An asynchronous multi-worker parameter server tf.distribute strategy.
tf.compat.v2.distribute.experimental.ParameterServerStrategy( cluster_resolver=None )
This strategy requires two jobs: workers and parameter servers. Variables and updates to those variables will be assigned to parameter servers and other operations are assigned to workers.
When each worker has more than one GPU, operations will be replicated on all GPUs. Even though operations may be replicated, variables are not and each worker shares a common view for which parameter server a variable is assigned to.
By default it uses
TFConfigClusterResolver to detect configurations for multi-worker training. This requires a 'TF_CONFIG' environment variable and the 'TF_CONFIG' must have a cluster spec.
This class assumes each worker is running the same code independently, but parameter servers are running a standard server. This means that while each worker will synchronously compute a single gradient update across all GPUs, updates between workers proceed asynchronously. Operations that occur only on the first replica (such as incrementing the global step), will occur on the first replica of every worker.
It is expected to call
call_for_each_replica(fn, ...) for any operations which potentially can be replicated across replicas (i.e. multiple GPUs) even if there is only CPU or one GPU. When defining the
fn, extra caution needs to be taken:
1) It is generally not recommended to open a device scope under the strategy's scope. A device scope (i.e. calling
tf.device) will be merged with or override the device for operations but will not change the device for variables.
2) It is also not recommended to open a colocation scope (i.e. calling
tf.compat.v1.colocate_with) under the strategy's scope. For colocating variables, use
strategy.extended.colocate_vars_with instead. Colocation of ops will possibly create device assignment conflicts.
Note: This strategy only works with the Estimator API. Pass an instance of this strategy to the
experimental_distributeargument when you create the
RunConfig. This instance of
RunConfigshould then be passed to the
Estimatorinstance on which
strategy = tf.distribute.experimental.ParameterServerStrategy() run_config = tf.estimator.RunConfig( experimental_distribute.train_distribute=strategy) estimator = tf.estimator.Estimator(config=run_config) tf.estimator.train_and_evaluate(estimator,...) <!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2"><h2 class="add-link">Args</h2></th></tr> <tr> <td> `cluster_resolver` </td> <td> Optional <a href="../../../../../tf/distribute/cluster_resolver/ClusterResolver"><code>tf.distribute.cluster_resolver.ClusterResolver</code></a> object. Defaults to a <a href="../../../../../tf/distribute/cluster_resolver/TFConfigClusterResolver"><code>tf.distribute.cluster_resolver.TFConfigClusterResolver</code></a>. </td> </tr> </table> <!-- Tabular view --> <table class="responsive fixed orange"> <colgroup><col width="214px"><col></colgroup> <tr><th colspan="2"><h2 class="add-link">Attributes</h2></th></tr> <tr> <td> `extended` </td> <td> <a href="../../../../../tf/distribute/StrategyExtended"><code>tf.distribute.StrategyExtended</code></a> with additional methods. </td> </tr><tr> <td> `num_replicas_in_sync` </td> <td> Returns number of replicas over which gradients are aggregated. </td> </tr> </table> ## Methods <h3 id="experimental_distribute_dataset"><code>experimental_distribute_dataset</code></h3> <a target="_blank" href="https://github.com/tensorflow/tensorflow/blob/v1.15.0/tensorflow/python/distribute/distribute_lib.py#L614-L678">View source</a> <pre class="devsite-click-to-copy prettyprint lang-py tfo-signature-link"> <code>experimental_distribute_dataset( dataset ) </code></pre> Distributes a tf.data.Dataset instance provided via `dataset`. The returned distributed dataset can be iterated over similar to how regular datasets can. NOTE: Currently, the user cannot add any more transformations to a distributed dataset. The following is an example: ```python strategy = tf.distribute.MirroredStrategy() # Create a dataset dataset = dataset_ops.Dataset.TFRecordDataset([ "/a/1.tfr", "/a/2.tfr", "/a/3.tfr", "/a/4.tfr"]) # Distribute that dataset dist_dataset = strategy.experimental_distribute_dataset(dataset) # Iterate over the distributed dataset for x in dist_dataset: # process dataset elements strategy.experimental_run_v2(train_step, args=(x,))
We will assume that the input dataset is batched by the global batch size. With this assumption, we will make a best effort to divide each batch across all the replicas (one or more workers).
In a multi-worker setting, we will first attempt to distribute the dataset by attempting to detect whether the dataset is being created out of ReaderDatasets (e.g. TFRecordDataset, TextLineDataset, etc.) and if so, attempting to shard the input files. Note that there has to be at least one input file per worker. If you have less than one input file per worker, we suggest that you should disable distributing your dataset using the method below.
