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Synchronous training on TPUs and TPU Pods.
Inherits From: Strategy
tf.distribute.TPUStrategy(
tpu_cluster_resolver=None, experimental_device_assignment=None
)
Used in the notebooks
Used in the guide  Used in the tutorials 

To construct a TPUStrategy object, you need to run the initialization code as below:
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.TPUStrategy(resolver)
While using distribution strategies, the variables created within the strategy's scope will be replicated across all the replicas and can be kept in sync using allreduce algorithms.
To run TF2 programs on TPUs, you can either use .compile
and
.fit
APIs in tf.keras
with TPUStrategy, or write your own customized
training loop by calling strategy.run
directly. Note that
TPUStrategy doesn't support pure eager execution, so please make sure the
function passed into strategy.run
is a tf.function
or
strategy.run
is called inside a tf.function
if eager
behavior is enabled. See more details in https://www.tensorflow.org/guide/tpu.
distribute_datasets_from_function
and
experimental_distribute_dataset
APIs can be used to distribute the dataset
across the TPU workers when writing your own training loop. If you are using
fit
and compile
methods available in tf.keras.Model
, then Keras will
handle the distribution for you.
An example of writing customized training loop on TPUs:
with strategy.scope():
model = tf.keras.Sequential([
tf.keras.layers.Dense(2, input_shape=(5,)),
])
optimizer = tf.keras.optimizers.SGD(learning_rate=0.1)
def dataset_fn(ctx):
x = np.random.random((2, 5)).astype(np.float32)
y = np.random.randint(2, size=(2, 1))
dataset = tf.data.Dataset.from_tensor_slices((x, y))
return dataset.repeat().batch(1, drop_remainder=True)
dist_dataset = strategy.distribute_datasets_from_function(
dataset_fn)
iterator = iter(dist_dataset)
@tf.function()
def train_step(iterator):
def step_fn(inputs):
features, labels = inputs
with tf.GradientTape() as tape:
logits = model(features, training=True)
loss = tf.keras.losses.sparse_categorical_crossentropy(
labels, logits)
grads = tape.gradient(loss, model.trainable_variables)
optimizer.apply_gradients(zip(grads, model.trainable_variables))
strategy.run(step_fn, args=(next(iterator),))
train_step(iterator)
For the advanced use cases like model parallelism, you can set
experimental_device_assignment
argument when creating TPUStrategy to specify
number of replicas and number of logical devices. Below is an example to
initialize TPU system with 2 logical devices and 1 replica.
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=1)
strategy = tf.distribute.TPUStrategy(
resolver, experimental_device_assignment=device_assignment)
Then you can run a tf.add
operation only on logical device 0.
@tf.function()
def step_fn(inputs):
features, _ = inputs
output = tf.add(features, features)
# Add operation will be executed on logical device 0.
output = strategy.experimental_assign_to_logical_device(output, 0)
return output
dist_dataset = strategy.distribute_datasets_from_function(
dataset_fn)
iterator = iter(dist_dataset)
strategy.run(step_fn, args=(next(iterator),))
Args  

tpu_cluster_resolver

A
tf.distribute.cluster_resolver.TPUClusterResolver instance, which
provides information about the TPU cluster. If None, it will assume
running on a local TPU worker.

experimental_device_assignment

Optional
tf.tpu.experimental.DeviceAssignment to specify the placement of
replicas on the TPU cluster.

Attributes  

cluster_resolver

Returns the cluster resolver associated with this strategy.
In general, when using a multiworker Strategies that intend to have an associated
Singleworker strategies usually do not have a
The
For more information, please see

extended

tf.distribute.StrategyExtended with additional methods.

