CLAIMED - The Component Library for AI, Machine Learning, ETL, and Data Science

TL;DR

set of re-usable coarse-grained components (just a bunch of code)
think of tasks, not functions (e.g., read from a database, transform data, train model, deploy model, store result to cloud object storage)
write once, runs everywhere: Kubeflow, Apache Airflow, CLI, KNative, Docker, Kubernetes
orchestrate with anything: shell script, Kubeflow, Airflow, Argo, Tekton
persistence layer / queue agnostic: Cloud Object Storage, file systems, PVC, Kafka, MQTT
just use Python - no other skills required (no Kubeflow component YAML, maven, Java)
1st class citizen in JupyterLab and the Elyra Pipeline Editor (creating a low code / no code IDE for data science)
upstream repository to IBM Watson Studio Pipelines contributed components in IBM Cloud Pak for Data

You also might wanna have a look at our FAQ

CLAIMED is a operator component framework for artificial intelligence, machine learning, "extract, transform, load" processes, and data science. The goal is to enable low-code/no-code rapid prototyping style programming to seamlessly CI/CD into production. The open source CLAIMED component library provides ready-made components for various business domains, supports multiple computer languages, works on different data flow editors and command line tools, and runs on various execution engines including Kubernetes (Job), Knative (Pod), Kubeflow (KFP component) and on the CLI using docker or podman. In addition to the open source library, clients create their private operator libraries using in conjunction with the open source one. Core of CLAIMED is C3, the Claimed Component Compiler which takes source code in the form of jupyter notebooks or source files and compiles it to various target runtime environments, allowing to add additional targets as plugins. Currently, C3 supports python notebooks and python scripts as input and Kubernetes/OpenShift, Kubeflow Pipeline Components/RedHat OpenShift DataScience Pipelines, and plain docker/podmain (via CLAIMED CLI) C3 does the following steps:

extract meta information (name, description, interface, dependencies (requirements)) from source
create (docker) container with dependencies and source added
push container to registry
create kubernetes-job.yaml
create kubeflow-pipeline-component.yaml

To demonstrate its utility, we constructed a workflow composed exclusively of this library's components. To display the capabilities of this library, we made use of a publicly available Computed Tomography (CT) scan dataset [covidata]. We created a deep learning model, which is supposed to classify exams as either COVID-19 positive or negative. We built the pipeline with Elyra's Pipeline Visual Editor, with support for local, Airflow, and Kubeflow execution https://arxiv.org/abs/2103.03281.

Low Code / No Code pipeline creation tool for data science

Bring the latest and greatest libraries to the hands of everybody.

AIX360/LIME highlights a poor deep learning covid classification model looking at bones only

Components of this library can be exported as:

Kubeflow pipeline components
Apache Airflow components
Standalone graphical components for the Elyra pipeline editor
Standalone components to be run from the command line
Standalone components to be run as docker containers
Standalone components to be run as Kubernetes Service
Standalone components to be run as KNative Application or Job
Components to consume from or publish to Queue Managers like Kafka or MQTT
Components deployed to Kubernets wrapped into DAPR (as service or message consumer/producer)

Visually create pipelines from notebooks and run them everywhere

Each notebook is following a similar format.

The first cell contains a description of the component itself.
The second cell installs all dependencies using pip3.
The third cell imports all dependencies.
The fourth cell contains a list of dependencies, input parameters, and return values as Python comments
The fifth cell reads the input parameters from environment variables.

Export notebooks and files as runtime components for different engines

To learn more on how this library works in practice, please have a look at the following video

Achievements

🏆 IEEE OSS AWARD 2023

Kienzler, R., Khan, R., Nilmeier, J., Nesic, I., & Haddad, I. (IEEE OSS 2023). Claimed -- the open source framework for building coarse-grained operators for accelerated discovery in science. arXiv:2307.06824

Related work

Ploomber

Orchest

[covidata] Joseph Paul Cohen et al. COVID-19 Image Data Collection: Prospective Predictions Are the Future, arXiv:2006.11988, 2020

Getting Help

We welcome your questions, ideas, and feedback. Please create an issue or a discussion thread. Please see VULNERABILITIES.md for reporting vulnerabilities.

Contributing to CLAIMED

Interested in helping make CLAIMED better? We encourage you to take a look at our Contributing page.

License