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Jacqueline Z Cardoso

Newly Elected ONNX Steering Committee Announced!

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Author(s): The ONNX Steering Committee

The ONNX community continues to grow with new tools supporting the spec and nearly two hundred individuals from one hundred organizations attending the April 2020 community meeting. Along with the strong growth of this open source project, we are excited to announce that the governance structure is working well and elections have resulted in newly appointed steering committee members. This is another important step to ensure an open, adaptive, sustainable future for the ONNX project.

The ONNX steering committee as of June 1st are: 

The community expresses sincere gratitude to the three former members, both for exemplary service as well as continuing participation and support for ONNX spec and community: 

The past and present steering committee members wish to thank all those who self-nominated as well as those who voted in the election. Solid contributions to SIGS, Working Groups, and Community Meetings continue to be the best way to grow eminence in the ONNX community. For those who plan to self-nominate in next year’s election, participation is essential.   Also, community outreach to other projects in the LF AI Foundation and contributions to defining the ONNX Roadmap are encouraged.

ONNX is an open format to represent and optimize deep learning and machine learning models that deploy and execute on diverse hardware platforms and clouds. ONNX allows AI developers to more easily move AI models between tools that are part of trusted AI/ML/DL workflows. The ONNX community was established in 2017 to create an open ecosystem for interchangeable models, and quickly grew as tool vendors and enterprises adopted ONNX for their products and internal processes. Support for ONNX spec as an industry standard continues to grow with the support of contributors from across geographies and industry sectors. ONNX is a graduated project of the LF AI Foundation under multi-vendor open governance, in accordance with industry best practice. ONNX community values are: Open, welcoming, respectful, transparent, accessible, meritorious, and speedy. In accordance with our ONNX community principle of being welcoming, all ONNX Steering Committee meetings are open to the community to attend. We welcome your contributions to ONNX.

Congrats to everyone involved and thank you for your contributions to the ONNX project!

The ONNX Steering Committee

ONNX Key Links

LF AI Resources

ONNX 1.7 Now Available!

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ONNX, an LF AI Foundation Graduated Project, has released version 1.7 and we’re thrilled to see this latest set of improvements. ONNX is an open format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. 

In version 1.7, you can find the following:

  • Model training introduced as a technical preview, which expands ONNX beyond its original inference capabilities 
  • New and updated operators to support more models and data types
  • Functions are enhanced to enable dynamic function body registration and multiple operator sets
  • Operator documentation is also updated with more details to clarify the expected behavior

To learn more about the ONNX 1.7 release, check out the full release notes. Want to get involved with ONNX? Be sure to join the ONNX Announce and ONNX Technical-Discuss mailing lists to join the community and stay connected on the latest updates. 

Congratulations to the ONNX team and we look forward to continued growth and success as part of the LF AI Foundation! To learn about hosting an open source project with us, visit the LF AI Foundation website.

ONNX Key Links

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Angel 3.1.0 Release Now Available!

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Angel, an LF AI Foundation Graduated Project, has released version 3.1.0 and we’re thrilled to see lots of momentum within this community. The Angel Project is a high-performance distributed machine learning platform based on Parameter Server, running on YARN and Apache Spark. It is tuned for performance with big data and provides advantages in handling higher dimension models. It supports big and complex models with billions of parameters, partitions parameters of complex models into multiple parameter-server nodes, and implements a variety of machine learning algorithms using efficient model-updating interfaces and functions, as well as flexible consistency models for synchronization.

In version 3.1.0, Angel adds a variety of improvements, including: 

  • Features in graph learning with the trend of graph data structure adopted for many applications such as social network analysis and recommendation systems
  • Publishing a collection of well implemented graph algorithms such as traditional learning, graph embedding, and graph deep learning – These algorithms can be used directly in the production model by calling with simple configurations
  • Providing an operator API for graph manipulations including building graph, and operating the vertices and edges
  • Enabling the support of GPU devices within the PyTorch-on-Angel running mode – With this feature it’s possible to leverage the hardwares to speed up the computation intensive algorithms

The Angel Project invites you to adopt or upgrade Angel of version 3.1.0 in your application, and welcomes feedback. To learn more about the Angel 3.1.0 release, check out the full release notes. Want to get involved with Angel? Be sure to join the Angel-Announce and Angel Technical-Discuss mailing lists to join the community and stay connected on the latest updates. 

