# tf.distribute.MultiWorkerMirroredStrategy

<br />

|---|
| [![](https://www.tensorflow.org/images/GitHub-Mark-32px.png) View source on GitHub](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/distribute/collective_all_reduce_strategy.py#L56-L216) |

A distribution strategy for synchronous training on multiple workers.

Inherits From: [`Strategy`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/Strategy)

    tf.distribute.MultiWorkerMirroredStrategy(
        cluster_resolver=None, communication_options=None
    )

This strategy implements synchronous distributed training across multiple
workers, each with potentially multiple GPUs. Similar to
[`tf.distribute.MirroredStrategy`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/MirroredStrategy), it replicates all variables and computations
to each local device. The difference is that it uses a distributed collective
implementation (e.g. all-reduce), so that multiple workers can work together.

You need to launch your program on each worker and configure
`cluster_resolver` correctly. For example, if you are using
[`tf.distribute.cluster_resolver.TFConfigClusterResolver`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/cluster_resolver/TFConfigClusterResolver), each worker needs to
have its corresponding `task_type` and `task_id` set in the `TF_CONFIG`
environment variable. An example TF_CONFIG on worker-0 of a two worker cluster
is:

    TF_CONFIG = '{"cluster": {"worker": ["localhost:12345", "localhost:23456"]}, "task": {"type": "worker", "index": 0} }'

Your program runs on each worker as-is. Note that collectives require each
worker to participate. All [`tf.distribute`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute) and non [`tf.distribute`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute) API may use
collectives internally, e.g. checkpointing and saving since reading a
[`tf.Variable`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/Variable) with [`tf.VariableSynchronization.ON_READ`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/VariableSynchronization#ON_READ) all-reduces the value.
Therefore it's recommended to run exactly the same program on each worker.
Dispatching based on `task_type` or `task_id` of the worker is error-prone.

`cluster_resolver.num_accelerators()` determines the number of GPUs the
strategy uses. If it's zero, the strategy uses the CPU. All workers need to
use the same number of devices, otherwise the behavior is undefined.

This strategy is not intended for TPU. Use [`tf.distribute.TPUStrategy`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/TPUStrategy)
instead.

After setting up TF_CONFIG, using this strategy is similar to using
[`tf.distribute.MirroredStrategy`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/MirroredStrategy) and [`tf.distribute.TPUStrategy`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/TPUStrategy).

    strategy = tf.distribute.MultiWorkerMirroredStrategy()

    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)

    model.compile()
    model.fit(dist_dataset)

You can also write your own training loop:

    @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),))

    for _ in range(NUM_STEP):
      train_step(iterator)

See
[Multi-worker training with Keras](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras)
for a detailed tutorial.

**Saving**

You need to save and checkpoint on all workers instead of just one. This is
because variables whose synchronization=ON_READ triggers aggregation during
saving. It's recommended to save to a different path on each worker to avoid
race conditions. Each worker saves the same thing. See
[Multi-worker training with Keras](https://www.tensorflow.org/tutorials/distribute/multi_worker_with_keras#model_saving_and_loading)
tutorial for examples.

**Known Issues**

- [`tf.distribute.cluster_resolver.TFConfigClusterResolver`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/cluster_resolver/TFConfigClusterResolver) does not return the correct number of accelerators. The strategy uses all available GPUs if `cluster_resolver` is [`tf.distribute.cluster_resolver.TFConfigClusterResolver`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/cluster_resolver/TFConfigClusterResolver) or `None`.
- In eager mode, the strategy needs to be created before calling any other Tensorflow API.

<br />

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<br />

| ## Args ||
|---|---|
| `cluster_resolver` | optional [`tf.distribute.cluster_resolver.ClusterResolver`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/cluster_resolver/ClusterResolver). If `None`, [`tf.distribute.cluster_resolver.TFConfigClusterResolver`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/cluster_resolver/TFConfigClusterResolver) is used. |
| `communication_options` | optional [`tf.distribute.experimental.CommunicationOptions`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/experimental/CommunicationOptions). This configures the default options for cross device communications. It can be overridden by options provided to the communication APIs like [`tf.distribute.ReplicaContext.all_reduce`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/ReplicaContext#all_reduce). See [`tf.distribute.experimental.CommunicationOptions`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/experimental/CommunicationOptions) for details. |

