text: # This file is MACHINE GENERATED! Do not edit.
  # Generated by: tensorflow/python/tools/api/generator/create_python_api.py script.
  """Library for running a computation across multiple devices.
  
  See the guide for overview and examples:
  [TensorFlow v2.x](https://www.tensorflow.org/guide/distributed_training),
  [TensorFlow v1.x](https://github.com/tensorflow/docs/blob/master/site/en/r1/guide/distribute_strategy.ipynb).  # pylint: disable=line-too-long
  
  The intent of this library is that you can write an algorithm in a stylized way
  and it will be usable with a variety of different `tf.distribute.Strategy`
  implementations. Each descendant will implement a different strategy for
  distributing the algorithm across multiple devices/machines.  Furthermore, these
  changes can be hidden inside the specific layers and other library classes that
  need special treatment to run in a distributed setting, so that most users'
  model definition code can run unchanged. The `tf.distribute.Strategy` API works
  the same way with eager and graph execution.
  
  *Glossary*
  
  * _Data parallelism_ is where we run multiple copies of the model
    on different slices of the input data. This is in contrast to
    _model parallelism_ where we divide up a single copy of a model
    across multiple devices.
    Note: we only support data parallelism for now, but
    hope to add support for model parallelism in the future.
  * A _device_ is a CPU or accelerator (e.g. GPUs, TPUs) on some machine that
    TensorFlow can run operations on (see e.g. `tf.device`). You may have multiple
    devices on a single machine, or be connected to devices on multiple
    machines. Devices used to run computations are called _worker devices_.
    Devices used to store variables are _parameter devices_. For some strategies,
    such as `tf.distribute.MirroredStrategy`, the worker and parameter devices
    will be the same (see mirrored variables below). For others they will be
    different.  For example, `tf.distribute.experimental.CentralStorageStrategy`
    puts the variables on a single device (which may be a worker device or may be
    the CPU), and `tf.distribute.experimental.ParameterServerStrategy` puts the
    variables on separate machines called parameter servers (see below).
  * A _replica_ is one copy of the model, running on one slice of the
    input data. Right now each replica is executed on its own
    worker device, but once we add support for model parallelism
    a replica may span multiple worker devices.
  * A _host_ is the CPU device on a machine with worker devices, typically
    used for running input pipelines.
  * A _worker_ is defined to be the physical machine(s) containing the physical
    devices (e.g. GPUs, TPUs) on which the replicated computation is executed. A
    worker may contain one or more replicas, but contains at least one
    replica. Typically one worker will correspond to one machine, but in the case
    of very large models with model parallelism, one worker may span multiple
    machines. We typically run one input pipeline per worker, feeding all the
    replicas on that worker.
  * _Synchronous_, or more commonly _sync_, training is where the updates from
    each replica are aggregated together before updating the model variables. This
    is in contrast to _asynchronous_, or _async_ training, where each replica
    updates the model variables independently. You may also have replicas
    partitioned into groups which are in sync within each group but async between
    groups.
  * _Parameter servers_: These are machines that hold a single copy of
    parameters/variables, used by some strategies (right now just
    `tf.distribute.experimental.ParameterServerStrategy`). All replicas that want
    to operate on a variable retrieve it at the beginning of a step and send an
    update to be applied at the end of the step. These can in priniciple support
    either sync or async training, but right now we only have support for async
    training with parameter servers. Compare to
    `tf.distribute.experimental.CentralStorageStrategy`, which puts all variables
    on a single device on the same machine (and does sync training), and
    `tf.distribute.MirroredStrategy`, which mirrors variables to multiple devices
    (see below).
  * _Mirrored variables_: These are variables that are copied to multiple
    devices, where we keep the copies in sync by applying the same
    updates to every copy. Normally would only be used with sync training.
  * Reductions and all-reduce: A _reduction_ is some method of aggregating
    multiple values into one value, like "sum" or "mean". If a strategy is doing
    sync training, we will perform a reduction on the gradients to a parameter
    from all replicas before applying the update. _All-reduce_ is an algorithm for
    performing a reduction on values from multiple devices and making the result
    available on all of those devices.
  
  Note that we provide a default version of `tf.distribute.Strategy` that is
  used when no other strategy is in scope, that provides the same API with
  reasonable default behavior.
  
  """
  
  from __future__ import print_function as _print_function
  
  import sys as _sys
  
  from . import cluster_resolver
  from . import experimental
  from tensorflow.python.distribute.cross_device_ops import CrossDeviceOps
  from tensorflow.python.distribute.cross_device_ops import HierarchicalCopyAllReduce
  from tensorflow.python.distribute.cross_device_ops import NcclAllReduce
  from tensorflow.python.distribute.cross_device_ops import ReductionToOneDevice
  from tensorflow.python.distribute.distribute_lib import InputContext
  from tensorflow.python.distribute.distribute_lib import InputReplicationMode
  from tensorflow.python.distribute.distribute_lib import ReplicaContext
  from tensorflow.python.distribute.distribute_lib import StrategyExtendedV1 as StrategyExtended
  from tensorflow.python.distribute.distribute_lib import StrategyV1 as Strategy
  from tensorflow.python.distribute.distribute_lib import get_loss_reduction
  from tensorflow.python.distribute.distribution_strategy_context import experimental_set_strategy
  from tensorflow.python.distribute.distribution_strategy_context import get_replica_context
  from tensorflow.python.distribute.distribution_strategy_context import get_strategy
  from tensorflow.python.distribute.distribution_strategy_context import has_strategy
  from tensorflow.python.distribute.distribution_strategy_context import in_cross_replica_context
  from tensorflow.python.distribute.mirrored_strategy import MirroredStrategyV1 as MirroredStrategy
  from tensorflow.python.distribute.one_device_strategy import OneDeviceStrategyV1 as OneDeviceStrategy
  from tensorflow.python.distribute.reduce_util import ReduceOp
  from tensorflow.python.training.server_lib import Server
  
  del _print_function
  