---
title: Blog posts tagged "TensorFlow"
description: Canonical makes open source secure, reliable and easy to use, providing
  support for Ubuntu and a portfolio of enterprise-grade technologies. Founded in
  2004, Canonical operates globally with team members in over 80 countries.
url: https://canonical.com/blog/tag/tensorflow
---

# Blog posts tagged "TensorFlow"

#### [AI, ML, & Ubuntu: Everything you need to know](https://canonical.com/blog/ai-ml-ubuntu-everything-you-need-to-know)

AI and ML adoption in the enterprise is exploding from Silicon Valley to Wall Street. Ubuntu is the premier platform for these ambitions — from developer workstations, to racks, to clouds and to the edge with smart connected IoT. One of the joys that come with new developer trends are a plethora of new technologies

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Cloud and server

#### [Kubernetes and OpenStack solving AI complexities at scale](https://canonical.com/blog/kubernetes-and-openstack-solving-ai-complexities-at-scale)

Stu Miniman and John Boyer of theCUBE interviewed Stephan Fabel, Director of Ubuntu Product and Development at the OpenStack Summit in Vancouver. Read on for the full interview, and to hear more on Kubernetes, Kubeflow and MicroK8s. OpenStack has entered a new phase, maturing to a point where those in attendance are serious about open

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Cloud and server

#### [NVIDIA GTC 2018](https://canonical.com/blog/nvidia-gtc-2018)

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

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Cloud and server

#### [Job concurrency in Kubernetes: LXD and CPU pinning to the rescue](https://canonical.com/blog/job-concurrency-in-kubernetes-lxd-cpu-pinning-to-the-rescue)

A few days ago, someone shared with me a project to run video transcoding jobs in Kubernetes. During her tests, made on a default Kubernetes installation on bare metal servers with 40 cores & 512GB RAM, she allocated 5 full CPU cores to each of the transcoding pods, then scaled up to 6 concurrent tasks

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Company

#### [GPUs and Kubernetes for deep learning — Part 3/3: Automating Tensorflow](https://canonical.com/blog/gpus-kubernetes-for-deep-learning-part-33-automating-tensorflow)

Here we are. After having spent 21min reading how to build a GPU Kubernetes cluster on AWS, 7min on adding EFS storage, you want to get to the real thing, which is actually DO something with it. So today we are going to define, design, deploy and operate a Deep Learning pipeline. So what is

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Cloud and server