cellmap_flow.cli.yaml_cli
=========================

.. py:module:: cellmap_flow.cli.yaml_cli

.. autoapi-nested-parse::

   YAML-based CLI for running multiple models.
   Similar to cli_v2 but uses YAML configuration files for batch processing.

   This dynamically discovers ModelConfig subclasses just like cli_v2,
   making it easy to add new model types without modifying this file.



Attributes
----------

.. autoapisummary::

   cellmap_flow.cli.yaml_cli.logger


Functions
---------

.. autoapisummary::

   cellmap_flow.cli.yaml_cli.run_multiple
   cellmap_flow.cli.yaml_cli.main


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

.. py:data:: logger

.. py:function:: run_multiple(models: List[cellmap_flow.models.models_config.ModelConfig], dataset_path: str, charge_group: str, queue: str, wrap_raw: bool = True) -> None

   Submit multiple model inference jobs.

   :param models: List of ModelConfig instances to run
   :param dataset_path: Base path to the dataset
   :param charge_group: Billing/chargeback group
   :param queue: Job queue name


.. py:function:: main(config_path: str, log_level: str, list_types: bool, validate_only: bool)

   Run multiple model inference jobs from a YAML configuration file.

   The YAML file should have the following structure:

   
   data_path: /path/to/data
   charge_group: my_group
   queue: gpu_h100  # optional, defaults to gpu_h100
   json_data: /path/to/config.json  # optional
   models:
     my_model:
       type: dacapo
       run_name: my_run
       iteration: 100
     fly_model:
       type: fly
       checkpoint: /path/to/checkpoint.ts
       classes: [mito, er, nucleus]
       resolution: [4, 4, 4]

   The model keys (my_model, fly_model) become the model names.

   Model types are automatically discovered from ModelConfig subclasses.
   Use --list-types to see all available types.

   Examples:

   
       cellmap_flow_yaml config.yaml
       cellmap_flow_yaml config.yaml --log-level DEBUG
       cellmap_flow_yaml --list-types
       cellmap_flow_yaml config.yaml --validate-only