If that attempt is unsuccessful (e.g. the dataset is created from a Dataset.range), we will shard the dataset evenly at the end by appending a
.shard operation to the end of the processing pipeline. This will cause the entire preprocessing pipeline for all the data to be run on every worker, and each worker will do redundant work. We will print a warning if this method of sharding is selected. In this case, consider using
You can disable dataset sharding across workers using the
auto_shard option in
Within each worker, we will also split the data among all the worker devices (if more than one a present), and this will happen even if multi-worker sharding is disabled using the method above.
If the above batch splitting and dataset sharding logic is undesirable, please use
experimental_distribute_datasets_from_function instead, which does not do any automatic splitting or sharding.
| || |
| A "distributed |
experimental_distribute_datasets_from_function( dataset_fn )
tf.data.Dataset instances created by calls to
dataset_fn will be called once for each worker in the strategy. Each replica on that worker will dequeue one batch of inputs from the local
Dataset (i.e. if a worker has two replicas, two batches will be dequeued from the
Dataset every step).
This method can be used for several purposes. For example, where
experimental_distribute_dataset is unable to shard the input files, this method might be used to manually shard the dataset (avoiding the slow fallback behavior in
experimental_distribute_dataset). In cases where the dataset is infinite, this sharding can be done by creating dataset replicas that differ only in their random seed.
experimental_distribute_dataset may also sometimes fail to split the batch across replicas on a worker. In that case, this method can be used where that limitation does not exist.
dataset_fn should take an
tf.distribute.InputContext instance where information about batching and input replication can be accessed:
def dataset_fn(input_context): batch_size = input_context.get_per_replica_batch_size(global_batch_size) d = tf.data.Dataset.from_tensors([[1.]]).repeat().batch(batch_size) return d.shard( input_context.num_input_pipelines, input_context.input_pipeline_id) inputs = strategy.experimental_distribute_datasets_from_function(dataset_fn) for batch in inputs: replica_results = strategy.experimental_run_v2(replica_fn, args=(batch,))
| || A function taking a |
| A "distributed |
experimental_local_results( value )
Returns the list of all local per-replica values contained in
Note: This only returns values on the worker initiated by this client. When using a
tf.distribute.experimental.MultiWorkerMirroredStrategy, each worker will be its own client, and this function will only return values computed on that worker.
| || A value returned by |
| A tuple of values contained in |
experimental_make_numpy_dataset( numpy_input )
tf.data.Dataset for input provided via a numpy array.
This avoids adding
numpy_input as a large constant in the graph, and copies the data to the machine or machines that will be processing the input.
Note that you will likely need to use
experimental_distribute_dataset with the returned dataset to further distribute it with the strategy.
numpy_input = np.ones(, dtype=np.float32) dataset = strategy.experimental_make_numpy_dataset(numpy_input) dist_dataset = strategy.experimental_distribute_dataset(dataset)
| || A nest of NumPy input arrays that will be converted into a dataset. Note that lists of Numpy arrays are stacked, as that is normal |
| A |
experimental_run_v2( fn, args=(), kwargs=None )
fn on each replica, with the given arguments.
Executes ops specified by
fn on each replica. If
kwargs have "per-replica" values, such as those produced by a "distributed
fn is executed on a particular replica, it will be executed with the component of those "per-replica" values that correspond to that replica.
fn may call
tf.distribute.get_replica_context() to access members such as
All arguments in
kwargs should either be nest of tensors or per-replica objects containing tensors or composite tensors.
| || The function to run. The output must be a |
| || (Optional) Positional arguments to |
| || (Optional) Keyword arguments to |
| Merged return value of |
reduce( reduce_op, value, axis )
value across replicas.
Given a per-replica value returned by
experimental_run_v2, 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. 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. By default,
reduce will just aggregate 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). More often you will want to aggregate across the global batch, which you can get by specifying the batch dimension as the
axis=0. In this case it would return a scalar
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
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
| || A |
| || A "per replica" value, e.g. returned by |
| || Specifies the dimension to reduce along within each replica's tensor. Should typically be set to the batch dimension, or |
| A |
Returns a context manager selecting this Strategy as current.
with strategy.scope(): code block, this thread will use a variable creator set by
strategy, and will enter its "cross-replica context".
|A context manager.|
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Code samples licensed under the Apache 2.0 License.