|TensorFlow 1 version||View source on GitHub|
A distribution strategy for synchronous training on multiple workers.
tf.distribute.experimental.MultiWorkerMirroredStrategy( communication=tf.distribute.experimental.CollectiveCommunication.AUTO, cluster_resolver=None )
This strategy implements synchronous distributed training across multiple
workers, each with potentially multiple GPUs. Similar to
tf.distribute.MirroredStrategy, it creates copies of all variables in the
model on each device across all workers.
It uses CollectiveOps's implementation of multi-worker all-reduce to to keep variables in sync. A collective op is a single op in the TensorFlow graph which can automatically choose an all-reduce algorithm in the TensorFlow runtime according to hardware, network topology and tensor sizes.
By default it uses all local GPUs or CPU for single-worker training.
When 'TF_CONFIG' environment variable is set, it parses cluster_spec, task_type and task_id from 'TF_CONFIG' and turns into a multi-worker strategy which mirrored models on GPUs of all machines in a cluster. In the current implementation, it uses all GPUs in a cluster and it assumes all workers have the same number of GPUs.
You can also pass a
when instantiating the strategy. The task_type, task_id etc. will be parsed
from the resolver instance instead of from the
TF_CONFIG env var.
It supports both eager mode and graph mode. However, for eager mode, it has to set up the eager context in its constructor and therefore all ops in eager mode have to run after the strategy object is created.
optional Enum of type
Returns the cluster resolver associated with this strategy.
As a multi-worker strategy,
||Returns number of replicas over which gradients are aggregated.|
experimental_assign_to_logical_device( tensor, logical_device_id )
Adds annotation that
tensor will be assigned to a logical device.
# Initializing TPU system with 2 logical devices and 4 replicas. resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='') tf.config.experimental_connect_to_cluster(resolver) topology = tf.tpu.experimental.initialize_tpu_system(resolver) device_assignment = tf.tpu.experimental.DeviceAssignment.build( topology, computation_shape=[1, 1, 1, 2], num_replicas=4) strategy = tf.distribute.TPUStrategy( resolver, experimental_device_assignment=device_assignment) iterator = iter(inputs) @tf.function() def step_fn(inputs): output = tf.add(inputs, inputs) # Add operation will be executed on logical device 0. output = strategy.experimental_assign_to_logical_device(output, 0) return output strategy.run(step_fn, args=(next(iterator),))
||Input tensor to annotate.|
||Id of the logical core to which the tensor will be assigned.|
||The logical device id presented is not consistent with total number of partitions specified by the device assignment.|
Annotated tensor with idential value as
experimental_distribute_dataset( dataset, options=None )
The following is an example:
strategy = tf.distribute.MirroredStrategy() # Create a dataset dataset = dataset_ops.Dataset.TFRecordDataset([ "/a/1.tfr", "/a/2.tfr", "/a/3.tfr", "/a/4.tfr"]) # Distribute that dataset dist_dataset = strategy.experimental_distribute_dataset(dataset) # Iterate over the `tf.distribute.DistributedDataset` for x in dist_dataset: # process dataset elements strategy.run(replica_fn, args=(x,))
In the code snippet above, the
dist_dataset is batched by
GLOBAL_BATCH_SIZE, and we iterate through it
for x in dist_dataset.
containing data for all replicas, which aggregates to a batch of
tf.distribute.Strategy.run will take care of feeding
the right per-replica data in
x to the right
replica_fn executed on each