Can I measure the execution time of individual operations with TensorFlow?

Question

I know I can measure the execution time of a call to sess.run(), but is it possible to get a finer granularity and measure the execution time of individual operations?

Answer 1

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 arguments options and run_metadata
• you then create a Timeline object with the run_metadata.step_stats data

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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) 

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You can then open Google Chrome, go to the page chrome://tracing and load the timeline.json file. You should see something like:

Answer 2

There is not yet a way to do this in the public release. We are aware that it's an important feature and we are working on it.