caikit.runtime.servicers.global_train_servicer
==============================================

.. py:module:: caikit.runtime.servicers.global_train_servicer


Attributes
----------

.. autoapisummary::

   caikit.runtime.servicers.global_train_servicer.log
   caikit.runtime.servicers.global_train_servicer.error
   caikit.runtime.servicers.global_train_servicer.NON_PRIMITIVE_TYPES


Classes
-------

.. autoapisummary::

   caikit.runtime.servicers.global_train_servicer.GlobalTrainServicer


Module Contents
---------------

.. py:data:: log

.. py:data:: error

.. py:data:: NON_PRIMITIVE_TYPES

.. py:class:: GlobalTrainServicer(training_service: caikit.runtime.service_factory.ServicePackage)

   Something something about the train servicer


   .. py:attribute:: _training_service


   .. py:attribute:: _model_manager
      :value: None



   .. py:attribute:: cdm


   .. py:attribute:: library


   .. py:property:: training_output_dir
      :type: str



   .. py:property:: save_with_id
      :type: str



   .. py:method:: Train(request: google.protobuf.message.Message, context: grpc.ServicerContext, *_, **__) -> caikit.interfaces.runtime.data_model.TrainingJob

      Global train RPC -- Mocks the invocation of a Caikit Library module.train()
      method for a loaded Caikit Library model

      Args:
          request (object): A deserialized RPC request message
          context (ServicerContext): Context object (contains request metadata, etc)

      Returns:
          caikit.interfaces.runtime.data_model.TrainingJob:
              A TrainingJob data model response object



   .. py:method:: run_training_job(request: google.protobuf.message.Message, module: Type[caikit.core.ModuleBase], training_output_dir: Optional[str] = None, *, context: Optional[grpc.ServicerContext] = None, wait: bool = False, **kwargs) -> caikit.interfaces.runtime.data_model.TrainingJob

      Builds the request dict for calling the train function asynchronously,
      then returns the thread id

      Args:
          request (ProtoMessageType): The message that stimulated this request
          module (Type[ModuleBase]): The module class to train
          training_output_dir (Optional[str]): The base directory where
              the trained model should be saved, falling back to the
              configured output dir if not given.

      Kwargs:
          context (Optional[ServicerContext]): The grpc context for the
              request if called from a grpc handler
          wait (bool): Whether or not to block until the training is complete

      Returns:
          training_job (TrainingJob): The job handle for the training with the
              job's ID and the model's name