Kari Briski's guest appearances

Kari Briski

Google Cloud Platform Podcast

Platforms Windows, Linux, Apple all the Tech Info you can Handle on the Various Platforms

Dave Elliott

Andrew Walker

Peter Norvig

Eric Anderson

John Bohannon

Manuel Lima

Margaret Mitchell

Jessie Frazelle

Joanna Smith

Juliet Hougland

Michelle Casbon

Jesse Houston

Mark Chmarny

Ville Aikas

Dale Markowitz

OwlTail

Ian Buck

Today on the podcast, we speak with Ian Buck and Kari Briski of NVIDIA about new updates and achievements in deep learning. Ian begins by telling hosts Jon and Mark about his first project at NVIDIA, CUDA, and how it has helped expand and pave the way for future projects in super computing, AI, and gaming. CUDA is used extensively in computer vision, speech and audio applications, and machine comprehension, Kari elaborates. NVIDIA recently announced their new Tensor Cores, which maximize their GPUs and make it easier for users to achieve peak performance. Working with the Tensor Cores, TensorFlow AMP is an acceleration into the TensorFlow Framework. It automatically makes the right choices for neural networks and maximizes performance, while still maintaining accuracy, with only a two line change in Tensor Flow script. Just last year, NVIDIA announced their T4 GPU with Google Cloud Platform. This product is designed for inferences, the other side of AI. Because AI is becoming so advanced, complicated, and fast, the GPUs on the inference side have to be able to handle the workload and produce inferences just as quickly. T4 and Google Cloud accomplish this together. Along with T4, NVIDIA has introduced TensorRT, a software framework for AI inference that’s integrated into TensorFlow. Ian Buck Ian Buck is general manager and vice president of Accelerated Computing at NVIDIA. He is responsible for the company’s worldwide datacenter business, including server GPUs and the enabling NVIDIA computing software for AI and HPC used by millions of developers, researchers and scientists. Buck joined NVIDIA in 2004 after completing his PhD in computer science from Stanford University, where he was development lead for Brook, the forerunner to generalized computing on GPUs. He is also the creator of CUDA, which has become the world’s leading platform for accelerated parallel computing. Buck has testified before the U.S. Congress on artificial intelligence and has advised the White House on the topic. Buck also received a BSE degree in computer science from Princeton University. Kari Briski Kari Briski is a Senior Director of Accelerated Computing Software Product Management at NVIDIA. Her talents and interests include Deep Learning, Accelerated Computing, Design Thinking, and supporting women in technology. Kari is also a huge Steelers fan. Cool things of the week  Kubernetes 1.14: Production-level support for Windows Nodes, Kubectl Updates, Persistent Local Volumes GA  blog Stadia  blog How Google Cloud helped Multiplay power a record-breaking Apex Legends launch  blog Massive Entertainment hosts Tom Clancy’s The Division 2 on Google Cloud Platform  blog  Interview  NVIDIA site NVIDIA Catalog site CUDA site Tensor Cores site TensorFlow sote Automatic Mixed Precision for Deep Learning site Automatic Mixed Precision for NVIDIA Tensor Core Architecture in TensorFlow  blog TensorFlow 2.0 on NVIDIA GPU video NVIDIA Volta site NVIDIA T4 site WaveNet blog BERT blog Compute Engine site T4 on GCP site Webinar On Demand: Accelerate Your AI Models with Automatic Mixed-Precision Training in PyTorch  site PyTorch site NVIDIA TensorRT site TensorRT 5.1  site Kubernetes site Rapids site NVIDIA GTC site Deep Learning Institute site KubeFlow Pipeline Docs site KubeFlow Pipelines on GitHub site NVIDIA RTX site  Question of the week Where can we learn more about Stadia?  general info developer access  Where can you find us next? Mark will be at Cloud NEXT, ECGC, and IO. Jon may be going to Unite Shanghai and will definitely be at Cloud NEXT, ECGC, and IO. NVIDIA will be at Cloud NEXT and  KubeCon, as well as International Conference on Machine Learning, The International Conference on Learning Representations, and CVPR

Today on the podcast, we speak with <a href="https://twitter.com/ia_buck" rel="nofollow" target="_blank">Ian Buck</a> and <a href="https://twitter.com/karibriski" rel="nofollow" target="_blank">Kari Briski</a> of NVIDIA about new updates and achievements in deep learning. Ian begins by telling hosts <a href="https://twitter.com/syntxerror1" rel="nofollow" target="_blank">Jon</a> and <a href="https://twitter.com/Neurotic" rel="nofollow" target="_blank">Mark</a> about his first project at NVIDIA, CUDA, and how it has helped expand and pave the way for future projects in super computing, AI, and gaming. CUDA is used extensively in computer vision, speech and audio applications, and machine comprehension, Kari elaborates. NVIDIA recently announced their new Tensor Cores, which maximize their GPUs and make it easier for users to achieve peak performance. Working with the Tensor Cores, TensorFlow AMP is an acceleration into the TensorFlow Framework. It automatically makes the right choices for neural networks and maximizes performance, while still maintaining accuracy, with only a two line change in Tensor Flow script. Just last year, NVIDIA announced their T4 GPU with Google Cloud Platform. This product is designed for inferences, the other side of AI. Because AI is becoming so advanced, complicated, and fast, the GPUs on the inference side have to be able to handle the workload and produce inferences just as quickly. T4 and Google Cloud accomplish this together. Along with T4, NVIDIA has introduced TensorRT, a software framework for AI inference that’s integrated into TensorFlow. Ian Buck <a href="https://twitter.com/ia_buck" rel="nofollow" target="_blank">Ian Buck</a> is general manager and vice president of Accelerated Computing at NVIDIA. He is responsible for the company’s worldwide datacenter business, including server GPUs and the enabling NVIDIA computing software for AI and HPC used by millions of developers, researchers and scientists. Buck joined NVIDIA in 2004 after completing his PhD in computer science from Stanford University, where he was development lead for Brook, the forerunner to generalized computing on GPUs. He is also the creator of CUDA, which has become the world’s leading platform for accelerated parallel computing. Buck has testified before the U.S. Congress on artificial intelligence and has advised the White House on the topic. Buck also received a BSE degree in computer science from Princeton University. Kari Briski <a href="https://twitter.com/karibriski" rel="nofollow" target="_blank">Kari Briski</a> is a Senior Director of Accelerated Computing Software Product Management at NVIDIA. Her talents and interests include Deep Learning, Accelerated Computing, Design Thinking, and supporting women in technology. Kari is also a huge Steelers fan. Cool things of the week <ul> <li>Kubernetes 1.14: Production-level support for Windows Nodes, Kubectl Updates, Persistent Local Volumes GA <a href="https://kubernetes.io/blog/2019/03/25/kubernetes-1-14-release-announcement/" rel="nofollow" target="_blank"> blog</a></li> <li>Stadia <a href="https://www.blog.google/products/stadia/stadia-a-new-way-to-play/" rel="nofollow" target="_blank"> blog</a></li> <li>How Google Cloud helped Multiplay power a record-breaking Apex Legends launch <a href="https://cloud.google.com/blog/topics/customers/how-google-cloud-helped-multiplay-power-a-record-breaking-apex-legends-launch" rel="nofollow" target="_blank"> blog</a></li> <li>Massive Entertainment hosts Tom Clancy’s The Division 2 on Google Cloud Platform <a href="https://cloud.google.com/blog/topics/customers/massive-entertainment-hosts-tom-clancys-the-division-2-on-google-cloud-platform" rel="nofollow" target="_blank"> blog</a></li> </ul> Interview <ul> <li>NVIDIA <a href="https://www.nvidia.com/en-us/" rel="nofollow" target="_blank">site</a></li> <li>NVIDIA Catalog <a href="https://ngc.nvidia.com/catalog/landing" rel="nofollow" target="_blank">site</a></li> <li>CUDA <a href="https://developer.nvidia.com/cuda-toolkit" rel="nofollow" target="_blank">site</a></li> <li>Tensor Cores <a href="https://developer.nvidia.com/tensor-cores" rel="nofollow" target="_blank">site</a></li> <li>TensorFlow <a href="https://www.tensorflow.org" rel="nofollow" target="_blank">sote</a></li> <li>Automatic Mixed Precision for Deep Learning <a href="https://developer.nvidia.com/automatic-mixed-precision" rel="nofollow" target="_blank">site</a></li> <li>Automatic Mixed Precision for NVIDIA Tensor Core Architecture in TensorFlow <a href="https://devblogs.nvidia.com/nvidia-automatic-mixed-precision-tensorflow/" rel="nofollow" target="_blank"> blog</a></li> <li>TensorFlow 2.0 on NVIDIA GPU <a href="https://youtu.be/26t8MfP8Fo0" rel="nofollow" target="_blank">video</a></li> <li>NVIDIA Volta <a href="https://www.nvidia.com/en-us/data-center/volta-gpu-architecture/" rel="nofollow" target="_blank">site</a></li> <li>NVIDIA T4 <a href="https://www.nvidia.com/en-us/data-center/tesla-t4/" rel="nofollow" target="_blank">site</a></li> <li>WaveNet <a href="https://deepmind.com/blog/wavenet-generative-model-raw-audio/" rel="nofollow" target="_blank">blog</a></li> <li>BERT <a href="https://arxiv.org/pdf/1810.04805.pdf" rel="nofollow" target="_blank">blog</a></li> <li>Compute Engine <a href="https://cloud.google.com/compute/" rel="nofollow" target="_blank">site</a></li> <li>T4 on GCP <a href="https://cloud.google.com/nvidia/" rel="nofollow" target="_blank">site</a></li> <li>Webinar On Demand: Accelerate Your AI Models with Automatic Mixed-Precision Training in PyTorch <a href="https://info.nvidia.com/webinar-mixed-precision-with-pytorch-reg-page.html" rel="nofollow" target="_blank"> site</a></li> <li>PyTorch <a href="https://pytorch.org" rel="nofollow" target="_blank">site</a></li> <li>NVIDIA TensorRT <a href="https://www.developer.nvidia.com/tensorrt" rel="nofollow" target="_blank">site</a></li> <li>TensorRT 5.1 <a href="https://news.developer.nvidia.com/speed-up-new-models-with-tensorrt-updates/" rel="nofollow" target="_blank"> site</a></li> <li>Kubernetes <a href="https://kubernetes.io" rel="nofollow" target="_blank">site</a></li> <li>Rapids <a href="https://developer.nvidia.com/rapids" rel="nofollow" target="_blank">site</a></li> <li>NVIDIA GTC <a href="https://www.nvidia.com/en-us/gtc/" rel="nofollow" target="_blank">site</a></li> <li>Deep Learning Institute <a href="https://www.nvidia.com/en-us/deep-learning-ai/education/" rel="nofollow" target="_blank">site</a></li> <li>KubeFlow Pipeline Docs <a href="https://www.kubeflow.org/docs/pipelines/pipelines-overview/" rel="nofollow" target="_blank">site</a></li> <li>KubeFlow Pipelines on GitHub <a href="https://github.com/kubeflow/pipelines" rel="nofollow" target="_blank">site</a></li> <li>NVIDIA RTX <a href="https://developer.nvidia.com/rtx" rel="nofollow" target="_blank">site</a></li> </ul> Question of the week Where can we learn more about Stadia? <ul> <li><a href="https://stadia.dev" rel="nofollow" target="_blank">general info</a></li> <li><a href="https://stadia.dev/apply" rel="nofollow" target="_blank">developer access</a></li> </ul> Where can you find us next? Mark will be at <a href="https://cloud.withgoogle.com/next/sf" rel="nofollow" target="_blank">Cloud NEXT</a>, <a href="http://ecgconf.com" rel="nofollow" target="_blank">ECGC</a>, and <a href="https://events.google.com/io/" rel="nofollow" target="_blank">IO</a>. Jon may be going to <a href="https://connect.unity.com/events/UniteShanghai2019" rel="nofollow" target="_blank">Unite Shanghai</a> and will definitely be at <a href="https://cloud.withgoogle.com/next/sf" rel="nofollow" target="_blank">Cloud NEXT</a>, <a href="http://ecgconf.com" rel="nofollow" target="_blank">ECGC</a>, and <a href="https://events.google.com/io/" rel="nofollow" target="_blank">IO</a>. NVIDIA will be at <a href="https://cloud.withgoogle.com/next/sf" rel="nofollow" target="_blank">Cloud NEXT</a> and <a href="https://events.linuxfoundation.org/events/kubecon-cloudnativecon-north-america-2019/" rel="nofollow" target="_blank"> KubeCon</a>, as well as <a href="https://icml.cc" rel="nofollow" target="_blank">International Conference on Machine Learning</a>, <a href="https://iclr.cc" rel="nofollow" target="_blank">The International Conference on Learning Representations</a>, and <a href="http://cvpr2019.thecvf.com" rel="nofollow" target="_blank">CVPR</a>

NVIDIA T4 with Ian Buck and Kari Briski