I have used the Timeline
object to get the time of execution for each node in the graph:
- you use a classic
sess.run()
but also specify the optional argumentsoptions
andrun_metadata
- you then create a
Timeline
object with therun_metadata.step_stats
data
Here is an example program that measures the performance of a matrix multiplication:
import tensorflow as tf
from tensorflow.python.client import timeline
x = tf.random_normal([1000, 1000])
y = tf.random_normal([1000, 1000])
res = tf.matmul(x, y)
# Run the graph with full trace option
with tf.Session() as sess:
run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
run_metadata = tf.RunMetadata()
sess.run(res, options=run_options, run_metadata=run_metadata)
# Create the Timeline object, and write it to a json
tl = timeline.Timeline(run_metadata.step_stats)
ctf = tl.generate_chrome_trace_format()
with open('timeline.json', 'w') as f:
f.write(ctf)
You can then open Google Chrome, go to the page chrome://tracing
and load the timeline.json
file.
You should see something like: