content: # Copyright 2019 Google LLC. All Rights Reserved.
  #
  # 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
  #
  #     https://www.apache.org/licenses/LICENSE-2.0
  #
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  # 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.
  
  #  Copyright (c) ZenML GmbH 2022. All Rights Reserved.
  #
  #  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:
  #
  #       https://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.
  
  # Minor parts of the `prepare_or_run_pipeline()` method of this file are
  # inspired by the kubeflow dag runner implementation of tfx
  """Implementation of the Kubeflow orchestrator."""
  
  import os
  import types
  from typing import (
      TYPE_CHECKING,
      Any,
      Dict,
      List,
      Optional,
      Tuple,
      Type,
      cast,
  )
  from uuid import UUID
  
  import kfp
  import requests
  import urllib3
  from kfp import dsl
  from kfp.client import Client as KFPClient
  from kfp.compiler import Compiler
  from kfp_server_api.exceptions import ApiException
  from kubernetes import client as k8s_client
  from kubernetes import config as k8s_config
  
  from zenml.client import Client
  from zenml.config.resource_settings import ResourceSettings
  from zenml.constants import (
      METADATA_ORCHESTRATOR_URL,
  )
  from zenml.entrypoints import StepEntrypointConfiguration
  from zenml.enums import StackComponentType
  from zenml.environment import Environment
  from zenml.integrations.kubeflow.flavors.kubeflow_orchestrator_flavor import (
      KubeflowOrchestratorConfig,
      KubeflowOrchestratorSettings,
  )
  from zenml.io import fileio
  from zenml.logger import get_logger
  from zenml.metadata.metadata_types import MetadataType, Uri
  from zenml.orchestrators import ContainerizedOrchestrator, SubmissionResult
  from zenml.orchestrators.utils import get_orchestrator_run_name
  from zenml.stack import StackValidator
  from zenml.utils import io_utils, settings_utils
  
  if TYPE_CHECKING:
      from zenml.models import PipelineRunResponse, PipelineSnapshotResponse
      from zenml.stack import Stack
  
  
  logger = get_logger(__name__)
  
  KFP_POD_LABELS = {
      "add-pod-env": "true",
      "pipelines.kubeflow.org/pipeline-sdk-type": "zenml",
  }
  
  ENV_KFP_RUN_ID = "KFP_RUN_ID"
  KFP_ACCELERATOR_NODE_SELECTOR_CONSTRAINT_LABEL = "accelerator"
  
  
  class KubeClientKFPClient(kfp.Client):  # type: ignore[misc]
      """KFP client initialized from a Kubernetes client.
  
      This is a workaround for the fact that the native KFP client does not
      support initialization from an existing Kubernetes client.
      """
  
      def __init__(
          self, client: k8s_client.ApiClient, *args: Any, **kwargs: Any
      ) -> None:
          """Initializes the KFP client from a Kubernetes client.
  
          Args:
              client: pre-configured Kubernetes client.
              args: standard KFP client positional arguments.
              kwargs: standard KFP client keyword arguments.
          """
          self._k8s_client = client
          super().__init__(*args, **kwargs)
  
      def _load_config(self, *args: Any, **kwargs: Any) -> Any:
          """Loads the KFP configuration.
  
          Initializes the KFP configuration from the Kubernetes client.
  
          Args:
              args: standard KFP client positional arguments.
              kwargs: standard KFP client keyword arguments.
  
          Returns:
              The KFP configuration.
          """
          from kfp_server_api.configuration import Configuration
  
          kube_config = self._k8s_client.configuration
  
          host = (
              kube_config.host
              + "/"
              + self._KUBE_PROXY_PATH.format(kwargs.get("namespace", "kubeflow"))
          )
  
          config = Configuration(
              host=host,
              api_key=kube_config.api_key,
              api_key_prefix=kube_config.api_key_prefix,
              username=kube_config.username,
              password=kube_config.password,
              discard_unknown_keys=kube_config.discard_unknown_keys,
          )
  
          # Extra attributes not present in the Configuration constructor
          keys = ["ssl_ca_cert", "cert_file", "key_file", "verify_ssl"]
          for key in keys:
              if key in kube_config.__dict__:
                  setattr(config, key, getattr(kube_config, key))
  
          return config
  
  
  class KubeflowOrchestrator(ContainerizedOrchestrator):
      """Orchestrator responsible for running pipelines using Kubeflow."""
  
      _k8s_client: Optional[k8s_client.ApiClient] = None
  
      def _get_kfp_client(
          self,
          settings: KubeflowOrchestratorSettings,
      ) -> kfp.Client:
          """Creates a KFP client instance.
  
          Args:
              settings: Settings which can be used to
                  configure the client instance.
  
          Returns:
              A KFP client instance.
  
          Raises:
              RuntimeError: If the linked Kubernetes connector behaves
                  unexpectedly.
          """
          connector = self.get_connector()
          client_args = settings.client_args.copy()
  
          # The kube_context, host and namespace are stack component
          # configurations that refer to the Kubeflow deployment. We don't want
          # these overwritten on a run by run basis by user settings
          client_args["namespace"] = self.config.kubeflow_namespace
  
          if connector:
              client = connector.connect()
              if not isinstance(client, k8s_client.ApiClient):
                  raise RuntimeError(
                      f"Expected a k8s_client.ApiClient while trying to use the "
                      f"linked connector, but got {type(client)}."
                  )
              return KubeClientKFPClient(
                  client=client,
                  **client_args,
              )
  
          elif self.config.kubernetes_context:
              client_args["kube_context"] = self.config.kubernetes_context
  
          elif self.config.kubeflow_hostname:
              client_args["host"] = self.config.kubeflow_hostname
  
              # Handle username and password, ignore the case if one is passed and
              # not the other. Also do not attempt to get cookie if cookie is
              # already passed in client_args
              if settings.client_username and settings.client_password:
                  # If cookie is already set, then ignore
                  if "cookie" in client_args:
                      logger.warning(
                          "Cookie already set in `client_args`, ignoring "
                          "`client_username` and `client_password`..."
                      )
                  else:
                      session_cookie = self._get_session_cookie(
                          username=settings.client_username,
                          password=settings.client_password,
                      )
  
                      client_args["cookies"] = session_cookie
          return KFPClient(**client_args)
  
      @property
      def config(self) -> KubeflowOrchestratorConfig:
          """Returns the `KubeflowOrchestratorConfig` config.
  
          Returns:
              The configuration.
          """
          return cast(KubeflowOrchestratorConfig, self._config)
  
      def get_kubernetes_contexts(self) -> Tuple[List[str], Optional[str]]:
          """Get the list of configured Kubernetes contexts and the active context.
  
          Returns:
              A tuple containing the list of configured Kubernetes contexts and
              the active context.
          """
          try:
              contexts, active_context = k8s_config.list_kube_config_contexts()
          except k8s_config.config_exception.ConfigException:
              return [], None
  
          context_names = [c["name"] for c in contexts]
          active_context_name = active_context["name"]
          return context_names, active_context_name
  
      @property
      def settings_class(self) -> Type[KubeflowOrchestratorSettings]:
          """Settings class for the Kubeflow orchestrator.
  
          Returns:
              The settings class.
          """
          return KubeflowOrchestratorSettings
  
      @property
      def validator(self) -> Optional[StackValidator]:
          """Validates that the stack contains a container registry.
  
          Also check that requirements are met for local components.
  
          Returns:
              A `StackValidator` instance.
          """
          msg = f"'{self.name}' Kubeflow orchestrator error: "
  
          def _validate_kube_context(
              kubernetes_context: str,
          ) -> Tuple[bool, str]:
              contexts, active_context = self.get_kubernetes_contexts()
  
              if kubernetes_context and kubernetes_context not in contexts:
                  if not self.config.is_local:
                      return False, (
                          f"{msg}could not find a Kubernetes context named "
                          f"'{kubernetes_context}' in the local Kubernetes "
                          f"configuration. Please make sure that the Kubernetes "
                          f"cluster is running and that the kubeconfig file is "
                          f"configured correctly. To list all configured "
                          f"contexts, run:\n\n"
                          f"  `kubectl config get-contexts`\n"
                      )
              elif (
                  kubernetes_context
                  and active_context
                  and kubernetes_context != active_context
              ):
                  logger.warning(
                      f"{msg}the Kubernetes context '{kubernetes_context}' "  # nosec
                      f"configured for the Kubeflow orchestrator is not the "
                      f"same as the active context in the local Kubernetes "
                      f"configuration. If this is not deliberate, you should "
                      f"update the orchestrator's `kubernetes_context` field by "
                      f"running:\n\n"
                      f"  `zenml orchestrator update {self.name} "
                      f"--kubernetes_context={active_context}`\n"
                      f"To list all configured contexts, run:\n\n"
                      f"  `kubectl config get-contexts`\n"
                      f"To set the active context to be the same as the one "
                      f"configured in the Kubeflow orchestrator and silence "
                      f"this warning, run:\n\n"
                      f"  `kubectl config use-context "
                      f"{kubernetes_context}`\n"
                  )
  
              return True, ""
  
          def _validate_local_requirements(stack: "Stack") -> Tuple[bool, str]:
              container_registry = stack.container_registry
  
              # should not happen, because the stack validation takes care of
              # this, but just in case
              assert container_registry is not None
  
              kubernetes_context = self.config.kubernetes_context
  
              if not self.connector:
                  if (
                      not kubernetes_context
                      and not self.config.kubeflow_hostname
                  ):
                      return False, (
                          f"{msg}the Kubeflow orchestrator is incompletely "
                          "configured. For a multi-tenant Kubeflow deployment, "
                          "you must set the `kubeflow_hostname` attribute in the "
                          "orchestrator configuration. For a single-tenant "
                          "deployment, you must either set the "
                          "`kubernetes_context` attribute in the orchestrator "
                          "configuration to the name of the Kubernetes config "
                          "context pointing to the cluster where you would like "
                          "to run pipelines or link this stack component to a "
                          "Kubernetes cluster via a service connector (see the "
                          "'zenml orchestrator connect' CLI command)."
                      )
  
                  if kubernetes_context:
                      valid, err = _validate_kube_context(kubernetes_context)
                      if not valid:
                          return False, err
  
              silence_local_validations_msg = (
                  f"To silence this warning, set the "
                  f"`skip_local_validations` attribute to True in the "
                  f"orchestrator configuration by running:\n\n"
                  f"  'zenml orchestrator update {self.name} "
                  f"--skip_local_validations=True'\n"
              )
  
              if not self.config.is_local:
                  # if the orchestrator is not running in a local k3d cluster,
                  # we cannot have any other local components in our stack,
                  # because we cannot mount the local path into the container.
                  # This may result in problems when running the pipeline,
                  # because the local components will not be available inside the
                  # Kubeflow containers.
  
                  # go through all stack components and identify those that
                  # advertise a local path where they persist information that
                  # they need to be available when running pipelines.
                  for stack_comp in stack.components.values():
                      local_path = stack_comp.local_path
                      if not local_path:
                          continue
                      return False, (
                          f"{msg}the Kubeflow orchestrator is configured to run "
                          f"pipelines in a remote Kubernetes cluster but the "
                          f"'{stack_comp.name}' {stack_comp.type.value} is a "
                          "local stack component and will not be available in "
                          "the Kubeflow pipeline step.\n"
                          "Please ensure that you always use non-local "
                          f"stack components with a remote Kubeflow "
                          f"orchestrator, otherwise you may run into pipeline "
                          f"execution problems. You should use a flavor of "
                          f"{stack_comp.type.value} other than "
                          f"'{stack_comp.flavor}'.\n"
                          + silence_local_validations_msg
                      )
  
                  # if the orchestrator is remote, the container registry must
                  # also be remote.
                  if container_registry.config.is_local:
                      return False, (
                          f"{msg}the Kubeflow orchestrator is configured to run "
                          f"pipelines in a remote Kubernetes cluster, but the "
                          f"'{container_registry.name}' container registry URI "
                          f"'{container_registry.config.uri}' "
                          f"points to a local container registry. Please ensure "
                          f"that you always use non-local stack components with "
                          f"a remote Kubeflow orchestrator, otherwise you will "
                          f"run into problems. You should use a flavor of "
                          f"container registry other than "
                          f"'{container_registry.flavor}'.\n"
                          + silence_local_validations_msg
                      )
  
              return True, ""
  
          return StackValidator(
              required_components={
                  StackComponentType.CONTAINER_REGISTRY,
                  StackComponentType.IMAGE_BUILDER,
              },
              custom_validation_function=_validate_local_requirements,
          )
  
      @property
      def root_directory(self) -> str:
          """Path to the root directory for all files concerning this orchestrator.
  
          Returns:
              Path to the root directory.
          """
          return os.path.join(
              io_utils.get_global_config_directory(),
              "kubeflow",
              str(self.id),
          )
  
      @property
      def pipeline_directory(self) -> str:
          """Returns path to a directory in which the kubeflow pipeline files are stored.
  
          Returns:
              Path to the pipeline directory.
          """
          return os.path.join(self.root_directory, "pipelines")
  
      def _create_dynamic_component(
          self,
          image: str,
          command: List[str],
          arguments: List[str],
          component_name: str,
      ) -> dsl.PipelineTask:
          """Creates a dynamic container component for a Kubeflow pipeline.
  
          Args:
              image: The image to use for the component.
              command: The command to use for the component.
              arguments: The arguments to use for the component.
              component_name: The name of the component.
  
          Returns:
              The dynamic container component.
          """
  
          def dynamic_container_component() -> dsl.ContainerSpec:
              """Dynamic container component.
  
              Returns:
                  The dynamic container component.
              """
              return dsl.ContainerSpec(
                  image=image,
                  command=command,
                  args=arguments,
              )
  
          # Change the name of the function
          new_container_spec_func = types.FunctionType(
              dynamic_container_component.__code__,
              dynamic_container_component.__globals__,
              name=component_name,
              argdefs=dynamic_container_component.__defaults__,
              closure=dynamic_container_component.__closure__,
          )
          pipeline_task = dsl.container_component(new_container_spec_func)
  
          return pipeline_task
  
      def submit_pipeline(
          self,
          snapshot: "PipelineSnapshotResponse",
          stack: "Stack",
          base_environment: Dict[str, str],
          step_environments: Dict[str, Dict[str, str]],
          placeholder_run: Optional["PipelineRunResponse"] = None,
      ) -> Optional[SubmissionResult]:
          """Submits a pipeline to the orchestrator.
  
          This method should only submit the pipeline and not wait for it to
          complete. If the orchestrator is configured to wait for the pipeline run
          to complete, a function that waits for the pipeline run to complete can
          be passed as part of the submission result.
  
          Args:
              snapshot: The pipeline snapshot to submit.
              stack: The stack the pipeline will run on.
              base_environment: Base environment shared by all steps. This should
                  be set if your orchestrator for example runs one container that
                  is responsible for starting all the steps.
              step_environments: Environment variables to set when executing
                  specific steps.
              placeholder_run: An optional placeholder run for the snapshot.
  
          Raises:
              RuntimeError: If trying to run a pipeline in a notebook
                  environment.
  
          Returns:
              Optional submission result.
          """
          # First check whether the code running in a notebook
          if Environment.in_notebook():
              raise RuntimeError(
                  "The Kubeflow orchestrator cannot run pipelines in a notebook "
                  "environment. The reason is that it is non-trivial to create "
                  "a Docker image of a notebook. Please consider refactoring "
                  "your notebook cells into separate scripts in a Python module "
                  "and run the code outside of a notebook when using this "
                  "orchestrator."
              )
  
          assert stack.container_registry
  
          # Create a callable for future compilation into a dsl.Pipeline.
          orchestrator_run_name = get_orchestrator_run_name(
              pipeline_name=snapshot.pipeline_configuration.name
          ).replace("_", "-")
  
          def _create_dynamic_pipeline() -> Any:
              """Create a dynamic pipeline including each step.
  
              Returns:
                  pipeline_func
              """
              step_name_to_dynamic_component: Dict[str, Any] = {}
              node_selector_constraint: Optional[Tuple[str, str]] = None
  
              for step_name, step in snapshot.step_configurations.items():
                  image = self.get_image(
                      snapshot=snapshot,
                      step_name=step_name,
                  )
                  command = StepEntrypointConfiguration.get_entrypoint_command()
                  arguments = (
                      StepEntrypointConfiguration.get_entrypoint_arguments(
                          step_name=step_name,
                          snapshot_id=snapshot.id,
                      )
                  )
                  dynamic_component = self._create_dynamic_component(
                      image, command, arguments, step_name
                  )
                  step_settings = cast(
                      KubeflowOrchestratorSettings, self.get_settings(step)
                  )
                  pod_settings = step_settings.pod_settings
                  if pod_settings:
                      ignored_fields = pod_settings.model_fields_set - {
                          "node_selectors"
                      }
                      if ignored_fields:
                          logger.warning(
                              f"The following pod settings are not supported in "
                              f"Kubeflow with Kubeflow Pipelines 2.x and will be "
                              f"ignored: {list(ignored_fields)}."
                          )
  
                      # apply pod settings
                      if (
                          KFP_ACCELERATOR_NODE_SELECTOR_CONSTRAINT_LABEL
                          in pod_settings.node_selectors.keys()
                      ):
                          node_selector_constraint = (
                              KFP_ACCELERATOR_NODE_SELECTOR_CONSTRAINT_LABEL,
                              pod_settings.node_selectors[
                                  KFP_ACCELERATOR_NODE_SELECTOR_CONSTRAINT_LABEL
                              ],
                          )
  
                  step_name_to_dynamic_component[step_name] = dynamic_component
  
              @dsl.pipeline(  # type: ignore[misc]
                  display_name=orchestrator_run_name,
              )
              def dynamic_pipeline() -> None:
                  """Dynamic pipeline."""
                  # iterate through the components one by one
                  # (from step_name_to_dynamic_component)
                  for (
                      component_name,
                      component,
                  ) in step_name_to_dynamic_component.items():
                      step_environment = step_environments[component_name]
                      # for each component, check to see what other steps are
                      # upstream of it
                      step = snapshot.step_configurations[component_name]
                      upstream_step_components = [
                          step_name_to_dynamic_component[upstream_step_name]
                          for upstream_step_name in step.spec.upstream_steps
                      ]
                      task = (
                          component()
                          .set_display_name(
                              name=component_name,
                          )
                          .set_caching_options(enable_caching=False)
                          .set_env_variable(
                              name=ENV_KFP_RUN_ID,
                              value=dsl.PIPELINE_JOB_NAME_PLACEHOLDER,
                          )
                          .after(*upstream_step_components)
                      )
                      for key, value in step_environment.items():
                          task = task.set_env_variable(name=key, value=value)
  
                      self._configure_container_resources(
                          task,
                          step.config.resource_settings,
                          node_selector_constraint,
                      )
  
              return dynamic_pipeline
  
          # Get a filepath to use to save the finished yaml to
          fileio.makedirs(self.pipeline_directory)
          pipeline_file_path = os.path.join(
              self.pipeline_directory, f"{orchestrator_run_name}.yaml"
          )
  
          # write the argo pipeline yaml
          Compiler().compile(
              pipeline_func=_create_dynamic_pipeline(),
              package_path=pipeline_file_path,
              pipeline_name=orchestrator_run_name,
          )
  
          logger.info(
              "Writing Kubeflow workflow definition to `%s`.", pipeline_file_path
          )
  
          # using the kfp client uploads the pipeline to kubeflow pipelines and
          # runs it there
          return self._upload_and_run_pipeline(
              snapshot=snapshot,
              pipeline_file_path=pipeline_file_path,
              run_name=orchestrator_run_name,
          )
  
      def _upload_and_run_pipeline(
          self,
          snapshot: "PipelineSnapshotResponse",
          pipeline_file_path: str,
          run_name: str,
      ) -> Optional[SubmissionResult]:
          """Tries to upload and run a KFP pipeline.
  
          Args:
              snapshot: The pipeline snapshot.
              pipeline_file_path: Path to the pipeline definition file.
              run_name: The Kubeflow run name.
  
          Raises:
              RuntimeError: If Kubeflow API returns an error.
  
          Returns:
              Optional submission result.
          """
          pipeline_name = snapshot.pipeline_configuration.name
          settings = cast(
              KubeflowOrchestratorSettings, self.get_settings(snapshot)
          )
          user_namespace = settings.user_namespace
  
          kubernetes_context = self.config.kubernetes_context
          try:
              if kubernetes_context:
                  logger.info(
                      "Running in kubernetes context '%s'.",
                      kubernetes_context,
                  )
              elif self.config.kubeflow_hostname:
                  logger.info(
                      "Running on Kubeflow deployment '%s'.",
                      self.config.kubeflow_hostname,
                  )
              elif self.connector:
                  logger.info(
                      "Running with Kubernetes credentials from connector '%s'.",
                      str(self.connector),
                  )
  
