caikit.runtime.service_factory
==============================

.. py:module:: caikit.runtime.service_factory

.. autoapi-nested-parse::

   This module is responsible for creating service objects for the runtime to consume



Attributes
----------

.. autoapisummary::

   caikit.runtime.service_factory.log
   caikit.runtime.service_factory.error


Classes
-------

.. autoapisummary::

   caikit.runtime.service_factory.ServicePackage
   caikit.runtime.service_factory.ServicePackageFactory


Functions
---------

.. autoapisummary::

   caikit.runtime.service_factory.get_inference_request
   caikit.runtime.service_factory.get_train_request
   caikit.runtime.service_factory.get_train_params


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

.. py:data:: log

.. py:data:: error

.. py:class:: ServicePackage

   Container with references to:
   - A service Class
   - A service Descriptor
   - A grpc servicer registration function
   - A client stub
   - A client messages module


   .. py:attribute:: service
      :type:  Type[google.protobuf.service.Service]


   .. py:attribute:: descriptor
      :type:  google.protobuf.descriptor.ServiceDescriptor


   .. py:attribute:: registration_function
      :type:  Callable[[google.protobuf.service.Service, grpc.Server], None]


   .. py:attribute:: stub_class
      :type:  Type


   .. py:attribute:: messages
      :type:  types.ModuleType


   .. py:attribute:: caikit_rpcs
      :type:  Dict[str, caikit.runtime.service_generation.rpcs.CaikitRPCBase]


.. py:class:: ServicePackageFactory

   Factory responsible for yielding the correct concrete ServicePackage implementation


   .. py:attribute:: ServiceType


   .. py:method:: get_service_package(service_type: ServiceType, write_modules_file: bool = False) -> ServicePackage
      :classmethod:


      Public factory API. Returns a service package of the requested type, from the
      configured source.

      Args:
          service_type (ServicePackageFactory.ServiceType): The type of service to build,
              to match the servicer implementation that will handle it. e.g. the
              GlobalPredictServicer expects an "INFERENCE" service
          write_modules_file (bool): if set, write out a modules.json file to list the
              included modules in this service generation. See config to customize file name at
              runtime.service_generation.backwards_compatibility.current_modules_path

      Returns:
          ServicePackage: A container with properties referencing everything you need to bind a
              concrete Servicer implementation to a protobufs Service and grpc Server



   .. py:method:: _check_backwards_compatibility(caikit_config: aconfig.Config, clean_modules: Set[Type[caikit.core.ModuleBase]])
      :staticmethod:



   .. py:method:: _get_and_filter_modules(caikit_config: aconfig.Config, lib: str, write_modules_file: bool) -> Set[Type[caikit.core.ModuleBase]]
      :staticmethod:



.. py:function:: get_inference_request(task_or_module_class: Type[Union[caikit.core.ModuleBase, caikit.core.task.TaskBase]], input_streaming: bool = False, output_streaming: bool = False) -> Type[caikit.core.data_model.base.DataBase]

   Helper function to return the inference request DataModel for the Module or Task Class


.. py:function:: get_train_request(module_class: Type[caikit.core.ModuleBase]) -> Type[caikit.core.data_model.base.DataBase]

   Helper function to return the train request DataModel for the Module Class


.. py:function:: get_train_params(module_class: Type[caikit.core.ModuleBase]) -> Type[caikit.core.data_model.base.DataBase]

   Helper function to return the train parameters DataModel for the Module Class