Skip to main content
All Posts By


Integration Among Tools – Key to Machine Learning Implementation Success

By Blog

Guest Author(s): Dr. Ofer Hermoni, Director of Product Strategy in Amdocs’ CTO office,

Use and adoption of Machine Learning (ML) and Deep Learning (DL) technologies is exploding with the availability of dozens of such open source libraries, frameworks and platforms, not to mention all the proprietary solutions. While there are many applications and tools out there, the integration between them can be complicated, can pose additional challenges especially in relation to long term sustainability, and may present a barrier for and adoption as part of a commercial product/service. 

To help developers and data scientists make sense of the diversity of projects, the LF AI landscape (Figure 1) was originally published in December 2018 and has been continuously updated ever since.  The LF AI landscape is an interactive tool that shows both how fragmented the space as well as the wide range of projects in each technology category.

Figure 1: LF AI Landscape available via

Most open source AI projects started as proprietary efforts and are the result of years of investment and talent acquisition. At different points in time, the founding company (or companies) of such an effort decide to open source projects as a consequence of wanting to build an ecosystem around it and to collaborate with others on constructing a platform. The end result of that phenomenon is a large ecosystem of open source projects.

The important question from an adoption perspective is which open source project to adopt and how to integrate it with other open source solutions (libraries, frameworks, etc.) and internal proprietary stacks.  

Goal: Better Integration among Projects and Tools

One of the goals of LF AI Foundation is building integration among LF AI projects and generally available and open source solutions so users can easily take advantage of a wide array of options and further the adoption of open source for AI solutions. This effort to improve integration and collaboration is aimed at helping bring everyone up to the same level of understanding of common deployments of ML workflow. Few companies are willing or able to provide this. This filtering and analysis is uniquely suited to a foundation like the LF AI Foundation, since we can look across specialties and provide help and guidance.

In his talk “How Linux Foundation is Changing the (Machine-Learning) World,” Ofer Hermoni, Ph.D., Director of Product Strategy, CTO Office, Amdocs, and the Chairperson, of LF AI Technical Advisory Council, highlights one of the key goals of the LF AI: 

“Harmonization, Interoperability – Increase efforts to harmonize open source projects and reduce fragmentation; increase the interoperability among projects”

This has led to the LF AI Technical Advisory Committee (TAC) pushing to clarify the current landscape. First, what is a typical workflow? What projects are already available under the LF AI umbrella that can implement parts of that workflow? Finally, what open source projects are out there that help fill the gaps and provide good alternatives? This way, users can quickly start to understand the larger picture (landscape) and have a great understanding of not just available open source components in the AI/ML/DL space but also how to integrate them together in implementing an end-to-end ML workflow. At the same time, LF AI can better evaluate where integration is already strong and where there are gaps that can be opportunities to collaborate and fill following the open source approach for the benefit of the broader open source AI community.

The reference ML workflow produced by TAC is summed up in three main layers. 

We started with reviewing existing published flows. We then built on them and extended them to create an entire workflow that covers the lifetime of ML integration across three major phases, starting with data preparation including data governance, moving through model creation, including ethics management, and then moving toward solution rollout including security management.

Figure 2: ML Workflow as defined by the LF AI TAC

Second, the identification of existing LF AI hosted projects and where they fit in the ML workflow. 

Figure 3: ML Workflow showcasing the fit of the LF AI hosted projects (Acumos, EDL, Angel, Horovod and Pyro)  

And, third, the ML workflow highlighting other open source projects and where they fit in, such as TensorFlow, Keras, PyTorch, Kubeflow and many more.

Figure 4: Same ML Workflow highlighting the fit of other existing open source projects

The figures are a great way to quickly grasp the entire process and identify the scope of the applications and tools that are needed, and is especially helpful in identifying integration opportunities across these different projects. The result is a better understanding of the connections or lack of connections, and a path to create these connections or integration points. 

Who should use this?

We would like to hear from as many developers and data scientists as possible, since we are just getting started.  There are certainly more connections and gaps to be identified. Integration work takes time. It’s been built up over the past year. This activity is open not only to LF AI members, but to the entire community, and many companies already participate in the discussions.

How Does My Project Get Involved?

The ML Workflow effort is open for participation and we are soliciting feedback to improve our reference workflow. There are various ways in which you can participate and get involved:

Meet the LF AI Team in San Diego (August 20, 2019)

LF AI is hosting an open meeting in San Diego on August 20th with the goal to discuss the ongoing projects, explore new collaboration opportunities, and provide face-to-face feedback and updates on various Foundation ongoing technical efforts. We welcome you to join, get to meet our members, projects, and staff, and explore ways to get involved in our efforts. 

For more information please visit:

About the author

As the Director of Product Strategy in Amdocs’ CTO office, Dr. Ofer Hermoni is responsible for leading all of Amdocs’ activities in the Machine-Learning open-source community, including defining Amdocs’ product strategy in the area of AI/machine learning. In addition, he is the Chairperson of the LF AI Foundation Technical Advisory Council and a member of the LF AI Foundation Governing Board. Ofer is also an active contributor to the Acumos AI project, and a member of the Acumos AI Technical Steering Committee.

Let’s Talk Open Source AI! Open Invitation to the LF AI Meetings on August 20th in San Diego

By Blog

Come join us! The LF AI Meetings are being held in San Diego, Aug 20, 9am-12:30pm one day prior to Open Source Summit North America, San Diego (Aug 21-23). LF AI members meet and discuss ongoing projects, explore new collaboration opportunities, and provide face-to-face feedback and updates.

It’s a great opportunity to meet with AI developers working on LF AI hosted projects and LF AI staff, too!

Meet the developers! – Who’s at the LF AI Booth #43?

Time SlotWednesday – Aug 21
10:30 am – 12:30 pm Angel, Acumos
2:00 pm– 4:00 pmHorovod, Acumos
4:00 pm – 6:00 pmHorovod, Acumos
6:00 pm – 7:00 pmAcumos

*Booth Crawl & ELC Tech Showcase – 5:30 – 7:00 pm

Time SlotThursday – Aug 22
10:30 am – 12:30 pmAcumos
2:00 pm – 4:00 pmHorovod, Acumos
4:00 pm – 5:30 pmHorovod, Acumos
Time SlotFriday – Aug 23
10:30 am – 12:30 pmAngel, Acumos
2:00 pm – 4:00 pmAngel, Acumos

For registration information and location details, please see:

Looking forward to seeing you there!

Registration for LF AI Day – Paris Now Open! Expanding Open Source AI Engagement Across the Globe

By Blog

Register Now! 

Orange and the LF AI Foundation  are excited to announce the LF AI Day – Paris coming up on September 16 in Paris-Châtillon. LF AI Days are regional, one-day events hosted and organized by local members with support from LF AI and its hosted projects. These events are open to all for participation and have no cost to attend.

Hosted at the beautiful Orange Gardens, 44 Avenue de la République, LF AI Day – Paris will feature keynote speakers from leading operators and AI industry, including Orange, NTT, Nokia, Deutsche Telekom, LF AI Foundation, and more. The agenda will focus on open source strategies and ongoing technical developments in the areas of open source machine learning and deep learning. During this event, various AI topics will be covered, including technical presentations, demonstrations of Orange AI Marketplace based on Acumos, an LF AI Graduate project, and a Startups panel discussion.

Agenda (updated Sept 12)

The agenda for the full-day free event is as follows. 

