PyMicroChat

 

声明：本项目仅用于通信方面知识的学习交流，请勿用于非法用途.

Python3.3以上的版本通过venv模块原生支持虚拟环境，可以代替Python之前的virtualenv.低版本请用pip安装。

git clone https://github.com/InfiniteTsukuyomi/PyMicroChat.git
or git clone git@github.com:InfiniteTsukuyomi/PyMicroChat.git

Windows环境

创建Python3的venv虚拟环境

D:\VSCODE\PyMicroChat>python3 -m venv .

进入虚拟环境

D:\VSCODE\PyMicroChat>.\Scripts\activate

首次执行，配置依赖包，以后根据更新依赖环境运行

(PyMicroChat) D:\VSCODE\PyMicroChat>pip install -r requirements.txt

修改run.py相应的usrname & passwd，执行

(PyMicroChat) D:\VSCODE\PyMicroChat>python run.py

退出

(PyMicroChat) D:\VSCODE\PyMicroChat>deactivate

工程中的dll文件源码地址：https://github.com/InfiniteTsukuyomi/MicroChat/tree/master/test/ecdh

from  https://github.com/LSilent/PyMicroChat

awesome-deeplearning-resources

 Deep Learning and deep reinforcement learning research papers and some codes.

A list of recent papers regarding deep learning and deep reinforcement learning. They are sorted by time to see the recent papers first. I will renew the recent papers and add notes to these papers.

You should find the papers and software with star flag are more important or popular.

Table of Contents

• Papers
• Model Zoo
• Pretrained Model
• Courses
• Books
• Tutorials
• Software
• Applications
• Awesome Projects
• Corpus

Papers

• 2021 year
  • computer vision
  • natural language process
  • multi model
• 2020 year
  • deep learning
  • deep reinforcement learning
  • natural language process
  • computer vision
• 2019 year
  • deep learning
  • deep reinforcement learning
  • natural language process
  • computer vision
• 2018 year
  • deep learning
  • deep reinforcement learning
  • natural language process
  • computer vision
• 2017 year
  • deep learning
  • deep reinforcement learning
  • natural language process
  • computer vision
• 2016 year
  • deep learning
  • deep reinforcement learning
  • natural language process
  • computer vision
• 2015 year
• 2014 year
• 2013 year
• 2012 year
• 2011 year
• 2010 year
• before 2010 year

Model Zoo

• 2012 | AlexNet: ImageNet Classification with Deep Convolutional Neural Networks. pdf code
• 2013 | RCNN: Rich feature hierarchies for accurate object detection and semantic segmentation. arxiv code
• 2014 | CGNA: Conditional Generative Adversarial Nets. arxiv code
• 2014 | DeepFaceVariant: Deep Learning Face Representation from Predicting 10,000 Classes. pdf code
• 2014 | GAN: Generative Adversarial Networks. arxiv code
• 2014 | GoogLeNet: Going Deeper with Convolutions. pdf code

More details in Model Zoo

Pre Trained Model

• Aligning the fastText vectors of 78 languages
• Available pretrained word embeddings
• Inception-v3 of imagenet
• Caffe2 Model Repository

More details in Pretrained Model

Courses

• [Berkeley] CS294: Deep Reinforcement Learning
• [Berkeley] Stat212b：Topics Course on Deep Learning
• [CUHK] ELEG 5040: Advanced Topics in Signal Processing(Introduction to Deep Learning)
• [CMU] Deep Reinforcement Learning and Control
• [CMU] Neural networks for NLP )

More details in courses

Books

• Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 中文版本
• Deep Learning Tutorial by LISA lab, University of Montreal
• Deep Learning Crash Course
• Documentation on all topics that I learn on both Artificial intelligence and machine learning.
• Interpretable Machine Learning
• Deep Learning and the Game of Go
• Deep Learning for Search
• Deep Learning with PyTorch
• Deep Reinforcement Learning in Action
• Grokking Deep Reinforcement Lerning
• Grokking Deep Learning for Computer Vision
• Probabilistic Deep Learning with Python
• Math and Architectures of Deep Learning
• Inside Deep Learning
• Engineering Deep Learning Platforms
• Deep Learning with R, Second Edition
• Regularization in Deep Learning
• Jax in Action
• Deep Learning with PyTorch, Second Edition
• Deep Learning with Python, Third Edition
• The RLHF Book
• Time Series Forecasting Using Foundation Models

More details in books

Tutorials

• UFLDL Tutorial 1
• UFLDL Tutorial 2
• Deep Learning for NLP (without Magic)
• A Deep Learning Tutorial: From Perceptrons to Deep Networks
• Deep Learning from the Bottom up
• Theano Tutorial
• TensorFlow tutorials
• Deep Learning with R in Motion
• Grokking Deep Learning in Motion
• Machine Learning, Data Science and Deep Learning with Python

More details in tutorials

Software

• Keras Deep Learning library for Theano and TensorFlow. ⭐
• Kur Descriptive Deep Learning. ⭐
• Caffe Deep learning framework by the BVLC ⭐
• CNTK The Microsoft Cognitive Toolkit.
• Dlib A modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++.
• PyTorch Tensors and Dynamic neural networks in Python with strong GPU acceleration. ⭐
• Scikit-Learn Machine learning in Python. ⭐
  • Semisup-Learn Semi-supervised learning frameworks for Python
• Tensorflow An open source software library for numerical computation using data flow graph by Google ⭐

More details in software

Applications

• pytorch

  • 2D and 3D Face alignment library build using pytorch
  • Adversarial Autoencoders
  • A implementation of WaveNet with fast generation
  • A fast and differentiable QP solver for PyTorch.
  • A method to generate speech across multiple speakers
  • A model for style-specific music generation ⭐
  • A natural language processing toolkit using state-of-the-art deep learning models. ⭐
  • 使用PyTorch实现Char RNN生成古诗和周杰伦的歌词

• theano

  • CNN-yelp-challenge-2016-sentiment-classification
  • Deep learning tutorial for PyData
  • Deep Neural Network for Sentiment Analysis on Twitter
  • Implementations of many popular deep learning models in Theano+Lasagne

• tensorflow

  • A generic image detection program that uses tensorflow and a pre-trained Inception.
  • All kinds of text classificaiton models and more with deep learning ⭐
  • Applying transfer learning to a custom dataset by retraining Inception's final layer
  • An easy implement of VGG19 with tensorflow, which has a detailed explanation.
  • An experimentation system for Reinforcement Learning using OpenAI Gym, Tensorflow, and Keras. ⭐
  • An implementation of Pix2Pix in Tensorflow for use with frames from films

• Keras

  • A DCGAN to generate anime faces using custom mined dataset
  • A facial expression classification system that recognizes 6 basic emotions: happy, sad, surprise, fear, anger and neutral.
  • A neural network trained to help writing neural network code using autocomplete
  • Attention mechanism Implementation for Keras.
  • Automated deep neural network design with genetic programming ⭐
  • Attention based Neural Machine Translation for Keras
  • Keras Implementation of Ladder Network for Semi-Supervised Learning

• Mxnet

  • 使用MXNet的动态图接口Gluon实现Char RNN生成古诗和周杰伦的歌词

More details in applications

Awesome Projects

• 15 AI and Machine Learning Events
• 188 examples of artificial intelligence in action
• A curated list of automated machine learning papers, articles, tutorials, slides and projects ⭐
• A curated list of awesome Machine Learning frameworks, libraries and software.
• A curated list of awesome places to learn and/or practice algorithms.
• A curated list of awesome R packages and tools
• A curated list of awesome SLAM tutorials, projects and communities.
• A curated list of resources dedicated to bridge between coginitive science and deep learning
• A curated list of resources dedicated to Natural Language Processing (NLP)
• A curated list of resources for NLP (Natural Language Processing) for Chinese
• Another curated list of deep learning resources
• A list of artificial intelligence tools you can use today
• A list of deep learning implementations in biology
• Awesome-2vec
• Awesome Action Recognition

More details in awesome projects

Corpus

• 用于对话系统的中英文语料
• 搜狗实验室
• 情感分析︱网络公开的免费文本语料训练数据集汇总
• 中文情感分析用词语集
• 人民日报切分/标注语料库
• 哈工大信息检索研究中心(HIT CIR)语言技术平台共享资源
• 中文句结构树资料库
• 中文对白语料 chinese conversation corpus
• 中文语料小数据：Some useful Chinese corpus datasets
• 中文人名语料库。中文姓名,姓氏,名字,称呼,日本人名,翻译人名,英文人名
• 联合国平行语料库
• 保险行业语料库
• 用于训练中英文对话系统的语料库 Datasets for Training Chatbot System
• PTT 八卦版問答中文語料
• 3 Million Instacart Orders, Open Sourced
• ACM Multimedia Systems Conference Dataset Archive
• A dataset for book recommendations: ten thousand books, one million ratings
• A dataset for personalized highlight detection
• A dataset of 200k English plaintext jokes.
• A large-scale and high-qualityFMA: A Dataset For Music Analysis dataset of annotated musical notes.
• A large-scale dataset of manually annotated audio events ⭐
• Alphabetical list of free/public domain datasets with text data for use in NLP

