在CPU预处理期间使用多线程时,Tensorflow速度较慢

make_sample 不能 被翻译成张量流运算。由于样本生成非常耗时,我想从多个线程调用该函数来填充输入队列。

example given in the documentation 并得出以下玩具示例:

import numpy as np
import tensorflow as tf
import time

def make_sample():
  # something that takes time and needs to be on CPU w/o tf ops
  p = 1
  for n in range(1000000):
    p = (p + np.random.random()) * np.random.random()
  return np.float32(p)

read_threads = 1

with tf.device('/cpu:0'):
  example_list = [tf.py_func(make_sample, [], [tf.float32]) for _ in range(read_threads)]
  for ex in example_list:
    ex[0].set_shape(())
  batch_size = 3
  capacity = 30
  batch = tf.train.batch_join(example_list, batch_size=batch_size, capacity=capacity)

with tf.Session().as_default() as sess:
  tf.global_variables_initializer().run()
  coord = tf.train.Coordinator()
  threads = tf.train.start_queue_runners(sess=sess, coord=coord)
  try:
    # dry run, left out of timing
    sess.run(batch)
    start_time = time.time()
    for it in range(5):
      print(sess.run(batch))
  finally:
    duration = time.time() - start_time
    print('duration: {0:4.2f}s'.format(duration))
    coord.request_stop()
  coord.join(threads)

令我惊讶的是,当 read_threads

• read_threads=1 duration: 12s
• read_threads=2 duration: 46s
• read_threads=4 duration: 68s
• read_threads=8 duration: 112s