ccflow_celery.examples.fibonacci
================================

.. py:module:: ccflow_celery.examples.fibonacci

.. autoapi-nested-parse::

   Fibonacci example using ccflow-celery with local (eager) execution.

   Demonstrates:
   - Defining a CallableModel with __deps__ for dependency-graph discovery
   - Using CeleryGraphEvaluator in eager mode (no message broker required)
   - Graph-parallel dispatch of Fibonacci sub-problems

   Usage:
       python -m ccflow_celery.examples.fibonacci



Classes
-------

.. autoapisummary::

   ccflow_celery.examples.fibonacci.FibonacciModel


Functions
---------

.. autoapisummary::

   ccflow_celery.examples.fibonacci.main


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

.. py:class:: FibonacciModel(/, **data: Any)

   Bases: :py:obj:`ccflow.CallableModel`


   Compute Fibonacci numbers via ccflow dependency graphs.

   The graph evaluator calls __deps__ to discover the full dependency tree,
   then dispatches each node as a Celery task in topological order.

   Create a new model by parsing and validating input data from keyword arguments.

   Raises [`ValidationError`][pydantic_core.ValidationError] if the input data cannot be
   validated to form a valid model.

   `self` is explicitly positional-only to allow `self` as a field name.


   .. py:method:: __call__(context: ccflow.GenericContext[int]) -> ccflow.GenericResult[int]

      This method produces the result for the given context.

      Instead of passing the context, one can pass an object that pydantic will try to validate as the context.
      Additionally, if kwargs are passed instead of the context, it will use these to construct the context.



   .. py:method:: __deps__(context: ccflow.GenericContext[int])

      Overwrite this method to specify dependencies of this `CallableModel` that can then be used for parallelization
      of the implicit `CallableModel` graph. The 'call graph' of a `CallableModel` is implicitly defined by the calls
      made in the `__call__` function of a `CallableModel` to other `CallableModel`s. Since these dependencies can
      only be discovered at runtime, it is given as an option to the user that they specify a `CallableModel`s
      upstream dependencies in this function.



.. py:function:: main()