More details in corpus

Other Resources

• Synthical - AI-powered collaborative research environment. You can use it to get recommendations of articles based on reading history, simplify papers, find out what articles are trending, search articles by meaning (not just keywords), create and share folders of articles, see lists of articles from specific companies and universities, and so on.

from  https://github.com/endymecy/awesome-deeplearning-resources

一款实验性质的主机入侵检测系统驭龙HIDS

由于此项目缺少维护，建议仅用于参考学习和二次开发。

驭龙HIDS是一款由 YSRC 开源的入侵检测系统，由 Agent， Daemon， Server 和 Web 四个部分组成，集异常检测、监控管理为一体，拥有异常行为发现、快速阻断、高级分析等功能，可从多个维度行为信息中发现入侵行为。

Agent为采集者角色，收集服务器信息、开机启动项、计划任务、监听端口、服务、登录日志、用户列表，实时监控文件操作行为、网络连接、执行命令，初步筛选整理后通过RPC协议传输到Server节点。

Daemon为守护服务进程，为Agent提供进程守护、静默环境部署作用，其任务执行功能通过接收服务端的指令实现Agent热更新、阻断功能和自定义命令执行等，任务传输过程使用RSA进行加密。

Server为整套系统的大脑，支持横向扩展分布式部署，解析用户定义的规则（已内置部分基础规则）对从各Agent接收到的信息和行为进行分析检测和保存，可从各个维度的信息中发现webshell写入行为、异常登录行为、异常网络连接行为、异常命令调用行为等，从而实现对入侵行为实时预警。

文档

• 部署文档
• Docker快速体验部署文档
• 使用帮助
• 规则编写
• 编译指南
• Web安装向导指南
• Q&A

功能特点

• 实时监控、秒级响应
• 全局首次出现概念，可发现未知威胁
• 支持自定义规则，高扩展性
• 高级分析功能，可溯源
• 全局快速阻断（进程、文件）
• 威胁情报查询（可自定义接口）

相比其他同类应用，维护使用此系统需要有一定的安全判断分析能力。

 from https://github.com/ysrc/yulong-hids-archived

--------------------------------

 

编译指南

环境要求

Golang环境安装可以参照，各系统会与下面的示例稍有不同，Golang版本不可以低于1.9。

依赖

• Go依赖包都集成在相应工程的vendor目录中
• 编译Agent需要先安装libpcap-devel

Windows 下编译 Agent 需要 winpcap 支持。且受到 google/gopacket 影响可能会出现一些问题，具体请看 Q&A#Q1 Windows 下编译依赖gcc，可以通过mingw-w64 32位 64位安装

编译

客户端（Agent，Daemon、依赖）

• 下载安装对应安装包 https://golang.google.cn/dl/ 安装 golang
• windows 下编译需注意：32位版本须在 32 位版本 win7/2003 下编译。Win版本 agent 代码做了兼容，如只编译32位亦可在64位系统中正常工作
• go get github.com/ysrc/yulong-hids 之后在 {gopath}/src/github.com/ysrc/yulong-hids 下的项目编译后是会出现预期外异常的，请根据接下来的指导文档进行编译。

cd C:\Go\src
git clone https://github.com/ysrc/yulong-hids/

// 编译agent
go build -o yulong-hids\bin\win-64\agent.exe --ldflags="-w -s" yulong-hids\agent\agent.go

// 编译daemon
go build -o yulong-hids\bin\win-64\daemon.exe --ldflags="-w -s" yulong-hids\daemon\daemon.go

# linux 64
// 下载并解压
wget https://dl.google.com/go/go1.10.linux-amd64.tar.gz && tar -zxvf go1.10.linux-amd64.tar.gz -C /usr/local/

sudo vi /etc/profile 
// 并添加下面的内容：

export GOROOT=/usr/local/go
export GOBIN=$GOROOT/bin
export PATH=$PATH:$GOBIN
export GOPATH=$HOME/gopath (可选设置)

// 重新加载 profile 文件
source /etc/profile

cd /usr/local/go/src
git clone https://github.com/ysrc/yulong-hids/

// 编译agent
go build -o yulong-hids/bin/linux-64/agent --ldflags="-w -s" yulong-hids/agent/agent.go