Congratulations to the Angel team and we look forward to continued growth and success as part of the LF AI Foundation! To learn about hosting an open source project with us, visit the LF AI Foundation website.

Angel Key Links

LF AI Resources

Thank You IBM & ONNX for a Great LF AI Day

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A big thank you to IBM and ONNX for hosting a great virtual meetup! The LF AI Day ONNX Community Virtual Meetup was held on April 9, 2020 and was a great success with close to 200 attendees joining live. 

The meetup included ONNX Community updates, partner/end-user stories, and SIG/WG updates. The virtual meetup was an opportunity to connect with and hear from people working with ONNX across a variety of groups. A special thank you to Thomas Truong and Jim Spohrer from IBM for working closely with the ONNX Technical Steering Committee, SIG’s, and Working Groups to curate the content. 

Missed the meetup? Check out the recordings at bit.ly/lfaiday-onnxmeetup-040920.

This meetup took on a virtual format but we look forward to connecting again at another event in person soon. LF AI Day is a regional, one-day event hosted and organized by local members with support from LF AI, its members, and projects. If you are interested in hosting an LF AI Day please email info@lfai.foundation to discuss.

ONNX, an LF AI Foundation Graduated Project, is an open format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. Be sure to join the ONNX Announce mailing list and ONNX Gitter to join the community and stay connected on the latest updates. 

ONNX Key Links

LF AI Resources

sparklyr 1.2.0 Now Available!

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sparklyr, an LF AI Foundation Incubation Project, has released version 1.2.0 and we’re excited to see a great release with contributions from several members of the community. sparklyr is an R Language package that lets you analyze data in Apache Spark, the well-known engine for big data processing, while using familiar tools in R. The R Language is widely used by data scientists and statisticians around the world and is known for its advanced features in statistical computing and graphics. 

In version 1.2.0, sparklyr adds a variety of improvements, including: 

  • sparklyr now supports Databricks Connect 
  • A number of interop issues with Spark 3.0.0-preview were fixed
  • The `registerDoSpark` method was implemented to allow Spark to be used as a `foreach` parallel backend in Sparklyr (see registerDoSpark.Rd)
  • And more…A complete list of changes can be found in sparklyr 1.2.0 section of the NEWS.md file: sparklyr-1.2.0

The power of open source projects is the aggregate contributions originating from different community members and organizations that collectively help drive the advancement of the projects and their roadmaps. The sparklyr community is a great example of this process and was instrumental in producing this release. A special THANK YOU goes out to the following community members for their contributions of commits and pull request reviews!

To learn more about the sparklyr 1.2.0 release, check out the full release notes. Want to get involved with sparklyr? Be sure to join the sparklyr-Announce and sparklyr Technical-Discuss mailing lists to join the community and stay connected on the latest updates. 

Congratulations to the sparklyr team and we look forward to continued growth and success as part of the LF AI Foundation! To learn about hosting an open source project with us, visit the LF AI Foundation website.

sparklyr Key Links

LF AI Resources

ForestFlow Joins LF AI as New Incubation Project

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The LF AI Foundation (LF AI), the organization building an ecosystem to sustain open source innovation in artificial intelligence (AI), machine learning (ML), and deep learning (DL), today is announcing ForestFlow as its latest Incubation Project. ForestFlow is a scalable policy-based cloud-native machine learning model server. ForestFlow strives to strike a balance between the flexibility it offers data scientists and the adoption of standards while reducing friction between Data Science, Engineering and Operations teams. ForestFlow was released and open sourced by Dreamworks.

“We are very pleased to welcome ForestFlow to LF AI. ForestFlow provides an easy way to deploy ML models to production and realize business value on an open source platform that can scale as the user’s projects and requirements scale,” said Dr. Ibrahim Haddad, Executive Director of LF AI. “We look forward to supporting this project and helping it to thrive under a neutral, vendor-free, and open governance.” LF AI supports projects via a wide range of benefits; and the first step is joining as an Incubation Project. 

