mlcflow 1.1.8

An automation interface tailored for CPU/GPU benchmarking

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. You can use this tool for any of your workflow automation tasks.

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. MLCFlow is a complete replacement for the CMind package with an entirely new implementation led by Arjun Suresh and Anandhu Sooraj, with support from the MLCFlow community. The script automation used in MLCFlow is inherited from the CM project but is now modified to support MLC and provides a simpler interface to the devlopers. Please see the documentation for more details.

On February 9, 2025, MLCFlow released its first stable version, 1.0.0.

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.

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Status

MLCFlow is now fully equipped for workflow development, with complete support for all previously used CM scripts in MLPerf inference automation. If you're interested in discussions, join the MLCommons Benchmark Infra Discord channel, and check out the latest progress in Issues.

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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 as specified below.

Target	Action
script	run, find/search, rm, mv, cp, add, test, docker-run, show, experiment, doc
cache	find/search, rm, show
repo	pull, search, rm, list, find/search , add

MLC started with a compatibility layer where by it supported MLCommons CM automations - Script, Cache and Experiment. Now, MLCFLow has just the Script Automation which is an extension of the Script Automation from CM but with a cleaner integration of Cache Automation and Docker and Test extensions. The old CM scripts are now updated with the latest MLCFlow scripts in the MLPerf Automations repository.

Architectural Diagram

classDiagram
    class Action {
        +access(options)
        +find_target_folder(target)
        +load_repos_and_meta()
        +load_repos()
        +conflicting_repo(repo_meta)
        +register_repo(repo_meta)
        +unregister_repo(repo_path)
        +add(i)
        +rm(i)
        +save_new_meta(i, item_id, item_name, target_name, item_path, repo)
        +update(i)
        +is_uid(name)
        +cp(run_args)
        +copy_item(source_path, destination_path)
        +search(i)
    }
    class RepoAction {
        +find(run_args)
        +github_url_to_user_repo_format(url)
        +pull_repo(repo_url, branch, checkout)
        +pull(run_args)
        +list(run_args)
        +rm(run_args)
    }
    class ScriptAction {
        +search(i)
        +rm(i)
        +dynamic_import_module(script_path)
        +call_script_module_function(function_name, run_args)
        +docker(run_args)
        +run(run_args)
        +test(run_args)
        +list(args)
    }
    class CacheAction {
        +search(i)
        +find(i)
        +rm(i)
        +show(run_args)
        +list(args)
    }
    class ExperimentAction {
        +show(args)
        +list(args)
    }
    class Index {
        +add(meta, folder_type, path, repo)
        +get_index(folder_type, uid)
        +update(meta, folder_type, path, repo)
        +rm(meta, folder_type, path)
        +build_index()
    }
    class Item {
        +meta
        +path
        +repo
        +_load_meta()
    }
    class Repo {
        +path
        +meta
        +_load_meta()
    }
    class Automation {
        +action_object
        +automation_type
        +meta
        +path
        +_load_meta()
        +search(i)
    }

    Action <|-- RepoAction
    Action <|-- ScriptAction
    Action <|-- CacheAction
    RepoAction o-- Repo
    ScriptAction o-- Automation
    CacheAction o-- Index
    ExperimentAction o-- Index
    CfgAction o-- Index
    Index o-- Repo
    Index o-- Item
    Item o-- Repo
    Automation o-- Action