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https://github.com/mikeroyal/linode-guide

Linode Guide
https://github.com/mikeroyal/linode-guide

cloud linode linode-api linode-cli linode-platform

Last synced: 27 days ago
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Linode Guide

Host: GitHub
URL: https://github.com/mikeroyal/linode-guide
Owner: mikeroyal
Created: 2021-08-30T18:16:57.000Z (about 3 years ago)
Default Branch: main
Last Pushed: 2021-08-30T18:26:40.000Z (about 3 years ago)
Last Synced: 2024-10-10T23:09:47.757Z (about 1 month ago)
Topics: cloud, linode, linode-api, linode-cli, linode-platform
Homepage:
Size: 60.5 KB
Stars: 6
Watchers: 4
Forks: 2
Open Issues: 0
Metadata Files:
- Readme: README.md

Awesome Lists containing this project

README

Linode Guide

#### A guide for getting started with Linode including the Tools and Applications that will make you a better and more efficient engineer with Linode.

**Note: You can easily convert this markdown file to a PDF in [VSCode](https://code.visualstudio.com/) using this handy extension [Markdown PDF](https://marketplace.visualstudio.com/items?itemName=yzane.markdown-pdf).**

# Table of Contents

1. [Linode Learning Resources](https://github.com/mikeroyal/Linode-Guide#linode-learning-resources)

2. [Linode Tools](https://github.com/mikeroyal/Linode-Guide#linode-tools)

3. [Linode DevOps Tools Integration](https://github.com/mikeroyal/Linode-Guide#linode-devops-tools-integration)

4. [Networking](https://github.com/mikeroyal/Linode-Guide#networking)

5. [Kubernetes](https://github.com/mikeroyal/Linode-Guide#kubernetes)

6. [Machine Learning](https://github.com/mikeroyal/Linode-Guide#machine-learning)

# Linode Learning Resources
[Back to the Top](https://github.com/mikeroyal/Linode-Guide#table-of-contents)

[Linode](https://www.linode.com/) is a cloud hosting company that provides virtual private servers and variety of other cloud services.

[Linode Documentation](https://www.linode.com/docs)

[Linode Guides & Tutorials ](https://www.linode.com/docs/guides/)

[Linode API Guides](https://developers.linode.com/guides/)

[Linode - YouTube Channel](https://www.youtube.com/channel/UCf8uu3IE42b6hRUusufEH8g)

[Linode Cloud Community](https://www.linode.com/community/)

[Linode Developer Portal](https://www.linode.com/developers/)

[Linode Content Resources](https://www.linode.com/content/)

[Linode Video Channel](https://www.linode.com/video-channel/)

[Setup a Virtual Web Server using Linode or Digital Ocean | Udemy](https://www.udemy.com/course/setup-a-virtual-web-server-using-linode-or-digital-ocean/)

# Linode Tools
[Back to the Top](https://github.com/mikeroyal/Linode-Guide#table-of-contents)

[Linode Cloud Manager](https://www.linode.com/products/cloud-manager/) is a user- and mobile-friendly interface to deploy and manage virtual machines, configure networking, and control user accounts.

[Linode API](https://developers.linode.com/api/v4/) is a tool that makes easy to configure, manage, and deploy user management, billing, support tickets, and more with programmatic access to Linode products and services.

[Linode CLI](https://www.linode.com/docs/cli/) is a tool to deploy and manage Linux servers from Linode without leaving the command line.

[Linode Images](https://www.linode.com/products/images/) is a service to capture, store, and deploy your custom images across Linodes or data centers. Easily create your own raw disk image and upload a compressed .gz image file (up to 5 GB) using the Cloud Manager or API to easily deploy to the Linode size and data center you need.

[Linode Integrations](https://www.linode.com/products/integrations/) is a collection of integrations lets you connect infrastructure and dev tools to the Linode platform. That let's you manage your Linode resources using the tools you know and love.

[StackScripts](https://www.linode.com/products/stackscripts/) is a tool to automatically configure new Linode instances using simple scripts. Create [your own StackScript](https://www.linode.com/docs/platform/stackscripts/) or browse the community StackScript library.

[Dedicated CPU](https://www.linode.com/products/dedicated-cpu/) is a powerful infrastructure solution for CPU-intensive applications such as video encoding, machine learning, and data analytics processing.

[Shared CPU](https://www.linode.com/products/shared/) is a balanced array of resources that support a wide range of cloud applications. From personal projects to enterprise applications, Shared Instances can handle it.

[High Memory Linodes](https://www.linode.com/products/high-memory/) is a cost-effective way to run memory-intensive applications on dedicated CPUs. Perfect for when you need more RAM without increasing storage or vCPUs for an enterprise-level database solution.

[Dedicated Cloud GPU](https://www.linode.com/products/gpu/) is an on-demand GPUs for parallel processing workloads such as machine learning, scientific computing, and video processing. It offers GPU-optimized virtual machines accelerated by the NVIDIA Quadro RTX 6000, harnessing the power of CUDA, Tensor, and RT cores to execute complex processing, deep learning, and ray tracing workloads.

[Linode Bare Metal](https://www.linode.com/products/bare-metal/) is the single-tenant solution for applications and organizations with security, compliance, and performance needs. Bare Metal combines direct hardware access and the flexibility of a virtual machine.

[Linode Kubernetes Engine (LKE)](https://www.linode.com/products/kubernetes/) is a fully-managed container orchestration engine for deploying and managing containerized applications and workloads. LKE combines Linode’s ease of use and simple pricing with infrastructure efficiency. You can now get your infrastructure and workloads up and running in minutes instead of days.

[Linode Block Storage](https://www.linode.com/products/block-storage/) is Linode’s storage capacity by attaching additional high-speed volume sizes up to 10TB. Volumes are managed independently of Linodes, so your data persists even if you delete your Linode.

[Linode Object Storage](https://www.linode.com/products/object-storage/) is an S3-compatible Object Storage service that makes it easy and more affordable to manage unstructured data such as content assets, as well as sophisticated and data-intensive storage challenges around artificial intelligence and machine learning.

[Linode Cloud Firewall](https://www.linode.com/products/cloud-firewall/) is a service that makes it simple to control network traffic to and from your Linodes. Customize firewall rule sets and secure a Linode’s traffic based on trusted IP addresses, ports, and protocols.

[Linode Cloud DDoS Protection](https://www.linode.com/products/ddos/) is a service that automatically detects and mitigates distributed denial-of-service (DDoS) attacks on your infrastructure from large-volume traffic intended to make your service unavailable to legitimate users.

[Linode's DNS Manager](https://www.linode.com/products/dns-manager/) is a comprehensive interface within the Linode Cloud Manager that gives you complete oversight of DNS records.

[Linode NodeBalancers](https://www.linode.com/products/nodebalancers/) is a service that easily scales your app or website to thousands of users by automatically handle increases in load and ensure your site is highly available.

[Linode VLAN](https://www.linode.com/products/vlan/) is a service that helps increase cloud infrastructure security and streamline broadcast traffic with a free VLAN service from Linode.

[Linode Backups](https://www.linode.com/products/backups/) is a simple, scalable backup service. The subscription service automatically performs daily, weekly, and bi-weekly local backups of your Linode.

[Linode Managed](https://www.linode.com/products/managed/) is an incident response service designed to help businesses cut out costly downtime. It provides a highly experienced Managed Service team is here around the clock to help.

[Linode Professional Services](https://www.linode.com/products/pro-services/) is a service that provides in-house experts architect services, complete successful migrations, and deploy software. That help you achieve your short- and long-term goals in the cloud with Linode.

# Linode DevOps Tools Integration
[Back to the Top](https://github.com/mikeroyal/Linode-Guide#table-of-contents)

[Open Container Initiative](https://opencontainers.org/about/overview/) is an open governance structure for the express purpose of creating open industry standards around container formats and runtimes.