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<br />

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<br />

| ## Attributes ||
|---|---|
| `cluster_resolver` | Returns the cluster resolver associated with this strategy. <br /> As a multi-worker strategy, [`tf.distribute.MultiWorkerMirroredStrategy`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/MultiWorkerMirroredStrategy) provides the associated [`tf.distribute.cluster_resolver.ClusterResolver`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/cluster_resolver/ClusterResolver). If the user provides one in `__init__`, that instance is returned; if the user does not, a default `TFConfigClusterResolver` is provided. |
| `extended` | [`tf.distribute.StrategyExtended`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/StrategyExtended) with additional methods. |
| `num_replicas_in_sync` | Returns number of replicas over which gradients are aggregated. |

<br />

## Methods

### `distribute_datasets_from_function`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/distribute/distribute_lib.py#L1476-L1553)

    distribute_datasets_from_function(
        dataset_fn, options=None
    )

Distributes [`tf.data.Dataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/InputContext) argument and returns a [`tf.data.Dataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/Strategy#distribute_datasets_from_function) does
not batch or shard the [`tf.data.Dataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedDataset) returned by this API to query the
[`tf.TypeSpec`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/TypeSpec) of the elements returned by the iterator. This can be used to
set the `input_signature` property of a [`tf.function`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/function). Follow
[`tf.distribute.DistributedDataset.element_spec`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedDataset#element_spec) to see an example.

> [!IMPORTANT]
> **Important:** The [`tf.data.Dataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/data/Dataset) returned by `dataset_fn` should have a per-replica batch size, unlike `experimental_distribute_dataset`, which uses the global batch size. This may be computed using `input_context.get_per_replica_batch_size`.

> [!NOTE]
> **Note:** If you are using TPUStrategy, the order in which the data is processed by the workers when using [`tf.distribute.Strategy.experimental_distribute_dataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/Strategy#experimental_distribute_dataset) or [`tf.distribute.Strategy.distribute_datasets_from_function`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/Strategy#distribute_datasets_from_function) is not guaranteed. This is typically required if you are using [`tf.distribute`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute) to scale prediction. You can however insert an index for each element in the batch and order outputs accordingly. Refer to [this
> snippet](https://www.tensorflow.org/tutorials/distribute/input#caveats) for an example of how to order outputs.

> [!NOTE]
> **Note:** Stateful dataset transformations are currently not supported with `tf.distribute.experimental_distribute_dataset` or `tf.distribute.distribute_datasets_from_function`. Any stateful ops that the dataset may have are currently ignored. For example, if your dataset has a `map_fn` that uses [`tf.random.uniform`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/random/uniform) to rotate an image, then you have a dataset graph that depends on state (i.e the random seed) on the local machine where the python process is being executed.

For a tutorial on more usage and properties of this method, refer to the
[tutorial on distributed input](https://www.tensorflow.org/tutorials/distribute/input#tfdistributestrategyexperimental_distribute_datasets_from_function)).
If you are interested in last partial batch handling, read [this section](https://www.tensorflow.org/tutorials/distribute/input#partial_batches).

<br />

<br />

<br />

| Args ||
|---|---|
| `dataset_fn` | A function taking a [`tf.distribute.InputContext`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/InputContext) instance and returning a [`tf.data.Dataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/data/Dataset). |
| `options` | [`tf.distribute.InputOptions`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/InputOptions) used to control options on how this dataset is distributed. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A [`tf.distribute.DistributedDataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedDataset). ||

<br />

### `experimental_distribute_dataset`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/distribute/distribute_lib.py#L1355-L1474)

    experimental_distribute_dataset(
        dataset, options=None
    )

Creates [`tf.distribute.DistributedDataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedDataset) from [`tf.data.Dataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/data/Dataset).

The returned [`tf.distribute.DistributedDataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedDataset) can be iterated over
similar to regular datasets.
NOTE: The user cannot add any more transformations to a
[`tf.distribute.DistributedDataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedDataset). You can only create an iterator or
examine the [`tf.TypeSpec`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/TypeSpec) of the data generated by it. See API docs of
[`tf.distribute.DistributedDataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues) containing data for all replicas,
and each replica gets data of the new batch size.
[`tf.distribute.Strategy.run`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/experimental/MultiWorkerMirroredStrategy)
or [`tf.distribute.TPUStrategy`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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.

> [!NOTE]
> **Note:** for autosharding across multiple workers, the default mode is [`tf.data.experimental.AutoShardPolicy.AUTO`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/data/experimental/AutoShardPolicy#AUTO). This mode will attempt to shard the input dataset by files if the dataset is being created out of reader datasets (e.g. [`tf.data.TFRecordDataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/data/TFRecordDataset), [`tf.data.TextLineDataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/data/TextLineDataset), etc.) or otherwise shard the dataset by data, where each of the workers will read the entire dataset and only process the shard assigned to it. However, if you have less than one input file per worker, we suggest that you disable dataset autosharding across workers by setting the [`tf.data.experimental.DistributeOptions.auto_shard_policy`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/data/experimental/DistributeOptions#auto_shard_policy) to be [`tf.data.experimental.AutoShardPolicy.OFF`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/data/experimental/AutoShardPolicy#OFF).

By default, this method adds a prefetch transformation at the end of the
user provided [`tf.data.Dataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/Strategy#distribute_datasets_from_function)
instead, which does not do any automatic batching or sharding for you.

> [!NOTE]
> **Note:** If you are using TPUStrategy, the order in which the data is processed by the workers when using [`tf.distribute.Strategy.experimental_distribute_dataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/Strategy#experimental_distribute_dataset) or [`tf.distribute.Strategy.distribute_datasets_from_function`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/Strategy#distribute_datasets_from_function) is not guaranteed. This is typically required if you are using [`tf.distribute`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute) to scale prediction. You can however insert an index for each element in the batch and order outputs accordingly. Refer to [this
> snippet](https://www.tensorflow.org/tutorials/distribute/input#caveats) for an example of how to order outputs.

> [!NOTE]
> **Note:** Stateful dataset transformations are currently not supported with `tf.distribute.experimental_distribute_dataset` or `tf.distribute.distribute_datasets_from_function`. Any stateful ops that the dataset may have are currently ignored. For example, if your dataset has a `map_fn` that uses [`tf.random.uniform`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/random/uniform) to rotate an image, then you have a dataset graph that depends on state (i.e the random seed) on the local machine where the python process is being executed.

For a tutorial on more usage and properties of this method, refer to the
[tutorial on distributed input](https://www.tensorflow.org/tutorials/distribute/input#tfdistributestrategyexperimental_distribute_dataset).
If you are interested in last partial batch handling, read [this section](https://www.tensorflow.org/tutorials/distribute/input#partial_batches).

<br />

<br />

<br />

| Args ||
|---|---|
| `dataset` | [`tf.data.Dataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/data/Dataset) that will be sharded across all replicas using the rules stated above. |
| `options` | [`tf.distribute.InputOptions`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/InputOptions) used to control options on how this dataset is distributed. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A [`tf.distribute.DistributedDataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedDataset). ||

<br />

### `experimental_distribute_values_from_function`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/distribute/distribute_lib.py#L2036-L2113)

    experimental_distribute_values_from_function(
        value_fn
    )

Generates [`tf.distribute.DistributedValues`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues) from `value_fn`.

This function is to generate [`tf.distribute.DistributedValues`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues) to pass
into `run`, `reduce`, or other methods that take
distributed values when not using datasets.

<br />

<br />

<br />

| 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. |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A [`tf.distribute.DistributedValues`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues) containing a value for each replica. ||

<br />

#### 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`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/distribute/distribute_lib.py#L1900-L1918)

    experimental_local_results(
        value
    )

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

> [!NOTE]
> **Note:** This only returns values on the worker initiated by this client. When using a [`tf.distribute.Strategy`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/Strategy) like [`tf.distribute.experimental.MultiWorkerMirroredStrategy`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/experimental/MultiWorkerMirroredStrategy), each worker will be its own client, and this function will only return values computed on that worker.

<br />

<br />

<br />

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

<br />

<br />

<br />

<br />

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

<br />

### `gather`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/distribute/distribute_lib.py#L2115-L2220)

    gather(
        value, axis
    )

Gather `value` across replicas along `axis` to the current device.

