cellmap_flow.cli.yaml_cli

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

logger

Functions

run_multiple(→ None)

Submit multiple model inference jobs.

main(config_path, log_level, list_types, validate_only)

Run multiple model inference jobs from a YAML configuration file.

Module Contents

cellmap_flow.cli.yaml_cli.logger
cellmap_flow.cli.yaml_cli.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.

Parameters:
  • models – List of ModelConfig instances to run

  • dataset_path – Base path to the dataset

  • charge_group – Billing/chargeback group

  • queue – Job queue name

cellmap_flow.cli.yaml_cli.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