collective-knowledge

A collection of all known Alfred3 workflows

Collective Knowledge workflow for Caffe to automate installation across diverse platforms and to collaboratively evaluate and optimize Caffe-based workloads across diverse hardware, software and data sets (compilers, libraries, tools, models, inputs):

Virtual Reality Software Library

Collective Knowledge components for TensorFlow (code, data sets, models, packages, workflows):

CK repository with components and automation actions to enable portable workflows across diverse platforms including Linux, Windows, MacOS and Android. It includes software detection plugins and meta packages (code, data sets, models, scripts, etc) with the possibility of multiple versions to co-exist in a user or system environment:

CK automation actions to let users implement portable, customizable and reusable program workflows for reproducible, collaborative and multi-objective benchmarking, optimization and SW/HW co-design:

Collective Knowledge extension with unified and customizable benchmarks (with extensible JSON meta information) to be easily integrated with customizable and portable Collective Knowledge workflows. You can easily compile and run these benchmarks using different compilers, environments, hardware and OS (Linux, MacOS, Windows, Android). More info:

Collective Knowledge repository with actions to unify the access to different predictive analytics engines (scipy, R, DNN) from software, command line and web-services via CK JSON API:

Integration of Caffe2 to Collective Knowledge workflow framework to provide unified CK JSON API for AI (customized builds across diverse libraries and hardware, unified AI API, collaborative experiments, performance optimization and model/data set tuning):

Collective Knowledge crowd-tuning extension to let users crowdsource their experiments (using portable Collective Knowledge workflows) such as performance benchmarking, auto tuning and machine learning across diverse platforms with Linux, Windows, MacOS and Android provided by volunteers. Demo of DNN crowd-benchmarking and crowd-tuning:

Collective Knowledge web extension to browse CK repositories, visualize interactive graphs and articles, render CK-based websites, implement simple web services with JSON API (for example to crowdsource experiments or unify access to DNN). Demos of interactive articles, graphs and crowdsourced experiments:

Collective Knowledge extension to automate Docker "build","run" and "push" functions and packge CK repositories for collaborative and reproducible research:

Crowdsourcing video experiments (such as collaborative benchmarking and optimization of DNN algorithms) using Collective Knowledge Framework across diverse Android devices provided by volunteers. Results are continuously aggregated in the open repository:

Standalone, thin and portable server to let users participate in experiment crowdsourcing using Windows devices via Collective Knowledge Framework:

Meta information about platforms participating in experiment crowdsourcing using Collective Knowledge Framework (OS,CPU,GPU,GPGPU,NN...):

Collective Knowledge packages to build development versions of compilers (LLVM, GCC, etc) and use them in customizable and portable CK research workflows:

Collective Knowledge extension to crowdsource bug detection in OpenCL compilers using CLSmith tool from Imperial College London

Public data sets and their properties in the Collective Knowledge Format with JSON API and JSON meta information to be easily pluggable to customizable and reproducible CK experimental workflows (such as collaborative program analysis and optimization):

Shared artifacts in the Collective Knowledge Format as a proof-of-concept to reproduce our recent Collective Mind- and Collective Knowledge-related papers

Public scenarios to crowdsource experiments (such as DNN crowd-benchmarking and crowd-tuning) using Collective Knowledge Framework across diverse mobile devices provided by volunteers. Results are continuously aggregated at the open repository of knowledge:

Collective Knowledge workflow for ARM's workload automation tool: an open framework for gathering and sharing knowledge about system design and optimization using real-world workloads.

The procedures and a workflow to prepare Student Cluster Competition submissions:

Open Ideas Collective - A Collective of Thinkers and Doers

Integration of the Lift project with the Collective Knowledge (CK) framework

Knowledge Commons research group's repository

Development version of CodeReefied portable CK workflows for image classification and object detection. Stable "live" versions are available at CodeReef portal:

Extra resources in the Collective Knowledge Format for ARM's Workload Automation Framework:

Some tests and demo programs in CK-format.

ARM's workloads in the universal Collective Knowledge Format with JSON API and JSON meta information to power Workload Automation Framework

Public results in the Collective Knowledge Format (JSON meta data) from collaborative optimization of computer systems. See live repository: