text: # Copyright 2017 Uber Technologies, Inc. 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.
  # ==============================================================================
  
  import keras
  import keras.backend as K
  
  from horovod.tensorflow import init
  from horovod.tensorflow import shutdown
  from horovod.tensorflow import size
  from horovod.tensorflow import local_size
  from horovod.tensorflow import rank
  from horovod.tensorflow import local_rank
  from horovod.tensorflow import mpi_threads_supported, mpi_enabled, mpi_built
  from horovod.tensorflow import gloo_enabled, gloo_built
  from horovod.tensorflow import nccl_built, ddl_built, mlsl_built
  from horovod.tensorflow import Compression
  
  from horovod.keras import callbacks
  import horovod._keras as _impl
  
  
  def DistributedOptimizer(optimizer, name=None,
                           device_dense='', device_sparse='',
                           compression=Compression.none,
                           sparse_as_dense=False):
      """
      An optimizer that wraps another keras.optimizers.Optimizer, using an allreduce to
      average gradient values before applying gradients to model weights.
  
      Args:
          optimizer: Optimizer to use for computing gradients and applying updates.
          name: Optional name prefix for the operations created when applying
                gradients. Defaults to "Distributed" followed by the provided
                optimizer type.
          device_dense: Device to be used for dense tensors. Uses GPU by default
                        if Horovod was build with HOROVOD_GPU_ALLREDUCE.
          device_sparse: Device to be used for sparse tensors. Uses GPU by default
                         if Horovod was build with HOROVOD_GPU_ALLGATHER.
          compression: Compression algorithm used to reduce the amount of data
                       sent and received by each worker node.  Defaults to not
                       using compression.
          sparse_as_dense: Treat all sparse gradients as dense tensors.  This can
                           help improve performance and memory utilization if
                           the original sparse gradient has high density.
                           Defaults to false.
      """
      return _impl.create_distributed_optimizer(keras, optimizer, name,
                                                device_dense, device_sparse, compression,
                                                sparse_as_dense)
  
  
  def broadcast_global_variables(root_rank):
      """Broadcasts all global variables from root rank to all other processes.
  
      Arguments:
          root_rank: Rank of the process from which global variables will be broadcasted
                     to all other processes.
      """
      return _impl.broadcast_global_variables(K, root_rank)
  
  
  def allreduce(value, name=None, average=True):
      """
      Perform an allreduce on a tensor-compatible value.
  
      Arguments:
          value: A tensor-compatible value to reduce.
                 The shape of the input must be identical across all ranks.
          name: Optional name for the constants created by this operation.
          average: If True, computes the average over all ranks.
                   Otherwise, computes the sum over all ranks.
      """
      return _impl.allreduce(K, value, name, average)
  
  
  def allgather(value, name=None):
      """
      Perform an allgather on a tensor-compatible value.
  
      The concatenation is done on the first dimension, so the input values on the
      different processes must have the same rank and shape, except for the first
      dimension, which is allowed to be different.
  
      Arguments:
          value: A tensor-compatible value to gather.
          name: Optional name prefix for the constants created by this operation.
      """
      return _impl.allgather(K, value, name)
  
  
  def broadcast(value, root_rank, name=None):
      """
      Perform a broadcast on a tensor-compatible value.
  
      Arguments:
          value: A tensor-compatible value to reduce.
                 The shape of the input must be identical across all ranks.
          root_rank: Rank of the process from which global variables will be
                     broadcasted to all other processes.
          name: Optional name for the constants created by this operation.
      """
      return _impl.broadcast(K, value, root_rank, name)
  
  
  def load_model(filepath, custom_optimizers=None, custom_objects=None, compression=Compression.none):
      """
      Loads a saved Keras model with a Horovod DistributedOptimizer.
  
      The DistributedOptimizer will wrap the underlying optimizer used to train
      the saved model, so that the optimizer state (params and weights) will
      be picked up for retraining.
  
      By default, all optimizers in the module `keras.optimizers` will be loaded
      and wrapped without needing to specify any `custom_optimizers` or
      `custom_objects`.
  
      # Arguments
          filepath: One of the following:
              - string, path to the saved model, or
              - h5py.File object from which to load the model
          custom_optimizers: Optional list of Optimizer subclasses to support
              during loading.
          custom_objects: Optional dictionary mapping names (strings) to custom
              classes or functions to be considered during deserialization.
          compression: Compression algorithm used to reduce the amount of data
                       sent and received by each worker node.  Defaults to not
                       using compression.
  
      # Returns
          A Keras model instance.
  
      # Raises
          ImportError: If h5py is not available.
          ValueError: In case of an invalid savefile.
      """
      def wrap_optimizer(cls):
          return lambda **kwargs: DistributedOptimizer(cls(**kwargs), compression=compression)
      return _impl.load_model(keras, wrap_optimizer, filepath, custom_optimizers, custom_objects)
  