Adding hook for images/sec and other small changes

tutorials/image/cifar10_estimator/cifar10_main.py

@@ -36,6 +36,10 @@ import os
 import numpy as np
 from six.moves import xrange  # pylint: disable=redefined-builtin
 import tensorflow as tf
+from tensorflow.python.platform import tf_logging as logging
+from tensorflow.python.training import basic_session_run_hooks
+from tensorflow.python.training import session_run_hook
+from tensorflow.python.training import training_util
 
 import cifar10
 import cifar10_model
@@ -50,7 +54,7 @@ tf.flags.DEFINE_string('data_dir', '',
 tf.flags.DEFINE_string('model_dir', '',
                        'The directory where the model will be stored.')
 
-tf.flags.DEFINE_boolean('is_cpu_ps', False,
+tf.flags.DEFINE_boolean('is_cpu_ps', True,
                         'If using CPU as the parameter server.')
 
 tf.flags.DEFINE_integer('num_gpus', 1,
@@ -58,7 +62,7 @@ tf.flags.DEFINE_integer('num_gpus', 1,
 
 tf.flags.DEFINE_integer('num_layers', 44, 'The number of layers of the model.')
 
-tf.flags.DEFINE_integer('train_steps', 10000,
+tf.flags.DEFINE_integer('train_steps', 80000,
                         'The number of steps to use for training.')
 
 tf.flags.DEFINE_integer('train_batch_size', 128, 'Batch size for training.')
@@ -75,14 +79,16 @@ tf.flags.DEFINE_boolean('use_distortion_for_training', True,
 tf.flags.DEFINE_boolean('run_experiment', False,
                         'If True will run an experiment,'
                         'otherwise will run training and evaluation'
-                        'using the estimator interface')
+                        'using the estimator interface.'
+                        'Experiments perform training on several workers in parallel'
+                        ', in other words experiments know how to invoke train and'
+                        ' eval in a sensible fashion for distributed training.')
 
 tf.flags.DEFINE_boolean('sync', False,
                         'If true when running in a distributed environment'
                         'will run on sync mode')
 
-tf.flags.DEFINE_integer('num_workers', 1,
-                        'Number of workers')
+tf.flags.DEFINE_integer('num_workers', 1, 'Number of workers')
 
 # Perf flags
 tf.flags.DEFINE_integer('num_intra_threads', 1,
@@ -110,16 +116,80 @@ tf.flags.DEFINE_boolean('log_device_placement', False,
                         'Whether to log device placement.')
 
 
-# TODO(jamesqin): Replace with fix in b/62239022
-class ParamServerDeviceSetter(object):
-  """Helper class to assign variables on the least loaded ps-device."""
+class ExamplesPerSecondHook(session_run_hook.SessionRunHook):
+  """Hook to print out examples per second
+    
+    Total time is tracked and then divided by the total number of steps
+    to get the average step time and then batch_size is used to determine
+    the running average of examples per second. The examples per second for the
+    most recent interval is also logged.
+  """
 
-  def __init__(self, worker_device, ps_devices):
-    """Initializer for ParamServerDeviceSetter.
+  def __init__(
+      self,
+      batch_size,
+      every_n_steps=100,
+      every_n_secs=None,):
+    """Initializer for ExamplesPerSecondHook.
+    Args:
+      batch_size: Total batch size used to calculate examples/second from
+      global time.
+      every_n_steps: Log stats every n steps.
+      every_n_secs: Log stats every n seconds.
+      """
+    if (every_n_steps is None) == (every_n_secs is None):
+      raise ValueError(
+          'exactly one of every_n_steps and every_n_secs should be provided.')
+    self._timer = basic_session_run_hooks.SecondOrStepTimer(
+        every_steps=every_n_steps, every_secs=every_n_secs)
+
+    self._step_train_time = 0
+    self._total_steps = 0
+    self._batch_size = batch_size
+
+  def begin(self):
+    self._global_step_tensor = training_util.get_global_step()
+    if self._global_step_tensor is None:
+      raise RuntimeError(
+          'Global step should be created to use StepCounterHook.')
+
+  def before_run(self, run_context):  # pylint: disable=unused-argument
+    return basic_session_run_hooks.SessionRunArgs(self._global_step_tensor)
+
+  def after_run(self, run_context, run_values):
+    _ = run_context
+
+    global_step = run_values.results
+    if self._timer.should_trigger_for_step(global_step):
+      elapsed_time, elapsed_steps = self._timer.update_last_triggered_step(
+          global_step)
+      if elapsed_time is not None:
+        steps_per_sec = elapsed_steps / elapsed_time
+        self._step_train_time += elapsed_time
+        self._total_steps += elapsed_steps
+
+        average_examples_per_sec = self._batch_size * (
+            self._total_steps / self._step_train_time)
+        current_examples_per_sec = steps_per_sec * self._batch_size
+        # Average examples/sec followed by current examples/sec
+        logging.info('%s: %g (%g), step = %g', 'Average examples/sec',
+                     average_examples_per_sec, current_examples_per_sec,
+                     self._total_steps)
+
+
+class GpuParamServerDeviceSetter(object):
+  """Used with tf.device() to place variables on the least loaded GPU.
+    A common use for this class is to pass a list of GPU devices, e.g. ['gpu:0',
+    'gpu:1','gpu:2'], as ps_devices.  When each variable is placed, it will be
+    placed on the least loaded gpu. All other Ops, which will be the computation
+    Ops, will be placed on the worker_device.
+  """
 
