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An asynchronous multi-worker parameter server tf.distribute strategy.
Inherits From: 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 fn, extra
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
variables.
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
variables, use strategy.extended.colocate_vars_with instead. Colocation of
ops will possibly create device assignment conflicts.
For Example:
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,...)
Args | |
|---|---|
cluster_resolver
|
Optional
tf.distribute.cluster_resolver.ClusterResolver object. Defaults to a
tf.distribute.cluster_resolver.TFConfigClusterResolver.
|
Attributes | |
|---|---|
extended
|
tf.distribute.StrategyExtended with additional methods.
|
num_replicas_in_sync
|
Returns number of replicas over which gradients are aggregated. |
Methods
experimental_distribute_dataset
experimental_distribute_dataset(
dataset
)
Distributes a tf.data.Dataset instance provided via dataset.
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 tf.data.experimental.DistributeOptions.
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,
please use 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 input_signature property
of a tf.function.
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,))
| Args | |
|---|---|
dataset
|
tf.data.Dataset that will be sharded across all replicas using
the rules stated above.
|
| Returns | |
|---|---|
A "distributed Dataset", which acts like a tf.data.Dataset except
it produces "per-replica" values.
|
experimental_distribute_datasets_from_function
experimental_distribute_datasets_from_function(
dataset_fn
)
Distributes tf.data.Dataset instances created by calls to dataset_fn.
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
from the 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.
The 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 tf.function.
# 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),))
| Args | |
|---|---|
dataset_fn
|
A function taking a tf.distribute.InputContext instance and
returning a tf.data.Dataset.
|
| Returns | |
|---|---|
A "distributed Dataset", which acts like a tf.data.Dataset except
it produces "per-replica" values.
|
experimental_local_results
experimental_local_results(
value
)
Returns the list of all local per-replica values contained in value.
| Args | |
|---|---|
value
|
A value returned by experimental_run(), run(),
extended.call_for_each_replica(), or a variable created in scope.
|
| Returns | |
|---|---|
A tuple of values contained in value. If value represents a single
value, this returns (value,).
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experimental_make_numpy_dataset
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
the input.
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.
Example:
numpy_input = np.ones([10], dtype=np.float32)
dataset = strategy.experimental_make_numpy_dataset(numpy_input)
dist_dataset = strategy.experimental_distribute_dataset(dataset)
| Args | |
|---|---|
numpy_input
|
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
tf.data.Dataset behavior.
|
session
|
(TensorFlow v1.x graph execution only) A session used for initialization. |
| Returns | |
|---|---|
A tf.data.Dataset representing numpy_input.
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experimental_run
experimental_run(
fn, input_iterator=None
)
Runs ops in fn on each replica, with inputs from input_iterator.
DEPRECATED: This method is not available in TF 2.x. Please switch
to using run instead.
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
one get_next call on the input iterator.
fn may call tf.distribute.get_replica_context() to access members such
as replica_id_in_sync_group.
| Args | |
|---|---|
fn
|
The function to run. The inputs to the function must match the outputs
of input_iterator.get_next(). The output must be a tf.nest of
Tensors.
|
input_iterator
|
(Optional) input iterator from which the inputs are taken. |
| Returns | |
|---|---|
Merged return value of fn across replicas. The structure of the return
value is the same as the return value from fn. Each element in the
structure can either be PerReplica (if the values are unsynchronized),
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