I am running a variational auto-encoder in TensorFlow, which could take a long time. Thus I want to report the time the algorithm has been running for as a scalar on TensorBoard.
One dirty way is to hard-code the start time of the compilation into a global variable, or pass it as an argument to the model function and compute the difference with current time.
Does Tensorflow have a native way to do it?
There is the tf.train.ProfilerHook. Comes with release 1.14.
Example usage:
estimator = tf.estimator.LinearClassifier(...)
hooks = [tf.train.ProfilerHook(output_dir=model_dir, save_secs=600, show_memory=False)]
estimator.train(input_fn=train_input_fn, hooks=hooks)
Executing the hook will generate files timeline-xx.json in output_dir.
Then open chrome://tracing/ in chrome browser and load the file. You will get a time usage timeline like below.
