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A state & compute distribution policy on a list of devices.
tf.distribute.Strategy(
extended
)
See the guide
for overview and examples. See tf.distribute.StrategyExtended
and
tf.distribute
for a glossory of concepts mentioned on this page such as "per-replica",
replica, and reduce.
In short:
- To use it with Keras
compile
/fit
, please read. - You may pass descendant of
tf.distribute.Strategy
totf.estimator.RunConfig
to specify how atf.estimator.Estimator
should distribute its computation. See guide. - Otherwise, use
tf.distribute.Strategy.scope
to specify that a strategy should be used when building an executing your model. (This puts you in the "cross-replica context" for this strategy, which means the strategy is put in control of things like variable placement.) If you are writing a custom training loop, you will need to call a few more methods, see the guide:
Start by either creating a
tf.data.Dataset
normally or usingtf.distribute.experimental_make_numpy_dataset
to make a dataset out of anumpy
array.Use
tf.distribute.Strategy.experimental_distribute_dataset
to convert atf.data.Dataset
to something that produces "per-replica" values. If you want to manually specify how the dataset should be partitioned across replicas, usetf.distribute.Strategy.experimental_distribute_datasets_from_function
instead.Use
tf.distribute.Strategy.run
to run a function once per replica, taking values that may be "per-replica" (e.g. from atf.distribute.DistributedDataset
object) and returning "per-replica" values. This function is executed in "replica context", which means each operation is performed separately on each replica.Finally use a method (such as
tf.distribute.Strategy.reduce
) to convert the resulting "per-replica" values into ordinaryTensor
s.
A custom training loop can be as simple as:
with my_strategy.scope():
@tf.function
def distribute_train_epoch(dataset):
def replica_fn(input):
# process input and return result
return result
total_result = 0
for x in dataset:
per_replica_result = my_strategy.run(replica_fn, args=(x,))
total_result += my_strategy.reduce(tf.distribute.ReduceOp.SUM,
per_replica_result, axis=None)
return total_result
dist_dataset = my_strategy.experimental_distribute_dataset(dataset)
for _ in range(EPOCHS):
train_result = distribute_train_epoch(dist_dataset)
This takes an ordinary dataset
and replica_fn
and runs it
distributed using a particular tf.distribute.Strategy
named
my_strategy
above. Any variables created in replica_fn
are created
using my_strategy
's policy, and library functions called by
replica_fn
can use the get_replica_context()
API to implement
distributed-specific behavior.
You can use the reduce
API to aggregate results across replicas and use
this as a return value from one iteration over a
tf.distribute.DistributedDataset
. Or
you can use tf.keras.metrics
(such as loss, accuracy, etc.) to
accumulate metrics across steps in a given epoch.
See the custom training loop tutorial for a more detailed example.
Attributes | |
---|---|
cluster_resolver
|
Returns the cluster resolver associated with this strategy.
In general, when using a multi-worker Strategies that intend to have an associated
Single-worker 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
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.
# 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 idential 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 how regular datasets can.
NOTE: The user cannot add any more transformations to a
tf.distribute.DistributedDataset
.
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 `tf.distribute.DistributedDataset`
for x in dist_dataset:
# process dataset elements
strategy.run(replica_fn, args=(x,))
In the code snippet above, the tf.distribute.DistributedDataset
dist_dataset
is batched by GLOBAL_BATCH_SIZE
, and we iterate through it
using for x in dist_dataset
. x
a tf.distribute.DistributedValues
containing data for all replicas, which aggregates to a batch of
GLOBAL_BATCH_SIZE
. tf.distribute.Strategy.run
will take care of feeding
the right per-replica data in x
to the right replica_fn
executed on each
replica.
What's under the hood of this method, when we say the tf.data.Dataset
instance - dataset
- gets distributed? It depends on how you set the
tf.data.experimental.AutoShardPolicy
through
tf.data.experimental.DistributeOptions
. By default, it is set to
tf.data.experimental.AutoShardPolicy.AUTO
. In a multi-worker setting, we
will first attempt to distribute dataset
by detecting whether dataset
is
being created out of reader datasets (e.g. tf.data.TFRecordDataset
,
tf.data.TextLineDataset
, etc.) and if so, try 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 disable dataset
sharding across workers, by setting the
tf.data.experimental.DistributeOptions.auto_shard_policy
to be
tf.data.experimental.AutoShardPolicy.OFF
.
If the attempt to shard by file is unsuccessful (i.e. the dataset is not
read from files), 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 route is selected.
As mentioned before, within each worker, we will also split the data among all the worker devices (if more than one a present). This will happen even if