Self-Hosting-Guide
Self-Hosting Guide. Learn all about locally hosting (on premises & private web servers) and managing software applications by yourself or your organization. Including Cloud, LLMs, WireGuard, Automation, Home Assistant, and Networking.
https://github.com/mikeroyal/Self-Hosting-Guide
Last synced: 7 days ago
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Ansible DevOps Tools Integration
Interfaces
- Capistrano - default deployment workflows.
- ansible-role-wireguard
- wireguard_cloud_gateway
- Ansible Automation Hub
- Collections - of-collections).
- Ansible Lint - line tool for linting playbooks, roles and collections aimed towards any Ansible users. Its main goal is to promote proven practices, patterns and behaviors while avoiding common pitfalls that can easily lead to bugs or make code harder to maintain.
- Ansible cmdb
- Ansible Inventory Grapher
- Ansible Playbook Grapher
- Ansible Shell - in tab completion for all the modules.
- Ansible Silo - contained Ansible environment by Docker.
- Ansigenome
- Collections - of-collections).
- Ansible Lint - line tool for linting playbooks, roles and collections aimed towards any Ansible users. Its main goal is to promote proven practices, patterns and behaviors while avoiding common pitfalls that can easily lead to bugs or make code harder to maintain.
- Fabric - line features) and Paramiko (SSH protocol implementation), extending their APIs to complement one another and provide additional functionality.
Ansible Learning Resources
Interfaces
- Top Ansible Courses Online from Udemy
- Introduction to Ansible: The Fundamentals on Coursera
- Learning Ansible Fundamentals on Pluralsight
- Introducing Red Hat Ansible Automation Platform 2.1
- Ansible Documentation
- Ansible Galaxy User Guide
- Ansible Collections Overview
- Ansible Best Practices: Roles & Modules
- Using vault in playbooks with Ansible
- How to Use Ansible: An Ansible Cheat Sheet Guide from DigitalOcean
- Intro to Ansible on Linode | Spatial Labs
- Red Hat Training for Ansible
- Ansible Use Cases
- Ansible Integrations
- Ansible Integrations
- Ansible Use Cases
- Ansible Integrations
- Working with playbooks
- Ansible for DevOps Examples by Jeff Geerling
- Working With Modules in Ansible
- Working with command line tools for Ansible
- Encrypting content with Ansible Vault
- Ansible Best Practices: Roles & Modules
- Introducing Red Hat Ansible Automation Platform 2.1
- Working with playbooks
- Working With Modules in Ansible
- Working with command line tools for Ansible
Blockchain Learning Resources
In-memory data grids
- Blockchain
- Blockchain Developer Nanodegree program | Udacity
- Blockchain Programming: Build a blockchain and cryptocurrency from scratch using Python | Udemy
- Blockchain Courses by Coursera
- Blockchain Courses by edX
- Blockchain Certifications
- IBM Blockchain Essentials
- Blockchain Developer Nanodegree program | Udacity
Blockchain Tools, Libraries and Frameworks
In-memory data grids
- Lightning Network (LN)
- Hyperledger Fabric - grade permissioned distributed ledger framework for developing solutions and applications. Its modular and versatile design satisfies a broad range of industry use cases. It offers a unique approach to consensus that enables performance at scale while preserving privacy.
- Geth
- Diesel - lang.org/).
- Tendermint Core
- Squeezer - entry blockchain developers to build serverless dApps easily.
- Quorum - based distributed ledger protocol with transaction/contract privacy and new consensus mechanisms.
- Hyperledger Fabric - grade permissioned distributed ledger framework for developing solutions and applications. Its modular and versatile design satisfies a broad range of industry use cases. It offers a unique approach to consensus that enables performance at scale while preserving privacy.
- Ethers.js - side JavaScript based wallets, keeping the private key on the owner’s machine at all times.
- OpenEthereum - rich multi-network Ethereum client.
- BlockShell
- inDEX
C/C++ Learning Resources
In-memory data grids
- C - purpose, high-level language that was originally developed by Dennis M. Ritchie to develop the UNIX operating system at Bell Labs. It supports structured programming, lexical variable scope, and recursion, with a static type system. C also provides constructs that map efficiently to typical machine instructions, which makes it one was of the most widely used programming languages today.
