Introduction

What is the Collective Knowledge project (CK)

Developing complex computational systems for emerging workloads (ML, AI, Quantum, IoT) and moving them to production is a very tedious, ad-hoc and time consuming process due to continuously changing software, hardware, models, data sets and research techniques.

After struggling with these problems for many years, we started the Collective Knowledge project (CK) to decompose complex systems and research projects into reusable, portable, customizable and non-virtualized CK components with the unified automation actions, Python APIs, CLI and JSON meta description.

Our idea is to gradually abstract all existing artifacts (software, hardware, models, data sets, results) and use the DevOps methodology to connect such components together into functional solutions that can automatically adapt to evolving models, data sets and bare-metal platforms with the help of customizable program workflows, a list of all dependencies (models, data sets, frameworks), and a portable meta package manager.

CK is basically our intermediate language to connect researchers and practitioners to collaboratively design, benchmark, optimize and validate innovative computational systems. It then makes it possible to find the most efficient system configurations on a Pareto frontier (trading off speed, accuracy, energy, size and different costs) using an open repository of knowledge with live SOTA scoreboards and reproducible papers.

We hope that such approach will make it possible to better understand what is happening inside complex and "black box" computational systems, integrate them with production and legacy systems, use them inside Docker and Kubernetes, share them along with published papers, and apply the DevOps methodology in deep tech research and computational science. We also use CK to complement related reproducibility initiatives including MLPerf, PapersWithCode, ACM artifact review and badging and artifact evaluation.

See the real CK use cases from our partners and try our MLPerf automation demo on your platform. You can learn more about our project in this documentation and the following presentations and white papers: 2019, 2018, 2017, 2009.

Even though the CK technology is used in production for more than 5 years, it is still a proof-of-concept prototype requiring further improvements and standardization. Depending on the available resources, we plan to develop a new, backward-compatible and more user-friendly version - please get in touch if you are interested to know more!

cKnowledge platform concept

Glossary

CK framework

The CK framework is a low-level CLI-based SDK to share reusable CK components and R&D automation actions in a human-readable format with a unified CLI, Python API, and JSON meta descriptions.

CK portal

The CK portal is an open platform to share and interconnect reusable CK components (models, data sets, packages, scripts) from research projects in the common CK format required to enable portable, customizable and reproducible benchmarking pipelines.

CK component

A CK component is an abstraction of any existing artifact (code, model, data set, script, result) consisting of a CK module, CK data and a collection of native files and directories from a given artifact. See the example of a CK package component "package:compiler-llvm-8.0.0-universal" at GitHub and on cKnowledge.io platform

  • CK module provides a unified Python API with JSON input/output and a Command Line Interface for a given computational component.
  • CK data provides a unified JSON meta description of a given computational component and connects it with native files and directories of this component.
    • CK UID is a unique ID of 16 lowercase hexadecimal digits automatically assigned to all CK modules and data during creation. This is needed to keep track of all distributed components.
    • CK UOA is an abbreviation when one can use Unique ID Or Alias where alias is a user-friendly name of a given module and data.
    • CID is a combination of {module UOA}:{data UOA} to uniquely identify and find any CK component.
  • CK action describes all public automation functions from a given CK module applied to a given CK data
  • CK soft components automatically detect required software for a given computational system on a given platform (models, data sets, frameworks, libraries ...)
  • CK package components automatically install missing software for a given computational system on a given platform (models, data sets, frameworks, libraries ...)

CK repository

A CK repository is a directory with a collection of CK components. After such a repository is installed by CK, a user has a unified access to all new CK components inside this repository. Such repository receives the following badge on GitHub:

cKnowledge compatible badge

Reproducible benchmarking pipeline and autotuning workflow

The Reproducible benchmarking pipeline

The unified API and JSON meta of all CK components allow us to connect them into customizable benchmarking pipelines. It is also possible to expose all design and optimization parameters of all components (models, frameworks, compilers, run-time systems, hardware) in a unified way and enable complete system autotuning to find the most efficient solutions on a Pareto frontier in terms of speed, accuracy, energy and other costs also exposed via unified APIs. Furthermore, unified APIs allow one to keep track of all information passed between components to ensure reproducibility of results.

cKnowledge pipeline

See how CK was used to autotune ML models for MLPerf submission and for reproducible and Pareto-efficient ML/SW/HW co-design tournaments.

cBench

cBench is a wrapper around the low-level CK SDK to connect it with the CK portal while improving and simplifying the user experience during collaborative benchmarking and optimization of complex computational systems.

Live SOTA dashboards

Live SOTA dashboards are connected with cBench to aggregate information about the behavior of computational systems across diverse software, hardware, models and data sets similar to SETI@home. They are also used for our reproducibility initiatives to validate results from published papers at systems and ML conferences.

Reproduced research papers

Reproduced research papers are published on our portal if it was reproduced using the standard ACM/cTuning evaluaiton procedure with the unified Artifact Appendix and reproducibility checklist which we helped to prepare in collaboration with the ACM taskforce on reproducibility.

Portable CK solution

A Portable CK solution is a set of JSON files describing how to download, build, benchmark and use AI, ML, quantum, IoT and other emerging technologies across diverse hardware, software, models and data sets. It includes:

We need it to provide a common format for research code and results shared along with published papers during our reproducibility initiatives for systems and ML conferences.

CK solutions for MLPerf

CK portable solutions for MLPerf demonstrates how to use our CK solutions with cBench to automatically build, run and crowd-benchmark object detection based on SSD-Mobilenets, TensorFlow and COCO dataset from the latest MLPerf inference benchmark across a wide range of platforms from Raspberry Pi, Android phones and IoT devices to HPC servers with GPUs.

The meaning behind the "c"

The lowercase c (cKnowledge, cBench, cTuning) means "collective", "collaborative" or "crowd" based on our vision of collaborative and reproducible benchmarking and optimization of complex computational systems: 2009, 2017, 2018, 2019.

Why CK?

The CK project was heavily motivated by our painful experience validating innovative ideas from research papers at ML and systems conferences and moving them to production. We have noticed that while finding the code from research papers is not a problem anymore thanks to ArXiv, PapersWithCode and MLPerf, it is only a small tip of the iceberg. The major challenge afterwards is to figure out how to find and connect all compatible dependencies (code, data, models) for numerous and rapidly evolving heterogeneous systems with continuously changing interfaces and data formats, integrate them with complex applications, customize them and run them in the most efficient way.

Existing useful tools such as MLFlow, Kedro and Amazon SageMaker help to automate training and other high-level ML operations while Docker, Kubernetes and other container based tools can hide software chaos but there is a lack of an intermediate framework and format to bridge the growing gap between deep tech research and real computer systems, particularly when targeting resource constrained embedded devices and IoT!

All these problems motivated Grigori Fursin to start the Collective Knowledge Project and pursue his vision of the collaborative and reproducible development of efficient computational systems (AI, ML, quantum, IoT) with the help of a common research SDK, an open repository of knowledge and collaborative validation of research results from published papers (2009, 2014, 2017, 2019).

The idea is to help the community exchange all the basic blocks (CK components) needed to assemble portable workflows for complex systems, collaboratively benchmark them and compare their performance using live SOTA scoreboards.

One of the current practical applications of the CK technology is to enable portable MLSysOps, i.e. cross-platform MLOps with the automated deployment of ML models in production across diverse systems from IoT to data centers in the most efficient way.

We also work with the community to reproduce and compare SOTA results from AI, ML and systems research using our (CK solutions) with a common API (compile, run, benchmark, validate) in terms of speed, accuracy, latency, energy usage, memory consumption and costs.

Why open CK format?

We have been working on a common format for collaborative benchmarking and optimization since 2009. In 2015 we prototyped the new, open and human-readable CK format to share computational components and workflows. It turned out to be flexible and customizable enough to support many [real use cases] from our academic and industrial partners. For example, the authors of 15+ research papers from different systems and ML conferences used CK to share their research code, data and results.

CK concept is to gradually convert ad-hoc research projects into file-based databases of reusable components including code, data, models, pre-/post-processing scripts, experimental results, best research practices to reproduce results, and auto-generated papers with unified Python APIs, CLI-based automation actions and JSON meta information.

CK also features plugins to automatically detect different software, models and data sets on a user machine and install the missing ones while supporting different operating systems (Linux, Windows, MacOS, Android) and heterogeneous hardware from all major vendors including Intel, Arm, Nvidia and AMD.

Such approach allows researchers and practitioners to create and share flexible APIs with JSON input/output for different AI/ML frameworks, libraries, compilers, models and data sets, and connect them together into portable and unified workflows instead of hardwired scripts and data formats.

Our goals and plans

Even though the CK technology is stable and used in production, there is still a lot to be done and we are only at the beginning of this long-term project.

We continue improving cBench as a more user-friendly wrapper around CK, and we continue improving the open cKnowledge.io portal as a centralized place to aggregate, version, test and create all components and workflows necessary to design complex computational systems.

We are making a special effort to ensure that the CK technology is non-intrusive, i.e. that it complements, abstracts and interconnects all existing tools including MLFlow, MLPerf, Docker, Kubernetes, GitHub actions making them more system aware rather than replacing or rewriting them.

Our goal is to help researchers and practitioners to collaboratively validate innovative techniques, benchmark and optimize novel computational systems, and find the most efficient ones in terms of speed, accuracy, energy, size and different costs using a public repository of knowledge with live SOTA scoreboards and reproducible papers.

Based on your feedback we plan to add the following features to our platform in 2020: