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Horovod v0.21: Optimizing Network Utilization with Local Gradient Aggregation and Grouped Allreduce

We originally open-sourced Horovod in 2017, and since then it has grown to become the standard solution in industry for scaling deep learning training to hundreds of GPUs.  With Horovod, you can reduce training times from days or weeks to hours or minutes by adding just a few lines of Python code to an existing TensorFlow, PyTorch, or Apache...

Turning Metadata Into Insights with Databook

Every day in over 10,000 cities around the world, millions of people rely on Uber to travel, order food, and ship cargo. Our apps and services are available in over 69 countries and run 24 hours a day. At our global scale, these activities generate large amounts of logging & operational data that runs through our systems in real-time....

Meet the 2020 Safety Engineering Interns: COVID Edition

About the Safety team & What we do Uber is dedicated to keeping people safe on the road. The Safety and Insurance Engineering team is at the core of Uber’s business. We work to redefine what it takes to be safe on the roads at a global scale. Our technology enables us to focus on rider safety before, during, and...

Operating Apache Pinot @ Uber Scale

Introduction Uber has a complex marketplace consisting of riders, drivers, eaters, restaurants and so on. Operating that marketplace at a global scale requires real-time intelligence and decision making. For instance, identifying delayed Uber Eats orders or abandoned carts helps to enable our community operations team to take corrective action. Having a real-time dashboard of different events such as consumer demand,...

Building from the Baltics: Meet the Uber Engineering Team in Vilnius, Lithuania

The Uber Vilnius office is home to members of our Production Engineering, Infrastructure, Storage Platform, and Developer Tools team.

Ludwig v0.3 Introduces Hyperparameter Optimization, Transformers and TensorFlow 2 support

In February 2019, Uber released Ludwig, an open source, code-free deep learning (DL) toolbox that gives non-programmers and advanced machine learning (ML) practitioners alike the power to develop models for a variety of DL tasks. With use cases spanning text classification, natural language understanding, image classification, and time series forecasting, among many others, Ludwig gives users the ability to...

Revolutionizing Money Movements at Scale with Strong Data Consistency

Uber as a platform invites its users to leverage it, earn from it, and be delighted by it. Serving more than 18 million requests per day, in 10,000+ cities, has enabled people to move freely and to think broadly while earning a livelihood on it. As one of the underlying engines, Uber Money fulfills some of the most important...

Spearheading Open Source: A Conversation with Jim Jagielski, Staff Technical Program Manager with the Uber Open Source Program Office

Jim Jagielski's fascination with open source software began out of necessity. He was working at NASA Goddard in the 1980s, and the agency had just received fancy new Macintosh computers loaded with Apple's new A/UX operating system. There was only one problem: None of the tools Jagielski needed ran on A/UX. It fell to Jagielski to port everything himself. "That’s...

Designing Edge Gateway, Uber’s API Lifecycle Management Platform

The making of Edge Gateway, the highly-available and scalable self-serve gateway to configure, manage, and monitor APIs of every business domain at Uber. Evolution of Uber's API gateway In October 2014, Uber had started its journey of scale in what would eventually turn out to be one of the most impressive growth phases in the company. Over time we were scaling...

Standing for Safety: Meet the Uber Sao Paulo Tech Team

Located in the heart of Latin America’s largest city, the Uber Sao Paulo Tech Center was founded in late 2018 as a company-wide hub for Safety Tech. The team is composed of product managers, UX designers, engineers and data scientists. As part of Uber’s mission to put the safety of our users first, our Sao Paulo-based Tech team is...

Introducing Domain-Oriented Microservice Architecture

Introduction Recently there has been substantial discussion around the downsides of service oriented architectures and microservice architectures in particular. While only a few years ago, many people readily adopted microservice architectures due to the numerous benefits they provide such as flexibility in the form of independent deployments, clear ownership, improvements in system stability, and better separation of concerns, in recent...

Engineering Failover Handling in Uber’s Mobile Networking Infrastructure

  Millions of users use Uber’s applications everyday across the globe, accessing seamless transportation or meal delivery at the push of a button. To achieve this accessibility at scale, our mobile apps require low-latency and highly reliable network communication, regardless of where customers use our services. The network communication for all of Uber’s mobile applications are powered by the edge and...

Fiber: Distributed Computing for AI Made Simple

Project Homepage: GitHub Over the past several years, increasing processing power of computing machines has led to an increase in machine learning advances. More and more, algorithms exploit parallelism and rely on distributed training to process an enormous amount of data. However, the resulting need to increase both data and training impose great challenges on the software that manages and...

Editing Massive Geospatial Data Sets with nebula.gl

Uber built and open sourced nebula.gl, a tool set for full-featured geospatial editing in the web browser, to better visualize large-scale data sets.

Profiles in Coding: Diana Yanakiev, Uber ATG, Pittsburgh

Self-driving cars have long been considered the future of transportation, but they’re becoming more present everyday. Uber ATG (Advanced Technologies Group) is at the forefront of this technology, helping bring safe, reliable self-driving vehicles to the streets. Of course, this wouldn’t be possible without the work of the engineers building the ATG platform’s underlying technologies.  As the senior manager for...

Building a Large-scale Transactional Data Lake at Uber Using Apache Hudi

The Apache Hudi team at Uber reflects on the open source project's history as it graduates to a Top Level Project under the Apache Software Foundation.

Introducing Neuropod, Uber ATG’s Open Source Deep Learning Inference Engine

Developed by Uber ATG, Neuropod is an abstraction layer that provides a universal interface to run models across any deep learning framework.

Inside Uber ATG’s Data Mining Operation: Identifying Real Road Scenarios at Scale for Machine Learning

Uber ATG's self-driving vehicles measure a multitude of possible scenario variations to answer the age-old question: "how does the pedestrian cross the road?"

Meta-Graph: Few-Shot Link Prediction Using Meta-Learning

Uber AI introduces Meta-Graph, a new few-shot link prediction framework that facilitates the more accurate training of ML models that quickly adapt to new graph data.

Profiles in Coding: Christabelle Bosson, Uber Elevate, San Francisco

Christabelle Bosson, a senior advanced airspaces services engineer, discusses her journey from NASA to Uber Elevate, what excites her about the future of aerial ridesharing, and advice for aspiring aerospace engineers.

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