- Embedded C - committee) to address issues that exist between C extensions for different embedded systems. The extensions hep enhance microprocessor features such as fixed-point arithmetic, multiple distinct memory banks, and basic I/O operations. This makes Embedded C the most popular embedded software language in the world.
- C & C++ Developer Tools from JetBrains
- Open source C++ libraries on cppreference.com
- C++ Graphics libraries
- C++ Libraries in MATLAB
- Google C++ Style Guide
- C++ style guide for Fuchsia
- Chromium C++ Style Guide
- C++ Core Guidelines
- C++ Style Guide for ROS
- Learn C++
- Learn C : An Interactive C Tutorial
- C++ Online Training Courses on LinkedIn Learning
- C++ Tutorials on W3Schools
- Learn C Programming Online Courses on edX
- Learn C++ with Online Courses on edX
- Learn C++ on Codecademy
- Coding for Everyone: C and C++ course on Coursera
- C++ For C Programmers on Coursera
- C++ Online Courses on Udemy
- Top C Courses on Udemy
- C++ For Programmers Course on Udacity
- C++ Fundamentals Course on Pluralsight
- C++ - platform language that can be used to build high-performance applications developed by Bjarne Stroustrup, as an extension to the C language.
- C++ Tools and Libraries Articles
- C++ Style Guide for ROS
C/C++ Tools
In-memory data grids
- Visual Studio Code
- ReSharper C++
- CLion - platform IDE for C and C++ developers developed by JetBrains.
- Code::Blocks
- Conan
- High Performance Computing (HPC) SDK
- Boost - edge C++. Boost has been a participant in the annual Google Summer of Code since 2007, in which students develop their skills by working on Boost Library development.
- Automake
- Cmake - source, cross-platform family of tools designed to build, test and package software. CMake is used to control the software compilation process using simple platform and compiler independent configuration files, and generate native makefiles and workspaces that can be used in the compiler environment of your choice.
- GDB
- GCC - C, Fortran, Ada, Go, and D, as well as libraries for these languages.
- GSL - squares fitting. There are over 1000 functions in total with an extensive test suite.
- OpenGL Extension Wrangler Library (GLEW) - platform open-source C/C++ extension loading library. GLEW provides efficient run-time mechanisms for determining which OpenGL extensions are supported on the target platform.
- Libtool
- Maven
- TAU (Tuning And Analysis Utilities) - based sampling. All C++ language features are supported including templates and namespaces.
- Clang - C, C++ and Objective-C++ compiler when targeting X86-32, X86-64, and ARM (other targets may have caveats, but are usually easy to fix). Clang is used in production to build performance-critical software like Google Chrome or Firefox.
- OpenCV - time applications. Cross-Platform C++, Python and Java interfaces support Linux, MacOS, Windows, iOS, and Android.
- ANTLR (ANother Tool for Language Recognition)
- Oat++ - efficient web application. It's zero-dependency and easy-portable.
- Cython
- Infer - C, and C. Infer is written in OCaml.
- Vcpkg
- CppSharp
- Thrust - level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. Interoperability with established technologies such as CUDA, TBB, and OpenMP integrates with existing software.
- JavaCPP
- Spdlog - only/compiled, C++ logging library.
Differential Privacy Learning Resources
Interfaces
- Statistical Noise
- Laplacian Noise - distributed noise to a function.
