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An asynchronous multi-worker parameter server tf.distribute strategy.
tf.compat.v1.distribute.experimental.ParameterServerStrategy( cluster_resolver=None )
This strategy requires two roles: workers and parameter servers. Variables and updates to those variables will be assigned to parameter servers and other operations are assigned to workers.
When each worker has more than one GPU, operations will be replicated on all GPUs. Even though operations may be replicated, variables are not and each worker shares a common view for which parameter server a variable is assigned to.
By default it uses
TFConfigClusterResolver to detect configurations for
multi-worker training. This requires a 'TF_CONFIG' environment variable and
the 'TF_CONFIG' must have a cluster spec.
This class assumes each worker is running the same code independently, but parameter servers are running a standard server. This means that while each worker will synchronously compute a single gradient update across all GPUs, updates between workers proceed asynchronously. Operations that occur only on the first replica (such as incrementing the global step), will occur on the first replica of every worker.
It is expected to call
call_for_each_replica(fn, ...) for any
operations which potentially can be replicated across replicas (i.e. multiple
GPUs) even if there is only CPU or one GPU. When defining the
caution needs to be taken:
1) It is generally not recommended to open a device scope under the strategy's
scope. A device scope (i.e. calling
tf.device) will be merged with or
override the device for operations but will not change the device for
2) It is also not recommended to open a colocation scope (i.e. calling
tf.compat.v1.colocate_with) under the strategy's scope. For colocating
strategy.extended.colocate_vars_with instead. Colocation of
ops will possibly create device assignment conflicts.
strategy = tf.distribute.experimental.ParameterServerStrategy() run_config = tf.estimator.RunConfig( experimental_distribute.train_distribute=strategy) estimator = tf.estimator.Estimator(config=run_config) tf.estimator.train_and_evaluate(estimator,...)
||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.run(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.
You can disable dataset sharding across workers using the
auto_shard_policy option in
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.
You can also use the
element_spec property of the distributed dataset
returned by this API to query the
tf.TypeSpec of the elements returned
by the iterator. This can be used to set the
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) @tf.function(input_signature=[dist_dataset.element_spec]) def train_step(inputs): # train model with inputs return # Iterate over the distributed dataset for x in dist_dataset: # process dataset elements strategy.run(train_step, args=(x,))
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.run(replica_fn, args=(batch,))
To query the
tf.TypeSpec of the elements in the distributed dataset
returned by this API, you need to use the
element_spec property of the
distributed iterator. This
tf.TypeSpec can be used to set the
input_signature property of a
# If you want to specify `input_signature` for a `tf.function` you must # first create the iterator. iterator = iter(inputs) @tf.function(input_signature=[iterator.element_spec]) def replica_fn_with_signature(inputs): # train the model with inputs return for _ in range(steps): strategy.run(replica_fn_with_signature, args=(next(iterator),))
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 |