// 编译daemon
go build -o yulong-hids/bin/linux-64/daemon --ldflags="-w -s" yulong-hids/daemon/daemon.go

编译后需压缩为不同系统的zip文件（如agent.exe、daemon.exe、data.zip），在向导过程中上传。
为了方便大家，我们提供了一个编译打包脚本，在build目录下运行 python3 build.py 就能编译并打包。
但是由于data.zip中提供的依赖文件不一定适配，可能需要你自行编译，所以这个文件需要手动打包进最终的部署包。

服务端（Server、Web）

go build -o yulong-hids/bin/server --ldflags="-w -s" yulong-hids/server/server.go
go build -o yulong-hids/web/web --ldflags="-w -s" yulong-hids/web/main.go

内核、驱动

win下的驭龙驱动文件pro.sys我们已经编译好了现成的放在了data.zip中，如果自行编译的话需要购买代码签名证书进行签名，否则无法正常加载使用。

下载地址：wdk 7600

// win驱动
准备2008或win7 x64系统，安装 GRMWDK 7600。
从开始菜单中选择进入对应系统的控制台，如
Windows Driver Kits->WDK 7600.16385.1->Build Environments->Windows Vista and Windows Server 2008->x64 Checked
Build Environment
具体根据你的系统选择Build Environments，要编译32位的就选x86。

cd C:\Go\src\yulong-hids\driver\
build

// Linux内核
我们在bin\linux-64\data.zip中已经提供了一些编译好的对应内核版本的ko文件，直接用的话需要确认内核版本完全一致。
实际部署过程中需要 uname -r 统计下需要部署的机器linux内核版本，然后需要找到完全匹配对应版本的kernel-devel包
下下来并安装，yum安装的不一定完全匹配。虽然软链接到不一致的版本也能编译出来ko并加载，但是不知道会不会有影响，
稳妥起见还是参照上面的做法。
rpm -ivh kernel-devel-3.10.0-327.el7.x86_64.rpm

debian系的也类似 用 uname -r 的结果替换 4.9.0-3-amd64 
apt-get install linux-headers-4.9.0-3-amd64

openSUSE
zypper install kernel-devel

安装好对应kernel-devel包后cd进目录就可以编译了
cd /usr/local/go/src/yulong-hids/syscall_hook
make

编译好的内核和驱动需要替换进对应系统目录下的data.zip内。

驱动签名的我们给两个示例，具体命令会因证书商不一样而稍有不同。

signtool sign /v /ac "MSCV-ThawteClass3.cer" /a /s MY /n "Tongcheng Network Technology Co., Ltd" /fd sha256 /tr http://sha256timestamp.ws.symantec.com/sha256/timestamp/ "e:\pro.sys"

signtool sign /v /ac "DigiCert High Assurance EV Root CA.crt" /tr http://timestamp.digicert.com /td sha256 /fd sha256 /f "e:\sha256new.p12" /p 证书密码 "e:\pro.sys"

 from https://github.com/ysrc/yulong-hids-archived/blob/master/docs/build.md

 

installing Calico

  installing Calico for policy and flannel for networking for up to date installation directions and manifests. 

 

docker-gocd-server

 Docker server image for GoCD。

 https://hub.docker.com/r/gocd/gocd-server/

GoCD Server Container image

A Wolfi-based container image for GoCD server.

Issues, feedback?

Please make sure to log them at https://github.com/gocd/gocd.

Usage

Start the container with this:

docker run -d -p8153:8153 gocd/gocd-server:v26.1.0

This will expose container port 8153 (http) onto your server. You can now open http://localhost:8153

Available configuration options

Mounting volumes

The GoCD server will store all configuration, pipeline history database, artifacts, plugins, and logs into /godata. If you'd like to provide secure credentials like SSH private keys among other things, you can mount /home/go

docker run -v /path/to/godata:/godata -v /path/to/home-dir:/home/go gocd/gocd-server:v26.1.0

Note: Ensure that /path/to/home-dir and /path/to/godata is accessible by the go user in container (go user - uid 1000).

Installing plugins

All plugins can be installed under /godata.

Installing plugins using an environment configuration

To install plugins, just add an ENV variable with the prefix GOCD_PLUGIN_INSTALL_ and your name as suffix and the value being the download URL. The plugin will only be downloaded if not yet present.