Given a [`tf.distribute.DistributedValues`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues) or [`tf.Tensor`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/TPUStrategy), it is the first TPU host. For
multi-client [`tf.distribute.MultiWorkerMirroredStrategy`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/ReplicaContext#all_gather).

> [!NOTE]
> **Note:** For all strategies except [`tf.distribute.TPUStrategy`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/TPUStrategy), the input `value` on different replicas must have the same rank, and their shapes must be the same in all dimensions except the `axis`-th dimension. In other words, their shapes cannot be different in a dimension `d` where `d` does not equal to the `axis` argument. For example, given a [`tf.distribute.DistributedValues`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues) with component tensors of shape `(1, 2, 3)` and `(1, 3, 3)` on two replicas, you can call `gather(..., axis=1, ...)` on it, but not `gather(..., axis=0, ...)` or `gather(..., axis=2, ...)`. However, for [`tf.distribute.TPUStrategy.gather`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/TPUStrategy#gather), all tensors must have exactly the same rank and same shape.

> [!NOTE]
> **Note:** Given a [`tf.distribute.DistributedValues`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues) `value`, its component tensors must have a non-zero rank. Otherwise, consider using [`tf.expand_dims`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/expand_dims) before gathering them.

    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)>

<br />

<br />

<br />

| Args ||
|---|---|
| `value` | a [`tf.distribute.DistributedValues`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues) instance, e.g. returned by [`Strategy.run`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/MirroredStrategy#run), to be combined into a single tensor. It can also be a regular tensor when used with [`tf.distribute.OneDeviceStrategy`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/OneDeviceStrategy) or the default strategy. The tensors that constitute the DistributedValues can only be dense tensors with non-zero rank, NOT a [`tf.IndexedSlices`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/IndexedSlices). |
| `axis` | 0-D int32 Tensor. Dimension along which to gather. Must be in the range \[0, rank(value)). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `Tensor` that's the concatenation of `value` across replicas along `axis` dimension. ||

<br />

### `reduce`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/distribute/distribute_lib.py#L1681-L1875)

    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.

> [!NOTE]
> **Note:** The result is copied to the "current" device - which would typically be the CPU of the worker on which the program is running. For `TPUStrategy`, it is the first TPU host. For multi client `MultiWorkerMirroredStrategy`, this is CPU of each worker.

There are a number of different tf.distribute APIs for reducing values
across replicas:

- [`tf.distribute.ReplicaContext.all_reduce`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/ReplicaContext#all_reduce): This differs from [`Strategy.reduce`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/MirroredStrategy#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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/StrategyExtended#reduce_to) and [`tf.distribute.StrategyExtended.batch_reduce_to`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/StrategyExtended#batch_reduce_to): These APIs are more advanced versions of [`Strategy.reduce`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/MirroredStrategy#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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`.

<br />

<br />

<br />

| Args ||
|---|---|
| `reduce_op` | a [`tf.distribute.ReduceOp`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues) instance, e.g. returned by [`Strategy.run`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/MirroredStrategy#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). |

<br />

<br />

<br />

<br />

| Returns ||
|---|---|
| A `Tensor`. ||

<br />

### `run`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/distribute/distribute_lib.py#L1563-L1679)

    run(
        fn, args=(), kwargs=None, options=None
    )

Invokes `fn` on each replica, with the given arguments.

This method is the primary way to distribute your computation with a
tf.distribute object. It invokes `fn` on each replica. If `args` or `kwargs`
have [`tf.distribute.DistributedValues`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues), such as those produced by a
[`tf.distribute.DistributedDataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedDataset) from
[`tf.distribute.Strategy.experimental_distribute_dataset`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/Strategy#experimental_distribute_dataset) or
[`tf.distribute.Strategy.distribute_datasets_from_function`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues) that correspond to that
replica.

`fn` is invoked under a replica context. `fn` may call
[`tf.distribute.get_replica_context()`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/get_replica_context) to access members such as
`all_reduce`. Please see the module-level docstring of tf.distribute for the
concept of replica context.

All arguments in `args` or `kwargs` can be a nested structure of tensors,
e.g. a list of tensors, in which case `args` and `kwargs` will be passed to
the `fn` invoked on each replica. Or `args` or `kwargs` can be
[`tf.distribute.DistributedValues`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues) containing tensors or composite tensors,
i.e. [`tf.compat.v1.TensorInfo.CompositeTensor`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/compat/v1/TensorInfo/CompositeTensor), in which case each `fn` call
will get the component of a [`tf.distribute.DistributedValues`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues) corresponding
to its replica. Note that arbitrary Python values that are not of the types
above are not supported.

