Using Elyra for COVID-19 Analytics
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The document discusses Elyra, an open-source set of AI-focused extensions for JupyterLab designed to streamline the development and deployment of machine learning models, particularly in scenarios like COVID-19 analytics. Elyra enhances productivity through features like hybrid runtime support, visual pipeline editors, and Git integration, simplifying collaboration and resource management. It also highlights IBM's commitment to open-source contributions and innovation within the AI and data science community.
Using Elyra for COVID-19 Analytics
- 1. Elyra - Extending JupyterLab for AI Using Elyra for COVID-19 Analytics — Luciano Resende IBM - CODAIT
- 2. About me - Luciano Resende Open Source AI Platform Architect – IBM – CODAIT • Senior Technical Staff Member at IBM, contributing to open source for over 10 years • Currently contributing to : Jupyter Notebook ecosystem, Apache Bahir, Apache Toree, Apache Spark among other projects related to AI/ML platforms lresende@us.ibm.com https://www.linkedin.com/in/lresende @lresende1975 https://github.com/lresende
- 3. IBM Open Source Participation Learn Open Source @ IBM Program touches 78,000 IBMers annually Consume Virtually all IBM products contain some open source • 40,363 pkgs Per Year Contribute • >62K OS Certs per year • ~10K IBM commits per month Connect > 1000 active IBM Contributors Working in key OS projects
- 4. IBM Open Source Participation IBM generated open source innovation • 137 IBM Open Code projects w/1000+ Github projects • Projects graduates into full open governance: Node-Red, OpenWhisk, SystemML, Blockchain fabric among others • developer.ibm.com/code/open/code/ Community • IBM focused on 18 strategic communities • Drive open governance in “Centers of Gravity” • IBM Leaders drive key technologies and assure freedom of action The IBM OS Way is now open sourced • Training, Recognition, Tooling • Organization, Consuming, Contributing
- 5. Center for Open Source Data and AI Technologies 5 CODAIT aims to make AI solutions dramatically easier to create, deploy, and manage in the enterprise Relaunch of the Spark Technology Center (STC) to reflect expanded mission CODAIT http://codait.org codait (French) = coder/coded https://m.interglot.com/fr/en/codait
- 6. Agenda - Introduction to the COVID-19 scenario - Introduction to Elyra - Walkthrough the COVID- 19 analytic scenario exploring the Elyra features
- 8. IBM Developer / CODAIT / © 2020 IBM Corporation 8 Leveraging analytics to navigate unprecedented times - Getty Frederick Reiss IBM CODAIT Romeo Kienzler IBM CODAIT
- 9. Challenges implementing COVID-notebooks 9 - How to break apart tasks that are expensive to run - Data preparation - Once data is prepared, analytics can run multiple times on that same data - Data updates are frequently - When data is updated, how to ensure all tasks are executed on the right order - How to collaborate and share my artifacts
- 10. But what is Elyra ?
- 11. Elyra Elyra is a set of AI centric extensions for JupyterLab Elyra was officially announced as an open source project by IBM on April 29th. The name Elyra is a word play with one of the Jupyter moons “Elara” where we introduce the “y” from “Jupyter” to make it “Elyra”
- 12. JupyterLab JupyterLab is the next generation UI for the Jupyter Ecosystem. Bring all the previous improvements into a single unified platform plus more! Provides a modular, extensible architecture Retains backward compatibility with the old notebook we know and love
- 13. JupyterLab File Explorer Widgets / Rich Output Tabbed Workspaces Text Editor Console/Terminal
- 14. Elyra at GitHub https://github.com/elyra-ai/elyra Elyra Documentation https://elyra.readthedocs.io/en/latest/ Elyra Elyra is a set of AI centric extensions to JupyterLab. It aims to help data scientists, machine learning engineers and AI developer’s through the model development life cycle complexities.
- 15. ELYRA Hybrid Runtime Support Notebook Pipelines JupyterLab Extensions Hybrid runtime support It simplifies the task of running the notebooks interactively on cloud machines, improving productivity by leveraging the power of cloud-based resources Versioning using git Simplify tracking changes, enabling better sharing among teammates Elyra provides a visual editor for building Notebook- based AI pipelines, enabling the conversion of multiple notebooks into batch jobs or workflows. Notebook Pipelines editor Notebook as batch jobs Elyra extends the notebook UI to simplify the submission of notebooks as a batch job for model training Python script execution Exposes Python Scripts as first-class citizens allowing users to locally edit their scripts and execute them against local or cloud-based resources seamlessly. Fork me at: github.com/elyra-ai
- 17. Resources Elyra source code at GitHub https://github.com/elyra-ai/elyra Elyra Documentation https://elyra.readthedocs.io/en/latest/ Elyra announcement and demo video https://www.youtube.com/watch?v=PuGNijkV5PQ COVID-19 analytics scenario using Elyra by Fred Reiss https://www.youtube.com/watch?v=CbcgyzB8c4M&t STAR US & FORK US ON GITHUB
- 18. Backup Slides
- 19. Pipelines
- 20. AI / Deep Learning Workloads Resource intensive workloads Requires expensive hardware (GPU, TPU) Heterogeneous frameworks Long Running training jobs – Simple MNIST takes over one hour WITHOUT a decent GPU – Other non complex deep learning model training can easily take over a day WITH GPUs
- 21. Training/Deploying Models requires a lot of DevOPS Model Serving Monitoring Resource Management Configuration Hyperparameter Optimization Reproducibility
- 22. AI / Deep Learning Workloads Challenges • How to isolate the training environments to multiple jobs, based on different deep learning frameworks (and/or releases) can be submitted/trained on the same time. • Ability to allocate individual system level resources such as GPUs, TPUs, etc with different kernels for a period of time. • Ability to allocate and free up system level resources such as GPUs, TPUs, etc as they stop being used or when they are idle for a period of time.
