MLCFlow: Simplifying MLPerf Automations

License Downloads

MLC core actions test MLC script automation features test MLPerf inference resnet50

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.

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.

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.

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, list, prune, mark-tmp
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

Project links

Data verified by PyPI on Aug 17, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

2 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for arjunsuresh from gravatar.com arjunsuresh Avatar for mlcommons from gravatar.com mlcommons

Credits

Author: MLCommons

GitHub Statistics

Data verified by PyPI on Aug 17, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License

Apache Software License (Apache License)

Requires

Python >=3.7

Tags

mlc mlcflow pypi package automation

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

mlcflow-1.3.6.tar.gz (197.2 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

mlcflow-1.3.6-py3-none-any.whl (195.8 kB view details)

Uploaded Python 3

File details

Details for the file mlcflow-1.3.6.tar.gz.

File metadata

  • Download URL: mlcflow-1.3.6.tar.gz
  • Upload date:
  • Size: 197.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.13

File hashes

Hashes for mlcflow-1.3.6.tar.gz
Algorithm Hash digest
SHA256 4c1d4f39f5cb56c4d3634ee6ed0c348f9e5b07b7ad5f089d298f2beaabec16e7
MD5 e51a18cc3bf7e7fcbf2eea7696af2bb2
BLAKE2b-256 79fdbeec6c8038b08ccaa40cab4f079019b6a94e3d66722d962c33dca300003c

See more details on using hashes here.

Provenance

The following attestation bundles were made for mlcflow-1.3.6.tar.gz:

Publisher: build_wheels.yml on mlcommons/mlcflow

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file mlcflow-1.3.6-py3-none-any.whl.

File metadata

  • Download URL: mlcflow-1.3.6-py3-none-any.whl
  • Upload date:
  • Size: 195.8 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/6.1.0 CPython/3.13.13

File hashes

Hashes for mlcflow-1.3.6-py3-none-any.whl
Algorithm Hash digest
SHA256 de70251f453389b6a867acffc130d52b98dce14a08617f88f11ad93e09ce2ab7
MD5 4dc0536f6456dd5b2c761764a0c1d44f
BLAKE2b-256 dd5a7b91216ecd6414037a1d8a0b2ed06f285ba7fc7f69fe79ab54cf16dbdf45

See more details on using hashes here.

Provenance

The following attestation bundles were made for mlcflow-1.3.6-py3-none-any.whl:

Publisher: build_wheels.yml on mlcommons/mlcflow

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

Pre-release

1.4.0a3

2 files

Pre-release

1.4.0a2

2 files

Pre-release

1.4.0a1

2 files

This release

1.3.6 This release

2 files

1.3.5

2 files

1.3.3

2 files

1.3.2

2 files

1.3.1

2 files

1.3.0

2 files

1.2.9

2 files

1.2.8

2 files

1.2.7

2 files

1.2.6

2 files

1.2.5

2 files

1.2.4

2 files

1.2.3

2 files

1.2.2

2 files

1.2.1

2 files

1.2.0

2 files

1.1.21

2 files

1.1.20

2 files

1.1.19

2 files

1.1.18

2 files

1.1.17

2 files

1.1.16

2 files

1.1.15

2 files

1.1.14

2 files

1.1.13

2 files

1.1.12

2 files

1.1.11

2 files

1.1.10

2 files

1.1.9

2 files

1.1.8

2 files

1.1.7

2 files

1.1.6

2 files

1.1.5

2 files

1.1.4

2 files

1.1.3

2 files

1.1.2

2 files

1.1.1

2 files

1.0.24

2 files

1.0.23

2 files

1.0.22

2 files

1.0.21

2 files

1.0.20

2 files

1.0.19

2 files

1.0.18

2 files

1.0.17

2 files

1.0.16

2 files

1.0.15

2 files

1.0.14

2 files

1.0.13

2 files

1.0.12

2 files

1.0.11

2 files

1.0.10

2 files

1.0.9

2 files

1.0.8

2 files

1.0.6

2 files

1.0.5

2 files

1.0.4

2 files

1.0.3

2 files

1.0.0

2 files

0.1.28

2 files

0.1.27

2 files

0.1.26

2 files

0.1.25

2 files

0.1.24

1 file

0.1.23

1 file

0.1.22

1 file

0.1.21

1 file

0.1.20

1 file

0.1.19

1 file

0.1.18

1 file

0.1.17

1 file

0.1.16

1 file

0.1.14

1 file

0.1.13

1 file

0.1.12

1 file

0.1.11

1 file

0.1.10

1 file

0.1.9

1 file

0.1.8

1 file

0.1.7

1 file

0.1.6

1 file

0.1.4

1 file

0.1.2

1 file

0.1.1

1 file

0.1.0

1 file

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page