|TensorFlow 1 version||View source on GitHub|
Abstract class for all implementations of ClusterResolvers.
Compat aliases for migration
See Migration guide for more details.
This defines the skeleton for all implementations of ClusterResolvers. ClusterResolvers are a way for TensorFlow to communicate with various cluster management systems (e.g. GCE, AWS, etc...) and gives TensorFlow necessary information to set up distributed training.
By letting TensorFlow communicate with these systems, we will be able to automatically discover and resolve IP addresses for various TensorFlow workers. This will eventually allow us to automatically recover from underlying machine failures and scale TensorFlow worker clusters up and down.
Note to Implementors of
subclass: In addition to these abstract methods, when task_type, task_id, and
rpc_layer attributes are applicable, you should also implement them either as
properties with getters or setters, or directly set the attributes
self._rpc_layer so the base class'
getters and setters are used. See
tf.distribute.clusterresolver.SimpleClusterResolver.init_ for an
In general, multi-client tf.distribute strategies such as
tf.distribute.experimental.MultiWorkerMirroredStrategy require task_type and
task_id properties to be available in the
ClusterResolver they are using. On
the other hand, these concepts are not applicable in single-