[Buildah](https://buildah.io/) is a command line tool to build Open Container Initiative (OCI) images. It can be used with Docker, Podman, Kubernetes.

[Podman](https://podman.io/) is a daemonless, open source, Linux native tool designed to make it easy to find, run, build, share and deploy applications using Open Containers Initiative (OCI) Containers and Container Images. Podman provides a command line interface (CLI) familiar to anyone who has used the Docker Container Engine.

[Containerd](https://containerd.io)is a daemon that manages the complete container lifecycle of its host system, from image transfer and storage to container execution and supervision to low-level storage to network attachments and beyond. It is available for Linux and Windows.

[OKD](https://okd.io/) is a community distribution of Kubernetes optimized for continuous application development and multi-tenant deployment. OKD adds developer and operations-centric tools on top of Kubernetes to enable rapid application development, easy deployment and scaling, and long-term lifecycle maintenance for small and large teams.

[Red Hat OpenShift](https://www.openshift.com/) is an open source container application platform based on the Kubernetes container orchestrator for enterprise app development and deployment in the hybrid cloud Red Hat OpenShift, the open hybrid cloud platform built on Kubernetes.

[OpenShift CLI (oc)](https://docs.openshift.com/container-platform/4.4/cli_reference/openshift_cli/getting-started-cli.html) is a command line interface tool that extends the capabilities of kubectl with [many convenience functions](https://docs.openshift.com/container-platform/4.4/cli_reference/openshift_cli/usage-oc-kubectl.html) that make interacting with both Kubernetes and OpenShift clusters easier.

[OpenShift Serverless CLI (kn)](https://docs.openshift.com/container-platform/4.4/serverless/serverless-getting-started.html) is a command line interface tool to deploy serverless applications, then you’ll want access and control via the kn command.

[OpenShift Pipelines CLI (tkn)](https://docs.openshift.com/container-platform/4.4/pipelines/understanding-openshift-pipelines.html) is a command line interface tool for using Tekton to provide cloud-native CI/CD functionality within the cluster. The tkn command is used to manage the functionality from the CLI.

[Red Hat CodeReady Containers](https://developers.redhat.com/products/codeready-containers) is an option to host a local, all-in-one OpenShift 4 cluster on your workstation. CodeReady Containers replaces [minishift](https://www.okd.io/minishift/), used to run OpenShift 3 clusters on your workstation, as a quick and easy method of creating test and development clusters.

[Helm CLI](https://docs.openshift.com/container-platform/4.4/cli_reference/helm_cli/getting-started-with-helm-on-openshift-container-platform.html) is a command line interface tool for deploying and managing Kubernetes applications to your clusters.

[OpenShift Hive](https://github.com/openshift/hive) is an operator which runs as a service on top of Kubernetes/OpenShift. The Hive service can be used to provision and perform initial configuration of OpenShift 4 clusters.

[OpenShift Service Mesh](https://www.openshift.com/blog/introducing-openshift-service-mesh-2.0) is a tool that provides a layer on top of OpenShift for securely connecting services in a consistent manner. This provides centralized control, security and observability across your services without having to modify your applications.

[Red Hat OpenShift Service on AWS (ROSA)](https://www.openshift.com/products/amazon-openshift) is a fully-managed and jointly supported Red Hat OpenShift offering that combines the power of Red Hat OpenShift, the industry's most comprehensive enterprise Kubernetes platform, and the AWS public cloud.

[Red Hat® Quay](https://www.openshift.com/products/quay) is a secure, private container registry that builds, analyzes and distributes container images. It provides a high level of automation and customization.

[Kata Operator](https://github.com/openshift/kata-operator) is an operator to perform lifecycle management (install/upgrade/uninstall) of [Kata Runtime](https://katacontainers.io/) on Openshift as well as Kubernetes cluster.

[Ansible](https://www.ansible.com/)is a simple IT automation engine that automates cloud provisioning, configuration management, application deployment, intra-service orchestration, and many other IT needs. It uses a very simple language (YAML, in the form of Ansible Playbooks) that allows you to describe your automation jobs in a way that approaches plain English. Anisble works on Linux (Red Hat EnterPrise Linux(RHEL) and Ubuntu) and Microsoft Windows.

[Ansible cmdb](https://github.com/fboender/ansible-cmdb) is a tool that takes the output of Ansible’s fact gathering and converts it into a static HTML overview page containing system configuration information.

[Ansible Inventory Grapher](https://github.com/willthames/ansible-inventory-grapher) visually displays inventory inheritance hierarchies and at what level a variable is defined in inventory.

[Ansible Playbook Grapher](https://github.com/haidaraM/ansible-playbook-grapher) is a command line tool to create a graph representing your Ansible playbook tasks and roles.

[Ansible Shell](https://github.com/dominis/ansible-shell) is an interactive shell for Ansible with built-in tab completion for all the modules.

[Ansible Silo](https://github.com/groupon/ansible-silo) is a self-contained Ansible environment by [Docker](https://www.docker.com/).

[Ansigenome](https://github.com/nickjj/ansigenome) is a command line tool designed to help you manage your Ansible roles.

[ARA](https://github.com/openstack/ara) is a records Ansible playbook runs and makes the recorded data available and intuitive for users and systems by integrating with Ansible as a callback plugin.

[GitHub](https://github.com/) provides hosting for software development version control using Git. It offers all of the distributed version control and source code management functionality of Git as well as adding its own features. It provides access control and several collaboration features such as bug tracking, feature requests, task management, and wikis for every project.

[GitHub Codespaces](https://docs.github.com/en/free-pro-team@latest/github/developing-online-with-codespaces) is an integrated development environment(IDE) on GitHub. That allows developers to develop entirely in the cloud using Visual Studio and Visual Studio Code.

[GitHub Actions](https://docs.github.com/en/actions) will automate, customize, and execute your software development workflows right in your repository with GitHub Actions. You can discover, create, and share actions to perform any job you'd like, including CI/CD, and combine actions in a completely customized workflow.[GitHub Actions for Azure](https://docs.microsoft.com/en-us/azure/developer/github/github-actions) you can create workflows that you can set up in your repository to build, test, package, release and deploy to Azure.Learn more about all other integrations with Azure.

[GitLab](https://about.gitlab.com/) is a web-based DevOps lifecycle tool that provides a Git-repository manager providing wiki, issue-tracking and CI/CD pipeline features, using an open-source license, developed by GitLab Inc.

[Jenkins](https://jenkins.io/) is a free and open source automation server. Jenkins helps to automate the non-human part of the software development process, with continuous integration and facilitating technical aspects of continuous delivery.

[Bitbucket](https://bitbucket.org/) is a web-based version control repository hosting service owned by Atlassian, for source code and development projects that use either Mercurial or Git revision control systems. Bitbucket offers both commercial plans and free accounts. It offers free accounts with an unlimited number of private repositories. Bitbucket integrates with other Atlassian software like Jira, HipChat, Confluence and Bamboo.

[Bamboo](https://www.atlassian.com/software/bamboo) is a continuous integration (CI) server that can be used to automate the release management for a software application, creating a continuous delivery pipeline.

[Codecov](https://codecov.io/) is the leading, dedicated code coverage solution. It provides highly integrated tools to group, merge, archive and compare coverage reports. Whether your team is comparing changes in a pull request or reviewing a single commit, Codecov will improve the code review workflow and quality.

[Drone](https://drone.io/) is a Continuous Delivery system built on container technology. Drone uses a simple YAML configuration file, a superset of docker-compose, to define and execute Pipelines inside Docker containers.

[Travis CI](https://travis-ci.org/) is a hosted continuous integration service used to build and test software projects hosted at GitHub.

[Circle CI](https://circleci.com/) is a continuous integration and continuous delivery platform that helps software teams work smarter, faster.

[Zuul-CI](https://zuul-ci.org/index.html) is a program that drives continuous integration, delivery, and deployment systems with a focus on project gating and interrelated projects. Using the same [Ansible playbooks](https://docs.ansible.com/ansible/latest/user_guide/playbooks.html) to deploy your system and run your tests.

[Artifactory](https://jfrog.com/artifactory/) is a Universal Artifact Repository Manager developed by JFrog. It supports all major packages, enterprise ready security, clustered, HA, Docker registry, multi-site replication and scalable.

[Azure DevOps](https://azure.microsoft.com/en-us/services/devops/?nav=min) is a set of services for teams to share code, track work, and ship software; CLIs Build, deploy, diagnose, and manage multi-platform, scalable apps and services; Azure Pipelines Continuously build, test, and deploy to any platform and cloud; Azure Lab Services Set up labs for classrooms, trials, development and testing, and other scenarios.

[Team City](https://www.jetbrains.com/teamcity/) is a build management and continuous integration server from JetBrains.

[Shippable](https://www.shippable.com/) simplifies DevOps and makes it systematic with an Assembly Line platform that is heterogeneous, flexible, and provides complete visibility across your DevOps workflows.

[Spinnaker](https://www.spinnaker.io/) is an open source, multi-cloud continuous delivery platform for releasing software changes with high velocity and confidence.

[Selenium](https://www.seleniumhq.org/) is a free (open source) automated testing suite for web applications across different browsers and platforms.

[Cucumber](https://cucumber.io/) is a tool based on Behavior Driven Development (BDD) framework which is used to write acceptance tests for the web application. It allows automation of functional validation in easily readable and understandable format (like plain English) to Business Analysts, Developers, and Testers.

[JUnit](https://junit.org/junit5/) is a unit testing framework for the Java programming language.

[Mocha](https://mochajs.org/) is a JavaScript test framework for Node.js programs, featuring browser support, asynchronous testing, test coverage reports, and use of any assertion library.

[Karma](https://karma-runner.github.io/latest/index.html) is a simple tool that allows you to execute JavaScript code in multiple real browsers.

[Jasmine](https://jasmine.github.io/) is an open source testing framework for JavaScript. It aims to run on any JavaScript-enabled platform, to not intrude on the application nor the IDE, and to have easy-to-read syntax.

[Maven](https://maven.apache.org/) is a build automation tool used primarily for Java projects. Maven can also be used to build and manage projects written in C#, Ruby, Scala, and other languages. The Maven project is hosted by the Apache Software Foundation.

[Gradle](https://gradle.org/) is an open-source build-automation system that builds upon the concepts of Apache Ant and Apache Maven and introduces a Groovy-based domain-specific language instead of the XML form used by Apache Maven for declaring the project configuration.

[Chef](https://www.chef.io/) is an effortless Infrastructure Suite offers visibility into security and compliance status across all infrastructure and makes it easy to detect and correct issues long before they reach production.

[Puppet](https://puppet.com/) is an open source tool that makes continuous integration and delivery of your software on traditional or containerized infrastructure easy by pulling together all your existing tools and giving you flexibility to deploy your way.

[KubeInit](https://github.com/kubeinit/kubeinit) provides Ansible playbooks and roles for the deployment and configuration of multiple Kubernetes distributions.

[Salt](https://www.saltstack.com/) is Python-based, open-source software for event-driven IT automation, remote task execution, and configuration management. Supporting the "Infrastructure as Code" approach to data center system and network deployment and management, configuration automation, SecOps orchestration, vulnerability remediation, and hybrid cloud control.

[Terraform](https://www.terraform.io/) is an open-source infrastructure as code software tool created by HashiCorp.It enables users to define and provision a datacenter infrastructure using a high-level configuration language known as Hashicorp Configuration Language (HCL), or optionally JSON.

[Consul](https://www.consul.io) is a service networking solution to connect and secure services across any runtime platform and public or private cloud.

[Packer](https://www.packer.io/) is lightweight, runs on every major operating system, and is highly performant, creating machine images for multiple platforms in parallel. Packer does not replace configuration management like Chef or Puppet. In fact, when building images, Packer is able to use tools like Chef or Puppet to install software onto the image.

[Nomad](https://www.nomadproject.io/) is a highly available, distributed, data-center aware cluster and application scheduler designed to support the modern datacenter with support for long-running services, batch jobs, and much more.

[Vagrant](https://www.vagrantup.com/) is a tool for building and managing virtual machine environments in a single workflow. With an easy-to-use workflow and focus on automation, Vagrant lowers development environment setup time and increases production parity.

[Vault](https://www.hashicorp.com/products/vault/) is a tool for securely accessing secrets. A secret is anything that you want to tightly control access to, such as API keys, passwords, certificates, and more. Vault provides a unified interface to any secret, while providing tight access control and recording a detailed audit log.

[CFEngine](https://cfengine.com/) is an open-source configuration management system, written by Mark Burgess.Its primary function is to provide automated configuration and maintenance of large-scale computer systems, including the unified management of servers, desktops, consumer and industrial devices, embedded networked devices, mobile smartphones, and tablet computers.

[Octpus Deploy](https://octopus.com/) is the deployment automation server for your entire team, designed to make it easy to orchestrate releases and deploy applications, whether on-premises or in the cloud.

[Kubernetes](https://kubernetes.io/) is an open-source container-orchestration system for automating application deployment, scaling, and management. It was originally designed by Google, and is now maintained by the Cloud Native Computing Foundation.

[Docker](https://www.docker.com/) is a set of platform as a service products that use OS-level virtualization to deliver software in packages called containers. Containers are isolated from one another and bundle their own software, libraries and configuration files; they can communicate with each other through well-defined channels. All containers are run by a single operating-system kernel and are thus more lightweight than virtual machines.

[PowerShell/PowerShell Core](https://docs.microsoft.com/en-us/powershell/) is a cross-platform (Windows, Linux, and macOS) automation and configuration tool/framework that works well with your existing tools and is optimized for dealing with structured data (e.g. JSON, CSV, XML, etc.), REST APIs, and object models. It includes a command-line shell, an associated scripting language and a framework for processing cmdlets.

[Hyper-V](https://docs.microsoft.com/en-us/virtualization/hyper-v-on-windows/) creates virtual machines on Windows 10. Hyper-V can be enabled in many ways including using the Windows 10 control panel, PowerShell or using the Deployment Imaging Servicing and Management tool (DISM).

[Cloud Hypervisor](https://github.com/cloud-hypervisor/cloud-hypervisor) is an open source Virtual Machine Monitor (VMM) that runs on top of [KVM](https://www.kernel.org/doc/Documentation/virtual/kvm/api.txt). The project focuses on exclusively running modern, cloud workloads, on top of a limited set of hardware architectures and platforms. Cloud workloads refers to those that are usually run by customers inside a cloud provider. Cloud Hypervisor is implemented in [Rust](https://www.rust-lang.org/) and is based on the [rust-vmm](https://github.com/rust-vmm) crates.

[VMware vSphere Hypervisor](https://www.vmware.com/products/vsphere-hypervisor.html) is a bare-metal hypervisor that virtualizes servers; allowing you to consolidate your applications while saving time and money managing your IT infrastructure.

[VMware vSphere](https://www.vmware.com/products/vsphere.html) is the industry-leading compute virtualization platform, and your first step to application modernization. It has been rearchitected with native Kubernetes to allow customers to modernize the 70 million+ workloads now running on vSphere.

[VMware Tanzu](https://tanzu.vmware.com/tanzu) is a centralized management platform for consistently operating and securing your Kubernetes infrastructure and modern applications across multiple teams and private/public clouds.

[Rancher](https://rancher.com/) is a complete software stack for teams adopting containers. It addresses the operational and security challenges of managing multiple Kubernetes clusters, while providing DevOps teams with integrated tools for running containerized workloads.

[K3s](https://github.com/rancher/k3s) is a highly available, certified Kubernetes distribution designed for production workloads in unattended, resource-constrained, remote locations or inside IoT appliances.

[Rook](https://rook.io/) is an open source cloud-native storage orchestrator for Kubernetes that turns distributed storage systems into self-managing, self-scaling, self-healing storage services. It automates the tasks of a storage administrator: deployment, bootstrapping, configuration, provisioning, scaling, upgrading, migration, disaster recovery, monitoring, and resource management.

[Google Kubernetes Engine (GKE)](https://cloud.google.com/kubernetes-engine/) is a managed, production-ready environment for deploying containerized applications.

[Anthos](https://cloud.google.com/anthos/docs/concepts/overview) is a modern application management platform that provides a consistent development and operations experience for cloud and on-premises environments.

[Apache Mesos](http://mesos.apache.org/) is a cluster manager that provides efficient resource isolation and sharing across distributed applications, or frameworks. It can run Hadoop, Jenkins, Spark, Aurora, and other frameworks on a dynamically shared pool of nodes.

[Apache Spark](https://spark.apache.org/) is a unified analytics engine for big data processing, with built-in modules for streaming, SQL, machine learning and graph processing.

[Apache Hadoop](http://hadoop.apache.org/) is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures.

[Microsoft Azure](https://azure.microsoft.com/en-us/) is a cloud computing service created by Microsoft for building, testing, deploying, and managing applications and services through Microsoft-managed data centers.

[Azure Functions](https://azure.microsoft.com/en-us/services/functions/) is a solution for easily running small pieces of code, or "functions," in the cloud. You can write just the code you need for the problem at hand, without worrying about a whole application or the infrastructure to run it.

[Rkt](https://coreos.com/rkt/) is a pod-native container engine for Linux. It is composable, secure, and built on standards.

[Helm](https://helm.sh/) is the Kubernetes Package Manager.

[Kubespray](https://kubespray.io/) is a tool that combines Kubernetes and Ansible to easily install Kubernetes clusters that can be deployed on [AWS](https://github.com/kubernetes-sigs/kubespray/blob/master/docs/aws.md), GCE, [Azure](https://github.com/kubernetes-sigs/kubespray/blob/master/docs/azure.md), [OpenStack](https://github.com/kubernetes-sigs/kubespray/blob/master/docs/openstack.md), [vSphere](https://github.com/kubernetes-sigs/kubespray/blob/master/docs/vsphere.md), [Packet](https://github.com/kubernetes-sigs/kubespray/blob/master/docs/packet.md) (bare metal), Oracle Cloud Infrastructure (Experimental), or Baremetal

[Odo](https://odo.dev/) is a fast, iterative, and straightforward CLI tool for developers who write, build, and deploy applications on Kubernetes and OpenShift.

[Knative](https://knative.dev/) is a Kubernetes-based platform to build, deploy, and manage modern serverless workloads. Knative takes care of the operational overhead details of networking, autoscaling (even to zero), and revision tracking.

[Etcd](https://etcd.io/) is a distributed key-value store that provides a reliable way to store data that needs to be accessed by a distributed system or cluster of machines. Etcd is used as the backend for service discovery and stores cluster state and configuration for Kubernetes.

[OpenStack](https://www.openstack.org/) is a free and open-source software platform for cloud computing, mostly deployed as infrastructure-as-a-service that controls large pools of compute, storage, and networking resources throughout a datacenter, managed through a dashboard or via the OpenStack API. OpenStack works with popular enterprise and open source technologies making it ideal for heterogeneous infrastructure.

[Cloud Foundry](https://www.cloudfoundry.org/) is an open source, multi cloud application platform as a service that makes it faster and easier to build, test, deploy and scale applications, providing a choice of clouds, developer frameworks, and application services. It is an open source project and is available through a variety of private cloud distributions and public cloud instances.

[Splunk](https://www.splunk.com/) software is used for searching, monitoring, and analyzing machine-generated big data, via a Web-style interface.

[Prometheus](https://prometheus.io/) is a free software application used for event monitoring and alerting. It records real-time metrics in a time series database (allowing for high dimensionality) built using a HTTP pull model, with flexible queries and real-time alerting.

[Loki](https://grafana.com/oss/loki/) is a horizontally-scalable, highly-available, multi-tenant log aggregation system inspired by Prometheus. It is designed to be very cost effective and easy to operate. It does not index the contents of the logs, but rather a set of labels for each log stream.

[Thanos](https://thanos.io/) is a set of components that can be composed into a highly available metric system with unlimited storage capacity, which can be added seamlessly on top of existing Prometheus deployments.

[Container Storage Interface (CSI)](https://www.architecting.it/blog/container-storage-interface/) is an API that lets container orchestration platforms like Kubernetes seamlessly communicate with stored data via a plug-in.

[OpenEBS](https://openebs.io/) is a Kubernetes-based tool to create stateful applications using Container Attached Storage.

[ElasticSearch](https://www.elastic.co/) is a search engine based on the Lucene library. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents. Elasticsearch is developed in Java.

[Logstash](https://www.elastic.co/products/logstash) is a tool for managing events and logs. When used generically, the term encompasses a larger system of log collection, processing, storage and searching activities.

[Kibana](https://www.elastic.co/products/kibana) is an open source data visualization plugin for Elasticsearch. It provides visualization capabilities on top of the content indexed on an Elasticsearch cluster. Users can create bar, line and scatter plots, or pie charts and maps on top of large volumes of data.

[New Relic](https://newrelic.com/) is a SaaS-based monitoring tool that fully supports the way DevOps teams work in the modern enterprise by streamlining your workflows with today's collaboration software and orchestration tools like Puppet, Chef, and Ansible.

[Nagios](https://www.nagios.org/) is a free and open source computer-software application that monitors systems, networks and infrastructure. Nagios offers monitoring and alerting services for servers, switches, applications and services. It alerts users when things go wrong and alerts them a second time when the problem has been resolved.

[SonarQube](https://www.sonarqube.org/) is an open-source platform developed by SonarSource for continuous inspection of code quality to perform automatic reviews with static analysis of code to detect bugs, code smells, and security vulnerabilities on 20+ programming languages.

[Genie](https://netflix.github.io/genie) is a federated job orchestration engine developed by Netflix. Genie provides REST APIs to run a variety of big data jobs like Hadoop, Pig, Hive, Spark, Presto, Sqoop and more. It also provides APIs for managing the metadata of many distributed processing clusters and the commands and applications which run on them.

[Inviso](https://github.com/Netflix/inviso) is a lightweight tool that provides the ability to search for Hadoop jobs, visualize the performance, and view cluster utilization.

[Fenzo](https://github.com/Netflix/Fenzo) is a scheduler Java library for Apache Mesos frameworks that supports plugins for scheduling optimizations and facilitates cluster autoscaling.

[Dynomite](https://github.com/Netflix/dynomite) is a thin, distributed dynamo layer for different storage engines and protocols, which includes [Redis](http://redis.io/) and [Memcached](http://www.memcached.org/). Dynomite supports multi-datacenter replication and is designed for High Availability(HA).

[Dyno](https://github.com/Netflix/dynomite) is a tool that is used to scale a Java client application utilizing [Dynomite](https://github.com/Netflix/dynomite).

[Raigad](https://github.com/Netflix/Raigad) is a process/tool that runs alongside Elasticsearch to automate backup/recovery, Deployments and Centralized Configuration management.

[Priam](https://github.com/Netflix/Priam) is a process/tool that runs alongside Apache Cassandra to automate backup/recovery, Deployments and Centralized Configuration management.