Check-in and registration
Welcome Message,
Nicolas Demassieux SVP, Orange Labs Research
Building Sustainable Open Source AI Ecosystem,
Ibrahim Haddad, Executive Director, LF AI Foundation
Orange AI activities,
Steve Jarrett VP, Orange Data & AI
NTT’s Challenges of AI for Innovative Network Operation
Masakatsu Fujiwara, Project Manager,
NTT Network Technology Laboratories
Coffee Break
Acumos AI – Platform Overview, Releases and Use Cases
Anwar Aftab, Director, Inventive Science, AT&T Labs
We make AI accessible
Jamil Chawki, Intrapreneur-CEO Orange AI Marketplace
and Chair of the LF AI Outreach Committee
Trusted AI – Reproducible, Unbiased and Robust AI Pipelines using Open Source
Romeo Kienzler, Chief Data Scientist
IBM Center for Open Source Data and AI Technologies
Activities in LF AI and Acumos,
Sahar Tahvili, PhD. Lead Data Scientist, Ericsson,
Global Artificial Intelligence Accelerator (GAIA), Sweden
Acumos & Orange AI Marketplace demonstration
Philippe Dooze, Project Technical Lead,
Orange Labs Networks
Nokia, AI and Open Source
Philippe Carré, Senior Specialist Open Source, Nokia Bell-Labs & CTO
Startups Panel Discussion, Barriers for AI development,
François Tillerot, Intrapreneur-CMO,
Orange AI Marketplace

Rahul Chakkara, Co-Founder, Manas AI
Laurent Depersin, Research & Innovation Home Lab Director, Interdigital
Marion Carré, CEO Ask Mona
Sana Ben Jemaa, Project Manager Radio & AI, Orange Labs Networks
Open Discussion and Closing Session

There will be a welcome reception after the event. Details will be posted on the event’s page

For questions, please contact

To view LF AI Days happening is other geographical regions, please visit the LF AI Events page. 

Register Now! 

AT&T, Orange, Tech Mahindra Adoption of Acumos AI Builds Foundation for Growth

By Blog

by John Murray, Assistant Vice President of Inventive Science, Intelligent Systems and Incubation, AT&T

With the release of Acumos AI in late 2018, the core idea was to create a sustainable open source AI ecosystem by making it easy to create AI products and services using open source technologies in a neutral environment. Acumos AI was aimed squarely at reducing the need for specialists and lowering the barriers to AI.

Fundamentally, lowering barriers to AI means making it easier to create and train models.

The new Boreas release, just announced in June, does exactly that. Users now have readily available tools to create and train models, enabling the full lifecycle of development from model onboarding and designing, to sharing and deploying. Jupyter Notebooks and NiFi, two popular and well-known document and graphics tools, are now integrated in the pipeline. Access by users through an enhanced UX in the portal provide publishing, unpublishing, deploying and onboarding, model building, chaining, and more.

At the same time, AI model suppliers will be able to provide a software license with their models to ensure that the user has acquired the right to use the model. This is key for marketplace-like transactions. Boreas explicitly supports licenses and Right-To-Use for proprietary models. It also now supports license scans of models and metadata.

The new features in Boreas move AI development forward significantly, allowing developers and data scientists who are not AI specialists to develop and deploy apps.

Leadership and Real World Implementations

The LF AI Foundation charter promises to connect members and contributors with the innovative technical projects, companies, and developer communities that are transforming AI and Machine Learning. But the question is always, is it being used? And how does it perform in the real world?

AT&T, Orange and Tech Mahindra are three great examples how Acumos AI has jumped forward quickly in the last 6 months. All three companies are founding members of the LF AI Foundation and have been providing leadership in both development resources and real world implementations of the Acumos AI framework and marketplace. The reach of their current deployments is distinctly international and extremely ambitious.

AT&T – Infusing AI Across Operation – Big and Small

Two years ago, AT&T saw an opportunity to make AI more accessible and reduce barriers to this exciting industry. Together with Tech Mahindra and The Linux Foundation, AT&T developed Acumos AI to serve as an open marketplace for innovators to create and exchange the best AI solutions possible. Two years and two releases later, we’ve seen firsthand the success of this open approach. It’s led to the creation of new solutions from students, developers, startups and several groups across AT&T’s varied business.

At AT&T, we’re not only helping to improve the Acumos AI code for the public, we’re also using it to improve efficiencies in our own organization. In the past year, AT&T has leveraged Acumos models across customer care, network security, and a variety of different aspects of the business. And, with each release comes additional enhancements, capabilities and opportunities to infuse AI across operations – big and small.

Orange – We make AI accessible and ready for 5G

Orange is using Acumos for its new AI Marketplace. Orange is a leading telecommunications company with 273 million customers worldwide and revenue of €41B (2017). The Orange AI Marketplace is an AI app store where developers can publish and share AI services that can be quickly and easily deployed by customers.

Orange has increased involvement in Acumos significantly. Orange’s contributions to Acumos AI include the onboarding enhancements seen in the new Acumos Boreas release. After testing the publication and export of AI model for operations use cases – such as incident detection and tickets classification – Acumos was deployed as the basis for the Orange AI Marketplace.

The second half of 2019 will see even more implementations and further growth by Acumos AI. Please come back to find out more information here on the LF AI Foundation blog covering innovative use cases and key implementations of Acumos AI worldwide.

Acumos was also proposed by Orange as an AI platform for the European research project AI4EU.  The goals of AI4EU are ambitious, including making the promosis of AI “real” for the EU, and creating a collaborative AI European platform to nurture economic growth. Involving 80 partners, covering 21 countries, the project kicked off in January 2019 and will run for three years and it is expected to implement Acumos by the end of 2019.

Tech Mahindra GAiA – Democratizing AI

Tech Mahindra GAiA is the first enterprise-grade open source AI platform. It hosts a marketplace of AI models which can be applied to use cases in multiple industry verticals. These are used as the basis for building, sharing and rapidly deploying AI-driven services and applications to solve business critical problems.

GAiA is available for commercial products and services and supports open source distribution at the same time. Tech Mahindra is aiming to fully democratize AI. The core concept behind GAiA is that the knowledge and expertise around AI should be universally accessible.

The launch of the GAiA platform is in line with Tech Mahindra’s TechMNxt charter which focuses on leveraging next generation technologies like AI to address real world problems and meet the customer’s evolving and dynamic needs.

Getting Involved in the LF AI Foundation and Acumos

What to get involved? It’s easy to get started! You can get involved with specific projects with development, review, events, documentation, and much more. You can participate in the Technical Advisory Committee (TAC) by joining the discussions on bi-weekly calls, identifying collaboration opportunities, inviting speakers to outside events, evaluating new projects, and more more, And you can take advantage of marketing and outreach provided by the LF AI Foundation. 

The full “Getting Involved Guide” is available for current and prospective members.

“We’ve written this guide to provide you a complete reference to the LF AI community. You will learn how to engage with your communities of interest, all the different ways you can contribute, and how to get help when you need it. If you have suggestions for enhancing this guide, please get in touch with LF AI staff.”

If you are interested in joining the LF AI Foundation:

John Murray Bio

John Murray is the Assistant Vice President of Inventive Science, Intelligent Systems and Incubation at AT&T. He leads the Intelligent Systems and Incubation organization which uses software, platforms, data, analytics and AI and machine learning to deliver solutions that address AT&T’s needs. He is an expert in design and building advanced communications systems and is involved in key initiatives such as ONAP, Acumos, data management, and automation and communications systems.

Are you in Government or the Public Sector? The Call for Participation for the AAAI AI in Government and Public Sector Fall Symposium is Open!

By Blog

Government is at the front lines of the democratization of AI. The scale of participation and the importance in citizens lives means that government and public section approaches to open source AI will be a central component of how development changes and evolves in the coming years.

The Association for the Advancement of Artificial Intelligence (AAAI) is holding its 2019 Fall Symposium Series in Washington, DC, Nov 7–9, 2019.

This symposium will focus on a wide array of government and public sector AI topics. From the Call for Papers (see attached PDF for more information)

“There are hundreds of open source AI related projects focusing on several AI sub-domains such as deep learning, machine learning, models, natural language processing, speech recognition, data, reinforcement learning, notebook environments, ethics and many more.  How can government entities leverage the abundance of open source AI projects and solutions in building their own platforms and services? Based on which criteria should we evaluate various projects aiming to solve same or similar problems? What kind of framework should be in place to validate these projects, and allowing the trust in AI code that will be deployed for public service?”

Submit your proposal by July 26 through the AAAI site choosing the AAAI/FSS-19 Artificial Intelligence in Government and Public Sector track:

Contact Frank Stein ( with any questions.

