How to implement guide with tensorflow's fit() method hanging when training on a large dataset
I'm prototyping a solution and I'm performance testing and I'm maintaining legacy code that I'm currently working on a machine learning model using TensorFlow 2.9.0, and I've encountered a frustrating scenario where the `model.fit()` method hangs indefinitely when training on a large dataset of images (about 100,000 samples, each 128x128 pixels with 3 color channels). I’ve confirmed that my dataset is properly loaded and preprocessed using `tf.data.Dataset`. Here's a simplified version of my model training code: ```python import tensorflow as tf from tensorflow.keras import layers, models # Create a simple CNN model model = models.Sequential([ layers.Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)), layers.MaxPooling2D(pool_size=(2, 2)), layers.Flatten(), layers.Dense(64, activation='relu'), layers.Dense(10, activation='softmax') ]) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Assume `train_dataset` is a tf.data.Dataset object loaded with the training data model.fit(train_dataset, epochs=10) ``` I’ve made sure that `train_dataset` is created with `batch_size=32`, and I've also tried increasing the `prefetch_buffer_size` in the `tf.data` pipeline to help with performance: ```python train_dataset = train_dataset.batch(32).prefetch(tf.data.AUTOTUNE) ``` Despite these efforts, the training process often hangs after one or two epochs without any behavior messages, and the CPU utilization drops to near zero. I monitored the logs but didn't see any warnings or errors that indicate what the question might be. Has anyone else experienced similar issues with large datasets in TensorFlow? Any tips on how to debug this or improve performance would be greatly appreciated! I'm using Python 3.9 in this project. Could this be a known issue? How would you solve this?