Google's Infrastructure and Specific IoT ServicesIntel? Software
?
This document discusses Google Cloud Platform's Internet of Things (IoT) solutions. It describes IoT Core, which handles device management and communication, including the Device Manager for registering devices and MQTT Broker for bidirectional messaging. It explains how IoT Core collects analog sensor data from devices and transforms it into useful business insights and intelligence through data processing and analytics services like Cloud Dataflow, BigQuery, and Cloud ML.
Building Voice Controls and Integrating with Automation Actions on an IoT Net...Intel? Software
?
Voice recognition is a natural method that people can use to interact with and automate smart devices. In this session, we build a microservice for automation of IoT using local fog computing resources and cloud-based serverless functions. We also create a voice-enabled chatbot that triggers automatic actions on an IoT network.
The State of High-Performance Computing in the Open-Source R EcosystemIntel? Software
?
The document discusses the state of high performance computing (HPC) capabilities within the open source R software environment. It provides an overview of the various R packages available that enable advanced computing techniques like parallel and distributed processing. Specifically, it outlines packages for out-of-core computing, leveraging accelerators like GPUs, and performing remote and distributed evaluations across multiple nodes. The document aims to convince the audience that R is a legitimate option for HPC work and give a broad view of its growing ecosystem of related packages.
Yeoman - A Node.js cli tool for web developersCaesar Chi
?
Yeoman introduction.
as a web developers you have to know web development tool for work simple and quick.
Yeoman is, it included web base scaffolding, front end packages, back end command line for JSON style.
Introduce Node.js and how nvm work for you, developer should know `nvm` if you are developing with Node.js.
Finally we built a simple todo list example. feel free to view github project and give us some advices
This document provides an overview of Elasticsearch, including that it is an open source, distributed search engine that uses JSON documents over HTTP. It discusses how Elasticsearch is document-based and has a dynamic schema, as opposed to relational databases that use tables and rows. Examples are given of creating, querying, and searching documents using the REST API, Python elasticsearch and elasticsearch-dsl libraries, and the query domain specific language.
The document discusses Django Channels, which is an initiative to rewrite Django's core to support WebSockets and background tasks. Django was awarded a $150,000 grant to fund the development of Channels. Channels uses three separate layers - interface servers, a channel backend, and workers. It allows defining consumer functions for different channel types. Groups can be used to broadcast messages to multiple consumers instead of sending individually. The document provides an example chat room application built with Channels.
This document provides an overview of third party packages that can be used with Django projects. It lists packages for debugging, forms, REST APIs, testing, administration, background tasks, authentication, and social authentication. It then provides installation and configuration instructions for the django-allauth package for social authentication, including Facebook authentication. The document ends with a note about where the code examples are hosted on GitHub and a request for questions.
The document discusses the top 10 security issues from the OWASP 2013 report and provides solutions for securing a Django application. It covers issues like injection, broken authentication, cross-site scripting, sensitive data exposure, and insecure configurations. The document emphasizes that software security is difficult but important, and recommends following best practices like input validation, access control, and using security features built into Django.
This document discusses search and building an inverted index for searching. It introduces inverted indexes which map terms to documents. It also covers phrase searching, building an inverted index by splitting text into tokens, removing stop words, normalization, and stemming. The document then introduces ElasticSearch for building a distributed, document-based search index and using django-haystack and Elasticsearch to enable search in a Django application.
The document discusses the challenges of continuous integration, continuous deployment, and testing. It states that writing tests, setting up Jenkins, and automating deployments can be difficult. It then provides basic examples of writing a Django test and using Jenkins. It concludes with mentioning a demo of an automatic deployment for a voting website project containing details on authors, pull requests, commits, and changed files.
Andy gave a presentation at a Python Web Meetup on running Django in a heavy load environment. He demonstrated how to scale a Django application from 30 users per second to 400 users per second through load testing tools, using a load balancer like Nginx, and caching with Varnish. While he covered increasing performance through these methods, he noted that optimizing the database and Django application themselves were not covered.
This document discusses potential tradeoffs of replacing core Django components like the template engine and ORM. It recommends only making these changes if performance issues have been clearly identified and validated, caching and other optimizations have been exhausted, and the risks of losing compatibility are accepted. Specifically upgrading Django itself or replacing the template engine prematurely is not advised without ensuring the new approach is better suited to identified needs.
Introduce Node.js and how nvm work for you, developer should know `nvm` if you are developing with Node.js.
Finally we built a simple todo list example. feel free to view github project and give us some advices
This document provides an overview of Elasticsearch, including that it is an open source, distributed search engine that uses JSON documents over HTTP. It discusses how Elasticsearch is document-based and has a dynamic schema, as opposed to relational databases that use tables and rows. Examples are given of creating, querying, and searching documents using the REST API, Python elasticsearch and elasticsearch-dsl libraries, and the query domain specific language.
The document discusses Django Channels, which is an initiative to rewrite Django's core to support WebSockets and background tasks. Django was awarded a $150,000 grant to fund the development of Channels. Channels uses three separate layers - interface servers, a channel backend, and workers. It allows defining consumer functions for different channel types. Groups can be used to broadcast messages to multiple consumers instead of sending individually. The document provides an example chat room application built with Channels.
This document provides an overview of third party packages that can be used with Django projects. It lists packages for debugging, forms, REST APIs, testing, administration, background tasks, authentication, and social authentication. It then provides installation and configuration instructions for the django-allauth package for social authentication, including Facebook authentication. The document ends with a note about where the code examples are hosted on GitHub and a request for questions.
The document discusses the top 10 security issues from the OWASP 2013 report and provides solutions for securing a Django application. It covers issues like injection, broken authentication, cross-site scripting, sensitive data exposure, and insecure configurations. The document emphasizes that software security is difficult but important, and recommends following best practices like input validation, access control, and using security features built into Django.
This document discusses search and building an inverted index for searching. It introduces inverted indexes which map terms to documents. It also covers phrase searching, building an inverted index by splitting text into tokens, removing stop words, normalization, and stemming. The document then introduces ElasticSearch for building a distributed, document-based search index and using django-haystack and Elasticsearch to enable search in a Django application.
The document discusses the challenges of continuous integration, continuous deployment, and testing. It states that writing tests, setting up Jenkins, and automating deployments can be difficult. It then provides basic examples of writing a Django test and using Jenkins. It concludes with mentioning a demo of an automatic deployment for a voting website project containing details on authors, pull requests, commits, and changed files.
Andy gave a presentation at a Python Web Meetup on running Django in a heavy load environment. He demonstrated how to scale a Django application from 30 users per second to 400 users per second through load testing tools, using a load balancer like Nginx, and caching with Varnish. While he covered increasing performance through these methods, he noted that optimizing the database and Django application themselves were not covered.
This document discusses potential tradeoffs of replacing core Django components like the template engine and ORM. It recommends only making these changes if performance issues have been clearly identified and validated, caching and other optimizations have been exhausted, and the risks of losing compatibility are accepted. Specifically upgrading Django itself or replacing the template engine prematurely is not advised without ensuring the new approach is better suited to identified needs.
Build your own trello witihin 100 LOC (with Meteor)Andy Dai
?
This document discusses building a clone of the project management tool Trello using the Meteor framework. It describes Meteor's principles like data on the wire and one language across client and server. The document shows how to create a Meteor project, add templates with Handlebars, define data collections with MongoDB, add event handlers, and deploy the project. It demos a Trello clone built with Meteor in under 100 lines of code.
22. gunicorn
18
? http://gunicorn.org/
? Gunicorn 'Green Unicorn' is a Python
WSGI HTTP Server for UNIX. It's a pre-
fork worker model ported
? 總之就是一個效能很好的 wsgi web
server
13年8月6?日星期?二