tf.distribute.TPUStrategy

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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,
    experimental_spmd_xla_partitioning=False
)

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 all-reduce 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),))

experimental_spmd_xla_partitioning enables the experimental XLA SPMD feature for model parallelism. This flag can reduce the compilation time and HBM requirements. When running in this mode, every input tensor must either be partitioned (via strategy.experimental_split_to_logical_devices) or fully replicated (via strategy.experimental_replicate_to_logical_devices) to all logical devices. And calling strategy.experimental_assign_to_logical_device will result in a ValueError in this mode.

Methods

distribute_datasets_from_function

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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 per-replica 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.

experimental_assign_to_logical_device

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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 zero-th 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),))

experimental_distribute_dataset

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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 per-replica data in x to the right replica_fn executed on each replica.

Sharding contains autosharding across multiple workers and within every worker. First, in multi-worker 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 non-overlapping 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 multi-worker 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.

experimental_distribute_values_from_function

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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.

Example usage:

1. 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>)
       

2. 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)
       

3. 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)
       

4. 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

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experimental_local_results(
    value
)

Returns the list of all local per-replica values contained in value.

experimental_replicate_to_logical_devices

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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.

experimental_split_to_logical_devices

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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)
# Construct the TPUStrategy. Since we are going to split the image across
# logical devices, here we set `experimental_spmd_xla_partitioning=True`
# so that the partitioning can be compiled in SPMD mode, which usually
# results in faster compilation and smaller HBM requirement if the size of
# input and activation tensors are much bigger than that of the model
# parameters. Note that this flag is suggested but not a hard requirement
# for `experimental_split_to_logical_devices`.
strategy = tf.distribute.TPUStrategy(
    resolver, experimental_device_assignment=device_assignment,
    experimental_spmd_xla_partitioning=True)

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.

gather

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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 multi-client tf.distribute.MultiWorkerMirroredStrategy, this is the CPU of each worker.

This API can only be called in the cross-replica 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)>

reduce

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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 from Strategy.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 and tf.distribute.StrategyExtended.batch_reduce_to: These APIs are more advanced versions of Strategy.reduce as they allow customizing the destination of the result. They are also called in cross replica context.

What should axis be?

Given a per-replica value returned by run, 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 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.

run

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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()

scope

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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 no-op strategy.
• Entering the scope also enters the "cross-replica context". See tf.distribute.StrategyExtended for an explanation on cross-replica 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 like MirroredStrategy, TPUStrategy and MultiWorkerMiroredStrategy create variables replicated on each replica, whereas ParameterServerStrategy creates variables on the parameter servers. This is done using a custom tf.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 like strategy.run or keras.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 a tf.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 the strategy.scope can also work seamlessly, without the user having to enter the scope.
• Some strategy APIs (such as strategy.run and strategy.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 a strategy.scope, the Model object captures the scope information. When high level training framework methods such as model.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 calling model(..) does not automatically enter the captured scope -- only high level training framework APIs support this behavior: model.compile, model.fit, model.evaluate, model.predict and model.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.functions 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.