Update README.rst (flyteorg#269)

cookbook/integrations/kubernetes/kftensorflow/README.rst

@@ -1,6 +1,67 @@
-Executing Distributed Tensorflow training jobs on K8s
-==========================================================
+TF Operator
+===========
 
-.. NOTE::
+This plugin adds the capability of running distributed tensorflow training to Flyte using backend plugins, natively on
+Kubernetes. It leverages `TF Job <https://github.com/kubeflow/tf-operator>`_ Plugin from kubeflow.
 
-    Coming soon 🛠
+"""
+from dataclasses import dataclass
+from typing import Any, Callable, Dict, Optional
+
+from google.protobuf.json_format import MessageToDict
+
+from flytekit import PythonFunctionTask, Resources
+from flytekit.extend import SerializationSettings, TaskPlugins
+from flytekit.models import task as _task_model
+
+
+@dataclass
+class TfJob(object):
+    """
+    Configuration for an executable `TF Job <https://github.com/kubeflow/tf-operator>`_. Use this
+    to run distributed tensorflow training on k8s (with parameter server)
+    Args:
+        num_workers: integer determining the number of worker replicas spawned in the cluster for this job
+        (in addition to 1 master).
+        num_ps_replicas: Number of Parameter server replicas to use
+        num_chief_replicas: Number of chief replicas to use
+        per_replica_requests: [optional] lower-bound resources for each replica spawned for this job
+        (i.e. both for (main)master and workers).  Default is set by platform-level configuration.
+        per_replica_limits: [optional] upper-bound resources for each replica spawned for this job. If not specified
+        the scheduled resource may not have all the resources
+    """
+
+    num_workers: int
+    num_ps_replicas: int
+    num_chief_replicas: int
+    per_replica_requests: Optional[Resources] = None
+    per_replica_limits: Optional[Resources] = None
+
+
+class TensorflowFunctionTask(PythonFunctionTask[TfJob]):
+    """
+    Plugin that submits a TFJob (see https://github.com/kubeflow/tf-operator)
+        defined by the code within the _task_function to k8s cluster.
+    """
+
+    _TF_JOB_TASK_TYPE = "tensorflow"
+
+    def __init__(self, task_config: TfJob, task_function: Callable, **kwargs):
+        super().__init__(
+            task_type=self._TF_JOB_TASK_TYPE,
+            task_config=task_config,
+            task_function=task_function,
+            **{**kwargs, "requests": task_config.per_replica_requests, "limits": task_config.per_replica_limits}
+        )
+
+    def get_custom(self, settings: SerializationSettings) -> Dict[str, Any]:
+        job = _task_model.TensorFlowJob(
+            workers_count=self.task_config.num_workers,
+            ps_replicas_count=self.task_config.num_ps_replicas,
+            chief_replicas_count=self.task_config.num_chief_replicas,
+        )
+        return MessageToDict(job.to_flyte_idl())
+
+#%
+# Register the Tensorflow Plugin into the flytekit core plugin system
+TaskPlugins.register_pythontask_plugin(TfJob, TensorflowFunctionTask)