An example example would be GOCD_PLUGIN_INSTALL_docker-elastic-agents=https://github.com/gocd-contrib/docker-elastic-agents/releases/download/v0.8.0/docker-elastic-agents-0.8.0.jar:

docker run \
  -e GOCD_PLUGIN_INSTALL_docker-elastic-agents=https://github.com/gocd-contrib/docker-elastic-agents/releases/download/v0.8.0/docker-elastic-agents-0.8.0.jar \
  gocd/gocd-server:v26.1.0

To install multiple plugins, add several -e arguments as such:

docker run \
  -e GOCD_PLUGIN_INSTALL_a-plugin=https://example.com/a-plugin.jar \
  -e GOCD_PLUGIN_INSTALL_b-plugin=https://example.com/b-plugin.jar \
  gocd/gocd-server:v26.1.0

Installing plugins using a custom entry-point script (see below)

mkdir -p /godata/plugins/external
curl --location --fail https://example.com/plugin.jar > /path/to/godata/plugins/external/plugin.jar
chown -R 1000 /godata/plugins/external

Loading configuration from existing git repo

To load existing configuration from git repo, just add an ENV variable CONFIG_GIT_REPO. Auth token may be used to access private repo. Branch master would be cloned by default. To load another branch, define an ENV variable CONFIG_GIT_BRANCH. If /godata/config already is git repo then CONFIG_GIT_REPO will be ignored. Cloned repo must contain all files from /godata/config dir.

docker run \
  -e CONFIG_GIT_REPO=https://gocd_user:password_or_auth_token/config.git \
  -e CONFIG_GIT_BRANCH=branch_with_config \
  gocd/gocd-server:v26.1.0

Checkouted content would overwrite files in /godata/config/.

Running custom entrypoint scripts

To execute custom script(s) during the container boostrap, but before the GoCD server starts just add -v /path/to/your/script.sh:/docker-entrypoint.d/your-script.sh like so:

docker run -v /path/to/your/script.sh:/docker-entrypoint.d/your-script.sh ... gocd/gocd-server:v26.1.0

If you have several scripts in a directory that you'd like to execute:

docker run -v /path/to/script-dir:/docker-entrypoint.d ... gocd/gocd-server:v26.1.0

Note: Ensure that your scripts are executable chmod a+x — you can add as many scripts as you like, bash is available on the container. If your script uses other scripting language (perl, python), please ensure that the scripting language is installed in the container.

Tweaking JVM options (memory, heap etc)

JVM options can be tweaked using the environment variable GOCD_SERVER_JVM_OPTS.

docker run -e GOCD_SERVER_JVM_OPTS="-Xmx4096mb -Dfoo=bar" gocd/gocd-server:v26.1.0

Under the hood

The GoCD server runs as the go user, the location of the various directories is:

Directory	Description
/godata/artifacts	the directory where GoCD artifacts are stored
/godata/config	the directory where the GoCD configuration is stored
/godata/db	the directory where the GoCD database and configuration change history is stored
/godata/logs	the directory where GoCD logs will be written out to
/godata/plugins	the directory containing GoCD plugins
/home/go	the home directory for the GoCD server

Determine Server IP and Ports on Host

Once the GoCD server is up, we should be able to determine its ip address and the ports mapped onto the host by doing the following: The IP address and ports of the GoCD server in a docker container are important to know as they will be used by the GoCD agents to connect to it. If you have started the container with

docker run --name server -it -p8153:8153 gocd/gocd-server:v26.1.0

Then, the below commands will determine to GoCD server IP, server port and ssl port

docker inspect --format='{{(index (index .NetworkSettings.IPAddress))}}' server
docker inspect --format='{{(index (index .NetworkSettings.Ports "8153/tcp") 0).HostPort}}' server

Troubleshooting

The GoCD server does not come up

• Check if the docker container is running docker ps -a
• Check the STDOUT to see if there is any output that indicates failures docker logs CONTAINER_ID
• Check the server logs docker exec -it CONTAINER_ID tail -f /godata/logs/go-server.log (or check the log file in the volume mount, if you're using one)

from  https://github.com/gocd/docker-gocd-server

Spinnaker

 

Spinnaker is an open source, multi-cloud continuous delivery platform for releasing software changes with high velocity and confidence.

www.spinnaker.io/

 

Welcome to the Spinnaker Project

Spinnaker is an open-source continuous delivery platform for releasing software changes with high velocity and confidence. Spinnaker provides a single platform that allows you to safely deploy and manage your application in multiple cloud environments (AWS, GCP, Kubernetes and more!). With automated canary analysis and multiple ways to initiate deployments, you can safely promote your code from a git commit into a dev environment all the way through multiple production environments with confidence in the results. Spinnaker is the most mature and widely productionalized continuous delivery platform built upon the work of Netflix, Google, Microsoft, Amazon and numerous other companies to allow fast, safe deploys. Used by numerous organizations including Target, Salesforce, Airbnb, Cerner, Adobe, JPMorgan Chase and more!

