|TensorFlow 2 version||View source on GitHub|
TPU distribution strategy implementation.
`tf.contrib.distribute.TPUStrategy`Compat aliases for migration
See Migration guide for more details.
tf.distribute.experimental.TPUStrategy( tpu_cluster_resolver=None, steps_per_run=None, device_assignment=None )
||A tf.distribute.cluster_resolver.TPUClusterResolver, which provides information about the TPU cluster.|
||Number of steps to run on device before returning to the host. Note that this can have side-effects on performance, hooks, metrics, summaries etc. This parameter is only used when Distribution Strategy is used with estimator or keras.|
||Returns number of replicas over which gradients are aggregated.|
||DEPRECATED: use .extended.steps_per_run instead.|
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 )
See base class.
make_dataset_iterator( dataset )
Makes an iterator for input provided via
DEPRECATED: This method is not available in TF 2.x.
Data from the given dataset will be distributed evenly across all the
compute replicas. 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).
If this effort fails, an error will be thrown, and the user should instead
make_input_fn_iterator which provides more control to the user, and
does not try to divide a batch across replicas.
The user could also use
make_input_fn_iterator if they want to
customize which input is fed to which replica/worker etc.
make_input_fn_iterator( input_fn, replication_mode=tf.distribute.InputReplicationMode.PER_WORKER )
Returns an iterator split across replicas created from an input function.
DEPRECATED: This method is not available in TF 2.x.
input_fn should take an
tf.distribute.InputContext object where
information about batching and input sharding can be accessed:
def input_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) with strategy.scope(): iterator = strategy.make_input_fn_iterator(input_fn) replica_results = strategy.experimental_run(replica_fn, iterator)
tf.data.Dataset returned by
input_fn should have a per-replica
batch size, which may be computed using
A function taking a
an enum value of
An iterator object that should first be
reduce( reduce_op, value, axis=None )
value across replicas.
Given a per-replica value returned by
experimental_run_v2, say a
per-example loss, the batch will be divided across all the replicas. This
function allows you to aggregate across replicas and optionally also across
batch elements. For example, if you have a global batch size of 8 and 2
replicas, values for examples
[0, 1, 2, 3] will be on replica 0 and
[4, 5, 6, 7] will be on replica 1. By default,
reduce will just
aggregate across replicas, returning
[0+4, 1+5, 2+6, 3+7]. This is useful
when each replica is computing a scalar or some other value that doesn't
have a "batch" dimension (like a gradient). More often you will want to
aggregate across the global batch, which you can get by specifying the batch
dimension as the
axis=0. In this case it would return a
If there is a last partial batch, you will need to specify an axis so
that the resulting shape is consistent across replicas. So if the last
batch has size 6 and it is divided into [0, 1, 2, 3] and [4, 5], you
would get a shape mismatch unless you specify
axis=0. If you specify
axis=0 will use the correct
denominator of 6. Contrast this with computing
reduce_mean to get a
scalar value on each replica and this function to average those means,
which will weigh some values
1/8 and others
A "per replica" value, e.g. returned by
Specifies the dimension to reduce along within each
replica's tensor. Should typically be set to the batch dimension, or
Returns a context manager selecting this Strategy as current.
with strategy.scope(): code block, this thread
will use a variable creator set by
strategy, and will
enter its "cross-replica