[Chaos Monkey](https://github.com/Netflix/chaosmonkey) is a resiliency tool used to randomly terminates virtual machine instances and containers that run inside of your production environment. Chaos Monkey should work with any backend that [Spinnaker](http://www.spinnaker.io/) supports (AWS, Google Compute Engine, Microsoft Azure, Kubernetes, and Cloud Foundry).

[Falcor](https://netflix.github.io/falcor/) is a JavaScript library for efficient data fetching. Falcor lets you represent all your remote data sources as a single domain model via a virtual JSON graph, whether in memory on the client or over the network on the server.

[Restify](https://github.com/restify/node-restify) is a framework, utilizing [connect](https://github.com/senchalabs/connect) style middleware for building REST APIs.

[Traefik](https://traefik.io/traefik/) is an open source Edge Router that makes publishing your services a fun and easy experience. It receives requests on behalf of your system and finds out which components are responsible for handling them. What sets Traefik apart, besides its many features, is that it automatically discovers the right configuration for your services.

[Jira](https://www.atlassian.com/software/jira) is a proprietary issue tracking product developed by Atlassian that allows bug tracking and agile project management.

[Pivotal Tracker](https://www.pivotaltracker.com/) is the agile project management tool of choice for developers around the world for real-time collaboration around a shared, prioritized backlog.

# Networking
[Back to the Top](https://github.com/mikeroyal/Linode-Guide#table-of-contents)

## Network Learning Resources

[AWS Certified Security - Specialty Certification](https://aws.amazon.com/certification/certified-security-specialty/)

[Microsoft Certified: Azure Security Engineer Associate](https://docs.microsoft.com/en-us/learn/certifications/azure-security-engineer)

[Google Cloud Certified Professional Cloud Security Engineer](https://cloud.google.com/certification/cloud-security-engineer)

[Cisco Security Certifications](https://www.cisco.com/c/en/us/training-events/training-certifications/certifications/security.html)

[The Red Hat Certified Specialist in Security: Linux](https://www.redhat.com/en/services/training/ex415-red-hat-certified-specialist-security-linux-exam)

[Linux Professional Institute LPIC-3 Enterprise Security Certification](https://www.lpi.org/our-certifications/lpic-3-303-overview)

[Cybersecurity Training and Courses from IBM Skills](https://www.ibm.com/skills/topics/cybersecurity/)

[Cybersecurity Courses and Certifications by Offensive Security](https://www.offensive-security.com/courses-and-certifications/)

[Citrix Certified Associate – Networking(CCA-N)](http://training.citrix.com/cms/index.php/certification/networking/)

[Citrix Certified Professional – Virtualization(CCP-V)](https://www.globalknowledge.com/us-en/training/certification-prep/brands/citrix/section/virtualization/citrix-certified-professional-virtualization-ccp-v/)

[CCNP Routing and Switching](https://learningnetwork.cisco.com/s/ccnp-enterprise)

[Certified Information Security Manager(CISM)](https://www.isaca.org/credentialing/cism)

[Wireshark Certified Network Analyst (WCNA)](https://www.wiresharktraining.com/certification.html)

[Juniper Networks Certification Program Enterprise (JNCP)](https://www.juniper.net/us/en/training/certification/)

[Networking courses and specializations from Coursera](https://www.coursera.org/browse/information-technology/networking)

[Network & Security Courses from Udemy](https://www.udemy.com/courses/it-and-software/network-and-security/)

[Network & Security Courses from edX](https://www.edx.org/learn/cybersecurity)

## Networking Tools & Concepts

[cURL](https://curl.se/) is a computer software project providing a library and command-line tool for transferring data using various network protocols(HTTP, HTTPS, FTP, FTPS, SCP, SFTP, TFTP, DICT, TELNET, LDAP LDAPS, MQTT, POP3, POP3S, RTMP, RTMPS, RTSP, SCP, SFTP, SMB, SMBS, SMTP or SMTPS). cURL is also used in cars, television sets, routers, printers, audio equipment, mobile phones, tablets, settop boxes, media players and is the Internet transfer engine for thousands of software applications in over ten billion installations.

[cURL Fuzzer](https://github.com/curl/curl-fuzzer) is a quality assurance testing for the curl project.

[DoH](https://github.com/curl/doh) is a stand-alone application for DoH (DNS-over-HTTPS) name resolves and lookups.

[HTTPie](https://github.com/httpie/httpie) is a command-line HTTP client. Its goal is to make CLI interaction with web services as human-friendly as possible. HTTPie is designed for testing, debugging, and generally interacting with APIs & HTTP servers.

[HTTPStat](https://github.com/reorx/httpstat) is a tool that visualizes curl statistics in a simple layout.

[Wuzz](https://github.com/asciimoo/wuzz) is an interactive cli tool for HTTP inspection. It can be used to inspect/modify requests copied from the browser's network inspector with the "copy as cURL" feature.

[Websocat](https://github.com/vi/websocat) is a ommand-line client for WebSockets, like netcat (or curl) for ws:// with advanced socat-like functions.

• Connection: In networking, a connection refers to pieces of related information that are transferred through a network. This generally infers that a connection is built before the data transfer (by following the procedures laid out in a protocol) and then is deconstructed at the at the end of the data transfer.

• Packet: A packet is, generally speaking, the most basic unit that is transferred over a network. When communicating over a network, packets are the envelopes that carry your data (in pieces) from one end point to the other.

Packets have a header portion that contains information about the packet including the source and destination, timestamps, network hops. The main portion of a packet contains the actual data being transferred. It is sometimes called the body or the payload.

• Network Interface: A network interface can refer to any kind of software interface to networking hardware. For instance, if you have two network cards in your computer, you can control and configure each network interface associated with them individually.

A network interface may be associated with a physical device, or it may be a representation of a virtual interface. The "loop-back" device, which is a virtual interface to the local machine, is an example of this.

• LAN: LAN stands for "local area network". It refers to a network or a portion of a network that is not publicly accessible to the greater internet. A home or office network is an example of a LAN.

• WAN: WAN stands for "wide area network". It means a network that is much more extensive than a LAN. While WAN is the relevant term to use to describe large, dispersed networks in general, it is usually meant to mean the internet, as a whole.
If an interface is connected to the WAN, it is generally assumed that it is reachable through the internet.

• Protocol: A protocol is a set of rules and standards that basically define a language that devices can use to communicate. There are a great number of protocols in use extensively in networking, and they are often implemented in different layers.

Some low level protocols are TCP, UDP, IP, and ICMP. Some familiar examples of application layer protocols, built on these lower protocols, are HTTP (for accessing web content), SSH, TLS/SSL, and FTP.

• Port: A port is an address on a single machine that can be tied to a specific piece of software. It is not a physical interface or location, but it allows your server to be able to communicate using more than one application.

• Firewall: A firewall is a program that decides whether traffic coming into a server or going out should be allowed. A firewall usually works by creating rules for which type of traffic is acceptable on which ports. Generally, firewalls block ports that are not used by a specific application on a server.

• NAT: Network address translation is a way to translate requests that are incoming into a routing server to the relevant devices or servers that it knows about in the LAN. This is usually implemented in physical LANs as a way to route requests through one IP address to the necessary backend servers.

• VPN: Virtual private network is a means of connecting separate LANs through the internet, while maintaining privacy. This is used as a means of connecting remote systems as if they were on a local network, often for security reasons.

## Network Layers

While networking is often discussed in terms of topology in a horizontal way, between hosts, its implementation is layered in a vertical fashion throughout a computer or network. This means is that there are multiple technologies and protocols that are built on top of each other in order for communication to function more easily. Each successive, higher layer abstracts the raw data a little bit more, and makes it simpler to use for applications and users. It also allows you to leverage lower layers in new ways without having to invest the time and energy to develop the protocols and applications that handle those types of traffic.

As data is sent out of one machine, it begins at the top of the stack and filters downwards. At the lowest level, actual transmission to another machine takes place. At this point, the data travels back up through the layers of the other computer. Each layer has the ability to add its own "wrapper" around the data that it receives from the adjacent layer, which will help the layers that come after decide what to do with the data when it is passed off.

One method of talking about the different layers of network communication is the OSI model. OSI stands for Open Systems Interconnect.This model defines seven separate layers. The layers in this model are:

• Application: The application layer is the layer that the users and user-applications most often interact with. Network communication is discussed in terms of availability of resources, partners to communicate with, and data synchronization.

• Presentation: The presentation layer is responsible for mapping resources and creating context. It is used to translate lower level networking data into data that applications expect to see.

• Session: The session layer is a connection handler. It creates, maintains, and destroys connections between nodes in a persistent way.

• Transport: The transport layer is responsible for handing the layers above it a reliable connection. In this context, reliable refers to the ability to verify that a piece of data was received intact at the other end of the connection. This layer can resend information that has been dropped or corrupted and can acknowledge the receipt of data to remote computers.

• Network: The network layer is used to route data between different nodes on the network. It uses addresses to be able to tell which computer to send information to. This layer can also break apart larger messages into smaller chunks to be reassembled on the opposite end.

• Data Link: This layer is implemented as a method of establishing and maintaining reliable links between different nodes or devices on a network using existing physical connections.

• Physical: The physical layer is responsible for handling the actual physical devices that are used to make a connection. This layer involves the bare software that manages physical connections as well as the hardware itself (like Ethernet).

The TCP/IP model, more commonly known as the Internet protocol suite, is another layering model that is simpler and has been widely adopted.It defines the four separate layers, some of which overlap with the OSI model:

• Application: In this model, the application layer is responsible for creating and transmitting user data between applications. The applications can be on remote systems, and should appear to operate as if locally to the end user.
The communication takes place between peers network.

• Transport: The transport layer is responsible for communication between processes. This level of networking utilizes ports to address different services. It can build up unreliable or reliable connections depending on the type of protocol used.

• Internet: The internet layer is used to transport data from node to node in a network. This layer is aware of the endpoints of the connections, but does not worry about the actual connection needed to get from one place to another. IP addresses are defined in this layer as a way of reaching remote systems in an addressable manner.

• Link: The link layer implements the actual topology of the local network that allows the internet layer to present an addressable interface. It establishes connections between neighboring nodes to send data.

## Interfaces
**Interfaces** are networking communication points for your computer. Each interface is associated with a physical or virtual networking device. Typically, your server will have one configurable network interface for each Ethernet or wireless internet card you have. In addition, it will define a virtual network interface called the "loopback" or localhost interface. This is used as an interface to connect applications and processes on a single computer to other applications and processes. You can see this referenced as the "lo" interface in many tools.

## Network Protocols

Networking works by piggybacks on a number of different protocols on top of each other. In this way, one piece of data can be transmitted using multiple protocols encapsulated within one another.

**Media Access Control(MAC)** is a communications protocol that is used to distinguish specific devices. Each device is supposed to get a unique MAC address during the manufacturing process that differentiates it from every other device on the internet. Addressing hardware by the MAC address allows you to reference a device by a unique value even when the software on top may change the name for that specific device during operation. Media access control is one of the only protocols from the link layer that you are likely to interact with on a regular basis.

**The IP protocol** is one of the fundamental protocols that allow the internet to work. IP addresses are unique on each network and they allow machines to address each other across a network. It is implemented on the internet layer in the IP/TCP model. Networks can be linked together, but traffic must be routed when crossing network boundaries. This protocol assumes an unreliable network and multiple paths to the same destination that it can dynamically change between. There are a number of different implementations of the protocol. The most common implementation today is IPv4, although IPv6 is growing in popularity as an alternative due to the scarcity of IPv4 addresses available and improvements in the protocols capabilities.

**ICMP: internet control message protocol** is used to send messages between devices to indicate the availability or error conditions. These packets are used in a variety of network diagnostic tools, such as ping and traceroute. Usually ICMP packets are transmitted when a packet of a different kind meets some kind of a problem. Basically, they are used as a feedback mechanism for network communications.

**TCP: Transmission control protocol** is implemented in the transport layer of the IP/TCP model and is used to establish reliable connections. TCP is one of the protocols that encapsulates data into packets. It then transfers these to the remote end of the connection using the methods available on the lower layers. On the other end, it can check for errors, request certain pieces to be resent, and reassemble the information into one logical piece to send to the application layer. The protocol builds up a connection prior to data transfer using a system called a three-way handshake. This is a way for the two ends of the communication to acknowledge the request and agree upon a method of ensuring data reliability. After the data has been sent, the connection is torn down using a similar four-way handshake. TCP is the protocol of choice for many of the most popular uses for the internet, including WWW, FTP, SSH, and email. It is safe to say that the internet we know today would not be here without TCP.

**UDP: User datagram protocol** is a popular companion protocol to TCP and is also implemented in the transport layer. The fundamental difference between UDP and TCP is that UDP offers unreliable data transfer. It does not verify that data has been received on the other end of the connection. This might sound like a bad thing, and for many purposes, it is. However, it is also extremely important for some functions. It’s not required to wait for confirmation that the data was received and forced to resend data, UDP is much faster than TCP. It does not establish a connection with the remote host, it simply fires off the data to that host and doesn't care if it is accepted or not. Since UDP is a simple transaction, it is useful for simple communications like querying for network resources. It also doesn't maintain a state, which makes it great for transmitting data from one machine to many real-time clients. This makes it ideal for VOIP, games, and other applications that cannot afford delays.

**HTTP: Hypertext transfer protocol** is a protocol defined in the application layer that forms the basis for communication on the web. HTTP defines a number of functions that tell the remote system what you are requesting. For instance, GET, POST, and DELETE all interact with the requested data in a different way.

**FTP: File transfer protocol** is in the application layer and provides a way of transferring complete files from one host to another. It is inherently insecure, so it is not recommended for any externally facing network unless it is implemented as a public, download-only resource.

**DNS: Domain name system** is an application layer protocol used to provide a human-friendly naming mechanism for internet resources. It is what ties a domain name to an IP address and allows you to access sites by name in your browser.

**SSH: Secure shell** is an encrypted protocol implemented in the application layer that can be used to communicate with a remote server in a secure way. Many additional technologies are built around this protocol because of its end-to-end encryption and ubiquity. There are many other protocols that we haven't covered that are equally important. However, this should give you a good overview of some of the fundamental technologies that make the internet and networking possible.

[JSON Web Token (JWT)](https://jwt.io) is a compact URL-safe means of representing claims to be transferred between two parties. The claims in a JWT are encoded as a JSON object that is digitally signed using JSON Web Signature (JWS).

[OAuth 2.0](https://oauth.net/2/) is an open source authorization framework that enables applications to obtain limited access to user accounts on an HTTP service, such as Amazon, Google, Facebook, Microsoft, Twitter GitHub, and DigitalOcean. It works by delegating user authentication to the service that hosts the user account, and authorizing third-party applications to access the user account.

# Kubernetes
[Back to the Top](https://github.com/mikeroyal/Linode-Guide#table-of-contents)

[Kubernetes (K8s)](https://kubernetes.io/) is an open-source system for automating deployment, scaling, and management of containerized applications.

**Building Highly-Availability(HA) Clusters with kubeadm. Source: [Kubernetes.io](https://kubernetes.io/docs/setup/production-environment/tools/kubeadm/high-availability/)**

[Google Kubernetes Engine (GKE)](https://cloud.google.com/kubernetes-engine/) is a managed, production-ready environment for running containerized applications.

[Azure Kubernetes Service (AKS)](https://azure.microsoft.com/en-us/services/kubernetes-service/) is serverless Kubernetes, with a integrated continuous integration and continuous delivery (CI/CD) experience, and enterprise-grade security and governance. Unite your development and operations teams on a single platform to rapidly build, deliver, and scale applications with confidence.