Project status and updates

Note that this repo is only used for issue tracking across the various services at this time. Releases are not managed here nor are updates regularly made to this repository. Please see the following for more information about spinnaker and it’s state:

• Current available versions

• The spinnaker slack for help and discussions around spinnaker

• Issues page for bug reports, enhancements and similar

• Governance for the project as a whole There is a lot of activity and we’d welcome participation!

More about spinnaker

Manage your SDLC in Spinnaker using the GUI (graphical user interface), or config-as-code. View, manage, and construct application workflows involving one or all of these resources:

• Virtual machine (VM) deployments to a public cloud provider, "baked" as immutable infrastructure

• Container deployments to a cloud

• Container deployments to Kubernetes

• Load balancers

• Security groups

• Server groups

• Clusters

• Firewalls

• Functions

Facilitate GitOps, and grant developers control of provisioning infrastructure for apps. Use Spinnaker’s Clouddriver to deploy to all of the major public cloud providers and Kubernetes. You may even orchestrate configuration and firmware changes as part of Spinnaker delivery pipelines.

Why Do I Need Spinnaker?

Use Spinnaker to create a “paved road” for application delivery, with guardrails that ensure only valid infrastructure and configuration reach production.

Free development teams from burdensome ops provisioning while automating reinforcement of business and regulatory requirements. Delivery automation strategies such as canary deployments provide the safety necessary to capture value from quick innovation, while protecting against business and end-user impact.

Tech Specs

Spinnaker consists of a number of independent microservices, with the Halyard CLI tool or the Kubernetes Operator (Beta) managing the lifecycle of the other services. A variety of SDLC tools integrate with Spinnaker, and its plugin framework makes Spinnaker more easily customizable to your needs. Plugins allow us to add system integrations without updating Spinnaker, broadening its potential to easily leverage the entire software delivery toolchain. With this in place, Spinnaker is evolving towards a smaller threat surface, enabling performance and operational advantages. Meanwhile, managed delivery, a newer Spinnaker featureset, provides declarative definitions of common infrastructure and other requirements; users can declare requirements using those prebuilt definitions and move changes through environments via a visual interface.

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This repository centralizes issue tracking across Spinnaker for each microservice. The core code making up Spinnaker’s microservices is found in the other Spinnaker repositories.

Using Spinnaker

Spinnaker users should refer to the main Spinnaker site and Installation guide.

For more information on how Spinnaker is designed, see the Concepts topic in the documentation Overview.

If you’re interested in a detailed walkthrough of the Spinnaker systems, check the Spinnaker How-to guides, videos, and codelabs.

Setting Up Spinnaker For Development

To pull Spinnaker from source and set it up to run locally against any of the Cloud Providers, follow the Developer Setup Guide

FROM  https://github.com/spinnaker/spinnaker

velero, Backup and migrate Kubernetes applications

 

Backup and migrate Kubernetes applications and their persistent volumes.

velero.io

 

Overview

Velero (formerly Heptio Ark) gives you tools to back up and restore your Kubernetes cluster resources and persistent volumes. You can run Velero with a public cloud platform or on-premises.

Velero lets you:

• Take backups of your cluster and restore in case of loss.
• Migrate cluster resources to other clusters.
• Replicate your production cluster to development and testing clusters.

Velero consists of:

• A server that runs on your cluster
• A command-line client that runs locally

Documentation

The documentation provides a getting started guide and information about building from source, architecture, extending Velero and more.

Please use the version selector at the top of the site to ensure you are using the appropriate documentation for your version of Velero.

Troubleshooting

If you encounter issues, review the troubleshooting docs, file an issue, or talk to us on the #velero-users and #velero-dev channel on the Kubernetes Slack server.

Community

Velero is an open community and we welcome your participation. The best way to get involved is to join our bi-weekly community meetings:

• Join the Velero community meetings, held bi-weekly, alternating between Beijing-friendly and US/Europe-friendly time zones.
• Subscribe to the project meeting calendar.
• Chat with us on the Kubernetes Slack #velero-users channel and join the mailing list.

See the community page for the full schedule and details.

from  https://github.com/velero-io/velero