Performs a union on underlying ClusterResolvers.
This class performs a union given two or more existing ClusterResolvers. It merges the underlying ClusterResolvers, and returns one unified ClusterSpec when cluster_spec is called. The details of the merge function is documented in the cluster_spec function.
金铨达配资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.
__init__( *args, **kwargs )
Initializes a UnionClusterResolver with other ClusterResolvers.
ClusterResolverobjects to be unionized.
**kwargs: rpc_layer - (Optional) Override value for the RPC layer used by TensorFlow. task_type - (Optional) Override value for the current task type. task_id - (Optional) Override value for the current task index.
TypeError: If any argument is not a subclass of
ValueError: If there are no arguments passed.
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.
金铨达配资Otherwise, if you are implementing a ClusterResolver that will only work in open-source TensorFlow, you do not need to implement this property.
金铨达配资Returns a union of all the ClusterSpecs from the ClusterResolvers.
A ClusterSpec containing host information merged from all the underlying ClusterResolvers.
KeyError: If there are conflicting keys detected when merging two or more dictionaries, this exception is raised.
Note: If there are multiple ClusterResolvers exposing ClusterSpecs with the same job name, we will merge the list/dict of workers.
If all金铨达配资 underlying ClusterSpecs expose the set of workers as lists, we will concatenate the lists of workers, starting with the list of workers from the first ClusterResolver passed into the constructor.
If any of the ClusterSpecs expose the set of workers as a dict, we will
treat all the sets of workers as dicts (even if they are returned as lists)
and will only merge them into a dict if there is no conflicting keys. If
there is a conflicting key, we will raise a
master( task_type=None, task_id=None, rpc_layer=None )
金铨达配资Returns the master address to use when creating a session.
This usually returns the master from the first ClusterResolver passed in, but you can override this by specifying the task_type and task_id.
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 protocol for the given cluster.
The name or URL of the session master.
num_accelerators( task_type=None, task_id=None, config_proto=None )
金铨达配资Returns the number of accelerator cores per worker.
This returns the number of accelerator cores (such as GPUs and TPUs) available per worker.
Optionally, we allow callers to specify the task_type, and task_id, for if they want to target a specific TensorFlow process to query the number of accelerators. This is to support heterogenous environments, where the number of accelerators cores per host is different.
task_type: (Optional) The type of the TensorFlow task of the machine we want to query.
task_id: (Optional) The index of the TensorFlow task of the machine we want to query.
config_proto: (Optional) Configuration for starting a new session to query how many accelerator cores it has.
A map of accelerator types to number of cores.