FierceTelecom: AI Foundation offers second helping of Acumos with Boreas release

By In The News

The Linux Foundation’s AI Foundation project has dropped its second software release for its Acumos project, which includes a new licensing framework and partner catalogs.

The LF AI Foundation, which was previously known as LF the Deep Learning Foundation, was launched last year to spur innovation across artificial intelligence, machine learning and deep learning not just in the telecom industry, but across other industries as well.

Following up on the first release in November, which was called Athena, the second release, which is called Boreas, has added training and license verification for artificial intelligence (AI), machine learning (ML), and deep learning (DL) models and apps for use by community developers and data scientists.

“The key part about Boreas, or Acumos in general, is trying to address the real need to move AI as a more general purpose tool across the broader company and not just be applied in specialty areas,” said Jack Murray, assistant vice president of inventive science, intelligent systems and incubation within AT&T Labs.

Read more at FierceTelecom.

Light Reading: Acumos AI Project Adds Smarts With Boreas

By In The News

The Linux Foundation’s Artificial Intelligence Foundation (AIF) has announced the availability of the second release of Acumos AI, its open source platform for building, sharing and deploying artificial intelligence models. I’ve been following the Acumos project since its inception and wrote about the initial release, Athena, last November. Now, seven months later, some notable progress has been made. (See The Acumos AI Project Is On Track but Lacking ‘Apps’.)

This latest release, dubbed Boreas (in keeping with the Greek god theme), has two new key capabilities which should make the project more attractive to developers. As flagged by Orange executives in April, the latest release adds a licensing framework which facilitates the commercialization of AI models, and it integrates model training capabilities.

Read more at Light Reading.

The Linux Foundation’s Artificial Intelligence Community Announces New Acumos Release Focused on Creation of AI/ML Models

By Press Release

Second release of Acumos under the LF AI Foundation includes new pipeline tools to support creation and training of AI/ML models, points to adoption by telecom providers and vendors around the globe

San Francisco, Calif., June 24, 2019 – The LF AI Foundation, the organization building an open AI community to drive open source innovation in artificial intelligence (AI), machine learning (ML) and deep learning (DL), today announced the new release of Acumos code named Boreas. This latest release of the open source framework and marketplace will enable the creation, training and license verification of AI, ML and DL models and apps, among other benefits to the community of developers and data scientists.

“The technology industry is on the precipice of a major technological shift with AI, which is exactly the point in any technology evolution where open source software and community can accelerate development,” said Ibrahim Haddad, executive director of LF AI Foundation. “An open source framework that is iterated upon early and often is key in transforming the development work for data scientists and developers, and Acumos is that foundation for innovation.”

Acumos AI is a platform and open source framework that makes it easy to build, share, and deploy AI apps. Acumos standardizes the infrastructure stack and components required to run an out-of-the-box general AI environment. This frees data scientists and model trainers to focus on their core competencies and accelerates innovation.

Acumos is part of the LF AI Foundation, an umbrella organization within The Linux Foundation that supports and sustains open source innovation in AI, ML, and DL while striving to make these critical new technologies available to developers and data scientists everywhere.

The latest Acumos AI release includes:

  • Support for onboarding of ONNX, PFA and Dockerized models.
  • Enhanced Acumos platform peering through a controlled process of partner catalog publication and subscription.
    • Global catalog search capability
    • Federation of Catalogs
  • Support for AI/ML model suppliers to provide a commercial software license with their models in the Acumos marketplace.
    • Security scans of license metadata for models (Disabled with Security Verification turned off)
    • Support verification of licenses and Right-To-Use for commercial models**
    • Logging to enable model activity tracking and reporting
  • Support for ML Workbench to allow the creation and training of AI/ML models in Acumos platform.           
    • Support for Notebooks development environment (Jupyter)
    • Support for Pipeline (NiFi) tools are integrated with Acumos (NiFi Pipeline tools are available as a Beta Feature only under Kubernetes)
  • Enhanced user experience in portal.
    • Publishing, unpublishing, deploying , onboarding, model building, and chaining, etc.
  • Enhanced logging standards.
    • Log formats aligned with ONAP
    • Support for Log management tools
  • Enhanced support for deploying Acumos platform under Kubernetes.

Global Adoption of Acumos

Integration, adoption and deployment of Acumos around the world is well underway and demonstrates momentum for a common, open framework to accelerate innovation in the AI, ML and DL app space. Two key examples are Orange and Tech Mahindra. Orange is using Acumos for an AI Marketplace and is integrating the upcoming Acumos Clio release with ONAP in order to test it on ONAP OpenLab and the 5G research platform Plug’in. Orange’s contribution to the Acumos includes the Onboarding enhancements seen in Acumos Boreas. Tech Mahindra is integrating Acumos into a number of its initiatives. TechMahindra GAiA is the first enterprise-grade open source AI platform, hosting a marketplace of AI models for a wide group of industry verticals. These are used as the basis for building, sharing and rapidly deploying AI-driven services and applications to solve business critical problems.

Supporting Quotes


“Acumos Boreas represents a significant next step in open source AI and machine learning,” said Dr. Ofer Hermoni, Director of Product Strategy at Amdocs and Chair of the LF AI Technical Advisory Counsel. “With the ability to create and train models, we’re well on our way to providing all the essential tools for rapid innovation for data scientists and developers building AI and machine learning apps.”


“The second release of Acumos exemplifies the progress we’ve made as a community, and AT&T is proud to be a founding member of the project,” said Mazin Gilbert, vice president of Advanced Technology & Systems, AT&T Labs. Together, we’re lowering the barrier to entry for artificial intelligence by driving a collaborative open source community for developers, students and scientists. We’re honored to be a part of this community and committed to continued use of Acumos inside AT&T.” 


“Open Source based AI platform capabilities and ecosystem are an important part of Ericsson’s 5G platform strategy to address the needs of our customers globally.” said Anita Frisell, VP Head of Technology Development and Execution and LF AI Board Member. “Key part of these capabilities is to enable creation and commercialization of AI based solutions. Boreas release of Acumos takes a big step towards achieving this objective. Ericsson is pleased to be a key contributor of the licensing and security features of the Boreas release in collaboration with AT&T and the community.”


“Acumos with the Boreas Release offers a complete experience for ML modelers and model consumers beyond the marketplace. As a founding member of the LF AI, Nokia is excited about the possibilities of Acumos in powering ML model marketplaces around network automation and  evolved 5G RAN architecture’s near real-time RAN Intelligent Controller. We are working on several use cases together with our customers to help accelerate the adoption of AI based applications,” said Jonne Soininen, Head of Open Source Initiatives at Nokia.


“A unified development platform for AI was really needed. The Boreas release of Acumos AI is a major step forward. Orange has increased its involvement in Acumos in both execution and on-boarding. We have tested extensively the publication and exporting of AI models for operations use cases like incident detection and tickets classification and see Acumos as an excellent solution for the Orange AI Marketplace,” said Nicolas Demassieux, Senior Vice President, Orange Labs Research.


“We are proud to be a key contributor to the Acumos Boreas release, another milestone towards creating a collaborative ecosystem for AI along with AT&T and LF. At Tech Mahindra we are committed to leveraging technologies like AI, both on IT and networks side to help our customers RUN better, CHANGE faster and GROW greater. TechMahindra’s GAiA, powered by Acumos is another testimony of how we are accelerating innovation in the AI space,” said Dr. Satish Pai, senior VP and SBU head, Americas Communications, Media and Entertainment, Tech Mahindra.


“ZTE is excited to have witnessed the growth of Acumos in the past year. The Boreas release marks a significant step forward to break the barrier between model developers and model users with its flexible platform for model onboarding, designing, sharing and deploying, which will help create an ecosystem of AI in various vertical industries, especially in 5G. ZTE will continue to support the project and integrate it with our own AI solutions to accelerate 5G innovation with AI,” said Bingtao Han, Chief System Architecture Expert, ZTE.

