While fiddling around with TensorFlow, I noticed that a relatively simple task (batching some of our 3D accelerometer data and taking the sum of each epoch) was having relatively poor performance. Here's the essence of what I had running, once I got the (incredibly nifty!) Timeline functionality up:

import numpy as np
import tensorflow as tf
from tensorflow.python.client import timeline# Some dummy functions to compute "features" from the datadef compute_features( data ):feature_functions = [lambda x: test_sum( x, axis = 0 ),lambda x: test_sum( x, axis = 1 ),lambda x: test_sum( x, axis = 2 ),]return tf.convert_to_tensor( [ f( data ) for f in feature_functions ] )def test_sum( data, axis = 0 ):t, v = datareturn tf.reduce_sum( v[:, axis] )# Setup for using Timeline
sess = tf.Session()
run_options = tf.RunOptions( trace_level = tf.RunOptions.FULL_TRACE )
run_metadata = tf.RunMetadata()# Some magic numbers for our dataset
test_sampling_rate = 5000.0
segment_size = int( 60 * test_sampling_rate )# Load the dataset
with np.load( 'data.npz' ) as data:t_raw = data['t']v_raw = data['v']# Build the iterator
full_dataset = tf.data.Dataset.from_tensor_slices( (t_raw, v_raw) ).batch( segment_size )
dataset_iterator = full_dataset.make_initializable_iterator()
next_datum = dataset_iterator.get_next()sess.run( dataset_iterator.initializer )
i = 0
while True:try:print( sess.run( compute_features( next_datum ), options = run_options,run_metadata = run_metadata ) )# Write Timeline data to a file for analysis latertl = timeline.Timeline( run_metadata.step_stats )ctf = tl.generate_chrome_trace_format()with open( 'timeline_{0}.json'.format( i ), 'w' ) as f:f.write( ctf )i += 1except tf.errors.OutOfRangeError:break

Pulling this up in Chrome, I observed that in each iteration, IteratorGetNext was eating up the vast majority of the time:

Screenshot of Chrome displaying the timeline for one iteration

As you can see, the "main" part of the computation is shoved into the tiny blips on the right hand side, while the vast majority of the time for this cycle is stuck in IteratorGetNext.

I'm wondering if I'm missing anything obvious as far as the way I've constructed my graph that would cause performance to degrade so egregiously on this step. I'm a bit stumped as to why this setup is performing so poorly.

If IteratorGetNext shows up as a large event in the timeline, then your model is bottlenecked on input processing. In this case, the pipeline is quite simple, but it is bottleneck on copying 300,000 elements into a batch. You can move this copy off the critical path by adding a Dataset.prefetch(1) transformation to the dataset definition:

full_dataset = (tf.data.Dataset.from_tensor_slices((t_raw, v_raw)).batch(segment_size).prefetch(1))

For more performance suggestions, see the new Input Pipeline Performance Guide on tensorflow.org.

PS. Calling compute_features(next_datum) in a loop will cause your graph to grow, and the loop to slow down, over time. Rewriting it as follows will be more efficient:

next_computed_features = compute_features(next_datum)
while True:try:print(sess.run(next_computed_features, options=run_options,run_metadata=run_metadata))# ...except tf.errors.OutOfRangeError:break