6a0dda1ff86915d15cba0c9c12a9fc8a5e71a1a7,official/mnist/mnist.py,,run_mnist,#Any#,156

Before Change

  num_gpus = flags_core.get_num_gpus(flags_obj)
  multi_gpu = num_gpus > 1

  if multi_gpu:
    // Validate that the batch size can be split into devices.
    distribution_utils.per_device_batch_size(flags_obj.batch_size, num_gpus)

    // There are two steps required if using multi-GPU: (1) wrap the model_fn,
    // and (2) wrap the optimizer. The first happens here, and (2) happens
    // in the model_fn itself when the optimizer is defined.
    model_function = tf.contrib.estimator.replicate_model_fn(
        model_fn, loss_reduction=tf.losses.Reduction.MEAN,
        devices=["/device:GPU:%d" % d for d in range(num_gpus)])

  data_format = flags_obj.data_format
  if data_format is None:
    data_format = ("channels_first"
                   if tf.test.is_built_with_cuda() else "channels_last")

After Change

  distribution_strategy = distribution_utils.get_distribution_strategy(
      flags_core.get_num_gpus(flags_obj), flags_obj.all_reduce_alg)

  run_config = tf.estimator.RunConfig(
      train_distribute=distribution_strategy, session_config=session_config)

  data_format = flags_obj.data_format
  if data_format is None:
    data_format = ("channels_first"

Instances