tf.contrib.cluster_resolver.KubernetesClusterResolver

Class KubernetesClusterResolver

Inherits From: ClusterResolver

Aliases:

• Class tf.contrib.cluster_resolver.KubernetesClusterResolver
• Class tf.contrib.cluster_resolver.python.training.KubernetesClusterResolver

Defined in tensorflow/python/distribute/cluster_resolver/kubernetes_cluster_resolver.py.

Cluster Resolver for Kubernetes.

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.

__init__

__init__(
    job_to_label_mapping=None,
    tf_server_port=8470,
    rpc_layer='grpc',
    override_client=None
)

Initializes a new KubernetesClusterResolver.

This initializes a new Kubernetes Cluster Resolver. The Cluster Resolver will attempt to talk to the Kubernetes master to retrieve all the instances of pods matching a label selector.

Args:

• job_to_label_mapping: A mapping of TensorFlow jobs to label selectors. This allows users to specify many TensorFlow jobs in one Cluster Resolver, and each job can have pods belong with different label selectors. For example, a sample mapping might be

  {'worker': ['job-name=worker-cluster-a', 'job-name=worker-cluster-b'],
   'ps': ['job-name=ps-1', 'job-name=ps-2']}
  

• tf_server_port: The port the TensorFlow server is listening on.

• rpc_layer: (Optional) The RPC layer TensorFlow should use to communicate between tasks in Kubernetes. Defaults to 'grpc'.

• override_client: The Kubernetes client (usually automatically retrieved using from kubernetes import client as k8sclient). If you pass this in, you are responsible for setting Kubernetes credentials manually.

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.

Properties

environment

Returns the current environment which TensorFlow is running in.

For users in the Cloud environment, the environment property is always an empty string, and Google users will not use this ClusterResolver for running on internal systems.

Methods

tf.contrib.cluster_resolver.KubernetesClusterResolver.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.

tf.contrib.cluster_resolver.KubernetesClusterResolver.master

master(
    task_type=None,
    task_index=None,
    rpc_layer=None
)

Returns the master address to use when creating a session.

You must have set the task_type and task_index object properties before calling this function, or pass in the task_type and task_index 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_index: (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.

tf.contrib.cluster_resolver.KubernetesClusterResolver.num_accelerators

num_accelerators(
    task_type=None,
    task_index=None,
    accelerator_type='GPU',
    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. If workers only has CPU cores available, then this should return 0. This method will query the master for this information if it is not otherwise known.

Optionally, we allow callers to specify the task_type, task_index, and rpc_layer, 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.

Args:

• task_type: (Optional) The type of the TensorFlow task of the machine we want to query.
• task_index: (Optional) The index of the TensorFlow task of the machine we want to query.
• accelerator_type: (Optional) The type of accelerator we are trying to query (defaults to 'GPU').
• config_proto: (Optional) Configuration for starting a new session to query how many accelerator cores it has.