LF Deep Learning Foundation Technical Advisory Council Meeting

1. LF Deep Learning FoundationTechnical Advisory Council Meeting January 3, 2018

2. Recording of Calls • This is a reminder that we have decided to record TAC meetings and store them on the TAC Wiki.

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5. Agenda • Roll Call • Approval of Minutes • Project Updates • Acumos AI • Angel • Mission and Goals Statement • LF DL Interactive Landscape • Next Meeting Information • Open Discussion / Questions

6. TAC Member Directory *TAC Chairperson

7. Approval of Minutes • Draft minutes from the Sept 13, Oct 11, Oct 25, Nov 8, and Dec 6 meetings of the TAC were previously distributed to the TAC members • Proposed Resolution: • RESOLVED: That the minutes of the Sept 13, Oct 11, Oct 25, Nov 8, and Dec 6 meetings of the Technical Advisory Council of the LF Deep Learning Foundation are hereby approved

8. LFDL Overview • LF Deep Learning Foundation is an umbrella project of The Linux Foundation with the mission of supporting AI, ML and DL open source projects • LF DL currently has four projects and we are accepting contributions of additional projects A framework designed to help DL cloud service providers to build cluster cloud services using different DL frameworks. https://github.com/PaddlePaddle/edl Distributed training framework for TensorFlow, Keras, and PyTorch. https://github.com/uber/horovod Marketplace for ML models. https://github.com/acumos A high-performance distributed ML platform based on Parameter Server, running on YARN and Apache Spark. https://github.com/Angel-ML/angel

9. TAC Overview • The TAC is a committee within LF DL that is responsible for assisting in the coordination and communication of projects within LF DL. It is not a technical oversight body, all technical oversight is handled by the individual projects themselves according to their own governance • Since the March 2018 launch of LF DL, activity of the TAC has included: • approving a project lifecycle for projects of LF DL (incubation, graduation, archive stages); • reviewing and then approving contribution proposals for the Angel, EDL and Horovod projects; • open sourcing a landscape for open source AI projects; • exploring additional AI projects including Kubeflow, Pachyderm, ONNX, PMML, and so on, and • we are taking proposals for projects on LFLD github (https://github.com/LFDLFoundation/proposing-projects)

10. TAC Wiki • Visit the TAC Wiki for more information: (login to lists.deeplearning.org required): • https://lists.deeplearningfoundation.org/g/tac-general/wiki/home

11. Project Updates Acumos AI Project Angel Project

12. Acumos AI Project Update • Our Presenter: • John (Jack) Murray, jfm@research.att.com • https://wiki.acumos.org/display/AC

13. Acumos AI Project Update • First Community Release “Athena” Completed • https://wiki.acumos.org/ • https://docs.acumos.org/en/athena/ • https://marketplace.acumos.org/#/home

14. 8 Projects • 59 Repositories • 500K+ lines of code • 3K+ Commits (>170 LPC AVG) • 38 Committers • 69 Contributors • 78 Reviewers • 42 Epics, 176 US, 417 Tasks • 1350 Jira Items • 100+ Internal API Functions • 30+ External API’s • 40%+ Code Coverage • Platform Hardening • Platform Hardening • SSO, Personalized UI • Client Libraries (TensorFlow, ScikitLearn, H2O, R, Java) • Onboarding, Micro Services Generation • Enhanced Marketplace for AI/ML Models • Design Studio DAG Models Solutions • Model Connector, Runner, Probe • Federation • Cloud/Kubernetes Deployment • Dataset Management (Seed Code) • ML Workbench Tools (Seed Code) • One Click Platform Deployment • O&AM (Admin, Logging, Security) Key Stats Features Acumos AI Project Overview • Acumos released it first public release Athena on November 14th, 2018 • Maintenance release Dec 12th, 2018 to address initial defect list • What has the project focused on and accomplished since launch or last update? Boreas, JIRA and Defects

15. Key Stats Athena Athena Release AT&T • TSC, Architecture, Security, Common Services, DS, Licensing, CMLP, CDS/Federation Tech Mahindra • Release Manager, AcumosR, Acumos.org • Portal, Deployment, DS, Testing Amdocs • Product/Community Committee PTL Nokia • Training, Data Pipeline planning • Cloud Native/Kubernetes Orange • Onboarding, External Libraries Redhat • Kubernetes platform deployment • 8 Projects • 59 Repositories • 500K+ lines of code • 3K+ Commits • 38 Commiters • 69 Contributors • 78 Reviewers • 42 Epics, 176 US, 417 Tasks • 1350 Jira Items • 100+ Internal API Functional • 30+ External API’s • 40%+ Code Coverage Athena Contributors Boreas Release Amdocs • Product/Community Committee PTL AT&T • Licensing, Workbench Ericson • Licensing, Training and Data Pipeline • 5G Use Cases Huawei • 5G Use Cases Nokia • Training, Data Pipeline planning • 5G Use Cases NXP • Whitebox deployment Orange • Onboarding, External Libraries Redhat • Kubernetes model deployment and GPU support • Kubeflow Tech Mahindra • Release Manager • Portal, Deployment, DS, Testing

16. 2019 Key Goals • Acumos is targeting two release for 2019 • Release Boreas – May 2019 (Top 5) • 1 Training - Model Training Pipeline, onboarding improvement (Kubeflow) • 2 Licensing • 3 Data Pipeline – Training • 4 Model Evaluation and Validation – Model Evaluation and Test Harness • 5 Model Security – Validation and Citation • Release “C” – November 2019 • 1 Data Management and Tooling • 2 Data Catalog • 3 Model and Data Exchange • 4 Federated Training • 5 Enhanced Catalogs • What is the project looking for from the TAC and the community? • More Participation with Developers, Big issues- Model security, licensing, data policy

17. Boreas Priorities • 1 Training - Model Training Pipeline, onboarding improvement • 2 Data Pipeline - Training • 3 Model Evaluation and Validation – Model Evaluation and Test Harness • 4 Licensing • 5 Model Security - Security sub committee • 6 Model Deployment - K8S, Partner (openshift, whitebox), GPU • 7 Portal Architecture - UX/UI hardening, Public Federation checks, CMS removal • 8 OA&M - Performance/Optimization, Standard Logging (platform/models) • 9 Portal - Model Life Cycle Management- versioning notification, deletion/revoking, dashboard, composite public • 10 Model Builder - Jupyter, RCloud, RapidMiner, AutoML, Zeppelin, Design studio • 11 Data Management - Data stores, Data broker, Data Movement, Data catalog • 12 Design Studio/Model Interoperability - ONNX, PFA, etc. • 13 On-Boarding Model Type support - Caffe, MXNet, Paddle, PyTorch, etc. • 14 LF DL Projects Integration Tencent Angel, Baidu EDL, IBM FIDL • 15 Integration with 5G Use Cases • 16 OA&M- k8 tools for monitoring, metrics, backups, archival/purging, platform security • 17 Deploy - Serving Pipeline Training

18. Angel Project Update • Our Presenter: • Fitz Wang(王才华),fitzwang@Tencent.com • Acknowledgements: • Paynie Xiao, Lele Yu, Jeremy Jiang, Wenbin Wei • https://github.com/Angel-ML/angel

19. Angel Update - Agenda • Project summary • What the project focused on and accomplished • Key goals of Angel in 2019 • Help from TAC • Project statistics

20. Project Summary - Angel AutoML Serving Spark on Angel Angel PS Angel Native XXX on Angel A Flexible and Powerful Parameter Server for large-scale machine learning • PS: Angel provides a powerful parameter server • ML lib: Various of machine learning algorithms, specially for RS • SDK: Angel provide three levels of APIs for secondary development ML Core Tree Core Computing Graph Angel Math Library Level III Level I : PS + Math lib, for lower level development, that seeking for higher performance Level II: Level I + ML core, for platform development → XXX on Angel Level III: Level II + Out of the box algorithms → LR/FM/SVM/DAW/NFM/DFM/GBDT/LDA… Level II Level I

21. Ongoing focus areas and accomplishments • Computing graph: • Lightweight computing graph with automatic gradient calculation • JSON configuration for algorithm • New Optimizers: Adam, FTRL, Momentum, AdaGrad • Math library: optimize for sparse data calculation • Primitive types/Long key index/storage aware AutoML Serving Spark on Angel Angel PS Angel Native XXX on Angel ML Core Tree Core Computing Graph Angel Math Library

