I have read answers like:
import tensorflow as tf from keras.backend.tensorflow_backend import set_session config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.2 set_session(tf.Session(config=config))
But it just doesn’t work. There seems to be so much update in both keras and TF that almost anything written in 2017 doesn’t work!
So, how to limit memory usage?
One way to restrict reserving all GPU RAM in tensorflow is to grow the amount of reservation. This method will allow you to train multiple NN using same GPU but you cannot set a threshold on the amount of memory you want to reserve.
Using the following snippet before importing keras or just use
import tensorflow as tf gpus = tf.config.experimental.list_physical_devices('GPU') if gpus: try: for gpu in gpus: tf.config.experimental.set_memory_growth(gpu, True) except RuntimeError as e: print(e)
Answered By – Coderji