- Apple's Differential Privacy Overview
- Learning with Privacy at Scale with Apple Machine Learning
- Microsoft Research Differential Privacy Overview
- Preserve data privacy by using differential privacy and the SmartNoise package
- Introducing TensorFlow Privacy: Learning with Differential Privacy for Training Data
- Differentially-Private Stochastic Gradient Descent(DP-SGD)
- Learning Differential Privacy from Harvard University Privacy Tools Project
- Harvard University Privacy Tools Project Courses & Educational Materials
- The Weaknesses of Differential Privacy course on Coursera
- The Differential Privacy of Bayesian Inference
- Simultaneous private learning of multiple concepts
- The Complexity of Computing the Optimal Composition of Differential Privacy
- Order revealing encryption and the hardness of private learning
- SAP HANA data anonymization using SAP Software Solutions
- SAP HANA Security using their In-Memory Database
- DEFCON Differential Privacy Training Launch
- Secure and Private AI course on Udacity
- Differential Privacy - Security and Privacy for Big Data - Part 1 course on Coursera
- Differential Privacy - Security and Privacy for Big Data - Part 2 course on Coursera
- Certified Ethical Emerging Technologist Professional Certificate course on Coursera
- Responsible Machine Learning with Microsoft Azure
- Open Differential Privacy(OpenDP) Initiative by Microsoft and Harvard
- Google's Differential Privacy Library
- Open Differential Privacy(OpenDP) Initiative by Microsoft and Harvard
- Open Differential Privacy(OpenDP) Initiative by Microsoft and Harvard
- Open Differential Privacy(OpenDP) Initiative by Microsoft and Harvard
- Open Differential Privacy(OpenDP) Initiative by Microsoft and Harvard
- Responsible Machine Learning with Microsoft Azure
- Open Differential Privacy(OpenDP) Initiative by Microsoft and Harvard
- Open Differential Privacy(OpenDP) Initiative by Microsoft and Harvard
- Computing Private Statistics with Privacy on Beam from Google Codelabs
- Differential Privacy
- Responsible AI Resources with Microsoft AI
- TensorFlow Federated: Machine Learning on Decentralized Data
Differential Privacy Tools
Interfaces
- Privacy on Beam - to-end differential privacy solution built on Apache Beam. It is intended to be usable by all developers, regardless of their differential privacy expertise.
- Opacus
- PySyft - learning-collaborative.html), Differential Privacy, and Encrypted Computation (like Multi-Party Computation (MPC) and Homomorphic Encryption (HE) within the main Deep Learning frameworks like PyTorch and TensorFlow.
- TensorFlow Privacy
- PyDP
- Skorch - learn compatible neural network library that wraps PyTorch.
- Diffprivlib
- PyTorch Geometric (PyG)
- Smart Noise - of-the-art differential privacy (DP) techniques to inject noise into data, to prevent disclosure of sensitive information and manage exposure risk.
- TensorFlow Federated (TFF) - source framework for machine learning and other computations on decentralized data. TFF has been developed to facilitate open research and experimentation with Federated Learning (FL), an approach to machine learning where a shared global model is trained across many participating clients that keep their training data locally.
- BoTorch
- PennyLane - platform Python library for differentiable programming of quantum computers. By training a quantum computer the same way as a neural network.
Docker Learning Resources
Interfaces
Docker Tools
Interfaces
- Docker
- Docker Hub - source projects, and the community.
- Docker Compose - container applications. With Docker Compose, you can create a YAML file to define the services and with a single command, can spin everything up or tear it all down.
- Docker Swarm - native clustering system swarm is a simple tool which controls a cluster of Docker hosts and exposes it as a single "virtual" host.
- Dockerfile - line instructions in succession.
- Docker Network
- Docker Daemon
- Docker Storage
- Docker Images
- Docker Desktop
- Docker Containers
Grafana Learning Resources
Configure WDT service
- Grafana
- Getting Started with Grafana
- Grafana Community
- Grafana Professional Services Training | Grafana Labs
- Grafana Pro Training AWS | Grafana Labs
- Grafana Tutorials
- Top Grafana Courses on Udemy
- Grafana Online Training Courses | LinkedIn Learning
- Grafana Training Courses - NobleProg
- Setting Up Grafana to Visualize Our Metrics Course on Coursera
- Grafana Professional Services Training | Grafana Labs
- Setting Up Grafana to Visualize Our Metrics Course on Coursera
Grafana Tools
Configure WDT service
- Grafana Loki - scalable, highly-available(HA), multi-tenant log aggregation system inspired by Prometheus.
- Grafana
- Grafana
- Grafana Tempo - scale distributed tarcing backend. Tempo is cost-efficient, requiring only object storage to operate, and is deeply integrated with Grafana, Loki, and Prometheus.
- Grafana Tanka