cifar10_multi_gpu_train.py

# Copyright 2015 The TensorFlow Authors. All Rights Reserved.## Licensed under the Apache License, Version 2.0 (the "License");# you may not use this file except in compliance with the License.# You may obtain a copy of the License at##     http://www.apache.org/licenses/LICENSE-2.0## Unless required by applicable law or agreed to in writing, software# distributed under the License is distributed on an "AS IS" BASIS,# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.# See the License for the specific language governing permissions and# limitations under the License.# =============================================================================="""A binary to train CIFAR-10 using multiple GPUs with synchronous updates.Accuracy:cifar10_multi_gpu_train.py achieves ~86% accuracy after 100K steps (256epochs of data) as judged by cifar10_eval.py.Speed: With batch_size 128.System        | Step Time (sec/batch)  |     Accuracy--------------------------------------------------------------------1 Tesla K20m  | 0.35-0.60              | ~86% at 60K steps  (5 hours)1 Tesla K40m  | 0.25-0.35              | ~86% at 100K steps (4 hours)2 Tesla K20m  | 0.13-0.20              | ~84% at 30K steps  (2.5 hours)3 Tesla K20m  | 0.13-0.18              | ~84% at 30K steps4 Tesla K20m  | ~0.10                  | ~84% at 30K stepsUsage:Please see the tutorial and website for how to download the CIFAR-10data set, compile the program and train the model.http://tensorflow.org/tutorials/deep_cnn/"""from __future__ import absolute_importfrom __future__ import divisionfrom __future__ import print_functionfrom datetime import datetimeimport os.pathimport reimport timeimport numpy as npfrom six.moves import xrange  # pylint: disable=redefined-builtinimport tensorflow as tfimport cifar10FLAGS = tf.app.flags.FLAGStf.app.flags.DEFINE_string('train_dir', '/tmp/cifar10_train',                           """Directory where to write event logs """                           """and checkpoint.""")tf.app.flags.DEFINE_integer('max_steps', 1000000,                            """Number of batches to run.""")tf.app.flags.DEFINE_integer('num_gpus', 1,                            """How many GPUs to use.""")tf.app.flags.DEFINE_boolean('log_device_placement', False,                            """Whether to log device placement.""")def tower_loss(scope, images, labels):  """Calculate the total loss on a single tower running the CIFAR model.  Args:    scope: unique prefix string identifying the CIFAR tower, e.g. 'tower_0'    images: Images. 4D tensor of shape [batch_size, height, width, 3].    labels: Labels. 1D tensor of shape [batch_size].  Returns:     Tensor of shape [] containing the total loss for a batch of data  """  # Build inference Graph.  logits = cifar10.inference(images)  # Build the portion of the Graph calculating the losses. Note that we will  # assemble the total_loss using a custom function below.  _ = cifar10.loss(logits, labels)  # Assemble all of the losses for the current tower only.  losses = tf.get_collection('losses', scope)  # Calculate the total loss for the current tower.  total_loss = tf.add_n(losses, name='total_loss')  # Attach a scalar summary to all individual losses and the total loss; do the  # same for the averaged version of the losses.  for l in losses + [total_loss]:    # Remove 'tower_[0-9]/' from the name in case this is a multi-GPU training    # session. This helps the clarity of presentation on tensorboard.    loss_name = re.sub('%s_[0-9]*/' % cifar10.TOWER_NAME, '', l.op.name)    tf.summary.scalar(loss_name, l)  return total_lossdef average_gradients(tower_grads):  """Calculate the average gradient for each shared variable across all towers.  Note that this function provides a synchronization point across all towers.  Args:    tower_grads: List of lists of (gradient, variable) tuples. The outer list      is over individual gradients. The inner list is over the gradient      calculation for each tower.  Returns:     List of pairs of (gradient, variable) where the gradient has been averaged     across all towers.  """  average_grads = []  for grad_and_vars in zip(*tower_grads):    # Note that each grad_and_vars looks like the following:    #   ((grad0_gpu0, var0_gpu0), ... , (grad0_gpuN, var0_gpuN))    grads = []    for g, _ in grad_and_vars:      # Add 0 dimension to the gradients to represent the tower.      expanded_g = tf.expand_dims(g, 0)      # Append on a 'tower' dimension which we will average over below.      grads.append(expanded_g)    # Average over the 'tower' dimension.    grad = tf.concat(axis=0, values=grads)    grad = tf.reduce_mean(grad, 0)    # Keep in mind that the Variables are redundant because they are shared    # across towers. So .. we will just return the first tower's pointer to    # the Variable.    v = grad_and_vars[0][1]    grad_and_var = (grad, v)    average_grads.append(grad_and_var)  return average_gradsdef train():  """Train CIFAR-10 for a number of steps."""  with tf.Graph().as_default(), tf.device('/cpu:0'):    # Create a variable to count the number of train() calls. This equals the    # number of batches processed * FLAGS.num_gpus.    global_step = tf.get_variable(        'global_step', [],        initializer=tf.constant_initializer(0), trainable=False)    # Calculate the learning rate schedule.    num_batches_per_epoch = (cifar10.NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN /                             FLAGS.batch_size)    decay_steps = int(num_batches_per_epoch * cifar10.NUM_EPOCHS_PER_DECAY)    # Decay the learning rate exponentially based on the number of steps.    lr = tf.train.exponential_decay(cifar10.INITIAL_LEARNING_RATE,                                    global_step,                                    decay_steps,                                    cifar10.LEARNING_RATE_DECAY_FACTOR,                                    staircase=True)    # Create an optimizer that performs gradient descent.    opt = tf.train.GradientDescentOptimizer(lr)    # Get images and labels for CIFAR-10.    images, labels = cifar10.distorted_inputs()    batch_queue = tf.contrib.slim.prefetch_queue.prefetch_queue(          [images, labels], capacity=2 * FLAGS.num_gpus)    # Calculate the gradients for each model tower.    tower_grads = []    with tf.variable_scope(tf.get_variable_scope()):      for i in xrange(FLAGS.num_gpus):        with tf.device('/gpu:%d' % i):          with tf.name_scope('%s_%d' % (cifar10.TOWER_NAME, i)) as scope:            # Dequeues one batch for the GPU            image_batch, label_batch = batch_queue.dequeue()            # Calculate the loss for one tower of the CIFAR model. This function            # constructs the entire CIFAR model but shares the variables across            # all towers.            loss = tower_loss(scope, image_batch, label_batch)            # Reuse variables for the next tower.            tf.get_variable_scope().reuse_variables()            # Retain the summaries from the final tower.            summaries = tf.get_collection(tf.GraphKeys.SUMMARIES, scope)            # Calculate the gradients for the batch of data on this CIFAR tower.            grads = opt.compute_gradients(loss)            # Keep track of the gradients across all towers.            tower_grads.append(grads)    # We must calculate the mean of each gradient. Note that this is the    # synchronization point across all towers.    grads = average_gradients(tower_grads)    # Add a summary to track the learning rate.    summaries.append(tf.summary.scalar('learning_rate', lr))    # Add histograms for gradients.    for grad, var in grads:      if grad is not None:        summaries.append(tf.summary.histogram(var.op.name + '/gradients', grad))    # Apply the gradients to adjust the shared variables.    apply_gradient_op = opt.apply_gradients(grads, global_step=global_step)    # Add histograms for trainable variables.    for var in tf.trainable_variables():      summaries.append(tf.summary.histogram(var.op.name, var))    # Track the moving averages of all trainable variables.    variable_averages = tf.train.ExponentialMovingAverage(        cifar10.MOVING_AVERAGE_DECAY, global_step)    variables_averages_op = variable_averages.apply(tf.trainable_variables())    # Group all updates to into a single train op.    train_op = tf.group(apply_gradient_op, variables_averages_op)    # Create a saver.    saver = tf.train.Saver(tf.global_variables())    # Build the summary operation from the last tower summaries.    summary_op = tf.summary.merge(summaries)    # Build an initialization operation to run below.    init = tf.global_variables_initializer()    # Start running operations on the Graph. allow_soft_placement must be set to    # True to build towers on GPU, as some of the ops do not have GPU    # implementations.    sess = tf.Session(config=tf.ConfigProto(        allow_soft_placement=True,        log_device_placement=FLAGS.log_device_placement))    sess.run(init)    # Start the queue runners.    tf.train.start_queue_runners(sess=sess)    summary_writer = tf.summary.FileWriter(FLAGS.train_dir, sess.graph)    for step in xrange(FLAGS.max_steps):      start_time = time.time()      _, loss_value = sess.run([train_op, loss])      duration = time.time() - start_time      assert not np.isnan(loss_value), 'Model diverged with loss = NaN'      if step % 10 == 0:        num_examples_per_step = FLAGS.batch_size * FLAGS.num_gpus        examples_per_sec = num_examples_per_step / duration        sec_per_batch = duration / FLAGS.num_gpus        format_str = ('%s: step %d, loss = %.2f (%.1f examples/sec; %.3f '                      'sec/batch)')        print (format_str % (datetime.now(), step, loss_value,                             examples_per_sec, sec_per_batch))      if step % 100 == 0:        summary_str = sess.run(summary_op)        summary_writer.add_summary(summary_str, step)      # Save the model checkpoint periodically.      if step % 1000 == 0 or (step + 1) == FLAGS.max_steps:        checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')        saver.save(sess, checkpoint_path, global_step=step)def main(argv=None):  # pylint: disable=unused-argument  cifar10.maybe_download_and_extract()  if tf.gfile.Exists(FLAGS.train_dir):    tf.gfile.DeleteRecursively(FLAGS.train_dir)  tf.gfile.MakeDirs(FLAGS.train_dir)  train()if __name__ == '__main__':  tf.app.run()