Public API for tf._api.v2.distribute namespace
Modules
cluster_resolver
module: Public API for tf._api.v2.distribute.cluster_resolver namespace
coordinator
module: Public API for tf._api.v2.distribute.coordinator namespace
experimental
module: Public API for tf._api.v2.distribute.experimental namespace
Classes
class CrossDeviceOps
: Base class for cross-device reduction and broadcasting algorithms.
class DistributedDataset
: Represents a dataset distributed among devices and machines.
class DistributedIterator
: An iterator over tf.distribute.DistributedDataset
.
class DistributedValues
: Base class for representing distributed values.
class HierarchicalCopyAllReduce
: Hierarchical copy all-reduce implementation of CrossDeviceOps.
class InputContext
: A class wrapping information needed by an input function.
class InputOptions
: Run options for experimental_distribute_dataset(s_from_function)
.
class InputReplicationMode
: Replication mode for input function.
class MirroredStrategy
: Synchronous training across multiple replicas on one machine.
class MultiWorkerMirroredStrategy
: A distribution strategy for synchronous training on multiple workers.
class NcclAllReduce
: NCCL all-reduce implementation of CrossDeviceOps.
class OneDeviceStrategy
: A distribution strategy for running on a single device.
class ParameterServerStrategy
: An multi-worker tf.distribute strategy with parameter servers.
class ReduceOp
: Indicates how a set of values should be reduced.
class ReductionToOneDevice
: A CrossDeviceOps implementation that copies values to one device to reduce.
class ReplicaContext
: A class with a collection of APIs that can be called in a replica context.
class RunOptions
: Run options for strategy.run
.
class Server
: An in-process TensorFlow server, for use in distributed training.
class Strategy
: A state & compute distribution policy on a list of devices.
class StrategyExtended
: Additional APIs for algorithms that need to be distribution-aware.
class TPUStrategy
: Synchronous training on TPUs and TPU Pods.
Functions
experimental_set_strategy(...)
: Set a tf.distribute.Strategy
as current without with strategy.scope()
.
get_replica_context(...)
: Returns the current tf.distribute.ReplicaContext
or None
.
get_strategy(...)
: Returns the current tf.distribute.Strategy
object.
has_strategy(...)
: Return if there is a current non-default tf.distribute.Strategy
.
in_cross_replica_context(...)
: Returns True
if in a cross-replica context.