Which seeds have to be set where to realize 100% reproducibility of training results in tensorflow?

Question

In a general tensorflow setup like

model = construct_model()
with tf.Session() as sess:
    train_model(sess)

Where construct_model() contains the model definition including random initialization of weights (tf.truncated_normal) and train_model(sess) executes the training of the model -

Which seeds do I have to set where to ensure 100% reproducibility between repeated runs of the code snippet above? The documentation for tf.random.set_random_seed may be concise, but left me a bit confused. I tried:

tf.set_random_seed(1234)
model = construct_model()
    with tf.Session() as sess:
        train_model(sess)

But got different results each time.

Answer 1

The best solution which works as of today with GPU is to install tensorflow-determinism with the following:

pip install tensorflow-determinism

Then include the following code to your code

import tensorflow as tf
import os
os.environ['TF_DETERMINISTIC_OPS'] = '1'

source: https://github.com/NVIDIA/tensorflow-determinism

Answer 2

One possible reason is that when constructing the model, there are some code using numpy.random module. So maybe you can try to set the seed for numpy, too.