tf.distribute.cluster_resolver.SimpleClusterResolver

Simple implementation of ClusterResolver that accepts all attributes.

Inherits From: ClusterResolver

tf.distribute.cluster_resolver.SimpleClusterResolver(
    cluster_spec, master='', task_type=None, task_id=None,
    environment='', num_accelerators=None, rpc_layer=None
)

Please see the base class for documentation of arguments of its constructor.

It is useful if you want to specify some or all attributes.

Usage example with tf.distribute.Strategy:

  cluster = tf.train.ClusterSpec({"worker": ["worker0.example.com:2222",
                                             "worker1.example.com:2222"]})

  # On worker 0
  cluster_resolver = SimpleClusterResolver(cluster, task_type="worker",
                                           task_id=0,
                                           num_accelerators={"GPU": 8},
                                           rpc_layer="grpc")
  strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
      cluster_resolver=cluster_resolver)

  # On worker 1
  cluster_resolver = SimpleClusterResolver(cluster, task_type="worker",
                                           task_id=1,
                                           num_accelerators={"GPU": 8},
                                           rpc_layer="grpc")
  strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
      cluster_resolver=cluster_resolver)

environment Returns the current environment which TensorFlow is running in.

There are two possible return values, "google" (when TensorFlow is running in a Google-internal environment) or an empty string (when TensorFlow is running elsewhere).

If you are implementing a ClusterResolver that works in both the Google environment and the open-source world (for instance, a TPU ClusterResolver or similar), you will have to return the appropriate string depending on the environment, which you will have to detect.

Otherwise, if you are implementing a ClusterResolver that will only work in open-source TensorFlow, you do not need to implement this property.

rpc_layer

task_id Returns the task id this ClusterResolver indicates.

In TensorFlow distributed environment, each job may have an applicable task id, which is the index of the instance within its task type. This is useful when user needs to run specific code according to task index. For example,

cluster_spec = tf.train.ClusterSpec({
"ps": ["localhost:2222", "localhost:2223"],
"worker": ["localhost:2224", "localhost:2225", "localhost:2226"]
})

# SimpleClusterResolver is used here for illustration; other cluster
# resolvers may be used for other source of task type/id.
simple_resolver = SimpleClusterResolver(cluster_spec, task_type="worker",
task_id=0)

...

if cluster_resolver.task_type == 'worker' and cluster_resolver.task_id == 0:
# Perform something that's only applicable on 'worker' type, id 0. This
# block will run on this particular instance since we've specified this
# task to be a 'worker', id 0 in above cluster resolver.
else:
# Perform something that's only applicable on other ids. This block will
# not run on this particular instance.

Returns None if such information is not available or is not applicable in the current distributed environment, such as training with tf.distribute.cluster_resolver.TPUClusterResolver.

For more information, please see tf.distribute.cluster_resolver.ClusterResolver's class docstring.

task_type Returns the task type this ClusterResolver indicates.

In TensorFlow distributed environment, each job may have an applicable task type. Valid task types in TensorFlow include 'chief': a worker that is designated with more responsibility, 'worker': a regular worker for training/evaluation, 'ps': a parameter server, or 'evaluator': an evaluator that evaluates the checkpoints for metrics.

See Multi-worker configuration for more information about 'chief' and 'worker' task type, which are most commonly used.

Having access to such information is useful when user needs to run specific code according to task types. For example,

cluster_spec = tf.train.ClusterSpec({
"ps": ["localhost:2222", "localhost:2223"],
"worker": ["localhost:2224", "localhost:2225", "localhost:2226"]
})

# SimpleClusterResolver is used here for illustration; other cluster
# resolvers may be used for other source of task type/id.
simple_resolver = SimpleClusterResolver(cluster_spec, task_type="worker",
task_id=1)

...

if cluster_resolver.task_type == 'worker':
# Perform something that's only applicable on workers. This block
# will run on this particular instance since we've specified this task to
# be a worker in above cluster resolver.
elif cluster_resolver.task_type == 'ps':
# Perform something that's only applicable on parameter servers. This
# block will not run on this particular instance.

Returns None if such information is not available or is not applicable in the current distributed environment, such as training with tf.distribute.experimental.TPUStrategy.

For more information, please see tf.distribute.cluster_resolver.ClusterResolver's class doc.

Methods

cluster_spec

View source

cluster_spec()

Returns the ClusterSpec passed into the constructor.

master

View source

master(
    task_type=None, task_id=None, rpc_layer=None
)

Returns the master address to use when creating a session.

Args
task_type (Optional) The type of the TensorFlow task of the master.
task_id (Optional) The index of the TensorFlow task of the master.
rpc_layer (Optional) The RPC used by distributed TensorFlow.

Returns
The name or URL of the session master.

If a task_type and task_id is given, this will override the master string passed into the initialization function.

num_accelerators

View source

num_accelerators(
    task_type=None, task_id=None, config_proto=None
)

Returns the number of accelerator cores per worker.

The SimpleClusterResolver does not do automatic detection of accelerators, and thus all arguments are unused and we simply return the value provided in the constructor.

Args
task_type Unused.
task_id Unused.
config_proto Unused.