tf.distribute.experimental.ParameterServerStrategy

TensorFlow 1 version

View source on GitHub

An asynchronous multi-worker parameter server tf.distribute strategy.

Inherits From: Strategy

tf.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.

For Example:

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,...)

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<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>





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<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/v2.0.0/tensorflow/python/distribute/distribute_lib.py#L610-L674">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 experimental_distribute_datasets_from_function instead.

You can disable dataset sharding across workers using the auto_shard option in tf.data.experimental.DistributeOptions.

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.

experimental_distribute_datasets_from_function

View source

experimental_distribute_datasets_from_function(
    dataset_fn
)

Distributes tf.data.Dataset instances created by calls to dataset_fn.

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.

The 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,))

experimental_local_results

View source

experimental_local_results(
    value
)

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

experimental_make_numpy_dataset

View source

experimental_make_numpy_dataset(
    numpy_input
)

Makes a 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.

Example:

numpy_input = np.ones([10], dtype=np.float32)
dataset = strategy.experimental_make_numpy_dataset(numpy_input)
dist_dataset = strategy.experimental_distribute_dataset(dataset)

experimental_run_v2

View source

experimental_run_v2(
    fn, args=(), kwargs=None
)

Run fn on each replica, with the given arguments.

Executes ops specified by fn on each replica. If args or kwargs have "per-replica" values, such as those produced by a "distributed Dataset", when 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_reduce.

All arguments in args or kwargs should either be nest of tensors or per-replica objects containing tensors or composite tensors.

reduce

View source

reduce(
    reduce_op, value, axis
)

Reduce 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, typically axis=0. In this case it would return a scalar 0+1+2+3+4+5+6+7.

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.

scope

View source

scope()

Returns a context manager selecting this Strategy as current.

Inside a with strategy.scope(): code block, this thread will use a variable creator set by strategy, and will enter its "cross-replica context".