22. 2019 Plans AutoML Serving Spark on Angel Angel PS Angel Native XXX on Angel • AutoML: automatic hyperparameter tuning based on BO • Serving: a generic serving system for Angel (similar to TFS) • Kubernetes support: enable Angel run on cloud • Graph Learning Algorithms: LINE, GraphSAGE, etc. • Flink on Angel: (ongoing discussion) Support streaming on Angel ML Core Tree Core Computing Graph Angel Math Library Note: Graph Learning is based on Level I APIs, So it is not in the stack Kubernetes support

23. Requesting support from TAC • Seeking for cooperation in AutoML and Graph Learning • Achieve a generic model format, model compression

24. Project Statistics • Contributors: 30 (XXX % + XXX %) • Committers: 7 (100 % + 0 %) • Stars: 3851 • Commits: 1670 • Forks: 985 • Branches: 18 • PRs: 257 • Issues:248 • Releases: 10

25. Level III API (for algorithm user) AutoML Serving Spark on Angel Angel PS Angel Native XXX on Angel ML Core Tree Core Computing Graph Angel Math Library Level III Level II API (for platform development) Angel PS Level II ML Core Tree Core Computing Graph Angel Math Library Level I Level I API (for lower level development) Angel PS Angel Math Library

26. LF DL Mission Statement & Strategic Goals

27. LF DL Mission Build and support an open community of data scientists, researchers, and developers focused on AI, ML, and DL, and drive open source innovation in these domains by enabling collaboration and the creation of new opportunities for the community and their supporting organizations

29. A Neutral Environment to Accelerate Open Source AI/ML/DL Innovation • Establish a neutral environment that fosters collaboration on AI/ML/DL open source projects • Enable cross-pollination between AI/ML/DL open source projects in the drive for faster innovation cycles and a higher multiplier network effect • Support building a sustainable AI/ML/DL open source ecosystem and provide needed support to projects under the LF DL (legal, marketing, events, business development, developer relations, etc.)

30. Harmonize open source AI/ML/DL projects • Minimize and avoid fragmentation and redundancies among open source AI/ML/DL projects • Support efforts that focus on ensuring interoperability between the various components of the open source AI/ML/DL stack • Align vendors and providers of open source solutions on the larger AI/ML/DL ecosystem

31. Become the home of open source efforts for AI ethics and fairness • Advocate for AI ethic and fairness principles • Support open source AI/ML/DL projects in adopting and abiding by these principles • Support the development of tooling and libraries that address ethical and fairness concerns related to AI practices • Collaborate with academics and other participants engaged in research in this domain • Support efforts in the area of ML explainability leading to the development of open source methods and tools that will help scientists understand data sets, models’ predictions and uncover and correct for biases • ML privacy (TBD)

32. Data Lifecycle Management - Best Practices,Tools and Marketplace Open source data sets allow researchers and data scientists to improve models, increases transparency, and further validation of trained models • Encourage enterprises to publish their data under an open source license (CDLA) • Host open source data sets and make them accessible to the wider community of users • Support the creation of open source tools and mechanisms required to support hosting data Provide a marketplace for AI/ML/DL models: • Allow the creation, onboarding, enhancements, publication and deployment of models • Provide an economical approach to how the marketplace would operate and incentivize sharing and interchanging of models • Enable a federated marketplace that allows control on what models are visible to whom

33. Increase awareness on key open source AI/ML/DL projects and their dependencies • Provide a funding model to support projects critical in building AI/ML/DL open source stacks • Grow developer and user community of these key open source projects and their critical dependencies • Lower the barriers to participate in the development of AI/ML/DL open source projects • Provide guidelines, development best practices, design principles, training courses and other educational content to enable developers without an AI/ML/DL background to participate and be effective contributors in open source projects in these specialty domains • Create and maintain an industry landscape of AI/ML/DL open source projects and make it available to the public

34. LF DL Interactive Landscape

35. LF Deep Learning Foundation – Interactive Landscape • This landscape is intended as a map to explore open source artificial intelligence, machine learning, and deep learning projects; it also features the member companies of the LF Deep Learning Foundation. • https://l.lfdl.io • Please open a pull request to correct any issues.  

36. Try it at: https://l.lfdl.io

38. Next Meetings • Our next meeting is scheduled for January 17, 2019. • Presentation from Uber on PYRO • Pyro is a universal probabilistic programming language (PPL) written in Python and supported by PyTorch on the backend. Pyro enables flexible and expressive deep probabilistic modeling, unifying the best of modern deep learning and Bayesian modeling. • https://Pyro.ai • https://github.com/uber/pyro

39. Open Discussion / Questions

40. Addendum

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