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{% comment %} Copyright 2018-2021 Elyra Authors

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. {% endcomment %} -->

Elyra Component Library - 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)
  • 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

CLAIMED is a component library 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 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, KNative, Kubeflow, Airflow or plain docker. 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 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 AIX360/LIME highlights a poor deep learning covid classification model looking at bones only

Components of this library can be exported as:

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

Visually create pipelines from notebooks and run everywhere Visually create pipelines from notebooks and run them everywhere

Each notebook is following a similar format.

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

Export notebooks and files as runtime components for different engines 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

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.

Contributing to CLAIMED

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