Structuring a Keras project to achieve reproducible results in GPU

Question

I am writing a tensorflow.Keras wrapper to perform ML experiments.

I need my framework to be able to perform an experiment as specified in a configuration yaml file

Answer 1

Keras + Tensorflow.

Step 1, disable GPU.

import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = ""

Step 2, seed those libraries which are included in your code, say "tensorflow, numpy, random".

import tensorflow as tf
import numpy as np
import random as rn

sd = 1 # Here sd means seed.
np.random.seed(sd)
rn.seed(sd)
os.environ['PYTHONHASHSEED']=str(sd)

from keras import backend as K
config = tf.ConfigProto(intra_op_parallelism_threads=1,inter_op_parallelism_threads=1)
tf.set_random_seed(sd)
sess = tf.Session(graph=tf.get_default_graph(), config=config)
K.set_session(sess)

Make sure these two pieces of code are included at the start of your code, then the result will be reproducible.