metrical pipeline

Purpose

• Run a series of commands in sequence
• Store common pipeline configurations in version control

Usage

metrical pipeline <CONFIG>

Description

A pipeline is a series of commands that are run in sequence. These commands are just modes that have been serialized into JSON and stored in a file. This allows you to store common workflows in version control and run them with a single command.

Here's an example of a pipeline configuration file:

{
    "commands": [
        {
            "init": {
                "input_data_path": "fixtures/data_as.bag",
                "output_json": "/tmp/dummy_output_config.json",
                "plex_path": null,
                "preset_device": [],
                "topic_to_model": []
            }
        },
        {
            "calibrate": {
                "cache_dir": "cached_data",
                "cache_filtered_data": false,
                "disable_filter": false,
                "enable_ore_inference": false,
                "input_data_path": "fixtures/data_as.bag",
                "is_interactive": false,
                "max_iterations": 200,
                "object_space_path": "fixtures/obj.json",
                "output_json": "results.json",
                "plex_path": "fixtures/plex.json",
                "preserve_input_constraints": false,
                "render_socket": null,
                "should_render": false,
                "stillness_threshold": 1,
                "topic_to_component": []
            }
        }
    ],
    "license": null,
    "report_path": null
}

As you can see, it's just the CLI arguments for each mode serialized into JSON! Pretty easy, huh? And no, you can't have a pipeline for your pipeline; pipeline is the only command that isn't serialized. Sorry, all you recursion fans.

Note that commands will be run in the order they are listed in the commands array. A failed command will stop the entire pipeline and return the error.

Arguments

<CONFIG>

The configuration file describing the pipeline.

Examples

1. Run a pipeline

metrical pipeline my_pipeline.json