Ahmad Alkilani, Principal Architect and developer of ForestFlow at DreamWorks Animation, said, “We developed ForestFlow in response to our need to move ML models into production that affected the scheduling and placement of rendering jobs and the throughput of our rendering pipeline which has a material impact to our bottom line. Our focus was on maintaining our own teams’ agility and keeping ML models fresh in response to changes in data, features, or simply the production tools that historical data was associated with. Another pillar for developing ForestFlow was the openness of the solution we chose. We were looking to minimize vendor lock-in having a solution that was amenable to on-premise and cloud deployments all the same while offloading deployment complexities from the job description of a Data Scientist. We want our team to focus on extracting the most value they can out of the data we have and not have to worry about operational concerns. We also needed a hands-off approach to quickly iterate and promote or demote models based on observed metrics of staleness and performance. With these goals in mind, we also realize the value of open source software and the value the Linux Foundation brings to any project and specifically LF AI in this space. DreamWorks Animation is pleased that LF AI will manage the neutral open governance for ForestFlow to help foster the growth of the project.”

Continuous deployment and lifecycle management of Machine Learning/Deep Learning models is currently widely accepted as a primary bottleneck for gaining value out of ML projects. Hear from ForestFlow about why they set out to create this project: 

  • We wanted to reduce friction between our data science, engineering and operations teams
  • We wanted to give data scientists the flexibility to use the tools they wanted (H2O, TensorFlow, Spark export to PFA etc..)
  • We wanted to automate certain lifecycle management aspects of model deployments like automatic performance or time-based routing and retirement of stale models
  • We wanted a model server that allows easy A/B testing, Shadow (listen only) deployments and Canary deployments. This allows our Data Scientists to experiment with real production data without impacting production and using the same tooling they would when deployment to production.
  • We wanted something that was easy to deploy and scale for different deployment scenarios (on-prem local data center single instance, cluster of instances, Kubernetes managed, Cloud native etc..)
  • We wanted the ability to treat inference requests as a stream and log predictions as a stream. This allows us to test new models against a stream of older infer requests.
  • We wanted to avoid the “super-hero” data scientist that knows how to dockerize an application, apply the science, build an API and deploy to production. This does not scale well and is difficult to support and maintain.
  • Most of all, we wanted repeatability. We didn’t want to reinvent the wheel once we had support for a specific framework.

ForestFlow is policy-based to support the automation of Machine Learning/Deep Learning operations which is critical to scaling human resources. ForestFlow lends itself well to workflows based on automatic retraining, version control, A/B testing, Canary Model deployments, Shadow testing, automatic time or performance-based model deprecation and time or performance-based model routing in real-time. The aim for ForestFlow is to provide data scientists a simple means to deploy models to a production system with minimal friction accelerating the development to production value proposition. Check out the quickstart guide to get an overview of setting up ForestFlow and an example on inference. 

Learn more about ForestFlow here and be sure to join the ForestFlow-Announce and ForestFlow-Technical-Discuss mail lists to join the community and stay connected on the latest updates. 

A warm welcome to ForestFlow and we look forward to the project’s continued growth and success as part of the LF AI Foundation. To learn about how to host an open source project with us, visit the LF AI website.

ForestFlow Key Links

LF AI Resources

NNStreamer Joins LF AI as New Incubation Project

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The LF AI Foundation (LF AI), the organization building an ecosystem to sustain open source innovation in artificial intelligence (AI), machine learning (ML), and deep learning (DL), today is announcing NNStreamer as its latest Incubation Project. NNStreamer is a set of Gstreamer plugins that support ease and efficiency for Gstreamer developers adopting neural network models and neural network developers managing neural network pipelines and their filters. NNStreaner was released and open sourced by Samsung.

“We are very pleased to welcome NNStreamer to LF AI. Machine Learning applications often process online stream input data in real-time, which can create a complex system. NNStreamer can be used to easily represent and efficiently execute against these challenges,” said Dr. Ibrahim Haddad, Executive Director of LF AI. “We look forward to supporting this project and helping it to thrive under a neutral, vendor-free, and open governance.” LF AI supports projects via a wide range of benefits; and the first step is joining as an Incubation Project. Full details on why you should host your open source project with LF AI are available here.

NNStreamer promotes easier and more efficient development of on-device AI systems by allowing the description of general systems with various input, outputs, processors, and neural networks with the pipe-and-filter architecture. It provides easy-to-use APIs with corresponding SDKs as well: C-APIs (all platforms), Tizen.NET (C#), and Android (Java) along with a wide range of neural network frameworks and software platforms (Ubuntu, macOS, OpenEmbedded). NNStreamer became an open source project in 2018 and is under active development with the Tizen project and a wide range of consumer electronics devices. 

Learn more about NNStreamer via their GitHub. You can also check out the NNStreamer 2018 GStreamer Conference presentation recording here, as well as their presentation at the Samsung Developer Conference in 2019 here. And be sure to join the NNStreamer-Announce and NNStreamer-Technical-Discuss mail lists to join the community and stay connected on the latest updates. 

A warm welcome to NNStreamer and we look forward to the project’s continued growth and success as part of the LF AI Foundation. To learn about how to host an open source project with us, visit the LF AI website.

NNStreamer Key Links

LF AI Resources

Welcome New LF AI Members – Q1 2020

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We are excited to welcome three new members to the LF AI Foundation – Xenonstack as a General member and in our Associate category we now have AI for People and Ambianic. Learn a bit more about these organizations:

Xenonstack

Xenonstack joins LF AI as a General member. In their own words: A Product Engineering, Technology Services and Consulting company building Intelligent Distributed Systems at Scale, AI, and Data-driven Decision Platforms and Solutions. We are enabling enterprises for Digital transformation with Cloud, Data and AI Strategy and Enterprise Agility.

AI for People

AI for People joins LF AI as an Associate member. In their own words: Our mission is to learn, pose questions and take initiative on how Artificial Intelligent technology can be used for the social good.

Our strategy is to conduct impact analysis, projects and democratic policies that act at the crossing of Artificial Intelligence and society. We are a diverse team of motivated individuals that is dedicated to bring AI Policy to the people, in order to create positive change in society with technology, through and for the public.

Ambianic 

Ambianic joins LF AI as an Associate member. In their own words: Ambianic’s mission is to make our homes and workspaces a little cozier. Ambianic is an Open Source Ambient Intelligence platform that puts local control and privacy first. It enables users to train and share custom AI models without compromising privacy.

We look forward to partnering with these new LF AI Foundation members to help support open source innovation and projects within the artificial intelligence (AI), machine learning (ML), and deep learning (DL) space. Welcome to our new members!

Interested in joining the LF AI community as a member? Learn more here

LF AI Resources

Milvus Joins LF AI as New Incubation Project

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Milvus, an open source vector similarity search engine, was accepted by the LF AI Foundation (LF AI) as its latest incubation project after TAC voting. LF AI is the organization building an ecosystem to sustain open source innovation in artificial intelligence (AI), machine learning (ML), and deep learning (DL). 

Adopted by over 100 organizations and institutions worldwide, Milvus empowers applications in a variety of fields, including image processing, computer vision, natural language processing, voice recognition, recommender systems, drug discovery, etc. Milvus was originally developed by Zilliz, a Shanghai-based startup company, and open sourced in October 2019.

Zilliz, with the vision of “Reinvent data science”, develops open source data science software for the era of AI and 5G/IoT. “We are pushing forward a globalization strategy that fully incorporates global open source communities. We believe open development leads to greater implementation and greater good for all.” said Starlord, the founder & CEO of Zilliz. “We believe Milvus will help to accelerate the AI adoption for more organizations after joining LF AI.”

“We are very pleased to welcome Milvus to LF AI. Vector similarity search engine is an important component for processing rapidly growing unstructured data. Many AI domains, such as image processing, computer vision, NLP, recommendation systems, and more, could benefit from the capability of Milvus vector similarity search engine. Milvus can help to build up AI applications with open source AI technology,” said Dr. Ibrahim Haddad, Executive Director of LF AI. “We look forward to supporting this project and helping it to thrive under a neutral, vendor-free, and open governance.” 

