text: #!/usr/bin/python
  #
  # Copyright 2018-2022 Polyaxon, Inc.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  from marshmallow import fields, validate
  
  import polyaxon_sdk
  
  from polyaxon.k8s.k8s_schemas import V1Container
  from polyaxon.polyflow.run.base import BaseRun
  from polyaxon.polyflow.run.kinds import V1RunKind
  from polyaxon.polyflow.run.kubeflow.clean_pod_policy import V1CleanPodPolicy
  from polyaxon.polyflow.run.kubeflow.replica import KFReplicaSchema
  from polyaxon.polyflow.run.kubeflow.scheduling_policy import SchedulingPolicySchema
  from polyaxon.polyflow.run.resources import V1RunResources
  from polyaxon.polyflow.run.utils import DestinationImageMixin
  from polyaxon.schemas.base import BaseCamelSchema, BaseConfig
  
  
  class PytorchJobSchema(BaseCamelSchema):
      kind = fields.Str(allow_none=True, validate=validate.Equal(V1RunKind.PYTORCHJOB))
      clean_pod_policy = fields.Str(
          allow_none=True, validate=validate.OneOf(V1CleanPodPolicy.allowable_values)
      )
      scheduling_policy = fields.Nested(SchedulingPolicySchema, allow_none=True)
      master = fields.Nested(KFReplicaSchema, allow_none=True)
      worker = fields.Nested(KFReplicaSchema, allow_none=True)
  
      @staticmethod
      def schema_config():
          return V1PytorchJob
  
  
  class V1PytorchJob(
      BaseConfig, BaseRun, DestinationImageMixin, polyaxon_sdk.V1PytorchJob
  ):
      """Kubeflow Pytorch-Job provides an interface to train distributed experiments with Pytorch.
  
      Args:
          kind: str, should be equal `pytorchjob`
          clean_pod_policy: str, one of [`All`, `Running`, `None`]
          scheduling_policy: [V1SchedulingPolicy](/docs/experimentation/distributed/scheduling-policy/), optional  # noqa
          master: [V1KFReplica](/docs/experimentation/distributed/kubeflow-replica/), optional
          worker: [V1KFReplica](/docs/experimentation/distributed/kubeflow-replica/), optional
  
      ## YAML usage
  
      ```yaml
      >>> run:
      >>>   kind: pytorchjob
      >>>   cleanPodPolicy:
      >>>   schedulingPolicy:
      >>>   master:
      >>>   worker:
      ```
  
      ## Python usage
  
      ```python
      >>> from polyaxon.polyflow import V1KFReplica, V1PytorchJob
      >>> from polyaxon.k8s import k8s_schemas
      >>> pytorch_job = V1PytorchJob(
      >>>     clean_pod_policy='All',
      >>>     master=V1KFReplica(...),
      >>>     worker=V1KFReplica(...),
      >>> )
      ```
  
      ## Fields
  
      ### kind
  
      The kind signals to the CLI, client, and other tools that this
      component's runtime is a pytorchjob.
  
      If you are using the python client to create the runtime,
      this field is not required and is set by default.
  
      ```yaml
      >>> run:
      >>>   kind: pytorchjob
      ```
  
      ### cleanPodPolicy
  
      Controls the deletion of pods when a job terminates.
      The policy can be one of the following values: [`All`, `Running`, `None`]
  
  
      ```yaml
      >>> run:
      >>>   kind: pytorchjob
      >>>   cleanPodPolicy: 'All'
      >>>  ...
      ```
  
      ### schedulingPolicy
  
      SchedulingPolicy encapsulates various scheduling policies of the distributed training
      job, for example `minAvailable` for gang-scheduling.
  
  
      ```yaml
      >>> run:
      >>>   kind: pytorchjob
      >>>   schedulingPolicy:
      >>>     ...
      >>>  ...
      ```
  
      ### master
  
      The master replica in the distributed PytorchJob
  
      ```yaml
      >>> run:
      >>>   kind: pytorchjob
      >>>   master:
      >>>     replicas: 1
      >>>     container:
      >>>       ...
      >>>  ...
      ```
  
      ### worker
  
      The workers do the actual work of training the model.
  
      ```yaml
      >>> run:
      >>>   kind: pytorchjob
      >>>   worker:
      >>>     replicas: 3
      >>>     container:
      >>>       ...
      >>>  ...
      ```
      """
  
      SCHEMA = PytorchJobSchema
      IDENTIFIER = V1RunKind.PYTORCHJOB
      REDUCED_ATTRIBUTES = ["cleanPodPolicy", "schedulingPolicy", "master", "worker"]
  
      def apply_image_destination(self, image: str):
          if self.master:
              self.master.container = self.master.container or V1Container()
              self.master.container.image = image
          if self.worker:
              self.worker.container = self.worker.container or V1Container()
              self.worker.container.image = image
  
      def get_resources(self):
          resources = V1RunResources()
          if self.master:
              resources += self.master.get_resources()
          if self.worker:
              resources += self.worker.get_resources()
          return resources
  
      def get_all_containers(self):
          containers = []
          if self.master:
              containers += self.master.get_all_containers()
          if self.worker:
              containers += self.worker.get_all_containers()
          return containers
  
      def get_all_connections(self):
          connections = []
          if self.master:
              connections += self.master.get_all_connections()
          if self.worker:
              connections += self.worker.get_all_connections()
          return connections
  
      def get_all_init(self):
          init = []
          if self.master:
              init += self.master.get_all_init()
          if self.worker:
              init += self.worker.get_all_init()
          return init
  