python-tensorflowHow can I free up GPU memory when using Python and TensorFlow?
When using Python and TensorFlow, GPU memory can be freed up in a few ways.
- Release unneeded resources: To free up GPU memory, use the
tf.keras.backend.clear_session()function to release unneeded resources. This function will clear the Keras session, freeing up any GPU memory that was used during the session.
import tensorflow as tf # Create a Keras model model = tf.keras.Sequential([ tf.keras.layers.Dense(10, activation='relu', input_shape=(3,)) ]) # Use the model model.predict(tf.ones((3, 3))) # Clear the session tf.keras.backend.clear_session()
- Reduce the batch size: Another way to free up GPU memory is to reduce the batch size when training a model. This will reduce the amount of GPU memory that is used by the model.
# Create a Keras model model = tf.keras.Sequential([ tf.keras.layers.Dense(10, activation='relu', input_shape=(3,)) ]) # Compile the model model.compile(optimizer='adam', loss=tf.keras.losses.MeanSquaredError(), metrics=['accuracy']) # Train the model with a smaller batch size model.fit(x_train, y_train, batch_size=32)
Reduce the number of layers: Reducing the number of layers in a model can also help to free up GPU memory. This can be done by removing layers that are not necessary for the model's performance.
Reduce the number of parameters: Reducing the number of parameters in a model can also help to free up GPU memory. This can be done by reducing the size of the weights and biases in the model.
Use memory-efficient operations: Using memory-efficient operations such as
tf.math.reduce_mean()can help to reduce the amount of GPU memory that is used by the model.
Limit the GPU memory usage: It is also possible to limit the amount of GPU memory that is used by the model. This can be done by using the
# Limit the GPU memory usage gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: try: tf.config.experimental.set_virtual_device_configuration( gpus, [tf.config.experimental.VirtualDeviceConfiguration(memory_limit=1024)]) logical_gpus = tf.config.experimental.list_logical_devices('GPU') print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs") except RuntimeError as e: # Virtual devices must be set before GPUs have been initialized print(e)
1 Physical GPUs, 1 Logical GPUs
These are some of the ways to free up GPU memory when using Python and TensorFlow.
- TensorFlow Documentation - Clear session
- TensorFlow Documentation - Set Virtual Device Configuration
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