I work in an environment in which computational resources are shared, i.e., we have a few server machines equipped with a few Nvidia Titan X GPUs each.
For small to m
# allocate 60% of GPU memory from keras.backend.tensorflow_backend import set_session import tensorflow as tf config = tf.ConfigProto() config.gpu_options.per_process_gpu_memory_fraction = 0.6 set_session(tf.Session(config=config))