itwinai.cli

Command line interface for out Python application. You can call commands from the command line. Example

>>> $ itwinai --help

itwinai.cli.scalability_report(pattern: ~typing.Annotated[str, <typer.models.OptionInfo object at 0x7fe0b5723280>], plot_title: ~typing.Annotated[str | None, <typer.models.OptionInfo object at 0x7fe0b5721900>] | None = None, skip_id: ~typing.Annotated[int | None, <typer.models.OptionInfo object at 0x7fe0b5722d70>] | None = None, archive: ~typing.Annotated[str | None, <typer.models.OptionInfo object at 0x7fe0b57233d0>] | None = None)

Generate scalability report merging all CSVs containing epoch time records in sub-folders.

Example:

>>> itwinai scalability-report --pattern="^epoch.+\.csv$" --skip-id 0 \
>>>     --plot-title "Some title" --archive archive_name

itwinai.cli.exec_pipeline(config: ~pathlib.Annotated[~pathlib.Path, <typer.models.OptionInfo object at 0x7fe0b5723490>], pipe_key: ~typing.Annotated[str, <typer.models.OptionInfo object at 0x7fe0b5723520>] = 'pipeline', steps: ~typing.Annotated[str | None, <typer.models.OptionInfo object at 0x7fe0b57235b0>] | None = None, print_config: ~typing.Annotated[bool, <typer.models.OptionInfo object at 0x7fe0b5723640>] = False, overrides_list: ~typing.Annotated[~typing.List[str] | None, <typer.models.OptionInfo object at 0x7fe0b57236d0>] | None = None)

Execute a pipeline from configuration file. Allows dynamic override of fields.

itwinai.cli.mlflow_ui(path: str = <typer.models.OptionInfo object>)

Visualize Mlflow logs.

itwinai.cli.mlflow_server(path: str = <typer.models.OptionInfo object>, port: int = <typer.models.OptionInfo object>)

Spawn Mlflow server.

itwinai.cli.kill_mlflow_server(port: int = <typer.models.OptionInfo object>)

Kill Mlflow server.