Type GCEClusterResolver
Namespace tensorflow.distribute.cluster_resolver
Parent ClusterResolver
Interfaces IGCEClusterResolver
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 ClusterResolver object suitable for use for distributed
TensorFlow. 
			
		
		
			Methods
Properties
Public static methods
GCEClusterResolver NewDyn(object project, object zone, object instance_group, object port, ImplicitContainer<T> task_type, ImplicitContainer<T> task_id, ImplicitContainer<T> rpc_layer, ImplicitContainer<T> credentials, object service)
Creates a new GCEClusterResolver object.  This takes in a few parameters and creates a GCEClusterResolver project. It
will then use these parameters to query the GCE API for the IP addresses of
each instance in the instance group. 
			
				
		
	Parameters
- 
							objectproject
- Name of the GCE project.
- 
							objectzone
- Zone of the GCE instance group.
- 
							objectinstance_group
- Name of the GCE instance group.
- 
							objectport
- Port of the listening TensorFlow server (default: 8470)
- 
							ImplicitContainer<T>task_type
- Name of the TensorFlow job this GCE instance group of VM instances belong to.
- 
							ImplicitContainer<T>task_id
- The task index for this particular VM, within the GCE instance group. In particular, every single instance should be assigned a unique ordinal index within an instance group manually so that they can be distinguished from each other.
- 
							ImplicitContainer<T>rpc_layer
- The RPC layer TensorFlow should use to communicate across instances.
- 
							ImplicitContainer<T>credentials
- GCE Credentials. If nothing is specified, this defaults to GoogleCredentials.get_application_default().
- 
							objectservice
- The GCE API object returned by the googleapiclient.discovery function. (Default: discovery.build('compute', 'v1')). If you specify a custom service object, then the credentials parameter will be ignored.