Using allow_growth on keras with tensorflow

I’m using Keras with TensorFlow to train a large number of tiny networks (~4 layers, less than 30 nodes in each layer). Currently TF allocates all GPU memory to a single process and therefore prevents me from opening more learning processes in parallel. I found on TF document that I can use

config.gpu_options.allow_growth = True
session = tf.Session(config=config, ...)

to do this. However, I wasn’t able to integrate that into keras. Does someone know the way to initialize a tf session on keras? Thank you very much!

[zoltan-fedor]

@vijaycd , if you are still looking for an actual code you can copy-paste into your Keras code to have Tensorflow dynamically allocate the GPU memory:

import tensorflow as tf
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True  # dynamically grow the memory used on the GPU
config.log_device_placement = True  # to log device placement (on which device the operation ran)
                                    # (nothing gets printed in Jupyter, only if you run it standalone)
sess = tf.Session(config=config)
set_session(sess)  # set this TensorFlow session as the default session for Keras

[vijaycd]

I am sorry, I am new to KEras. How do I use it in my .py file. I need to have the equivalent of

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.log_device_placement=True

sess = tf.Session(config=config)  #With the two options defined above

with tf.Session() as sess:
...

Thanks.