[Amazon EKS](https://docs.aws.amazon.com/eks/latest/userguide/what-is-eks.html) is a tool that runs Kubernetes control plane instances across multiple Availability Zones to ensure high availability.

[AWS Controllers for Kubernetes (ACK)](https://aws.amazon.com/blogs/containers/aws-controllers-for-kubernetes-ack/) is a new tool that lets you directly manage AWS services from Kubernetes. ACK makes it simple to build scalable and highly-available Kubernetes applications that utilize AWS services.

[Container Engine for Kubernetes (OKE)](https://www.oracle.com/cloud-native/container-engine-kubernetes/) is an Oracle-managed container orchestration service that can reduce the time and cost to build modern cloud native applications. Unlike most other vendors, Oracle Cloud Infrastructure provides Container Engine for Kubernetes as a free service that runs on higher-performance, lower-cost compute.

[Red Hat Openshift](https://www.openshift.com/) is a fully managed Kubernetes platform that provides a foundation for on-premises, hybrid, and multicloud deployments.

[Odo](https://odo.dev/) is a fast, iterative, and straightforward CLI tool for developers who write, build, and deploy applications on Kubernetes and OpenShift.

[Rook](https://rook.io/) is a tool that turns distributed storage systems into self-managing, self-scaling, self-healing storage services. It automates the tasks of a storage administrator: deployment, bootstrapping, configuration, provisioning, scaling, upgrading, migration, disaster recovery, monitoring, and resource management.

[KubeInit](https://github.com/kubeinit/kubeinit) provides Ansible playbooks and roles for the deployment and configuration of multiple Kubernetes distributions.

[Helm](https://helm.sh/) is a Kubernetes Package Manager tool that makes it easier to install and manage Kubernetes applications.

[KubeFlow](https://www.kubeflow.org/) is a tool dedicated to making deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable.

[OpenEBS](https://openebs.io/) is a Kubernetes-based tool to create stateful applications using Container Attached Storage.

[MicroK8s](https://microk8s.io/) is a tool that delivers the full Kubernetes experience. In a Fully containerized deployment with compressed over-the-air updates for ultra-reliable operations. It is supported on Linux, Windows, and MacOS.

[Charmed Kubernetes](https://ubuntu.com/kubernetes/features) is a well integrated, turn-key, conformant Kubernetes platform, optimized for your multi-cloud environments developed by Canonical.

[Grafana Kubernetes App](https://grafana.com/grafana/plugins/grafana-kubernetes-app) is a toll that allows you to monitor your Kubernetes cluster's performance. It includes 4 dashboards, Cluster, Node, Pod/Container and Deployment. It allows for the automatic deployment of the required Prometheus exporters and a default scrape config to use with your in cluster Prometheus deployment.

[KubeEdge](https://kubeedge.io/en/) is an open source system for extending native containerized application orchestration capabilities to hosts at Edge.It is built upon kubernetes and provides fundamental infrastructure support for network, app. deployment and metadata synchronization between cloud and edge.

[Lens](https://k8slens.dev/) is the most powerful IDE for people who need to deal with Kubernetes clusters on a daily basis. It has support for MacOS, Windows and Linux operating systems.

[kind](https://kind.sigs.k8s.io/) is a tool for running local Kubernetes clusters using Docker container “nodes”. It was primarily designed for testing Kubernetes itself, but may be used for local development or CI.

[Flux CD](https://fluxcd.io/) is a tool that automatically ensures that the state of your Kubernetes cluster matches the configuration you've supplied in Git. It uses an operator in the cluster to trigger deployments inside Kubernetes, which means that you don't need a separate continuous delivery tool.

## Kubernetes Learning Resources

[Getting Kubernetes Certifications](https://training.linuxfoundation.org/certification/catalog/?_sft_technology=kubernetes)

[Getting started with Kubernetes on AWS](https://aws.amazon.com/kubernetes/)

[Kubernetes on Microsoft Azure](https://azure.microsoft.com/en-us/topic/what-is-kubernetes/)

[Intro to Azure Kubernetes Service](https://docs.microsoft.com/en-us/azure/aks/kubernetes-dashboard)

[Getting started with Google Cloud](https://cloud.google.com/learn/what-is-kubernetes)

[Getting started with Kubernetes on Red Hat](https://www.redhat.com/en/topics/containers/what-is-kubernetes)

[Getting started with Kubernetes on IBM](https://www.ibm.com/cloud/learn/kubernetes)

[YAML basics in Kubernetes](https://developer.ibm.com/technologies/containers/tutorials/yaml-basics-and-usage-in-kubernetes/)

[Elastic Cloud on Kubernetes](https://www.elastic.co/elastic-cloud-kubernetes)

[Docker and Kubernetes](https://www.docker.com/products/kubernetes)

[Deploy a model to an Azure Kubernetes Service cluster](https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-kubernetes-service?tabs=python)

[Simplify Machine Learning Inference on Kubernetes with Amazon SageMaker Operators](https://aws.amazon.com/blogs/machine-learning/simplify-machine-learning-inference-on-kubernetes-with-amazon-sagemaker-operators/)

[Running Apache Spark on Kubernetes](http://spark.apache.org/docs/latest/running-on-kubernetes.html)

[Kubernetes Across VMware vRealize Automation](https://blogs.vmware.com/management/2019/06/kubernetes-across-vmware-cloud-automation-services.html)

[VMware Tanzu Kubernetes Grid](https://tanzu.vmware.com/kubernetes-grid)

[All the Ways VMware Tanzu Works with AWS](https://tanzu.vmware.com/content/blog/all-the-ways-vmware-tanzutm-works-with-aws)

[VMware Tanzu Education](https://tanzu.vmware.com/education)

[Using Ansible in a Cloud-Native Kubernetes Environment](https://www.ansible.com/blog/how-useful-is-ansible-in-a-cloud-native-kubernetes-environment)

[Managing Kubernetes (K8s) objects with Ansible](https://docs.ansible.com/ansible/latest/collections/community/kubernetes/k8s_module.html)

[Setting up a Kubernetes cluster using Vagrant and Ansible](https://kubernetes.io/blog/2019/03/15/kubernetes-setup-using-ansible-and-vagrant/)

[Running MongoDB with Kubernetes](https://www.mongodb.com/kubernetes)

[Kubernetes Fluentd](https://docs.fluentd.org/v/0.12/articles/kubernetes-fluentd)

[Understanding the new GitLab Kubernetes Agent](https://about.gitlab.com/blog/2020/09/22/introducing-the-gitlab-kubernetes-agent/)

[Kubernetes Contributors](https://www.kubernetes.dev/)

[KubeAcademy from VMware](https://kube.academy/)

# Machine Learning
[Back to the Top](https://github.com/mikeroyal/Linode-Guide#table-of-contents)

## ML frameworks & applications

[TensorFlow](https://www.tensorflow.org) is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications.

[Tensorman](https://github.com/pop-os/tensorman) is a utility for easy management of Tensorflow containers by developed by [System76]( https://system76.com).Tensorman allows Tensorflow to operate in an isolated environment that is contained from the rest of the system. This virtual environment can operate independent of the base system, allowing you to use any version of Tensorflow on any version of a Linux distribution that supports the Docker runtime.

[Keras](https://keras.io) is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.It was developed with a focus on enabling fast experimentation. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.

[PyTorch](https://pytorch.org) is a library for deep learning on irregular input data such as graphs, point clouds, and manifolds. Primarily developed by Facebook's AI Research lab.

[Amazon SageMaker](https://aws.amazon.com/sagemaker/) is a fully managed service that provides every developer and data scientist with the ability to build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models.

