I need to use the Tensorflow profiler to profile some code that is running slowly for some reason. Unfortunately, the code in question uses tf.Estimator, so I can't figure out how to inject the run metadata object into the session run() call in order to get the information that the profiler needs.
What should I do?
tf.estimator use tf.train.ProfilerHook works!
just add a ProfilerHook in TrainSpec hooks!
hook = tf.train.ProfilerHook(
save_steps=20,
output_dir=os.path.join(args.model_dir, "tracing"),
show_dataflow=True,
show_memory=True)
hooks = [hook]
train_spec = tf.estimator.TrainSpec(
hooks=hooks,
input_fn=lambda: input_fn())
then, you could get the tracing file like timeline-{}.json in model_dir/tracing, and open chrome chrome://tracing to visual!
with tf.contrib.tfprof.ProfileContext('/tmp/train_dir', dump_steps=[10]) as pctx:
estimator.train() # any thing you want to profile
Then you will get a file at /tmp/train_dir/profile_10
Arguments are defined in https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/profiler/profile_context.py
Use ProfileContext, as described here: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/core/profiler . This allows you to profile without needing to get access to the session.