Update API docs of ClusterResolver and all its implementations.

PiperOrigin-RevId: 259246199

tensorflow/python/distribute/cluster_resolver/__init__.py

@@ -12,7 +12,14 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Library Imports for Cluster Resolvers."""
+"""Library imports for ClusterResolvers.
+
+  This library contains all implementations of ClusterResolvers.
+  ClusterResolvers are a way of specifying cluster information for distributed
+  execution. Built on top of existing `ClusterSpec` framework, ClusterResolvers
+  are a way for TensorFlow to communicate with various cluster management
+  systems (e.g. GCE, AWS, etc...).
+"""
 
 from __future__ import absolute_import
 from __future__ import division

tensorflow/python/distribute/cluster_resolver/cluster_resolver.py

@@ -90,7 +90,7 @@ class ClusterResolver(object):
 
   @abc.abstractmethod
   def cluster_spec(self):
-    """Retrieve the current state of the cluster and returns a ClusterSpec.
+    """Retrieve the current state of the cluster and return a ClusterSpec.
 
     Returns:
       A ClusterSpec representing the state of the cluster at the moment this
@@ -288,7 +288,7 @@ class UnionClusterResolver(ClusterResolver):
   when cluster_spec is called. The details of the merge function is
   documented in the cluster_spec function.
 
-  For additional Cluster Resolver properties such as task type, task index,
+  For additional ClusterResolver properties such as task type, task index,
   rpc layer, environment, etc..., we will return the value from the first
   ClusterResolver in the union.
   """

tensorflow/python/distribute/cluster_resolver/gce_cluster_resolver.py

@@ -12,7 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ==============================================================================
-"""Implementation of Cluster Resolvers for GCE Instance Groups."""
+"""Implementation of ClusterResolvers for GCE instance groups."""
 
 from __future__ import absolute_import
 from __future__ import division
@@ -33,12 +33,12 @@ except ImportError:
 
 @tf_export('distribute.cluster_resolver.GCEClusterResolver')
 class GCEClusterResolver(ClusterResolver):
-  """Cluster Resolver for Google Compute Engine.
+  """ClusterResolver for Google Compute Engine.
 
   This is an implementation of cluster resolvers for the Google Compute Engine
   instance group platform. By specifying a project, zone, and instance group,
   this will retrieve the IP address of all the instances within the instance
-  group and return a Cluster Resolver object suitable for use for distributed
+  group and return a ClusterResolver object suitable for use for distributed
   TensorFlow.
   """
 

tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py

@@ -33,7 +33,7 @@ except ImportError:
 
 @tf_export('distribute.cluster_resolver.KubernetesClusterResolver')
 class KubernetesClusterResolver(ClusterResolver):
-  """Cluster Resolver for Kubernetes.
+  """ClusterResolver for Kubernetes.
 
   This is an implementation of cluster resolvers for Kubernetes. When given the
   the Kubernetes namespace and label selector for pods, we will retrieve the
@@ -48,7 +48,7 @@ class KubernetesClusterResolver(ClusterResolver):
                override_client=None):
     """Initializes a new KubernetesClusterResolver.
 
-    This initializes a new Kubernetes Cluster Resolver. The Cluster Resolver
+    This initializes a new Kubernetes ClusterResolver. The ClusterResolver
     will attempt to talk to the Kubernetes master to retrieve all the instances
     of pods matching a label selector.
 

tensorflow/python/distribute/cluster_resolver/slurm_cluster_resolver.py

@@ -30,13 +30,13 @@ from tensorflow.python.util.tf_export import tf_export
 
 @tf_export('distribute.cluster_resolver.SlurmClusterResolver')
 class SlurmClusterResolver(ClusterResolver):
-  """Cluster Resolver for system with Slurm workload manager.
+  """ClusterResolver for system with Slurm workload manager.
 
   This is an implementation of cluster resolvers for Slurm clusters. This allows
   the specification of jobs and task counts, number of tasks per node, number of
-  GPUs on each node and number of GPUs for each task, It retrieves system
+  GPUs on each node and number of GPUs for each task. It retrieves system
   attributes by Slurm environment variables, resolves allocated computing node
-  names, construct a cluster and return a Cluster Resolver object which an be
+  names, constructs a cluster and returns a ClusterResolver object which can be
   use for distributed TensorFlow.
   """
 
@@ -61,15 +61,15 @@ class SlurmClusterResolver(ClusterResolver):
     """Creates a new SlurmClusterResolver object.
 
     This takes in parameters and creates a SlurmClusterResolver object. It uses
-    those parameters to check which nodes will processes reside and resolves
+    those parameters to check which nodes will processes reside on and resolves
     their hostnames. With the number of the GPUs on each node and number of GPUs
-    for each task it offsets the port number for each processes and allocate
+    for each task it offsets the port number for each process and allocates
     GPUs to tasks by setting environment variables. The resolver currently
     supports homogeneous tasks and default Slurm process allocation.
 
     Args:
       jobs: Dictionary with job names as key and number of tasks in the job as
-        value
+        value.
       port_base: The first port number to start with for processes on a node.
       gpus_per_node: Number of GPUs available on each node.
       gpus_per_task: Number of GPUs to be used for each task.

tensorflow/python/distribute/cluster_resolver/tfconfig_cluster_resolver.py

@@ -50,7 +50,12 @@ def _get_value_in_tfconfig(key, default=None):
 
 @tf_export('distribute.cluster_resolver.TFConfigClusterResolver')
 class TFConfigClusterResolver(ClusterResolver):
-  """Implementation of a ClusterResolver which reads the TF_CONFIG EnvVar."""
+  """Implementation of a ClusterResolver which reads the TF_CONFIG EnvVar.
+
+  This is an implementation of cluster resolvers when using TF_CONFIG to set
+  information about the cluster. The cluster spec returned will be
+  initialized from the TF_CONFIG environment variable.
+  """
 
   def __init__(self,
                task_type=None,

tensorflow/python/distribute/cluster_resolver/tpu_cluster_resolver.py

@@ -94,7 +94,7 @@ class TPUClusterResolver(ClusterResolver):
 
     This works around an issue where the underlying HTTP connection sometimes
     times out when the script has been running for too long. Other methods in
-    this object calls this method to get a new API object whenever they need
+    this object call this method to get a new API object whenever they need
     to communicate with the Cloud API.
 
     Returns:
@@ -206,7 +206,7 @@ class TPUClusterResolver(ClusterResolver):
     for the IP addresses and ports of each Cloud TPU listed.
 
     Args:
-      tpu: A string corresponding to the TPU to use. If the string is the empty
+      tpu: A string corresponding to the TPU to use. If the string is an empty
         string, the string 'local', or a string that begins with 'grpc://' or
           '/bns', then it is assumed to not correspond with a Cloud TPU and will
           instead be passed as the session master and no ClusterSpec propagation