mlcflow 0.1.11

An automation interface for ML applications

MLCFlow: Simplifying MLPerf Automations

MLCFlow is a versatile CLI and Python interface developed by MLCommons in collaboration with a dedicated team of volunteers (see Contributors). It serves as a streamlined replacement for the CMind tool, designed to drive the automation workflows of MLPerf benchmarks more efficiently.

The concept behind CMind originated from Grigori Fursin, while the MLPerf Automations project was created by Grigori Fursin and Arjun Suresh, whose collective contributions laid the foundation for modernizing MLPerf benchmarking tools.

Key Features

Building upon the core idea of CMind—wrapping native scripts with Python wrappers and YAML metadata—MLCFlow focuses exclusively on key automation components: Scripts, along with its complementary modules: Cache, Docker, and Experiments. This targeted design simplifies both implementation and interface, enabling a more user-friendly experience.

Status

MLCFlow is currently a work in progress and not yet ready for production use. If you are interested in contributing to its initial development, please email [e-mail] to join the daily development meetings and see Issues for seeing the development progress.

Getting Started

For early contributors, please use the mlc branch of the MLPerf Automations repository while working with MLCFlow.

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MLC CLI Overview

The MLC Command-Line Interface (CLI) enables users to perform actions on specified targets using a simple syntax:

mlc <action> <target> [options]

Key Components:

• <action>: The operation to be performed.
• <target>: The object on which the action is executed.
• [options]: Additional parameters passed to the action.

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Supported Targets and Actions

1. Repo

• Actions related to repositories, such as cloning or updating.

2. Script

• Manage or execute automation scripts.

3. Cache

• Handle cached data, including cleanup or inspection.

Each target has its own set of specific actions to tailor automation workflows.

CM compatibility layer

MLC has a compatibility layer where by it supports MLCommons CM automations - Script, Cache and Experiment.

Architectural Diagram

classDiagram
    class Action {
        -repos_path : str
        -cfg : dict
        -repos : list
        +execute(args)
        +access(options)
        +asearch(i)
        +find_target_folder(target)
        +load_repos_and_meta()
        +load_repos()
    }
    class RepoAction {
        +github_url_to_user_repo_format(url)
        +pull(args)
        +list(args)
    }
    class ScriptAction {
        +run(args)
        +list(args)
    }
    class CacheAction {
        +show(args)
        +list(args)
    }
    class ExperimentAction {
        +show(args)
        +list(args)
    }
    class CfgAction {
        +load(args)
        +unload(args)
    }
    class Repo {
        -path : str
        -meta : dict
    }
    class Automation {
        -cmind : Action
        +execute(args)
    }
    class Index {
        +find()
    }

    Action <|-- RepoAction
    Action <|-- ScriptAction
    Action <|-- CacheAction
    Action <|-- ExperimentAction
    Action <|-- CfgAction
    Repo "1" *-- Action
    Automation "1" *-- Action

    class get_action {
        +actions : dict
        +get_action(target)
    }

    main --> get_action
    get_action --> RepoAction
    get_action --> ScriptAction
    get_action --> CacheAction
    get_action --> ExperimentAction
    get_action --> CfgAction