[Azure Databricks](https://azure.microsoft.com/en-us/services/databricks/) is a fast and collaborative Apache Spark-based big data analytics service designed for data science and data engineering. Azure Databricks, sets up your Apache Spark environment in minutes, autoscale, and collaborate on shared projects in an interactive workspace. Azure Databricks supports Python, Scala, R, Java, and SQL, as well as data science frameworks and libraries including TensorFlow, PyTorch, and scikit-learn.

[Microsoft Cognitive Toolkit (CNTK)](https://docs.microsoft.com/en-us/cognitive-toolkit/) is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph. CNTK allows the user to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs) and recurrent neural networks (RNNs/LSTMs). CNTK implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.

[Apache Airflow](https://airflow.apache.org) is an open-source workflow management platform created by the community to programmatically author, schedule and monitor workflows. Install. Principles. Scalable. Airflow has a modular architecture and uses a message queue to orchestrate an arbitrary number of workers. Airflow is ready to scale to infinity.

[Open Neural Network Exchange(ONNX)](https://github.com/onnx) is an open ecosystem that empowers AI developers to choose the right tools as their project evolves. ONNX provides an open source format for AI models, both deep learning and traditional ML. It defines an extensible computation graph model, as well as definitions of built-in operators and standard data types.

[Apache MXNet](https://mxnet.apache.org/) is a deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, MXNet contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly. A graph optimization layer on top of that makes symbolic execution fast and memory efficient. MXNet is portable and lightweight, scaling effectively to multiple GPUs and multiple machines. Support for Python, R, Julia, Scala, Go, Javascript and more.

[AutoGluon](https://autogluon.mxnet.io/index.html) is toolkit for Deep learning that automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy deep learning models on tabular, image, and text data.

[Anaconda](https://www.anaconda.com/) is a very popular Data Science platform for machine learning and deep learning that enables users to develop models, train them, and deploy them.

[PlaidML](https://github.com/plaidml/plaidml) is an advanced and portable tensor compiler for enabling deep learning on laptops, embedded devices, or other devices where the available computing hardware is not well supported or the available software stack contains unpalatable license restrictions.

[OpenCV](https://opencv.org) is a highly optimized library with focus on real-time computer vision applications. The C++, Python, and Java interfaces support Linux, MacOS, Windows, iOS, and Android.

[Scikit-Learn](https://scikit-learn.org/stable/index.html) is a Python module for machine learning built on top of SciPy, NumPy, and matplotlib, making it easier to apply robust and simple implementations of many popular machine learning algorithms.

[Weka](https://www.cs.waikato.ac.nz/ml/weka/) is an open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j.

[Caffe](https://github.com/BVLC/caffe) is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors.

[Theano](https://github.com/Theano/Theano) is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently including tight integration with NumPy.

[nGraph](https://github.com/NervanaSystems/ngraph) is an open source C++ library, compiler and runtime for Deep Learning. The nGraph Compiler aims to accelerate developing AI workloads using any deep learning framework and deploying to a variety of hardware targets.It provides the freedom, performance, and ease-of-use to AI developers.

[NVIDIA cuDNN](https://developer.nvidia.com/cudnn) is a GPU-accelerated library of primitives for [deep neural networks](https://developer.nvidia.com/deep-learning). cuDNN provides highly tuned implementations for standard routines such as forward and backward convolution, pooling, normalization, and activation layers. cuDNN accelerates widely used deep learning frameworks, including [Caffe2](https://caffe2.ai/), [Chainer](https://chainer.org/), [Keras](https://keras.io/), [MATLAB](https://www.mathworks.com/solutions/deep-learning.html), [MxNet](https://mxnet.incubator.apache.org/), [PyTorch](https://pytorch.org/), and [TensorFlow](https://www.tensorflow.org/).

[Jupyter Notebook](https://jupyter.org/) is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Jupyter is used widely in industries that do data cleaning and transformation, numerical simulation, statistical modeling, data visualization, data science, and machine learning.

[Apache Spark](https://spark.apache.org/) is a unified analytics engine for large-scale data processing. It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing.

[Apache Spark Connector for SQL Server and Azure SQL](https://github.com/microsoft/sql-spark-connector) is a high-performance connector that enables you to use transactional data in big data analytics and persists results for ad-hoc queries or reporting. The connector allows you to use any SQL database, on-premises or in the cloud, as an input data source or output data sink for Spark jobs.

[Apache PredictionIO](https://predictionio.apache.org/) is an open source machine learning framework for developers, data scientists, and end users. It supports event collection, deployment of algorithms, evaluation, querying predictive results via REST APIs. It is based on scalable open source services like Hadoop, HBase (and other DBs), Elasticsearch, Spark and implements what is called a Lambda Architecture.

[Cluster Manager for Apache Kafka(CMAK)](https://github.com/yahoo/CMAK) is a tool for managing [Apache Kafka](https://kafka.apache.org/) clusters.

[BigDL](https://bigdl-project.github.io/) is a distributed deep learning library for Apache Spark. With BigDL, users can write their deep learning applications as standard Spark programs, which can directly run on top of existing Spark or Hadoop clusters.

[Koalas](https://pypi.org/project/koalas/) is project makes data scientists more productive when interacting with big data, by implementing the pandas DataFrame API on top of Apache Spark.

[Apache Spark™ MLflow](https://mlflow.org/) is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components:

**[MLflow Tracking](https://mlflow.org/docs/latest/tracking.html)**: Record and query experiments: code, data, config, and results.

**[MLflow Projects](https://mlflow.org/docs/latest/projects.html)**: Package data science code in a format to reproduce runs on any platform.

**[MLflow Models](https://mlflow.org/docs/latest/models.html)**: Deploy machine learning models in diverse serving environments.

**[Model Registry](https://mlflow.org/docs/latest/model-registry.html)**: Store, annotate, discover, and manage models in a central repository.

[Eclipse Deeplearning4J (DL4J)](https://deeplearning4j.konduit.ai/) is a set of projects intended to support all the needs of a JVM-based(Scala, Kotlin, Clojure, and Groovy) deep learning application. This means starting with the raw data, loading and preprocessing it from wherever and whatever format it is in to building and tuning a wide variety of simple and complex deep learning networks.

[Numba](https://github.com/numba/numba) is an open source, NumPy-aware optimizing compiler for Python sponsored by Anaconda, Inc. It uses the LLVM compiler project to generate machine code from Python syntax. Numba can compile a large subset of numerically-focused Python, including many NumPy functions. Additionally, Numba has support for automatic parallelization of loops, generation of GPU-accelerated code, and creation of ufuncs and C callbacks.

[Chainer](https://chainer.org/) is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the define-by-run approach (dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks. It also supports CUDA/cuDNN using [CuPy](https://github.com/cupy/cupy) for high performance training and inference.

[cuML](https://github.com/rapidsai/cuml) is a suite of libraries that implement machine learning algorithms and mathematical primitives functions that share compatible APIs with other RAPIDS projects. cuML enables data scientists, researchers, and software engineers to run traditional tabular ML tasks on GPUs without going into the details of CUDA programming. In most cases, cuML's Python API matches the API from scikit-learn.

## ML Learning Resources

[Machine Learning by Stanford University from Coursera](https://www.coursera.org/learn/machine-learning)

[Machine Learning Courses Online from Coursera](https://www.coursera.org/courses?query=machine%20learning&)

[Machine Learning Courses Online from Udemy](https://www.udemy.com/topic/machine-learning/)

[Learn Machine Learning with Online Courses and Classes from edX](https://www.edx.org/learn/machine-learning)

## Contribute

- [x] If would you like to contribute to this guide simply make a [Pull Request](https://github.com/mikeroyal/Linode-Guide/pulls).

## License
[Back to the Top](https://github.com/mikeroyal/Linode-Guide#table-of-contents)

Distributed under the [Creative Commons Attribution 4.0 International (CC BY 4.0) Public License](https://creativecommons.org/licenses/by/4.0/).