- 23. AI / Deep Learning Workloads Source: https://github.com/Langhalsdino/Kubernetes-GPU-Guide Containers and Kubernetes Platform - Containers simplify management of complicated and heterogenous AI/Deep Learning infrastructure providing a required isolation layer to different pods running different Deep Learning frameworks - Containers provides a flexible way to deploy applications and are here to stay - Kubernetes enables easy management of containerized applications and resources with the benefit of Elasticity and Quality of Services
- 24. AI Platforms AI/Deep Learning Platforms aim to abstract the DevOPS tasks from the Data Scientist providing a consistent way to develop AI models independent of the toolkit/framework being used.
- 25. Kubeflow • ML Toolkit for Kubernetes • Open source and community driven • Support multiple ML Frameworks • End-to-end workflows that can be shared, scaled and deployed
- 26. Kubeflow Pipelines Kubeflow Pipelines is a platform for building and deploying portable, scalable machine learning (ML) workflows based on Docker containers. • End-to-end orchestration: enabling and simplifying the orchestration of machine learning pipelines. • Easy experimentation: making it easy for you to try numerous ideas and techniques and manage your various trials/experiments. • Easy re-use: enabling you to re-use components and pipelines to quickly create end-to-end solutions without having to rebuild each time.
- 27. Kubeflow Pipelines Two key takeaways : A Pipeline and a Pipeline Component A pipeline is a description of a machine learning (ML) workflow, including all of the workflow components and how they work together.
- 28. Kubeflow Pipelines A pipeline component is an implementation of a pipeline task. A component represents a step in the workflow.
- 29. Kubeflow Pipelines Each pipeline component is a container that contains a program to perform the task required for that particular step of your workflow.
- 30. Model Training Model Validation Model Deployment Source A (Batch) Data Ingestion/Pr eparation Source B (Stream) Data Ingestion/Pr eparation Source C (Fetch) Data Ingestion/Pr eparation Model Testing Features (Snapshot) Decompose Schedule/Run
- 34. Notebook as batch jobs Model training can take hours, if not days. Elyra extends the Notebook UI with a new “submit notebook” button that simplify the submission of a single notebook as a batch job. Submit Notebook
- 36. Jupyter Enterprise Gateway website https://Jupyter.org/enterprise_gateway/ Jupyter Enterprise Gateway source code at GitHub https://github.com/jupyter/enterprise_gateway Jupyter Enterprise Gateway Documentation http://jupyter-enterprise-gateway.readthedocs.io/en/latest/ Supported Kernels Supported Platforms Jupyter Enterprise Gateway Spectrum Conductor A lightweight, multi-tenant, scalable and secure gateway that enables Jupyter Notebooks to share resources across an Apache Spark or Kubernetes cluster for Enterprise/Cloud use cases + +
- 37. Jupyter Enterprise Gateway Features Optimized Resource Allocation – Utilize resources on all cluster nodes by running kernels as Spark applications in YARN Cluster Mode. – Pluggable architecture to enable support for additional Resource Managers Enhanced Security – End-to-End secure communications - Secure socket communications - Encrypted HTTP communication using SSL Multiuser support with user impersonation – Enhance security and sandboxing by enabling user impersonation when running kernels (using Kerberos). – Individual HDFS home folder for each notebook user. – Use the same user ID for notebook and batch jobs. Kernel Kernel Kernel Kernel Kernel Kernel Kernel 16 32 48 64 0 20 40 60 80 4 Nodes 8 Nodes 12 Nodes 16 Nodes xKernels(4GBHeap) Cluster Size (32GB Nodes) MAXIMUM NUMBER OF SIMULTANEOUS KERNELS
- 38. Enterprise Gateway & Kubernetes Supported Platforms Before Jupyter Enterprise Gateway … - Resources required for all kernels needs to be allocated during Notebook Server pod creation - Resources limited to what is physically available on the host node that runs all kernels and associated Spark drivers After Jupyter Enterprise Gateway … - Gateway pod very lightweight - Kernels in their own pod, isolation - Kernel pods built from community images: Spark-on-K8s, TensorFlow, Keras, etc. Before Enterprise Gateway After Enterprise Gateway
- 39. Bob Alice Jupyter Enterprise Gateway Bob Alice Container images defined in kernelspec Community image Kernel Spark on Kubernetes Kernel Jupyter Enterprise Gateway - Kubernetes
- 40. Bob Alice Jupyter Enterprise Gateway Bob Alice Container images defined in kernelspec JupyterHub will provision custom images containing Notebook + NB2KG extension JupyterLab Jupyter Notebook Community image Kernel Spark on Kubernetes Kernel Jupyter Enterprise Gateway - Kubernetes
- 42. Python Script execution • Create new Python script from the workspace launcher • Navigate trough the script via Table of Content outline • Execute the script against local or cloud- based resources Execute Script Select Environment
- 43. Git Integration
- 44. Git Integration Elyra provides integrated support for git repositories simplifying tracking changes, allowing rollback to working versions of the code, backups
- 45. Resources Elyra source code at GitHub https://github.com/elyra-ai/elyra Elyra Documentation https://elyra.readthedocs.io/en/latest/ Elyra announcement and demo video https://developer.ibm.com/technologies/artificial-intelligence/blogs/open-source-elyra-ai-toolkit-simplifies-data-model-development/ COVID-19 analytics scenario using Elyra by Fred Reiss https://www.youtube.com/watch?v=CbcgyzB8c4M&t STAR US & FORK US ON GITHUB
- 46. Thank you!