|TensorFlow 1 version||View source on GitHub|
Simple implementation of ClusterResolver that accepts a ClusterSpec.
Compat aliases for migration
See Migration guide for more details.
tf.distribute.cluster_resolver.SimpleClusterResolver( cluster_spec, master='', task_type=None, task_id=None, environment='', num_accelerators=None, rpc_layer=None )
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.
Returns the ClusterSpec passed into the constructor.
master( task_type=None, task_id=None, rpc_layer=None )
Returns the master address to use when creating a session.
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.
The name or URL of the session master.
If a task_type and task_id is given, this will override the
string passed into the initialization function.
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, so a TensorFlow session will never be created, and thus all arguments are unused and we simply assume that the type of accelerator is a GPU and return the value in provided to us in the constructor.