GTC ON-DEMAND

 
SEARCH SESSIONS
SEARCH SESSIONS

Search All
 
Refine Results:
 
Year(s)

SOCIAL MEDIA

EMAIL SUBSCRIPTION

 
 

GTC ON-DEMAND

Presentation
Media
Abstract:
Well share our best practices for extending AI compute power to your teams without the need to build and manage a data center. Our innovative approaches will enable you to turn your NVIDIA DGX Station into a powerful departmental solution serving entire teams of developers from the convenience of an office environment. Teams building powerful AI applications might not need to own servers or depend on datacenter access. Well show how to use containers; orchestration tools such as Kubernetes and Kubeflow; and scheduling tools like Slum. Step-by-step demos will illustrate how to easily set up an AI workgroup.
Well share our best practices for extending AI compute power to your teams without the need to build and manage a data center. Our innovative approaches will enable you to turn your NVIDIA DGX Station into a powerful departmental solution serving entire teams of developers from the convenience of an office environment. Teams building powerful AI applications might not need to own servers or depend on datacenter access. Well show how to use containers; orchestration tools such as Kubernetes and Kubeflow; and scheduling tools like Slum. Step-by-step demos will illustrate how to easily set up an AI workgroup.  Back
 
Topics:
Accelerated Data Science, AI Application, Deployment & Inference
Type:
Talk
Event:
GTC Washington D.C.
Year:
2019
Session ID:
DC91209
Download:
Share:
 
Abstract:
Learn from NVIDIA customers who will share their best practices for extending AI compute power to their teams without the need to build and manage a data center. These organizations will describe innovative approaches that let them turn an NVIDIA DGX Station into a powerful solution serving entire teams of developers from the convenience of an office environment. Learn how teams building powerful AI applications may not need to own servers or depend on data center access and find out how to take advantage of containers, orchestration, monitoring, and scheduling tools. The organizations will also show demos of how to set up an AI work group with ease and cover best practices for AI developer productivity.
Learn from NVIDIA customers who will share their best practices for extending AI compute power to their teams without the need to build and manage a data center. These organizations will describe innovative approaches that let them turn an NVIDIA DGX Station into a powerful solution serving entire teams of developers from the convenience of an office environment. Learn how teams building powerful AI applications may not need to own servers or depend on data center access and find out how to take advantage of containers, orchestration, monitoring, and scheduling tools. The organizations will also show demos of how to set up an AI work group with ease and cover best practices for AI developer productivity.  Back
 
Topics:
Deep Learning & AI Frameworks, Data Center & Cloud Infrastructure
Type:
Talk
Event:
GTC Silicon Valley
Year:
2019
Session ID:
S9483
Streaming:
Download:
Share:
 
Abstract:

We'll do a dive deep into best practices and real world examples of leveraging the power and flexibility of local GPU workstations, such as the DGX Station, to rapidly develop and prototype deep learning applications. This journey will take you from experimenting and iterating fast and often, to obtaining a trained model, to eventually deploying scale-out GPU inference servers in a datacenter. Tools available, that will be explained, are NGC (NVIDIA GPU Cloud), TensorRT, TensorRT Inference Server, and YAIS. NGC is a cloud registry of docker images, TensorRT is an inference optimizing compiler, TensorRT Inference Server is a containerized microservice that maximizes GPU utilization and runs multiple models from different frameworks concurrently on a node, and YAIS is a C++ library for developing compute intensive asynchronous microservices using gRPC. This session will consist of a lecture, live demos, and detailed instructions about different inference compute options, pre- and post-processing considerations, inference serving options, monitoring considerations, and scalable workload orchestration using Kubernetes.

We'll do a dive deep into best practices and real world examples of leveraging the power and flexibility of local GPU workstations, such as the DGX Station, to rapidly develop and prototype deep learning applications. This journey will take you from experimenting and iterating fast and often, to obtaining a trained model, to eventually deploying scale-out GPU inference servers in a datacenter. Tools available, that will be explained, are NGC (NVIDIA GPU Cloud), TensorRT, TensorRT Inference Server, and YAIS. NGC is a cloud registry of docker images, TensorRT is an inference optimizing compiler, TensorRT Inference Server is a containerized microservice that maximizes GPU utilization and runs multiple models from different frameworks concurrently on a node, and YAIS is a C++ library for developing compute intensive asynchronous microservices using gRPC. This session will consist of a lecture, live demos, and detailed instructions about different inference compute options, pre- and post-processing considerations, inference serving options, monitoring considerations, and scalable workload orchestration using Kubernetes.

  Back
 
Topics:
Artificial Intelligence and Deep Learning, Data Center & Cloud Infrastructure, Developer Tools
Type:
Talk
Event:
GTC Washington D.C.
Year:
2018
Session ID:
DC8147
Streaming:
Share:
 
Abstract:
We'll do a dive deep into best practices and real world examples of leveraging the power and flexibility of local GPU workstations, such as the DGX Station, to rapidly develop and prototype deep learning applications. This journey will take you from experimenting and iterating fast and often, to obtaining a trained model, to eventually deploying scale-out GPU inference servers in a datacenter. Tools available, that will be explained, are NGC (NVIDIA GPU Cloud), TensorRT, TensorRT Inference Server, and YAIS.
We'll do a dive deep into best practices and real world examples of leveraging the power and flexibility of local GPU workstations, such as the DGX Station, to rapidly develop and prototype deep learning applications. This journey will take you from experimenting and iterating fast and often, to obtaining a trained model, to eventually deploying scale-out GPU inference servers in a datacenter. Tools available, that will be explained, are NGC (NVIDIA GPU Cloud), TensorRT, TensorRT Inference Server, and YAIS.  Back
 
Topics:
Artificial Intelligence and Deep Learning, HPC and Supercomputing
Type:
Talk
Event:
GTC Europe
Year:
2018
Session ID:
E8150
Streaming:
Download:
Share:
 
Abstract:
We'll do a dive deep into best practices and real world examples of leveraging the power and flexibility of local GPU workstations, such has the DGX Station, to rapidly develop and prototype deep learning applications. We'll demonstrate the setup of our small lab, which is capable of supporting a team of several developers/researchers, and our journey as we moved from lab to data center. Specifically, we'll walk through our experience of building the TensorRT Inference Demo, aka Flowers, used by Jensen to demonstrate the value of GPU computing throughout the world-wide GTCs. As an added bonus, get first-hand insights into the latest advancements coming to AI workstations this year. The flexibility for fast prototyping provided by our lab was an invaluable asset as we toyed with different software and hardware components. As the models and applications stabilized and we moved from lab to data center, we were able to run fully load-balanced over 64 V100s serving video inference demonstrating Software-in-the-Loop's (SIL) ReSim capabilities for Autonomous Vehicles at GTC EU. Real live examples will be given.
We'll do a dive deep into best practices and real world examples of leveraging the power and flexibility of local GPU workstations, such has the DGX Station, to rapidly develop and prototype deep learning applications. We'll demonstrate the setup of our small lab, which is capable of supporting a team of several developers/researchers, and our journey as we moved from lab to data center. Specifically, we'll walk through our experience of building the TensorRT Inference Demo, aka Flowers, used by Jensen to demonstrate the value of GPU computing throughout the world-wide GTCs. As an added bonus, get first-hand insights into the latest advancements coming to AI workstations this year. The flexibility for fast prototyping provided by our lab was an invaluable asset as we toyed with different software and hardware components. As the models and applications stabilized and we moved from lab to data center, we were able to run fully load-balanced over 64 V100s serving video inference demonstrating Software-in-the-Loop's (SIL) ReSim capabilities for Autonomous Vehicles at GTC EU. Real live examples will be given.  Back
 
Topics:
Deep Learning & AI Frameworks, HPC and AI
Type:
Talk
Event:
GTC Silicon Valley
Year:
2018
Session ID:
S8263
Streaming:
Share:
 
Abstract:

NVIDIA DGX Systems powered by Volta deliver breakthrough performance for today''s most popular deep learning frameworks. Attend this session to hear from DGX product experts and gain insights that will help researchers, developers, and data science practitioners accelerate training and iterate faster than ever. Learn (1) best practices for deploying an end-to-end deep learning practice, (2) how the newest DGX systems including DGX Station address the bottlenecks impacting your data science, and (3) how DGX software including optimized deep learning frameworks give your environment a performance advantage over GPU hardware alone.

NVIDIA DGX Systems powered by Volta deliver breakthrough performance for today''s most popular deep learning frameworks. Attend this session to hear from DGX product experts and gain insights that will help researchers, developers, and data science practitioners accelerate training and iterate faster than ever. Learn (1) best practices for deploying an end-to-end deep learning practice, (2) how the newest DGX systems including DGX Station address the bottlenecks impacting your data science, and (3) how DGX software including optimized deep learning frameworks give your environment a performance advantage over GPU hardware alone.

  Back
 
Topics:
Accelerated Data Science, Computer Vision, HPC and AI
Type:
Talk
Event:
GTC Europe
Year:
2017
Session ID:
23370
Download:
Share:
 
Abstract:

Deep learning practitioners have traditionally been forced to spend protracted cycle time cobbling together platforms using consumer-grade components and unsupported open source software. Learn (1) the benefits of rapid experimentation and deep learning framework optimization as a precursor to scalable production training in the data center, (2) the technical challenges that must be overcome for extending deep learning to more practitioners across the enterprise, and (3) how many organizations can benefit from a powerful enterprise-grade solution that's pre-built, simple to manage, and readily accessible to every practitioner.

Deep learning practitioners have traditionally been forced to spend protracted cycle time cobbling together platforms using consumer-grade components and unsupported open source software. Learn (1) the benefits of rapid experimentation and deep learning framework optimization as a precursor to scalable production training in the data center, (2) the technical challenges that must be overcome for extending deep learning to more practitioners across the enterprise, and (3) how many organizations can benefit from a powerful enterprise-grade solution that's pre-built, simple to manage, and readily accessible to every practitioner.

  Back
 
Topics:
Artificial Intelligence and Deep Learning
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7753
Download:
Share:
 
 
Previous
  • Amazon Web Services
  • IBM
  • Cisco
  • Dell EMC
  • Hewlett Packard Enterprise
  • Inspur
  • Lenovo
  • SenseTime
  • Supermicro Computers
  • Synnex
  • Autodesk
  • HP
  • Linear Technology
  • MSI Computer Corp.
  • OPTIS
  • PNY
  • SK Hynix
  • vmware
  • Abaco Systems
  • Acceleware Ltd.
  • ASUSTeK COMPUTER INC
  • Cray Inc.
  • Exxact Corporation
  • Flanders - Belgium
  • Google Cloud
  • HTC VIVE
  • Liqid
  • MapD
  • Penguin Computing
  • SAP
  • Sugon
  • Twitter
Next