👀 Views: 40 💬 Answers: 1 📅 Created: 2025-06-19

tensorflow data-augmentation memory-leak Python

I tried several approaches but none seem to work. I'm converting an old project and I'm experiencing a important memory leak while training a model using TensorFlow 2.12, specifically when applying a custom data augmentation pipeline through `tf.data.Dataset`. I have a dataset of images that I'm augmenting using a custom function as follows: ```python import tensorflow as tf import numpy as np # Custom augmentation function def augment(image): image = tf.image.random_flip_left_right(image) image = tf.image.random_brightness(image, max_delta=0.1) return image # Load dataset filenames = tf.io.gfile.glob('path/to/images/*') dataset = tf.data.Dataset.from_tensor_slices(filenames) dataset = dataset.map(lambda x: tf.io.read_file(x)) dataset = dataset.map(lambda x: augment(tf.image.decode_image(x))) dataset = dataset.batch(32).prefetch(tf.data.experimental.AUTOTUNE) ``` While the model compiles and starts training without any errors, I noticed that the memory usage keeps increasing over epochs, eventually leading to an `OutOfMemoryError`. I've tried placing `tf.data.Dataset` in the `tf.function` decorator to see if that would help, but the scenario continues. Additionally, I monitored the memory usage with tools like `memory_profiler`, which indicated that the `augment` function might be holding onto old tensor references. I've already attempted using `tf.data.Dataset.cache()` to cache the dataset, thinking it could help with performance, but this only seems to exacerbate the memory scenario. I also verified that my input images are correctly loaded and preprocessed before feeding them to the model. Has anyone run into similar problems with memory leaks while using TensorFlow's `tf.data.Dataset` API with custom augmentation? Are there specific practices I should follow to avoid this scenario? This is my first time working with Python 3.10. Thanks in advance! Is there a simpler solution I'm overlooking?