num_replicas_in_sync

Returns number of replicas over which gradients are aggregated. 
Methods
distribute_datasets_from_function
distribute_datasets_from_function(
dataset_fn, options=None
)
Distributes tf.data.Dataset
instances created by calls to dataset_fn
.
The argument dataset_fn
that users pass in is an input function that has a
tf.distribute.InputContext
argument and returns a tf.data.Dataset
instance. It is expected that the returned dataset from dataset_fn
is
already batched by perreplica batch size (i.e. global batch size divided by
the number of replicas in sync) and sharded.
tf.distribute.Strategy.distribute_datasets_from_function
does
not batch or shard the tf.data.Dataset
instance
returned from the input function. dataset_fn
will be called on the CPU
device of each of the workers and each generates a dataset where every
replica on that worker will dequeue one batch of inputs (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. First, it allows you to
specify your own batching and sharding logic. (In contrast,
tf.distribute.experimental_distribute_dataset
does batching and sharding
for you.) 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.
The dataset_fn
should take an tf.distribute.InputContext
instance where
information about batching and input replication can be accessed.
You can use element_spec
property of the
tf.distribute.DistributedDataset
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
. Follow
tf.distribute.DistributedDataset.element_spec
to see an example.
For a tutorial on more usage and properties of this method, refer to the tutorial on distributed input). If you are interested in last partial batch handling, read this section.
Args  

dataset_fn

A function taking a tf.distribute.InputContext instance and
returning a tf.data.Dataset .

options

tf.distribute.InputOptions used to control options on how this
dataset is distributed.

Returns  

A tf.distribute.DistributedDataset .

experimental_assign_to_logical_device
experimental_assign_to_logical_device(
tensor, logical_device_id
)
Adds annotation that tensor
will be assigned to a logical device.
This adds an annotation to tensor
specifying that operations on
tensor
will be invoked on logical core device id logical_device_id
.
When model parallelism is used, the default behavior is that all ops
are placed on zeroth 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),))
Args  

tensor

Input tensor to annotate. 
logical_device_id

Id of the logical core to which the tensor will be assigned. 
Raises  

ValueError

The logical device id presented is not consistent with total number of partitions specified by the device assignment. 
Returns  

Annotated tensor with identical value as tensor .

experimental_distribute_dataset
experimental_distribute_dataset(
dataset, options=None
)
Creates tf.distribute.DistributedDataset
from tf.data.Dataset
.
The returned tf.distribute.DistributedDataset
can be iterated over
similar to regular datasets.
NOTE: The user cannot add any more transformations to a
tf.distribute.DistributedDataset
. You can only create an iterator or
examine the tf.TypeSpec
of the data generated by it. See API docs of
tf.distribute.DistributedDataset
to learn more.
The following is an example:
global_batch_size = 2
# Passing the devices is optional.
strategy = tf.distribute.MirroredStrategy(devices=["GPU:0", "GPU:1"])
# Create a dataset
dataset = tf.data.Dataset.range(4).batch(global_batch_size)
# Distribute that dataset
dist_dataset = strategy.experimental_distribute_dataset(dataset)
@tf.function
def replica_fn(input):
return input*2
result = []
# Iterate over the `tf.distribute.DistributedDataset`
for x in dist_dataset:
# process dataset elements
result.append(strategy.run(replica_fn, args=(x,)))
print(result)
[PerReplica:{
0: <tf.Tensor: shape=(1,), dtype=int64, numpy=array([0])>,
1: <tf.Tensor: shape=(1,), dtype=int64, numpy=array([2])>
}, PerReplica:{
0: <tf.Tensor: shape=(1,), dtype=int64, numpy=array([4])>,
1: <tf.Tensor: shape=(1,), dtype=int64, numpy=array([6])>
}]
Three key actions happening under the hood of this method are batching, sharding, and prefetching.
In the code snippet above, dataset
is batched by global_batch_size
, and
calling experimental_distribute_dataset
on it rebatches dataset
to a
new batch size that is equal to the global batch size divided by the number
of replicas in sync. We iterate through it using a Pythonic for loop.
x
is a tf.distribute.DistributedValues
containing data for all replicas,
and each replica gets data of the new batch size.
tf.distribute.Strategy.run
will take care of feeding the right perreplica
data in x
to the right replica_fn
executed on each replica.
Sharding contains autosharding across multiple workers and within every
worker. First, in multiworker distributed training (i.e. when you use
tf.distribute.experimental.MultiWorkerMirroredStrategy
or tf.distribute.TPUStrategy
), autosharding a dataset over a set of
workers means that each worker is assigned a subset of the entire dataset
(if the right tf.data.experimental.AutoShardPolicy
is set). This is to
ensure that at each step, a global batch size of nonoverlapping dataset
elements will be processed by each worker. Autosharding has a couple of
different options that can be specified using
tf.data.experimental.DistributeOptions
. Then, sharding within each worker
means the method will split the data among all the worker devices (if more
than one a present). This will happen regardless of multiworker
autosharding.
By default, this method adds a prefetch transformation at the end of the
user provided tf.data.Dataset
instance. The argument to the prefetch
transformation which is buffer_size
is equal to the number of replicas in
sync.
If the above batch splitting and dataset sharding logic is undesirable,
please use
tf.distribute.Strategy.distribute_datasets_from_function
instead, which does not do any automatic batching or sharding for you.
For a tutorial on more usage and properties of this method, refer to the tutorial on distributed input. If you are interested in last partial batch handling, read this section.
Args  