+  def __init__(self, worker_device, ps_devices):
+    """Initializer for GpuParamServerDeviceSetter.
     Args:
-      worker_device: the device to use for computer ops.
-      ps_devices: a list of devices to use for Variable ops. Each variable is
+      worker_device: the device to use for computation Ops.
+      ps_devices: a list of devices to use for Variable Ops. Each variable is
       assigned to the least loaded device.
     """
     self.ps_devices = ps_devices
@@ -131,7 +201,8 @@ class ParamServerDeviceSetter(object):
       return op.device
     if op.type not in ['Variable', 'VariableV2', 'VarHandleOp']:
       return self.worker_device
-
+      
+    # Gets the least loaded ps_device
     device_index, _ = min(enumerate(self.ps_sizes), key=operator.itemgetter(1))
     device_name = self.ps_devices[device_index]
     var_size = op.outputs[0].get_shape().num_elements()
@@ -139,15 +210,16 @@ class ParamServerDeviceSetter(object):
 
     return device_name
 
-
-def _create_device_setter(is_cpu_ps, worker):
+def _create_device_setter(is_cpu_ps, worker, num_gpus):
   """Create device setter object."""
   if is_cpu_ps:
+    # tf.train.replica_device_setter supports placing variables on the CPU, all
+    # on one GPU, or on ps_servers defined in a cluster_spec.
     return tf.train.replica_device_setter(
         worker_device=worker, ps_device='/cpu:0', ps_tasks=1)
   else:
-    gpus = ['/gpu:%d' % i for i in range(FLAGS.num_gpus)]
-    return ParamServerDeviceSetter(worker, gpus)
+    gpus = ['/gpu:%d' % i for i in range(num_gpus)]
+    return GpuParamServerDeviceSetter(worker, gpus)
 
 
 def _resnet_model_fn(features, labels, mode):
@@ -181,7 +253,7 @@ def _resnet_model_fn(features, labels, mode):
   if num_gpus != 0:
     for i in range(num_gpus):
       worker = '/gpu:%d' % i
-      device_setter = _create_device_setter(is_cpu_ps, worker)
+      device_setter = _create_device_setter(is_cpu_ps, worker, FLAGS.num_gpus)
       with tf.variable_scope('resnet', reuse=bool(i != 0)):
         with tf.name_scope('tower_%d' % i) as name_scope:
           with tf.device(device_setter):
@@ -210,7 +282,7 @@ def _resnet_model_fn(features, labels, mode):
   ps_device = '/cpu:0' if is_cpu_ps else '/gpu:0'
   with tf.device(ps_device):
     with tf.name_scope('gradient_averaging'):
-      loss = tf.reduce_mean(tower_losses)
+      loss = tf.reduce_mean(tower_losses, name='loss')
       for zipped_gradvars in zip(*tower_gradvars):
         # Averaging one var's gradients computed from multiple towers
         var = zipped_gradvars[0][1]
@@ -403,7 +475,7 @@ def main(unused_argv):
   train_steps = FLAGS.train_steps
   eval_steps = num_eval_examples // FLAGS.eval_batch_size
 
-  # session configuration
+  # Session configuration.
   sess_config = tf.ConfigProto()
   sess_config.allow_soft_placement = True
   sess_config.log_device_placement = FLAGS.log_device_placement
@@ -411,10 +483,18 @@ def main(unused_argv):
   sess_config.inter_op_parallelism_threads = FLAGS.num_inter_threads
   sess_config.gpu_options.force_gpu_compatible = FLAGS.force_gpu_compatible
 
-  # log learning_rate
-  tensors_to_log = {'learning_rate': 'learning_rate'}
+  # Hooks that add extra logging that is useful to see the loss more often in
+  # the console as well as examples per second.
+  tensors_to_log = {'learning_rate': 'learning_rate',
+                    'loss': 'gradient_averaging/loss'}
+
   logging_hook = tf.train.LoggingTensorHook(
       tensors=tensors_to_log, every_n_iter=100)
+  
+  examples_sec_hook = ExamplesPerSecondHook(
+      FLAGS.train_batch_size, every_n_steps=10)
+
+  hooks = [logging_hook, examples_sec_hook]
 
   if FLAGS.run_experiment:
     config = tf.contrib.learn.RunConfig(model_dir=FLAGS.model_dir)
@@ -422,7 +502,7 @@ def main(unused_argv):
     tf.contrib.learn.learn_runner.run(
         get_experiment_fn(train_input_fn, eval_input_fn,
                           train_steps, eval_steps,
-                          [logging_hook]), run_config=config)
+                          hooks), run_config=config)
 
   else:
     config = tf.estimator.RunConfig()
@@ -433,7 +513,7 @@ def main(unused_argv):
     print('Starting to train...')
     classifier.train(input_fn=train_input_fn,
                      steps=train_steps,
-                     hooks=[logging_hook])
+                     hooks=hooks)
 
     print('Starting to evaluate...')
     eval_results = classifier.evaluate(