Milvus is easy-to-use, highly reliable, scalable, robust, and blazing fast, along with a rich list of features. 

  • Comprehensive Similarity Metrics – Milvus offers frequently used similarity metrics, including Euclidean distance, inner product, Hamming distance, Jaccard distance, etc., allowing you to explore vector similarity in the most effective and efficient way possible.
  • Leading-Edge Performance – Milvus is built on top of multiple optimized Approximate Nearest Neighbor Search (ANNS) indexing libraries, including faiss, annoy, hnswlib, etc., thus ensuring that you always get the best performance across various scenarios.   
  • Cost-Efficient – Milvus harnesses the parallelism of modern processors and enables billion-scale similarity searches in milliseconds on a single off-the-shelf server. 
  • Highly Scalable and Robust – You can deploy Milvus in a distributed environment. To increase the capacity and reliability of a Milvus cluster, you can simply add more nodes.
  • Cloud Native – Milvus is designed to run on public cloud, private cloud, or hybrid cloud.

Learn more about Milvus here and be sure to join the Milvus-Announce and Milvus-Technical-Discuss mail lists to join the community and stay connected on the latest updates. 

A warm welcome to Milvus and we look forward to the project’s continued growth and success as part of the LF AI Foundation. LF AI supports projects via a wide range of benefits; and the first step is joining as an Incubation Project. Full details on why you should host your open source project with LF AI are available here.

Milvus Key Links

LF AI Resources

Happy 2nd Birthday LF AI!

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Break out those party hats as we are thrilled to have celebrated the 2nd birthday of the LF AI Foundation! It’s hard to believe that two years have passed since the Foundation launched in March 2018. We invite you to go down memory lane with us and see the key milestones accomplished. 

Members – We launched with 10 members and now have 23 across our Premier, General, and Associate levels. We’ve seen a diverse group of companies getting involved across various industries and we welcome those interested in contributing to the support of open source projects within the artificial intelligence (AI), machine learning (ML), and deep learning (DL) space. 

Technical Projects – Our technical project portfolio grew from the single Acumos project to 10 projects; with a mix of 3 Graduated and 7 Incubation. Two of these projects were approved by the LF AI Technical Advisory Council (TAC) and are undergoing their onboarding and will be announced soon, stay tuned! The TAC is continually working to bring in new projects and we look forward to sharing those with you in the next few months. We’re always looking for new open source projects to host within LF AI, please email us info@lfai.foundation if you’d like to discuss joining us.

Interactive Landscape – The launch of the LF AI Interactive Landscape has become a great tool to gain insights into how LF AI projects, among many others, fit into the space of open source AI, ML, and DL. Explore the landscape and please reach out to help us expand it with your own open source project or others that should be included.

Initiatives – Through the great contributions across the LF AI Community we launched two very important initiatives. An ML Workflow Working Group with the goal of defining an ML Workflow and to promote cross project integration. As well as the Trusted AI Committee with the goal of creating policies, guidelines, tooling, and use cases by industry in this very important space. Both of these initiatives are open for participation and we encourage anyone interested to join the conversations by joining the mail lists or attending an upcoming meeting; check out their wiki pages for more information.

Events – There have been 11+ event opportunities to connect the LF AI Community face to face across the globe; including LF AI Days which are regional, one-day events hosted and organized by local members with support from LF AI and its projects. Visit our LF AI Events page for more details.

Community – In 2019, we rebranded the foundation from LF Deep Learning to LF AI Foundation and continued efforts to increase communication and collaboration within the LF AI Community. If you haven’t connected with us across the various channels please do so!

Lots of accomplishments and growth over the past two years! In the coming years there will be more exciting developments in the space of AI, ML, and DL, and we invite you to be a part of it through the LF AI Community. Check out our How to Get Involved Guide or email us at info@lfai.foundation for any questions. 

Happy 2nd Birthday LF AI! 

🥳 🎉🎂

LF AI Resources