Advancing tensorflow dataset iterator in python multiprocessing Queue

tensorflow_dataset_with_multiprocessing_queue.py

from multiprocessing import Process, Queue

import numpy as np
import tensorflow as tf


def store(next_m, queue):
	with tf.Session() as sess:
		while True:
			queue.put(sess.run(next_m))


if __name__ == '__main__':
	pqueue = Queue()
	a1 = np.arange(1000)

	m = tf.data.Dataset.from_tensor_slices(a1).repeat().batch(1)
	iter_m = m.make_one_shot_iterator()
	m_init_ops = iter_m.make_initializer(m)
	next_m = iter_m.get_next()

	pp_process = Process(target=store, args=(next_m, pqueue,))
	pp_process.daemon = True
	pp_process.start()  # <- Fork before starting this session!

	with tf.Session() as sess:
		for i in range(100000):
			print(pqueue.get())

This is the output I have from running your code with Visual Studio 2019.