OVN (Open Virtual Network) を用いる事により、OVS (Open vSwitch)が動作する複数のサーバー(Hypervisor/Chassis)を横断する仮想ネットワークを構築する事ができます。
本スライドはOVNを用いた論理ネットワークの構成と設定サンプルのメモとなります。
Using OVN, you can build logical network among multiple servers (Hypervisor/Chassis) running OVS (Open vSwitch).
This slide is describes HOW TO example of OVN configuration to create 2 logical switch connecting 4 VMs running on 2 chassis.
Open vSwitch is an open source virtual switch software that is compatible with the Linux standard bridge. The presentation will provide an overview of Open vSwitch, how to use its basic functions such as setting up bridges and ports, and its data structure that is managed in an ovsdb database.
Openstack Neutron, interconnections with BGP/MPLS VPNsThomas Morin
This document discusses the Openstack Neutron networking-bgpvpn project, which provides a Neutron API and service plugin that allows tenants to interconnect their Openstack networks and routers with BGP/MPLS VPNs. The API exposes constructs like BGPVPNs, network associations, and router associations. It works with drivers for Neutron/OVS, OpenDaylight, OpenContrail, and others. The goal is to provide a common way for tenants to control interconnections in a controller-agnostic manner. The project is part of Openstack and OPNFV, and provides a model for integrating telco functionality into Openstack.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/02/introduction-to-the-tvm-open-source-deep-learning-compiler-stack-a-presentation-from-octoml/
Luis Ceze, Co-founder and CEO of OctoML, a Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, and Venture Partner at Madrona Venture Group, presents the “Introduction to the TVM Open Source Deep Learning Compiler Stack” tutorial at the September 2020 Embedded Vision Summit.
There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narrow range of server-class GPUs. Deploying workloads to new platforms — such as mobile phones, embedded devices, and accelerators — requires significant manual effort.
In this talk, Ceze presents his work on the TVM stack, which exposes graph- and operator-level optimizations to provide performance portability for deep learning workloads across diverse hardware back-ends. TVM solves optimization challenges specific to deep learning, such as high-level operator fusion, mapping to arbitrary hardware primitives and memory latency hiding. It also automates optimization of low-level programs to hardware characteristics by employing a novel, learning-based cost modeling method for rapid exploration of optimizations.
Open vSwitch is an open source virtual switch software that is compatible with the Linux standard bridge. The presentation will provide an overview of Open vSwitch, how to use its basic functions such as setting up bridges and ports, and its data structure that is managed in an ovsdb database.
Openstack Neutron, interconnections with BGP/MPLS VPNsThomas Morin
This document discusses the Openstack Neutron networking-bgpvpn project, which provides a Neutron API and service plugin that allows tenants to interconnect their Openstack networks and routers with BGP/MPLS VPNs. The API exposes constructs like BGPVPNs, network associations, and router associations. It works with drivers for Neutron/OVS, OpenDaylight, OpenContrail, and others. The goal is to provide a common way for tenants to control interconnections in a controller-agnostic manner. The project is part of Openstack and OPNFV, and provides a model for integrating telco functionality into Openstack.
For the full video of this presentation, please visit:
https://www.edge-ai-vision.com/2021/02/introduction-to-the-tvm-open-source-deep-learning-compiler-stack-a-presentation-from-octoml/
Luis Ceze, Co-founder and CEO of OctoML, a Professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, and Venture Partner at Madrona Venture Group, presents the “Introduction to the TVM Open Source Deep Learning Compiler Stack” tutorial at the September 2020 Embedded Vision Summit.
There is an increasing need to bring machine learning to a wide diversity of hardware devices. Current frameworks rely on vendor-specific operator libraries and optimize for a narrow range of server-class GPUs. Deploying workloads to new platforms — such as mobile phones, embedded devices, and accelerators — requires significant manual effort.
In this talk, Ceze presents his work on the TVM stack, which exposes graph- and operator-level optimizations to provide performance portability for deep learning workloads across diverse hardware back-ends. TVM solves optimization challenges specific to deep learning, such as high-level operator fusion, mapping to arbitrary hardware primitives and memory latency hiding. It also automates optimization of low-level programs to hardware characteristics by employing a novel, learning-based cost modeling method for rapid exploration of optimizations.
This document provides an overview of OpenStack, including:
- The major components of OpenStack and how they work together through REST APIs and a message queue.
- Key concepts such as tenant virtual networks, private and floating IP addresses, virtual machine instance creation, block volumes, and template image registration.
- Examples of command line operations for the Keystone authentication service.
Your first TensorFlow programming with JupyterEtsuji Nakai
This document provides an introduction and overview of TensorFlow and how to use it with Jupyter notebooks on Google Cloud Platform (GCP). It explains that TensorFlow is Google's open source library for machine learning and was launched in 2015. It is used for many production machine learning projects. Jupyter is introduced as an interactive web-based platform for data analysis that can also be used as a TensorFlow runtime environment. The document then provides details on the programming paradigm and model of TensorFlow, giving an example of using it for a least squares method problem to predict temperatures. It explains the key components of defining a model, loss function, and training algorithm to optimize variables in a session.
Machine Learning Basics for Web Application DevelopersEtsuji Nakai
This document provides an overview of machine learning basics for web application developers. It discusses linear binary classifiers and logistic regression, how to measure model fitness with loss functions, and graphical understandings of linear classifiers. It then covers linear multiclass classifiers using softmax functions, image classification with neural networks, and ways to improve accuracy using convolutional neural networks. Finally, it discusses client applications that use pre-trained machine learning models through API services and examples of smile detection and cucumber classification.
This document provides a tutorial on the Hack programming language. It introduces Hack as a PHP extension created by Facebook that adds static typing. It also discusses HHVM, a virtual machine that compiles PHP and Hack to binary code for improved performance. The tutorial then walks through a series of exercises to demonstrate Hack features like type annotations, generics, and XHP for building HTML elements.
Introducton to Convolutional Nerural Network with TensorFlowEtsuji Nakai
Explaining basic mechanism of the Convolutional Neural Network with sample TesnsorFlow codes.
Sample codes: https://github.com/enakai00/cnn_introduction
This document provides an introduction to deep Q-networks (DQN) for beginners. It explains that DQNs can be used to learn optimal actions in video games by collecting data on screen states, player actions, rewards, and next states without knowing the game's rules. The key idea is to approximate a "Q function" that represents the total expected rewards if optimal actions are taken from each state onward. A deep neural network is used as the candidate function, and its parameters are adjusted using an error function to satisfy the Q-learning equation. To collect the necessary state-action data, the game is played with a mix of random exploration and exploiting the current best actions from the Q-network.
レッドハット 朝活セミナー(1/15, 2/18)の下記セッションでの発表予定資料です。
「Red Hat Enterprise Linux OpenStack Platform環境でのDocker活用テクニック」
https://www.redhat.com/ja/about/events/red-hat-asakatsu-seminar-2016
17. 17
テンプレートイメージの準備方法 (2)
■ Red Hat Enterprise Linux (RHEL)では、RHEL6.4より、OpenStackで利用可能なテン
プレートイメージが提供されています。
- これをダウンロードして、OpenStackに登録することで、すぐにRHELの仮想マシンを起動する
ことができます。