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_sum()
andtf.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
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.
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