Simple CPU/GPU TF comparison (TF 1.x)

gpu-cpu.py

# Simple CPU/GPU TF comparison

import tensorflow as tf
import timeit

# See https://www.tensorflow.org/tutorials/using_gpu#allowing_gpu_memory_growth
config = tf.ConfigProto()
config.gpu_options.allow_growth = True

with tf.device('/cpu:0'):
  random_image_cpu = tf.random_normal((100, 100, 100, 3))
  net_cpu = tf.layers.conv2d(random_image_cpu, 32, 7)
  net_cpu = tf.reduce_sum(net_cpu)

with tf.device('/gpu:0'):
  random_image_gpu = tf.random_normal((100, 100, 100, 3))
  net_gpu = tf.layers.conv2d(random_image_gpu, 32, 7)
  net_gpu = tf.reduce_sum(net_gpu)

sess = tf.Session(config=config)

# Test execution once to detect errors early.
try:
  sess.run(tf.global_variables_initializer())
except tf.errors.InvalidArgumentError:
  print(
      '\n\nThis error most likely means that this notebook is not '
      'configured to use a GPU.  Change this in Notebook Settings via the '
      'command palette (cmd/ctrl-shift-P) or the Edit menu.\n\n')
  raise

def cpu():
  sess.run(net_cpu)
  
def gpu():
  sess.run(net_gpu)
  
# Warm up
cpu()
gpu()
  
# Runs the op several times.
print('Time (s) to convolve 32x7x7x3 filter over random 100x100x100x3 images '
      '(batch x height x width x channel).\nSum of thirty runs.')
print('CPU (s):')
cpu_time = timeit.timeit('cpu()', number=30, setup="from __main__ import cpu")
print(cpu_time)
print('GPU (s):')
gpu_time = timeit.timeit('gpu()', number=30, setup="from __main__ import gpu")
print(gpu_time)
print('GPU speedup over CPU: {}x'.format(int(cpu_time/gpu_time)))

sess.close()