dataset

tf.data.Dataset that will be sharded across all replicas using
the rules stated above.

options

tf.distribute.InputOptions used to control options on how this
dataset is distributed.

Returns  

A tf.distribute.DistributedDataset .

experimental_distribute_values_from_function
experimental_distribute_values_from_function(
value_fn
)
Generates tf.distribute.DistributedValues
from value_fn
.
This function is to generate tf.distribute.DistributedValues
to pass
into run
, reduce
, or other methods that take
distributed values when not using datasets.
Args  

value_fn

The function to run to generate values. It is called for
each replica with tf.distribute.ValueContext as the sole argument. It
must return a Tensor or a type that can be converted to a Tensor.

Returns  

A tf.distribute.DistributedValues containing a value for each replica.

Example usage:
 Return constant value per replica:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
def value_fn(ctx):
return tf.constant(1.)
distributed_values = (
strategy.experimental_distribute_values_from_function(
value_fn))
local_result = strategy.experimental_local_results(distributed_values)
local_result
(<tf.Tensor: shape=(), dtype=float32, numpy=1.0>,
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>)
 Distribute values in array based on replica_id:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
array_value = np.array([3., 2., 1.])
def value_fn(ctx):
return array_value[ctx.replica_id_in_sync_group]
distributed_values = (
strategy.experimental_distribute_values_from_function(
value_fn))
local_result = strategy.experimental_local_results(distributed_values)
local_result
(3.0, 2.0)
 Specify values using num_replicas_in_sync:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
def value_fn(ctx):
return ctx.num_replicas_in_sync
distributed_values = (
strategy.experimental_distribute_values_from_function(
value_fn))
local_result = strategy.experimental_local_results(distributed_values)
local_result
(2, 2)
 Place values on devices and distribute:
strategy = tf.distribute.TPUStrategy()
worker_devices = strategy.extended.worker_devices
multiple_values = []
for i in range(strategy.num_replicas_in_sync):
with tf.device(worker_devices[i]):
multiple_values.append(tf.constant(1.0))
def value_fn(ctx):
return multiple_values[ctx.replica_id_in_sync_group]
distributed_values = strategy.
experimental_distribute_values_from_function(
value_fn)
experimental_local_results
experimental_local_results(
value
)
Returns the list of all local perreplica values contained in value
.
Args  

value

A value returned by experimental_run() , run(), or a variable
created in scope`.

Returns  

A tuple of values contained in value where ith element corresponds to
ith replica. If value represents a single value, this returns
(value,).

experimental_replicate_to_logical_devices
experimental_replicate_to_logical_devices(
tensor
)
Adds annotation that tensor
will be replicated to all logical devices.
This adds an annotation to tensor tensor
specifying that operations on
tensor
will be invoked on all logical devices.
# 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):
images, labels = inputs
images = strategy.experimental_split_to_logical_devices(
inputs, [1, 2, 4, 1])
# model() function will be executed on 8 logical devices with `inputs`
# split 2 * 4 ways.
output = model(inputs)
# For loss calculation, all logical devices share the same logits
# and labels.
labels = strategy.experimental_replicate_to_logical_devices(labels)
output = strategy.experimental_replicate_to_logical_devices(output)
loss = loss_fn(labels, output)
return loss
strategy.run(step_fn, args=(next(iterator),))
Args: tensor: Input tensor to annotate.
Returns  

Annotated tensor with identical value as tensor .