> [!IMPORTANT]
> **Important:** Depending on the implementation of [`tf.distribute.Strategy`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/Strategy) and whether eager execution is enabled, `fn` may be called one or more times. If `fn` is annotated with [`tf.function`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/function) or [`tf.distribute.Strategy.run`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/Strategy#run) is called inside a [`tf.function`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/function) (eager execution is disabled inside a [`tf.function`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/function) by default), `fn` is called once per replica to generate a Tensorflow graph, which will then be reused for execution with new inputs. Otherwise, if eager execution is enabled, `fn` will be called once per replica every step just like regular python code.

#### Example usage:

1. Constant tensor input.

       strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
       tensor_input = tf.constant(3.0)
       @tf.function
       def replica_fn(input):
         return input*2.0
       result = strategy.run(replica_fn, args=(tensor_input,))
       result
           PerReplica:{
             0: <tf.Tensor: shape=(), dtype=float32, numpy=6.0>,
             1: <tf.Tensor: shape=(), dtype=float32, numpy=6.0>
           }
           
2. DistributedValues input.

       strategy = tf.distribute.MirroredStrategy(["GPU:0", "GPU:1"])
       @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_fn2(input):
           return input*2
         return strategy.run(replica_fn2, args=(distributed_values,))
       result = run()
       result
           <tf.Tensor: shape=(), dtype=int32, numpy=4>
           
3. Use [`tf.distribute.ReplicaContext`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/ReplicaContext) to allreduce values.

       strategy = tf.distribute.MirroredStrategy(["gpu:0", "gpu:1"])
       @tf.function
       def run():
          def value_fn(value_context):
            return tf.constant(value_context.replica_id_in_sync_group)
          distributed_values = (
              strategy.experimental_distribute_values_from_function(
                  value_fn))
          def replica_fn(input):
            return tf.distribute.get_replica_context().all_reduce(
                "sum", input)
          return strategy.run(replica_fn, args=(distributed_values,))
       result = run()
       result
           PerReplica:{
             0: <tf.Tensor: shape=(), dtype=int32, numpy=1>,
             1: <tf.Tensor: shape=(), dtype=int32, numpy=1>
           }
           
<br />

<br />

<br />

| Args ||
|---|---|
| `fn` | The function to run on each replica. |
| `args` | Optional positional arguments to `fn`. Its element can be a tensor, a nested structure of tensors or a [`tf.distribute.DistributedValues`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues). |
| `kwargs` | Optional keyword arguments to `fn`. Its element can be a tensor, a nested structure of tensors or a [`tf.distribute.DistributedValues`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues). |
| `options` | An optional instance of [`tf.distribute.RunOptions`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/RunOptions) specifying the options to run `fn`. |

<br />

<br />

<br />

<br />

| 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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/distribute/DistributedValues), `Tensor` objects, or `Tensor`s (for example, if running on a single replica). ||

<br />

### `scope`

[View source](https://github.com/tensorflow/tensorflow/blob/v2.14.0/tensorflow/python/distribute/distribute_lib.py#L1229-L1321)

    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()`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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.

> [!NOTE]
> **Note:** Entering a scope does not automatically distribute a computation, except in the case of high level training framework like keras `model.fit`. If you're not using `model.fit`, you need to use `strategy.run` API to explicitly distribute that computation. See an example in the [custom training loop tutorial](https://www.tensorflow.org/tutorials/distribute/custom_training).

*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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/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()`](https://developers.generativeai.google/api/python/google/ai/generativelanguage/Model#__call__), tracing a [`tf.function`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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](https://www.tensorflow.org/tutorials/distribute/keras). 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.function`](https://www.tensorflow.org/versions/r2.14/api_docs/python/tf/function)s that represent your training step
  - Saving APIs such as [`tf.saved_model.save`](https://www.tensorflow.org/versions/r2.14/api_docs/python/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.

<br />

<br />

<br />

| Returns ||
|---|---|
| A context manager. ||

<br />