cMeta Project Home

Welcome to the Common Meta Framework (cMeta, also known as cX).

cMeta is a small, portable framework for unifying, interconnecting, and reusing code, data, models, agents, and knowledge across projects, platforms, and time through a uniform interface. It enables collaborative research and experimentation for developing self-optimizing and self-adapting software and hardware that automatically identify the most efficient and cost-effective ways to execute AI, ML, and other complex workloads.

Created, architected and developed by Grigori Fursin — originator of the long-term vision, concept, architecture and successive prototypes behind cMeta.

Core features

cMeta represents each part of a workflow as a uniform, composable, content-addressed, reproducible artifact, reachable through a single interface:

One uniform interface

cm.access({'category', 'command'}) in Python and cx <category> <command> on the command line reach everything: run a program, fetch a model, prepare a dataset, build a toolchain, invoke an agent.

Composable automations

Workflows are assembled from small, reusable tasks that declare what they use, instead of hard-coded scripts.

Extensible & pluggable

New capabilities are added as self-contained artifacts (categories, tasks, tools) with optional Python hooks, so the framework grows by plugging in components rather than modifying the core.

Metadata & tags

Structured, machine-readable identity makes any component discoverable and reusable, by tags rather than by hard-coded paths.

Content-addressed caching & better reproducibility

Identical work is not repeated, and the full context of a run is captured to help reproduce it. Full determinism across heterogeneous environments is hard; cMeta improves reproducibility but does not yet fully solve it — this remains ongoing community R&D.

Virtualized portability

Toolchains, compilers, drivers and runtimes are detected, isolated and pinned, abstracting away OS and accelerator differences.

Unifying agents

AI agents operate the same discovery, composition and execution surface that humans do, so automations can be driven by people and by agents through one interface.

Quickstart

Install:

pip install cmeta
cmeta --version

Command line:

cx --help
cx repo list

Python:

from cmeta import CMeta

cm = CMeta()
r = cm.access({'category': 'repo', 'command': 'list'})
print(r)

Project information

Author & vision

cMeta is the latest in a line of reproducible-research and automation frameworks that Grigori Fursin has envisioned, architected and prototyped over more than 15 years: Collective Knowledge (CK) → Collective Mind (CM) → CMX → cMeta.

He developed and donated the CM / CM4MLOps and MLPerf automations to MLCommons, and pioneered community Artifact Evaluation and reproducibility initiatives for ACM/IEEE conferences and journals. cMeta is the foundation for his ongoing, long-term work on self-optimizing and reproducible AI systems and AI-driven extreme software/hardware co-design.

If cMeta is useful in your work, you may cite:

Grigori Fursin. Enabling more efficient and cost-effective AI/ML systems with Collective Mind, virtualized MLOps, MLPerf, Collective Knowledge Playground and reproducible optimization tournaments. arXiv:2406.16791. https://arxiv.org/abs/2406.16791

and the earlier foundational work:

Grigori Fursin. Collective knowledge: organizing research projects as a database of reusable components and portable workflows with common interfaces. Philosophical Transactions of the Royal Society A, 379(2197):20200211, 2021. https://doi.org/10.1098/rsta.2020.0211