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.

1. 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()

2. 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)

3. 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.

4. 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.

5. Use memory-efficient operations: Using memory-efficient operations such as tf.math.reduce_sum() and tf.math.reduce_mean() can help to reduce the amount of GPU memory that is used by the model.

6. 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 tf.config.experimental.set_virtual_device_configuration() function.

# Limit the GPU memory usage
gpus = tf.config.experimental.list_physical_devices('GPU')
if gpus:
  try:
    tf.config.experimental.set_virtual_device_configuration(
        gpus[0],
        [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)

Output example

1 Physical GPUs, 1 Logical GPUs

These are some of the ways to free up GPU memory when using Python and TensorFlow.

Helpful links

• TensorFlow Documentation - Clear session
• TensorFlow Documentation - Set Virtual Device Configuration

Edit this code on GitHub