2019/08/23 第21回 Tokyo Jazug Night
https://jazug.connpass.com/event/139300/
動画: https://www.youtube.com/watch?v=YMAV8aqb9pk
Cluster API によるKubernetes環境のライフサイクル管理とマルチクラウド環境での適用Motonori Shindo
Cluster API は Kubernetes の宣言的APIとリソースの管理機能を活かし、Kubernetes環境のライフサイクル管理を行うもので、Kubernetesコミュニティで仕様の策定と開発が進められています。
これまでもKubernetes環境の構築を支援するツールはいくつかありましたが、Cluster APIはコミュニティからの大きな支持を得ており、Cluster APIのエコシステムが広がりつつあります。
本セッションでは Cluster API の概要と最新の動向、また、Cluster APIを利用した大規模マルチクラウド環境への適用などをデモを交えながら解説を行います。
本資料はCloud Operator Days Tokyo 2020登壇時の資料です。
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.
2019/08/23 第21回 Tokyo Jazug Night
https://jazug.connpass.com/event/139300/
動画: https://www.youtube.com/watch?v=YMAV8aqb9pk
Cluster API によるKubernetes環境のライフサイクル管理とマルチクラウド環境での適用Motonori Shindo
Cluster API は Kubernetes の宣言的APIとリソースの管理機能を活かし、Kubernetes環境のライフサイクル管理を行うもので、Kubernetesコミュニティで仕様の策定と開発が進められています。
これまでもKubernetes環境の構築を支援するツールはいくつかありましたが、Cluster APIはコミュニティからの大きな支持を得ており、Cluster APIのエコシステムが広がりつつあります。
本セッションでは Cluster API の概要と最新の動向、また、Cluster APIを利用した大規模マルチクラウド環境への適用などをデモを交えながら解説を行います。
本資料はCloud Operator Days Tokyo 2020登壇時の資料です。
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.
If you're new to openstack and you want get some hands on it then you have to install the Devstack. a bundled version for all openstack services and components in one software.
Systemd provides a more modular approach to system initialization and service management compared to SysVinit and Upstart. With systemd, various system initialization tasks and services are defined as independent "units" that have dependencies and startup ordering defined through configuration files. At boot, systemd analyzes the dependencies between units and starts them in parallel where possible to reduce startup time. It provides standardized methods for process supervision and resource management of services.
OpenContrail Users Event at OpenStack Summit Paris 行ってきましたTakashi Sogabe
2014年11月にOpenStack Summit Paris 会期中に行なわれた OpenContrail Users Event に参加してきました。本スライドは、OpenContrail Day Tokyo 2014 – Autumn のライトニングトークにて発表したUsers Eventのレポートになります。
July Tech Festa, August 2017
Alternate URL: https://speakerdeck.com/s1061123/kontenafalsenetutowakuintahuesu-sofalseshi-zhuang-shou-fa-tosofalseying-yong-nituite
HTML5 技術を利用してデスクトップ画面を、実時間で、数十台の端末に配信するシステムと、その管理システムを試作したことについて述べる。インターネットとプライベートネットワークのどちらにもサーバを配置することにより、授業や会議が遠隔地で分散して実施される場合にも対応できる。大量の端末に効率よくデータを配信するため、複数のサーバを利用するが、Web クライアントを自動的に適切なサーバに割り当てる機能も持っている。負荷分散機能も持っている。サーバを管理するため、Web 画面上でサーバを制御することができる。管理者が適切にサーバを加えたり減らしたりするため、端末数、Web クライアントで表示される単位時間あたりの表示画面枚数、Web クライアントにおけるネットワーク利用バンド幅などの変化も表示可能で、ログも採取できる。
Experimental Implementation of
a Real-time PC Screen Distribution System for Classes and Meetings using HTML5 Technology
Experimental implementation of a real-time PC screen distribution system for classes and meetings is discussed. This system uses HTML5 technology. So users of this system can use this system just using their own common Web browsers. Several tens web clients can share the screen of a PC. This system is a kind of CDN which unifies servers at the Internet and hierarchical private networks. An appropriate server of the CDN is selected automatically when a Web client is connected to the CDN. This system is also equipped with administration functions for managers of this system.
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
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.
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.
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.