experimental_split_to_logical_devices
experimental_split_to_logical_devices(
tensor, partition_dimensions
)
Adds annotation that tensor
will be split across logical devices.
This adds an annotation to tensor tensor
specifying that operations on
tensor
will be split among multiple logical devices. Tensor tensor
will
be split across dimensions specified by partition_dimensions
.
The dimensions of tensor
must be divisible by corresponding value in
partition_dimensions
.
For example, for system with 8 logical devices, if tensor
is an image
tensor with shape (batch_size, width, height, channel) and
partition_dimensions
is [1, 2, 4, 1], then tensor
will be split
2 in width dimension and 4 way in height dimension and the split
tensor values will be fed into 8 logical devices.
# Initializing TPU system with 8 logical devices and 1 replica.
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, 2, 2, 2],
num_replicas=1)
strategy = tf.distribute.TPUStrategy(
resolver, experimental_device_assignment=device_assignment)
iterator = iter(inputs)
@tf.function()
def step_fn(inputs):
inputs = strategy.experimental_split_to_logical_devices(
inputs, [1, 2, 4, 1])
# model() function will be executed on 8 logical devices with `inputs`
# split 2 * 4 ways.
output = model(inputs)
return output
strategy.run(step_fn, args=(next(iterator),))
Args:
tensor: Input tensor to annotate.
partition_dimensions: An unnested list of integers with the size equal to
rank of tensor
specifying how tensor
will be partitioned. The
product of all elements in partition_dimensions
must be equal to the
total number of logical devices per replica.
Raises  

ValueError

1) If the size of partition_dimensions does not equal to rank
of 
Returns  

Annotated tensor with identical value as tensor .

gather
gather(
value, axis
)
Gather value
across replicas along axis
to the current device.
Given a tf.distribute.DistributedValues
or tf.Tensor
like
object value
, this API gathers and concatenates value
across replicas
along the axis
th dimension. The result is copied to the "current" device,
which would typically be the CPU of the worker on which the program is
running. For tf.distribute.TPUStrategy
, it is the first TPU host. For
multiclient tf.distribute.MultiWorkerMirroredStrategy
, this is the CPU of
each worker.
This API can only be called in the crossreplica context. For a counterpart
in the replica context, see tf.distribute.ReplicaContext.all_gather
.
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
# A DistributedValues with component tensor of shape (2, 1) on each replica
distributed_values = strategy.experimental_distribute_values_from_function(lambda _: tf.identity(tf.constant([[1], [2]])))
@tf.function
def run():
return strategy.gather(distributed_values, axis=0)
run()
<tf.Tensor: shape=(4, 1), dtype=int32, numpy=
array([[1],
[2],
[1],
[2]], dtype=int32)>
Consider the following example for more combinations:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1", "GPU:2", "GPU:3"])
single_tensor = tf.reshape(tf.range(6), shape=(1,2,3))
distributed_values = strategy.experimental_distribute_values_from_function(lambda _: tf.identity(single_tensor))
@tf.function
def run(axis):
return strategy.gather(distributed_values, axis=axis)
axis=0
run(axis)
<tf.Tensor: shape=(4, 2, 3), dtype=int32, numpy=
array([[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]],
[[0, 1, 2],
[3, 4, 5]]], dtype=int32)>
axis=1
run(axis)
<tf.Tensor: shape=(1, 8, 3), dtype=int32, numpy=
array([[[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5],
[0, 1, 2],
[3, 4, 5]]], dtype=int32)>
axis=2
run(axis)
<tf.Tensor: shape=(1, 2, 12), dtype=int32, numpy=
array([[[0, 1, 2, 0, 1, 2, 0, 1, 2, 0, 1, 2],
[3, 4, 5, 3, 4, 5, 3, 4, 5, 3, 4, 5]]], dtype=int32)>
Args  

value

a tf.distribute.DistributedValues instance, e.g. returned by
Strategy.run , to be combined into a single tensor. It can also be a
regular tensor when used with tf.distribute.OneDeviceStrategy or the
default strategy. The tensors that constitute the DistributedValues
can only be dense tensors with nonzero rank, NOT a tf.IndexedSlices .

axis

0D int32 Tensor. Dimension along which to gather. Must be in the range [0, rank(value)). 
Returns  

