Use AUTOTUNE, remove noop take, and comment fixes

official/resnet/resnet_run_loop.py

@@ -68,36 +68,26 @@ def process_record_dataset(dataset, is_training, batch_size, shuffle_buffer,
     Dataset of (image, label) pairs ready for iteration.
   """
 
-  # We prefetch a batch at a time, This can help smooth out the time taken to
-  # load input files as we go through shuffling and processing.
+  # Sets tf.data to AUTOTUNE, e.g. num_parallel_batches in map_and_batch.
+  options = tf.data.Options()
+  options.experimental_autotune = True
+  dataset = dataset.with_options(options)
+
+  # Prefetches a batch at a time to smooth out the time taken to load input
+  # files for shuffling and processing.
   dataset = dataset.prefetch(buffer_size=batch_size)
   if is_training:
-    # Shuffle the records. Note that we shuffle before repeating to ensure
-    # that the shuffling respects epoch boundaries.
+    # Shuffles records before repeating to respect epoch boundaries.
     dataset = dataset.shuffle(buffer_size=shuffle_buffer)
 
-  # If we are training over multiple epochs before evaluating, repeat the
-  # dataset for the appropriate number of epochs.
+  # Repeats the dataset for the number of epochs to train.
   dataset = dataset.repeat(num_epochs)
 
-  if is_training and num_gpus and examples_per_epoch:
-    total_examples = num_epochs * examples_per_epoch
-    # Force the number of batches to be divisible by the number of devices.
-    # This prevents some devices from receiving batches while others do not,
-    # which can lead to a lockup. This case will soon be handled directly by
-    # distribution strategies, at which point this .take() operation will no
-    # longer be needed.
-    total_batches = total_examples // batch_size // num_gpus * num_gpus
-    dataset.take(total_batches * batch_size)
-
-  # Parse the raw records into images and labels. Testing has shown that setting
-  # num_parallel_batches > 1 produces no improvement in throughput, since
-  # batch_size is almost always much greater than the number of CPU cores.
+  # Parses the raw records into images and labels.
   dataset = dataset.apply(
       tf.contrib.data.map_and_batch(
           lambda value: parse_record_fn(value, is_training, dtype),
           batch_size=batch_size,
-          num_parallel_batches=1,
           drop_remainder=False))
 
   # Operations between the final prefetch and the get_next call to the iterator