View source on GitHub |
ClusterResolver for Google Compute Engine.
Inherits From: ClusterResolver
tf.distribute.cluster_resolver.GCEClusterResolver(
project,
zone,
instance_group,
port,
task_type='worker',
task_id=0,
rpc_layer='grpc',
credentials='default',
service=None
)
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.
Usage example with tf.distribute.Strategy:
# On worker 0
cluster_resolver = GCEClusterResolver("my-project", "us-west1",
"my-instance-group",
task_type="worker", task_id=0)
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
cluster_resolver=cluster_resolver)
# On worker 1
cluster_resolver = GCEClusterResolver("my-project", "us-west1",
"my-instance-group",
task_type="worker", task_id=1)
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
cluster_resolver=cluster_resolver)
Raises | |
---|---|
ImportError
|
If the googleapiclient is not installed. |
Attributes | |
---|---|
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. |
rpc_layer
|
|
task_id
|
Returns the task id this ClusterResolver indicates.
In TensorFlow distributed environment, each job may have an applicable task id, which is the index of the instance within its task type. This is useful when user needs to run specific code according to task index. For example,
Returns For more information, please see
|
task_type
|
Returns the task type this ClusterResolver indicates.
In TensorFlow distributed environment, each job may have an applicable task type. Valid task types in TensorFlow include 'chief': a worker that is designated with more responsibility, 'worker': a regular worker for training/evaluation, 'ps': a parameter server, or 'evaluator': an evaluator that evaluates the checkpoints for metrics. See Multi-worker configuration for more information about 'chief' and 'worker' task type, which are most commonly used. Having access to such information is useful when user needs to run specific code according to task types. For example,
Returns For more information, please see
|
Methods
cluster_spec
cluster_spec()
Returns a ClusterSpec object based on the latest instance group info.
This returns a ClusterSpec object for use based on information from the specified instance group. We will retrieve the information from the GCE APIs every time this method is called.
Returns | |
---|---|
A ClusterSpec containing host information retrieved from GCE. |
master
master(
task_type=None, task_id=None, rpc_layer=None
)
Retrieves the name or URL of the session master.
Args | |
---|---|
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. |
Returns | |
---|---|
The name or URL of the session master. |
Implementors of this function must take care in ensuring that the master returned is up-to-date at the time to calling this function. This usually means retrieving the master every time this function is invoked.
num_accelerators
num_accelerators(
task_type=None, task_id=None, config_proto=None
)
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 task to query the number of accelerators. This is to support heterogenous environments, where the number of accelerators cores per host is different.
Args | |
---|---|
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. |
Returns | |
---|---|
A map of accelerator types to number of cores. |