cellmap_flow.dashboard.finetune_layers
======================================

.. py:module:: cellmap_flow.dashboard.finetune_layers

.. autoapi-nested-parse::

   What the dashboard shows of a finetune job's model as it trains.

   The job manager (finetune.job_manager) knows nothing of the viewer:
   it tells its listeners when a job's inference server is up and when an
   iteration finishes (FinetuneJobListener). The dashboard's listener,
   ``FinetuneLayerListener``, answers each event with the dashboard's two
   views of the model:

   - ``add_finetuned_layer()``: its viewer layer, served by the job's server,
     replacing the previous iteration's; and its job among the session's jobs;
   - ``register_finetuned_model()``: its FinetuneModelConfig among the
     session's models, so the pipeline builder offers it.

   ``follow_jobs(manager)`` has the listener told of a manager's jobs; the
   routes that put jobs into the manager (submit, and finding the jobs of an
   earlier dashboard) call it first.



Attributes
----------

.. autoapisummary::

   cellmap_flow.dashboard.finetune_layers.logger


Classes
-------

.. autoapisummary::

   cellmap_flow.dashboard.finetune_layers.FinetuneLayerListener


Functions
---------

.. autoapisummary::

   cellmap_flow.dashboard.finetune_layers.add_finetuned_layer
   cellmap_flow.dashboard.finetune_layers.register_finetuned_model
   cellmap_flow.dashboard.finetune_layers.follow_jobs


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

.. py:data:: logger

.. py:function:: add_finetuned_layer(job, model_name)

   Add (or replace) the layer of ``job``'s model, ``model_name``.

   The layer is the one every model gets (viewer.layers.prediction_layer),
   over the job's inference server, in red; the previous iteration's layer
   (``job.finetuned_model_name``) goes. The session's jobs get a job for
   the server in place of any earlier iteration's, so Submit and the
   pipeline builder see the model as running. Nothing is added before the
   server is up.


.. py:function:: register_finetuned_model(job, model_name)

   Put a FinetuneModelConfig for ``job``'s model ``model_name`` among the
   session's models, in place of any earlier iteration's, so the pipeline
   builder offers it with its parameters filled in.


.. py:class:: FinetuneLayerListener



   The dashboard's listener: on each event, the model's layer and its
   pipeline-builder model. Either failing is logged, and does not stop the
   other.


   .. py:method:: on_server_ready(job, url, model_name)

      The job's inference server is up at ``url``, serving ``model_name``.



   .. py:method:: on_iteration_complete(job, model_name)

      The job finished a training iteration and named its model ``model_name``.



.. py:function:: follow_jobs(manager)

   Have the dashboard's listener told of ``manager``'s jobs, once however
   often this is called (request threads call it concurrently).