tf.distribute.cluster_resolver.KubernetesClusterResolver

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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.

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.

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.

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.

Methods

cluster_spec

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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

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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

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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 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_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.