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Mirrors vars to distribute across multiple devices and machines.
tf.contrib.distribute.MirroredStrategy( devices=None, num_gpus=None, num_gpus_per_worker=None, cross_device_ops=None, auto_shard_dataset=False, cross_tower_ops=None )
*** contrib version ***
This strategy uses one replica per device and sync replication for its multi-GPU version.
cluster_spec is given by the
configure method., it turns into the
mulit-worker version that works on multiple workers with in-graph replication.
configure will be called by higher-level APIs if running in
There are several important concepts for distributed TensorFlow, e.g.
in-graph replication and
synchronous training and they have already been defined in the
The distribution strategy inherits these concepts as well and in addition to
that we also clarify several more concepts:
- In-graph replication: the
clientcreates a single
tf.Graphthat specifies tasks for devices on all workers. The
clientthen creates a client session which will talk to the
masterservice of a
worker. Then the
masterwill partition the graph and distribute the work to all participating workers.
- Worker: A
workeris a TensorFlow
taskthat usually maps to one physical machine. We will have multiple
workers with different
taskindex. They all do similar things except for one worker checkpointing model variables, writing summaries, etc. in addition to its ordinary work.
The multi-worker version of this class maps one replica to one device on a
worker. It mirrors all model variables on all replicas. For example, if you
workers and each
worker has 4 GPUs, it will create 8 copies of
the model variables on these 8 GPUs. Then like in MirroredStrategy, each
replica performs their computation with their own copy of variables unless in
cross-replica model where variable or tensor reduction happens.
||a list of device strings.|
number of GPUs. For local training, either specify
number of GPUs per worker. This is the same as
optional, a descedant of
||whether to auto-shard the dataset when there are multiple workers.|
Deprecated alias for
||Returns number of replicas over which gradients are aggregated.|
experimental_distribute_dataset( dataset )
Distributes a tf.data.Dataset instance provided via
The returned distributed dataset can be iterated over similar to how regular datasets can. NOTE: Currently, the user cannot add any more transformations to a distributed dataset.
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 distributed dataset for x in dist_dataset: # process dataset elements strategy.experimental_run_v2(train_step, args=(x,))
We will assume that the input dataset is batched by the global batch size. With this assumption, we will make a best effort to divide each batch across all the replicas (one or more workers).
In a multi-worker setting, we will first attempt to distribute the dataset by attempting to detect whether the dataset is being created out of ReaderDatasets (e.g. TFRecordDataset, TextLineDataset, etc.) and if so, attempting to shard the input files. Note that there has to be at least one input file per worker. If you have less than one input file per worker, we suggest that you should disable distributing your dataset using the method below.
If that attempt is unsuccessful (e.g. the dataset is created from a
Dataset.range), we will shard the dataset evenly at the end by appending a
.shard operation to the end of the processing pipeline. This will cause
the entire preprocessing pipeline for all the data to be run on every
worker, and each worker will do redundant work. We will print a warning
if this method of sharding is selected. In this case, consider using
You can disable dataset sharding across workers using the
Within each worker, we will also split the data among all the worker devices (if more than one a present), and this will happen even if multi-worker sharding is disabled using the method above.
If the above batch splitting and dataset sharding logic is undesirable,
experimental_distribute_datasets_from_function instead, which
does not do any automatic splitting or sharding.
experimental_distribute_datasets_from_function( dataset_fn )
tf.data.Dataset instances created by calls to
dataset_fn will be called once for each worker in the strategy. Each
replica on that worker will dequeue one batch of inputs from the local
Dataset (i.e. if a worker has two replicas, two batches will be dequeued
Dataset every step).
This method can be used for several purposes. For example, where
experimental_distribute_dataset is unable to shard the input files, this
method might be used to manually shard the dataset (avoiding the slow
fallback behavior in
experimental_distribute_dataset). In cases where the
dataset is infinite, this sharding can be done by creating dataset replicas
that differ only in their random seed.
experimental_distribute_dataset may also sometimes fail to split the
batch across replicas on a worker. In that case, this method can be used
where that limitation does not exist.
dataset_fn should take an
tf.distribute.InputContext instance where
information about batching and input replication can be accessed:
def dataset_fn(input_context): batch_size = input_context.get_per_replica_batch_size(global_batch_size) d = tf.data.Dataset.from_tensors([[1.]]).repeat().batch(batch_size) return d.shard( input_context.num_input_pipelines, input_context.input_pipeline_id) inputs = strategy.experimental_distribute_datasets_from_function(dataset_fn) for batch in inputs: replica_results = strategy.experimental_run_v2(replica_fn, args=(batch,))
A function taking a
experimental_local_results( value )
Returns the list of all local per-replica values contained in
A value returned by
A tuple of values contained in
experimental_make_numpy_dataset( numpy_input, session=None )
Makes a tf.data.Dataset for input provided via a numpy array.
This avoids adding
numpy_input as a large constant in the graph,
and copies the data to the machine or machines that will be processing
Note that you will likely need to use tf.distribute.Strategy.experimental_distribute_dataset with the returned dataset to further distribute it with the strategy.
numpy_input = np.ones(, dtype=np.float32) dataset = strategy.experimental_make_numpy_dataset(numpy_input) dist_dataset = strategy.experimental_distribute_dataset(dataset)
A nest of NumPy input arrays that will be converted into a
dataset. Note that lists of Numpy arrays are stacked, as that is normal
||(TensorFlow v1.x graph execution only) A session used for initialization.|
experimental_run( fn, input_iterator=None )
Runs ops in
fn on each replica, with inputs from
DEPRECATED: This method is not available in TF 2.x. Please switch
When eager execution is enabled, executes ops specified by
fn on each
replica. Otherwise, builds a graph to execute the ops on each replica.
Each replica will take a single, different input from the inputs provided by
get_next call on the input iterator.
fn may call
tf.distribute.get_replica_context() to access members such
The function to run. The inputs to the function must match the outputs
||(Optional) input iterator from which the inputs are taken.|
Merged return value of
experimental_run_v2( fn, args=(), kwargs=None )
fn on each replica, with the given arguments.
Executes ops specified by
fn on each replica. If
"per-replica" values, such as those produced by a "distributed
fn is executed on a particular replica, it will be execu