How to resolve TensorFlow's 'ResourceExhaustedError' when training a CNN on a limited GPU?
After trying multiple solutions online, I still can't figure this out. I've been struggling with this for a few days now and could really use some help... I'm currently trying to train a convolutional neural network using TensorFlow 2.7 on a dataset of images, but I keep running into a `ResourceExhaustedError` due to GPU memory exhaustion. My model architecture is relatively standard, consisting of several convolutional layers followed by max pooling and finally a dense layer for classification. Here's a simplified version of my model: ```python import tensorflow as tf from tensorflow.keras import layers, models def create_model(): model = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(128, 128, 3))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(128, (3, 3), activation='relu')) model.add(layers.Flatten()) model.add(layers.Dense(128, activation='relu')) model.add(layers.Dense(10, activation='softmax')) return model model = create_model() model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) ``` I've tried reducing the batch size from 64 to 16 and even as low as 4, but I still encounter the same error message: ``` ResourceExhaustedError: OOM when allocating tensor with shape[32,128,128,32] and type float on /job:localhost/replica:0/task:0/device:GPU:0 ``` Additionally, I enabled mixed-precision training using the following lines: ```python tf.keras.mixed_precision.set_global_policy('mixed_float16') ``` However, it seems to have made little difference. I also made sure to clear the session before starting a new model training to free up resources. Here’s how I’m clearing the session: ```python tf.keras.backend.clear_session() ``` I have 8 GB of GPU memory, and I suspect my model might be too large. Is there a recommended approach to either optimize my model for performance or further manage GPU memory usage? Any suggestions or best practices would be greatly appreciated! Has anyone else encountered this? For context: I'm using Python on Linux.