A Tensor that's the concatenation of value across replicas along
axis dimension.

reduce
reduce(
reduce_op, value, axis
)
Reduce value
across replicas and return result on current device.
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
def step_fn():
i = tf.distribute.get_replica_context().replica_id_in_sync_group
return tf.identity(i)
per_replica_result = strategy.run(step_fn)
total = strategy.reduce("SUM", per_replica_result, axis=None)
total
<tf.Tensor: shape=(), dtype=int32, numpy=1>
To see how this would look with multiple replicas, consider the same example with MirroredStrategy with 2 GPUs:
strategy = tf.distribute.MirroredStrategy(devices=["GPU:0", "GPU:1"])
def step_fn():
i = tf.distribute.get_replica_context().replica_id_in_sync_group
return tf.identity(i)
per_replica_result = strategy.run(step_fn)
# Check devices on which per replica result is:
strategy.experimental_local_results(per_replica_result)[0].device
# /job:localhost/replica:0/task:0/device:GPU:0
strategy.experimental_local_results(per_replica_result)[1].device
# /job:localhost/replica:0/task:0/device:GPU:1
total = strategy.reduce("SUM", per_replica_result, axis=None)
# Check device on which reduced result is:
total.device
# /job:localhost/replica:0/task:0/device:CPU:0
This API is typically used for aggregating the results returned from different replicas, for reporting etc. For example, loss computed from different replicas can be averaged using this API before printing.
There are a number of different tf.distribute APIs for reducing values across replicas:
tf.distribute.ReplicaContext.all_reduce
: This differs fromStrategy.reduce
in that it is for replica context and does not copy the results to the host device.all_reduce
should be typically used for reductions inside the training step such as gradients.tf.distribute.StrategyExtended.reduce_to
andtf.distribute.StrategyExtended.batch_reduce_to
: These APIs are more advanced versions ofStrategy.reduce
as they allow customizing the destination of the result. They are also called in cross replica context.
What should axis be?
Given a perreplica value returned by run
, say a
perexample loss, the batch will be divided across all the replicas. This
function allows you to aggregate across replicas and optionally also across
batch elements by specifying the axis parameter accordingly.
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. With axis=None
, reduce
will
aggregate only 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 or loss).
strategy.reduce("sum", per_replica_result, axis=None)
Sometimes, you will want to aggregate across both the global batch and
all replicas. You can get this behavior by specifying the batch
dimension as the axis
, typically axis=0
. In this case it would return a
scalar 0+1+2+3+4+5+6+7
.
strategy.reduce("sum", per_replica_result, axis=0)
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
tf.distribute.ReduceOp.MEAN
, using 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 1/4
.
Args  

reduce_op

a tf.distribute.ReduceOp value specifying how values should
be combined. Allows using string representation of the enum such as
"SUM", "MEAN".

value

a tf.distribute.DistributedValues instance, e.g. returned by
Strategy.run , to be combined into a single tensor. It can also be a
regular tensor when used with OneDeviceStrategy or default strategy.

axis

specifies the dimension to reduce along within each
replica's tensor. Should typically be set to the batch dimension, or
None to only reduce across replicas (e.g. if the tensor has no batch
dimension).

Returns  

A Tensor .

run
run(
fn, args=(), kwargs=None, options=None
)
Run the computation defined by fn
on each TPU replica.
Executes ops specified by fn
on each replica. If args
or kwargs
have
tf.distribute.DistributedValues
, such as those produced by a
tf.distribute.DistributedDataset
from
tf.distribute.Strategy.experimental_distribute_dataset
or
tf.distribute.Strategy.distribute_datasets_from_function
,
when fn
is executed on a particular replica, it will be executed with the
component of tf.distribute.DistributedValues
that correspond to that
replica.
fn
may call tf.distribute.get_replica_context()
to access members such
as all_reduce
.
All arguments in args
or kwargs
should either be nest of tensors or
tf.distribute.DistributedValues
containing tensors or composite tensors.
Example usage:
resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='')
tf.config.experimental_connect_to_cluster(resolver)
tf.tpu.experimental.initialize_tpu_system(resolver)
strategy = tf.distribute.TPUStrategy(resolver)
@tf.function
def run():
def value_fn(value_context):
return value_context.num_replicas_in_sync
distributed_values = (
strategy.experimental_distribute_values_from_function(value_fn))
def replica_fn(input):
return input * 2
return strategy.run(replica_fn, args=(distributed_values,))
result = run()
Args  

fn

The function to run. The output must be a tf.nest of Tensor s.

args

(Optional) Positional arguments to fn .

kwargs

(Optional) Keyword arguments to fn .

options

(Optional) An instance of tf.distribute.RunOptions specifying
the options to run fn .

