How to resolve TensorFlow's 'ResourceExhaustedError' during model training with large datasets?
I've searched everywhere and can't find a clear answer. I'm currently training a convolutional neural network (CNN) using TensorFlow 2.4 on a dataset with over 100,000 images. Although I have a powerful GPU (NVIDIA RTX 3080), I keep encountering a `ResourceExhaustedError` during training, particularly when I try to increase the batch size. My model architecture is quite standard, with 5 convolutional layers followed by dense layers, and I am using the Adam optimizer with a learning rate of 0.001. I've tried reducing the batch size from 64 to 32 and even down to 16, which allows the training to proceed, but it makes the training time excessively long. Here's a snippet of the model setup and training loop: ```python import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense # Model definition model = Sequential([ Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3)), MaxPooling2D(pool_size=(2, 2)), Conv2D(64, (3, 3), activation='relu'), MaxPooling2D(pool_size=(2, 2)), Flatten(), Dense(128, activation='relu'), Dense(10, activation='softmax') ]) # Compile the model model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) # Training model.fit(train_dataset, epochs=10, batch_size=64) ``` I also attempted to use `tf.data` API for optimized data loading, but the error still persists. The training dataset is large, and I am loading images directly from disk. I'm concerned that the memory allocation for the GPU might not be managed well. Here’s the configuration for the `tf.data` pipeline: ```python def load_image(file_path): image = tf.io.read_file(file_path) image = tf.image.decode_jpeg(image, channels=3) image = tf.image.resize(image, [128, 128]) return image def prepare_dataset(file_paths): dataset = tf.data.Dataset.from_tensor_slices(file_paths) dataset = dataset.map(load_image, num_parallel_calls=tf.data.AUTOTUNE) dataset = dataset.batch(64) return dataset ``` While I understand that the `ResourceExhaustedError` typically indicates that I am trying to allocate more memory than is available on the GPU, is there a more efficient way to manage memory or adjust the model's input handling to avoid this? Are there any best practices for working with large datasets in TensorFlow that I might be overlooking? Am I missing something obvious?