EVOLUTION-MANAGER

Edit File: __init__.py

# 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. 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. *Guides* * [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) *Tutorials* * [Distributed Training Tutorials](https://www.tensorflow.org/tutorials/distribute/) The tutorials cover how to use `tf.distribute.Strategy` to do distributed training with native Keras APIs, custom training loops, and Esitmator APIs. They also cover how to save/load model when using `tf.distribute.Strategy`. *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). * _Replica context_ vs. _Cross-replica context_ vs _Update context_ A _replica context_ applies when you execute the computation function that was called with `strategy.run`. Conceptually, you're in replica context when executing the computation function that is being replicated. An _update context_ is entered in a `tf.distribute.StrategyExtended.update` call. An _cross-replica context_ is entered when you enter a `strategy.scope`. This is useful for calling `tf.distribute.Strategy` methods which operate across the replicas (like `reduce_to()`). By default you start in a _replica context_ (the "default single _replica context_") and then some methods can switch you back and forth. * _Distributed value_: Distributed value is represented by the base class `tf.distribute.DistributedValues`. `tf.distribute.DistributedValues` is useful to represent values on multiple devices, and it contains a map from replica id to values. Two representative kinds of `tf.distribute.DistributedValues` are "PerReplica" and "Mirrored" values. "PerReplica" values exist on the worker devices, with a different value for each replica. They are produced by iterating through a distributed dataset returned by `tf.distribute.Strategy.experimental_distribute_dataset` and `tf.distribute.Strategy.distribute_datasets_from_function`. They are also the typical result returned by `tf.distribute.Strategy.run`. "Mirrored" values are like "PerReplica" values, except we know that the value on all replicas are the same. We can safely read a "Mirrored" value in a cross-replica context by using the value on any replica. * _Unwrapping_ and _merging_: Consider calling a function `fn` on multiple replicas, like `strategy.run(fn, args=[w])` with an argument `w` that is a `tf.distribute.DistributedValues`. This means `w` will have a map taking replica id `0` to `w0`, replica id `1` to `w1`, etc. `strategy.run()` unwraps `w` before calling `fn`, so it calls `fn(w0)` on device `d0`, `fn(w1)` on device `d1`, etc. It then merges the return values from `fn()`, which leads to one common object if the returned values are the same object from every replica, or a `DistributedValues` object otherwise. * _Reductions_ and _all-reduce_: A _reduction_ is a 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. * _Mirrored variables_: These are variables that are created on multiple devices, where we keep the variables in sync by applying the same updates to every copy. Mirrored variables are created with `tf.Variable(...synchronization=tf.VariableSynchronization.ON_WRITE...)`. Normally they are only used in synchronous training. * _SyncOnRead variables_ _SyncOnRead variables_ are created by `tf.Variable(...synchronization=tf.VariableSynchronization.ON_READ...)`, and they are created on multiple devices. In replica context, each component variable on the local replica can perform reads and writes without synchronization with each other. When the _SyncOnRead variable_ is read in cross-replica context, the values from component variables are aggregated and returned. _SyncOnRead variables_ bring a lot of custom configuration difficulty to the underlying logic, so we do not encourage users to instantiate and use _SyncOnRead variable_ on their own. We have mainly used _SyncOnRead variables_ for use cases such as batch norm and metrics. For performance reasons, we often don't need to keep these statistics in sync every step and they can be accumulated on each replica independently. The only time we want to sync them is reporting or checkpointing, which typically happens in cross-replica context. _SyncOnRead variables_ are also often used by advanced users who want to control when variable values are aggregated. For example, users sometimes want to maintain gradients independently on each replica for a couple of steps without aggregation. * _Distribute-aware layers_ Layers are generally called in a replica context, except when defining a Keras functional model. `tf.distribute.in_cross_replica_context` will let you determine which case you are in. If in a replica context, the `tf.distribute.get_replica_context` function will return the default replica context outside a strategy scope, `None` within a strategy scope, and a `tf.distribute.ReplicaContext` object inside a strategy scope and within a `tf.distribute.Strategy.run` function. The `ReplicaContext` object has an `all_reduce` method for aggregating across all replicas. 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 ReplicaContextV1 as ReplicaContext from tensorflow.python.distribute.distribute_lib import RunOptions 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