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

---

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: