---
title: NVIDIA GTC 2018
description: 'Event Information Date: March 27 – 29 City/State: San Jose, CA Location:
  San Jose McEnery Convention Center Booth: #1227 GTC is the premier AI and deep learning
  conference, providing unparalleled training, industry insights, and direct access
  to NVIDIA and industry experts — all in one place. It features the latest breakthroughs
  in everyt […]'
url: https://canonical.com/blog/nvidia-gtc-2018?format=md
---

1. [Blog](https://canonical.com/blog)
2. Article

---

[Canonical](https://canonical.com/blog/author/canonical "More about Canonical")

14 March 2018

# NVIDIA GTC 2018

[cloud](https://canonical.com/blog/tag/cloud)
[GPU](https://canonical.com/blog/tag/gpu)
[IoT](https://canonical.com/blog/tag/iot)
[kubernetes](https://canonical.com/blog/tag/kubernetes)
[nvidia](https://canonical.com/blog/tag/nvidia)
[TensorFlow](https://canonical.com/blog/tag/tensorflow)

---

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## Event Information

**Date:** March 27 – 29

**City/State:** San Jose, CA

**Location:** San Jose McEnery Convention Center

**Booth:** #1227

GTC is the premier AI and deep learning conference, providing unparalleled training, industry insights, and direct access to NVIDIA and industry experts — all in one place. It features the latest breakthroughs in everything from healthcare to virtual reality, accelerated analytics, self-driving cars, and much more.

This year the Ubuntu team will be on site at **booth 1227**. Join us for demos on

* Using Kubeflow to manage Tensorflow jobs from desktop to servers and cloud.
* Building a complete, on premise AI platform using automation tools (MAAS and Juju) to deploy and manage Kubernetes on bare metal.
* How worker nodes with and without GPU’s are automatically identified to execute Tensorflow jobs.

## Talks:

[Using Containers for GPU Workloads:](https://2018gputechconf.smarteventscloud.com/connect/sessionDetail.ww?SESSION_ID=148747) **March 28, 10:00AM – 10:50AM – Room 210E**

**Presenters:** Christian Brauner – Software Engineer, Canonical Ltd. & Serge Hallyn: Principal Engineer, Cisco

Learn how to use containers for efficient GPU utilization to achieve bare-metal performance for computationally intensive workloads. We’ll show how NVIDIA tools and libraries can be used to achieve drop-in GPU support and efficient GPU feature integration for container runtimes, and illustrate how to leverage system containers to run complex statistical models on NVIDIA GPUs.

## Tell us about your project

Come discuss with our team to find out how Ubuntu can help you in your AI and deep learning projects.

[Book a meeting](https://www.ubuntu.com/cloud/contact-us)

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