![[Uber Seattle] Horovod: Distributed Deep Learning on Spark @UberEngineering](https://i.ytimg.com/vi/fZ9P1v1jtFM/hqdefault.jpg)
Uploaded May 2019 | Updated September 2026, 2 weeks ago
During this April 2019 meetup, Uber engineer Travis Addair introduces the concepts that make Horovod work, and walks through how to make use of Horovod on Spark to add distributed training to machine learning pipelines. Horovod is a distributed training framework for TensorFlow, PyTorch, Keras, and MXNet. Scaling to hundreds of GPUs, Horovod can reduce training time from hours to minutes with just a handful of lines added to existing single-GPU training processes.
During this April 2019 meetup, Uber engineer Travis Addair introduces the concepts that make Horovod work, and walks through how to make use of Horovod on Spark to add distributed training to machine learning pipelines. Horovod is a distributed training framework for TensorFlow, PyTorch, Keras, and MXNet. Scaling to hundreds of GPUs, Horovod can reduce training time from hours to minutes with just a handful of lines added to existing single-GPU training processes.
![[Uber Seattle] Horovod: Distributed Deep Learning on Spark](https://i.ytimg.com/vi/fZ9P1v1jtFM/mqdefault.jpg "[Uber Seattle] Horovod: Distributed Deep Learning on Spark
During this April 2019 meetup, Uber engineer Travis Addair introduces the concepts that make Horovod work, and walks through how to make use of Horovod on Spark to add distributed training to machine learning pipelines. Horovod is a distributed training framework for TensorFlow, PyTorch, Keras, and MXNet. Scaling to hundreds of GPUs, Horovod can reduce training time from hours to minutes with just a handful of lines added to existing single-GPU training processes.")
![[WiSDOM] An Intriguing Failing of Convolutional NNs & the CoordConv Solution Rosanne Liu](https://i.ytimg.com/vi/gMGL-shl3P8/mqdefault.jpg "[WiSDOM] An Intriguing Failing of Convolutional NNs & the CoordConv Solution Rosanne Liu
During an October 2018 meetup, Uber WiSDOM (Women in Statistics, Data Optimization, and Machine Learning) member & Uber AI researcher Rosanne Liu discuses how convolutional neural networks often fail to complete seemingly trivial tasks and introduces a novel solution to to improve their performance: the CoordConv layer.")
![[Go NYC] End-to-End Testing with Golang Adrian Witas](https://i.ytimg.com/vi/hfVAXTi39UA/mqdefault.jpg "[Go NYC] End-to-End Testing with Golang Adrian Witas
During an October 2018 Go Language NYC meetup, Adrian Witas, VP Software Architect at Viant, discusses how to conduct end-to-end testing with Go.")
![[Uber Mobility] Profiler Performance - Brian Attwell](https://i.ytimg.com/vi/ime9EBuvxDA/mqdefault.jpg "[Uber Mobility] Profiler Performance - Brian Attwell
In this presentation during a March 2018 meetup, staff engineer Brian Attwell introduces Nanoscope, a method tracing tool for Android that improves app performance.
Read the Eng Blog article: https://eng.uber.com/nanoscope/")
![[Visualization Nights] Designing for Complex Data Visualization - Elijah Meeks](https://i.ytimg.com/vi/iqWSJLFa9hg/mqdefault.jpg "[Visualization Nights] Designing for Complex Data Visualization - Elijah Meeks
While Netflix uses simple charts in various applications across, there are analytical views that require complex charts. During our June 2018 Visualization Night, Netflixs Elijah Meeks explores the design and implementation of network visualization to represent flow of Netflix users through different aspects of the product.")
![[Distributed Tracing NYC] Tracing @ Facebook Edison Gao & Michael Bevilacqua-Linn](https://i.ytimg.com/vi/j6no6JozkZU/mqdefault.jpg "[Distributed Tracing NYC] Tracing @ Facebook Edison Gao & Michael Bevilacqua-Linn
During an October 2018 Distributed Tracing NYC meetup, Facebook engineers Edison Gao & Michael Bevilacqua-Linn offer a brief overview of Facebooks distributed tracing system, Canopy, and how/why it differs from the more standard OpenTracing/Dapper model.")
![[MoneyCon 2019] Welcome & Keynote](https://i.ytimg.com/vi/jItgWGO5qHE/mqdefault.jpg "[MoneyCon 2019] Welcome & Keynote
The domain of Payments, Finance, and more generally FinTech, is a fast-growing industry that reached record global investment of $111.8B in 2018. The underlying technology that powers this incredible growth is also evolving rapidly. Hosted by Uber, MoneyCon 2019 brought together engineers from leading tech companies to present on the latest topics in payments engineering.
During MoneyCon 2019s opening keynote, Uber engineering director Lee Crawford provides an overview of the primary technical challenges Uber faces while solving global payments at scale.")
![[Visualization Nights] Introduction to Ubers Movement Speeds Dataset](https://i.ytimg.com/vi/jkhPdi3-W20/mqdefault.jpg "[Visualization Nights] Introduction to Ubers Movement Speeds Dataset
During this May 2019 meetup in New York City, the Uber Visualization gave a quick introduction to the new Uber Movement Street Speeds product and how to glean insights using Kepler.gl, a powerful, open source geospatial analysis tool for large-scale datasets.")
![[Destination:Web] Progressive Enhancement: Not Just For Websites](https://i.ytimg.com/vi/k0O-fE__17o/mqdefault.jpg "[Destination:Web] Progressive Enhancement: Not Just For Websites
During a May 2019 meetup hosted at Uber’s San Francisco office, Uber engineer Ryan Tsao discusses why progressive enhancement as a design principle has never been more relevant in the web development world.")
