# How reproducible should CNN models be? [closed]

I want to train several CNN architectures with Google Colab (GPU), Keras and Tensorflow. Since the trained models are not reproducible due to GPU support, I would like to train the models several times and determine the mean and the standard deviation of the results. I'm totally unsure if I should at least try to make the models minimal reproducible? For example with the following code at the beginning of the program:

import numpy
import tensorflow as tf
import random as rn
import os

os.environ['PYTHONHASHSEED']='0'
np.random.seed(1)
rn.seed(1)
tf.set_random_seed(1)

from keras import backend as K
if 'tensorflow' == K.backend():
import tensorflow as tf
tf.set_random_seed(1)
from keras.backend.tensorflow_backend import set_session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
config.gpu_options.visible_device_list = "0"
set_session(tf.Session(config=config)


I don't know if this makes sense. Would it be better if I did not use seeds at all? What do you think?