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 tf.distribute.DistributedValues , Tensor
objects, or Tensor s (for example, if running on a single replica).

scope
scope()
Context manager to make the strategy current and distribute variables.
This method returns a context manager, and is used as follows:
strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
# Variable created inside scope:
with strategy.scope():
mirrored_variable = tf.Variable(1.)
mirrored_variable
MirroredVariable:{
0: <tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>,
1: <tf.Variable 'Variable/replica_1:0' shape=() dtype=float32, numpy=1.0>
}
# Variable created outside scope:
regular_variable = tf.Variable(1.)
regular_variable
<tf.Variable 'Variable:0' shape=() dtype=float32, numpy=1.0>
What happens when Strategy.scope is entered?
strategy
is installed in the global context as the "current" strategy. Inside this scope,tf.distribute.get_strategy()
will now return this strategy. Outside this scope, it returns the default noop strategy. Entering the scope also enters the "crossreplica context". See
tf.distribute.StrategyExtended
for an explanation on crossreplica and replica contexts.  Variable creation inside
scope
is intercepted by the strategy. Each strategy defines how it wants to affect the variable creation. Sync strategies likeMirroredStrategy
,TPUStrategy
andMultiWorkerMiroredStrategy
create variables replicated on each replica, whereasParameterServerStrategy
creates variables on the parameter servers. This is done using a customtf.variable_creator_scope
.  In some strategies, a default device scope may also be entered: in
MultiWorkerMiroredStrategy
, a default device scope of "/CPU:0" is entered on each worker.
What should be in scope and what should be outside?
There are a number of requirements on what needs to happen inside the scope. However, in places where we have information about which strategy is in use, we often enter the scope for the user, so they don't have to do it explicitly (i.e. calling those either inside or outside the scope is OK).
 Anything that creates variables that should be distributed variables
must be called in a
strategy.scope
. This can be accomplished either by directly calling the variable creating function within the scope context, or by relying on another API likestrategy.run
orkeras.Model.fit
to automatically enter it for you. Any variable that is created outside scope will not be distributed and may have performance implications. Some common objects that create variables in TF are Models, Optimizers, Metrics. Such objects should always be initialized in the scope, and any functions that may lazily create variables (e.g.,Model.__call__()
, tracing atf.function
, etc.) should similarly be called within scope. Another source of variable creation can be a checkpoint restore  when variables are created lazily. Note that any variable created inside a strategy captures the strategy information. So reading and writing to these variables outside thestrategy.scope
can also work seamlessly, without the user having to enter the scope.  Some strategy APIs (such as
strategy.run
andstrategy.reduce
) which require to be in a strategy's scope, enter the scope automatically, which means when using those APIs you don't need to explicitly enter the scope yourself.  When a
tf.keras.Model
is created inside astrategy.scope
, the Model object captures the scope information. When high level training framework methods such asmodel.compile
,model.fit
, etc. are then called, the captured scope will be automatically entered, and the associated strategy will be used to distribute the training etc. See a detailed example in distributed keras tutorial. WARNING: Simply callingmodel(..)
does not automatically enter the captured scope  only high level training framework APIs support this behavior:model.compile
,model.fit
,model.evaluate
,model.predict
andmodel.save
can all be called inside or outside the scope.  The following can be either inside or outside the scope:
 Creating the input datasets
 Defining
tf.function
s that represent your training step  Saving APIs such as
tf.saved_model.save
. Loading creates variables, so that should go inside the scope if you want to train the model in a distributed way.  Checkpoint saving. As mentioned above 
checkpoint.restore
may sometimes need to be inside scope if it creates variables.
Returns  

A context manager. 