flip.nvflare.components.pt_model_persistor
==========================================

.. py:module:: flip.nvflare.components.pt_model_persistor


Classes
-------

.. autoapisummary::

   flip.nvflare.components.pt_model_persistor.InitialCheckpointPTModelPersistor


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

.. py:class:: InitialCheckpointPTModelPersistor(model=None, model_id: str = '', **kwargs)

   Bases: :py:obj:`nvflare.app_opt.pt.file_model_persistor.PTFileModelPersistor`


   Persistor that seeds the initial global model from a large backbone checkpoint
   staged **server-side**, so the checkpoint never has to be bundled into the job app
   (bundling a ~759 MiB file collapses NVFLARE's app-deploy to remote clients).

   The backbone filename is declared in the job's ``config.json`` under ``SERVER_CHECKPOINT``.
   At load time it is resolved in this order:

     1. ``<app>/custom/<checkpoint>`` — a checkpoint bundled in the app. Used by the local
        simulator (which copies the file into ``custom/``) and any legacy bundling.
     2. ``<SERVER_CHECKPOINT_ROOT>/<model_id>/<checkpoint>`` — the de-bundled checkpoint the
        FL API staged on the hub-local shared volume (production). Read straight from disk;
        the checkpoint is intentionally NOT shipped in the app bundle, so it never reaches
        the clients. Mirrors the Flower backend's ``/app/src`` shared mount and the eval
        ``EvaluationPTModelLocator``.

   The resolved checkpoint is loaded (``strict=False``) into the ``get_model()`` architecture
   so the round-0 global model that ScatterAndGather broadcasts carries the backbone **plus**
   freshly-initialised heads — a full state dict the clients can load. Clients therefore build
   only a bare architecture and receive all weights at round 0; they need no checkpoint file.

   When ``config.json`` declares no ``SERVER_CHECKPOINT`` (every other standard training job),
   this behaves exactly like the stock ``PTFileModelPersistor`` (initial weights from the model
   object), so it is a safe drop-in for the shared standard base app.


   .. py:attribute:: _model_id_arg
      :value: ''



   .. py:method:: _resolve_backbone(fl_ctx: nvflare.apis.fl_context.FLContext)

      Locate the declared backbone checkpoint, or ``None`` if none is declared/found.



   .. py:method:: load_model(fl_ctx: nvflare.apis.fl_context.FLContext) -> nvflare.app_common.abstract.model.ModelLearnable