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4 DGX POD Sessions from GTC 2019 You Can’t
Miss
How to Accelerate and Scale AI Deployment with
Proven Architecture Designs
While every enterprise is on a mission to infuse its
business with deep learning, few know how to
build the infrastructure to get there. Short-sighted
approaches to data center design can lead to
long-term consequences that make the ROI of AI
elusive. Learn about the insights and best
practices we at NVIDIA have gained from deep
learning deployments around the globe to shorten
deployment timeframes, improve developer
productivity, and streamline operations.
WATCH NOW
Building and Managing Scalable AI Infrastructure with
NVIDIA DGX POD and DGX POD Management Software
NVIDIA DGX POD is a new way of thinking about AI
infrastructure, combining DGX servers with networking
and storage to accelerate AI workflow deployment and
time to insight. Hear the lessons learned about
building, deploying, and managing AI infrastructure at
scale — from design to deployment to management and
monitoring. Our speakers show how the DGX Pod
Management software (DeepOps) along with our
storage partner reference-architectures can be used
for the deployment and management of multi-node
GPU clusters for Deep Learning and HPC environments,
in an on-premise, optionally air-gapped datacenter.
WATCH NOW
Edge to Core: A Meta Study of Data Complexity in AI
(Presented by DDN)
Learn about the challenges uncovered in AI and deep
learning workloads, the most efficient approaches to
handling data, and use cases in autonomous vehicles,
retail, healthcare, finance, and other markets. This
talk covers the complete requirements of the data
life cycle including initial acquisition, processing,
inference, long-term storage, and driving data back
into the field to sustain ever-growing processes of
improvement. DDN provides examples of data life
cycles in production triggering diverse architectures
from turnkey reference systems with DGX and DDN
A3I to tailor-made solutions.
WATCH NOW
Data Loading: The Next Frontier in Scale-Out Deep
Learning (Presented by Pure Storage)
Learn how to create efficient input pipelines that
are tailored to your training data. Gain guidance
on tradeoffs between pre-processing datasets and
in-line data processing, and review results from a
distributed training environment with multiple
NVIDIA DGX-1s and a Pure Storage FlashBlade to
highlight performance impact at scale. Learn how
to maximize time to accuracy and, ultimately,
time to shipping models.
WATCH NOW
EXPLORE THE FULL LIST
OF DGX SESSION REPLAYS
FROM GTC 2019
LEARN MORE

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DGX POD Top 4 Sessions From GTC 2019

  • 1. 4 DGX POD Sessions from GTC 2019 You Can’t Miss
  • 2. How to Accelerate and Scale AI Deployment with Proven Architecture Designs While every enterprise is on a mission to infuse its business with deep learning, few know how to build the infrastructure to get there. Short-sighted approaches to data center design can lead to long-term consequences that make the ROI of AI elusive. Learn about the insights and best practices we at NVIDIA have gained from deep learning deployments around the globe to shorten deployment timeframes, improve developer productivity, and streamline operations. WATCH NOW
  • 3. Building and Managing Scalable AI Infrastructure with NVIDIA DGX POD and DGX POD Management Software NVIDIA DGX POD is a new way of thinking about AI infrastructure, combining DGX servers with networking and storage to accelerate AI workflow deployment and time to insight. Hear the lessons learned about building, deploying, and managing AI infrastructure at scale — from design to deployment to management and monitoring. Our speakers show how the DGX Pod Management software (DeepOps) along with our storage partner reference-architectures can be used for the deployment and management of multi-node GPU clusters for Deep Learning and HPC environments, in an on-premise, optionally air-gapped datacenter. WATCH NOW
  • 4. Edge to Core: A Meta Study of Data Complexity in AI (Presented by DDN) Learn about the challenges uncovered in AI and deep learning workloads, the most efficient approaches to handling data, and use cases in autonomous vehicles, retail, healthcare, finance, and other markets. This talk covers the complete requirements of the data life cycle including initial acquisition, processing, inference, long-term storage, and driving data back into the field to sustain ever-growing processes of improvement. DDN provides examples of data life cycles in production triggering diverse architectures from turnkey reference systems with DGX and DDN A3I to tailor-made solutions. WATCH NOW
  • 5. Data Loading: The Next Frontier in Scale-Out Deep Learning (Presented by Pure Storage) Learn how to create efficient input pipelines that are tailored to your training data. Gain guidance on tradeoffs between pre-processing datasets and in-line data processing, and review results from a distributed training environment with multiple NVIDIA DGX-1s and a Pure Storage FlashBlade to highlight performance impact at scale. Learn how to maximize time to accuracy and, ultimately, time to shipping models. WATCH NOW
  • 6. EXPLORE THE FULL LIST OF DGX SESSION REPLAYS FROM GTC 2019 LEARN MORE