              # upload the pipeline to Kubeflow and start it
  
              client = self._get_kfp_client(settings=settings)
              if snapshot.schedule:
                  try:
                      experiment = client.get_experiment(
                          pipeline_name, namespace=user_namespace
                      )
                      logger.info(
                          "A recurring run has already been created with this "
                          "pipeline. Creating new recurring run now.."
                      )
                  except (ValueError, ApiException):
                      experiment = client.create_experiment(
                          pipeline_name, namespace=user_namespace
                      )
                      logger.info(
                          "Creating a new recurring run for pipeline '%s'.. ",
                          pipeline_name,
                      )
                  logger.info(
                      "You can see all recurring runs under the '%s' experiment.",
                      pipeline_name,
                  )
  
                  interval_seconds = (
                      snapshot.schedule.interval_second.seconds
                      if snapshot.schedule.interval_second
                      else None
                  )
                  result = client.create_recurring_run(
                      experiment_id=experiment.experiment_id,
                      job_name=run_name,
                      pipeline_package_path=pipeline_file_path,
                      enable_caching=False,
                      cron_expression=snapshot.schedule.cron_expression,
                      start_time=snapshot.schedule.utc_start_time,
                      end_time=snapshot.schedule.utc_end_time,
                      interval_second=interval_seconds,
                      no_catchup=not snapshot.schedule.catchup,
                  )
  
                  logger.info(
                      "Started recurring run with ID '%s'.",
                      result.recurring_run_id,
                  )
              else:
                  logger.info(
                      "No schedule detected. Creating a one-off pipeline run.."
                  )
                  try:
                      result = client.create_run_from_pipeline_package(
                          pipeline_file_path,
                          arguments={},
                          run_name=run_name,
                          enable_caching=False,
                          namespace=user_namespace,
                      )
                  except ApiException:
                      raise RuntimeError(
                          f"Failed to create {run_name} on kubeflow! "
                          "Please check stack component settings and "
                          "configuration!"
                      )
  
                  logger.info(
                      "Started one-off pipeline run with ID '%s'.", result.run_id
                  )
  
                  if settings.synchronous:
  
                      def _wait_for_completion() -> None:
                          client.wait_for_run_completion(
                              run_id=result.run_id, timeout=settings.timeout
                          )
  
                      return SubmissionResult(
                          wait_for_completion=_wait_for_completion
                      )
          except urllib3.exceptions.HTTPError as error:
              if kubernetes_context:
                  msg = (
                      f"Please make sure your kubernetes config is present and "
                      f"the '{kubernetes_context}' kubernetes context is "
                      "configured correctly."
                  )
              elif self.connector:
                  msg = (
                      f"Please check that the '{self.connector}' connector "
                      f"linked to this component is configured correctly with "
                      "valid credentials."
                  )
              else:
                  msg = ""
  
              logger.warning(
                  f"Failed to upload Kubeflow pipeline: {error}. {msg}",
              )
  
          return None
  
      def get_orchestrator_run_id(self) -> str:
          """Returns the active orchestrator run id.
  
          Raises:
              RuntimeError: If the environment variable specifying the run id
                  is not set.
  
          Returns:
              The orchestrator run id.
          """
          try:
              return os.environ[ENV_KFP_RUN_ID]
          except KeyError:
              raise RuntimeError(
                  "Unable to read run id from environment variable "
                  f"{ENV_KFP_RUN_ID}."
              )
  
      def _get_session_cookie(self, username: str, password: str) -> str:
          """Gets session cookie from username and password.
  
          Args:
              username: Username for kubeflow host.
              password: Password for kubeflow host.
  
          Raises:
              RuntimeError: If the cookie fetching failed.
  
          Returns:
              Cookie with the prefix `authsession=`.
          """
          if self.config.kubeflow_hostname is None:
              raise RuntimeError(
                  "You must configure the Kubeflow orchestrator "
                  "with the `kubeflow_hostname` parameter which usually ends "
                  "with `/pipeline` (e.g. `https://mykubeflow.com/pipeline`). "
                  "Please update the current kubeflow orchestrator with: "
                  f"`zenml orchestrator update {self.name} "
                  "--kubeflow_hostname=<MY_KUBEFLOW_HOST>`"
              )
  
          # Get cookie
          logger.info(
              f"Attempting to fetch session cookie from {self.config.kubeflow_hostname} "
              "with supplied username and password..."
          )
          session = requests.Session()
          try:
              response = session.get(self.config.kubeflow_hostname)
              response.raise_for_status()
          except (
              requests.exceptions.HTTPError,
              requests.exceptions.ConnectionError,
              requests.exceptions.Timeout,
  