View source on GitHub |
ClusterResolver for Kubernetes.
Inherits From: ClusterResolver
tf.distribute.cluster_resolver.KubernetesClusterResolver(
job_to_label_mapping=None,
tf_server_port=8470,
rpc_layer='grpc',
override_client=None
)
This is an implementation of cluster resolvers for Kubernetes. When given the the Kubernetes namespace and label selector for pods, we will retrieve the pod IP addresses of all running pods matching the selector, and return a ClusterSpec based on that information.
Usage example with tf.distribute.Strategy:
# On worker 0
cluster_resolver = KubernetesClusterResolver(
{"worker": ["job-name=worker-cluster-a", "job-name=worker-cluster-b"]})
cluster_resolver.task_type = "worker"
cluster_resolver.task_id = 0
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
cluster_resolver=cluster_resolver)
# On worker 1
cluster_resolver = KubernetesClusterResolver(
{"worker": ["job-name=worker-cluster-a", "job-name=worker-cluster-b"]})
cluster_resolver.task_type = "worker"
cluster_resolver.task_id = 1
strategy = tf.distribute.experimental.MultiWorkerMirroredStrategy(
cluster_resolver=cluster_resolver)
Raises | |
---|---|
ImportError
|
If the Kubernetes Python client is not installed and no
override_client is passed in.
|
RuntimeError
|
If autoresolve_task is not a boolean or a callable. |
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. |
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 info from Kubernetes.
We retrieve the information from the Kubernetes master every time this method is called.
Returns | |
---|---|
A ClusterSpec containing host information returned from Kubernetes. |
Raises | |
---|---|
RuntimeError
|
If any of the pods returned by the master is not in the
Running phase.
|
master
master(
task_type=None, task_id=None, rpc_layer=None
)
Returns the master address to use when creating a session.
You must have set the task_type and task_id object properties before
calling this function, or pass in the task_type
and task_id
parameters when using this function. If you do both, the function parameters
will override the object properties.
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. |
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. |