About LF AI Foundation The LF AI Foundation, a Linux Foundation project, accelerates and sustains the growth of Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) open source projects. Backed by many of the world’s largest technology leaders, LF AI is a neutral space for harmonization and ecosystem engagement to advance AI, ML and DL innovation. To get involved with the LF AI Foundation, please visit

LF Deep Learning Adds Two New Framework Projects to Expand Community and Ecosystem

By Press Release

Angel     Elastic Deep Learning

Contributed by Baidu and Tencent, new projects accelerate machine learning, artificial intelligence and deep learning innovation and opportunity

SAN FRANCISCO and RIO DE JANEIRO (VLDB 2018) – August 27, 2018 – The LF Deep Learning Foundation, a community umbrella project of The Linux Foundation that supports and sustains open source innovation in artificial intelligence, machine learning, and deep learning, announces that two additional projects have been accepted into the foundation: the Angel Project and the EDL Project. The LF Deep Learning Foundation is focused on building an ecosystem of AI, deep learning and machine learning projects, and today’s announcement represents a significant milestone toward achieving this vision.

The new LF Deep Learning Foundation includes the Acumos AI platform and open source framework that makes it easy to build, share and deploy AI apps. With these two new projects, the Foundation adds technology finely tuned for big data and deep learning via clusters with Baidu’s PaddlePaddle and Kubernetes container orchestration.

Angel Project Background

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.

Contributed to the LF Deep Learning Foundation by member Tencent, the project currently has more than 1,000 commits and is licensed under Apache-2.0.

“Angel shares a common goal with the LF Deep Learning Foundation: to make deep learning easier to use. By becoming a part of the LF Deep Learning Foundation, we believe Angel will be more active in the open source community, accumulate more use cases, expand usage scenarios and actively cooperate with other partners,” said Xiaolong Zhu, Tencent senior AI researcher and TAC member of the LF Deep Learning Foundation. “As a new project under the Foundation, Angel will continue working on a consistent and continuous user experience to make deep learning technology easier to apply and develop.”

The system is designed for efficient iteration computation, so that machine learning algorithms can benefit from it. Algorithms in Angel are out-of-the-box so analysts and data scientists can submit jobs without writing a single line code.

EDL Project Background

EDL is an Elastic Deep Learning framework designed to help deep learning cloud service providers to build cluster cloud services using deep learning frameworks such as PaddlePaddle and TensorFlow.

EDL includes a Kubernetes controller, PaddlePaddle auto-scaler, which changes the number of processes of distributed jobs to the idle hardware resource in the cluster, and a new fault-tolerable architecture.

Contributed by member Baidu, the project currently has nearly 1,000 commits and uses the Apache-2.0 license.

“We are excited to see that EDL has been accepted to LF Deep Learning Foundation,” said Yanjun Ma, Head of Deep Learning Technology Department, Baidu. “As an elastic deep learning framework for PaddlePaddle, we believe that EDL will substantially benefit the deployment of large-scale deep learning services, and the broader deep learning open source community.”

Organizations interested in contributing projects and learning more about LF Deep Learning Foundation, can go to

About LF Deep Learning

The LF Deep Learning Foundation, a Linux Foundation project, accelerates and sustains the growth of artificial intelligence, machine learning and deep learning open source projects. The initiative’s Acumos AI Project is a platform and open source framework that makes it easy to build, share and deploy AI models. Backed by many of the world’s largest technology leaders, LF Deep Learning is a neutral space for harmonization and ecosystem engagement to advance AI, DL and ML innovation. To get involved with the LF Deep Learning Foundation, please visit

About The Linux Foundation

The Linux Foundation is the organization of choice for the world’s top developers and companies to build ecosystems that accelerate open technology development and industry adoption. Together with the worldwide open source community, it is solving the hardest technology problems by creating the largest shared technology investment in history. Founded in 2000, The Linux Foundation today provides tools, training and events to scale any open source project, which together deliver an economic impact not achievable by any one company. More information can be found at

# # #

The Linux Foundation has registered trademarks and uses trademarks. For a list of trademarks of The Linux Foundation, please see our trademark usage page: Linux is a registered trademark of Linus Torvalds.