developers encountered this error:
tf.data.Dataset.shuffle segfaults on oversized buffer_size instead of raising an errorMar 24, 2026
tf.data.experimental.index_table_from_dataset aborts with std::bad_alloc on oversized vocab_size instead of raising an errorMar 24, 2026
tf.data.FixedLengthRecordDataset segfaults on oversized buffer_size and num_parallel_reads instead of raising an errorMar 24, 2026
tf.data.FixedLengthRecordDataset aborts with std::bad_alloc on oversized buffer_size instead of raising an errorMar 24, 2026
Timeline
First reported:Mar 24, 2026
Last reported:Mar 24, 2026