Search Results for "self-host"
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Showing 34 open source projects for "self-host"
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Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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1
Lightly
A python library for self-supervised learning on images
A python library for self-supervised learning on images. We, at Lightly, are passionate engineers who want to make deep learning more efficient. That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. -
2
Flax
Flax is a neural network library for JAX
...The library is widely used in vision, language, and reinforcement learning, often serving as a thin layer atop NumPy-like JAX primitives. Tutorials and examples show patterns for multi-host training, mixed precision, and advanced input pipelines that scale from laptops to TPUs. -
3
pyTermTk
Python Terminal Toolkit - a Spiced Up TUI Library
pyTermTk is a Text-based user interface library (TUI). Evolved from the discontinued project pyCuT and inspired by a mix of Qt5, GTK, and tkinter API definition with a touch of personal interpretation. -
4
how-to-live-better
Online single-page reading version of the 528-item "High-Value Life"
How to Live Better is a self-contained web reader for the open-source High Cost-Performance Life Guide. It renders all 32 sections and 528 recommendations from the upstream project into a single searchable HTML page. Each recommendation is presented as a card with evidence grade, value tier, and cost labels. Users can search the full guide, filter for top-grade evidence, or show only simplified explanations. -
Build Securely on Azure with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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5
n8n Workflows
All of the workflows of n8n i could find (also from the site itself)
This repository aggregates a well-organized collection of community-submitted automation workflows built for n8n, a fair-code, self-hostable automation tool. It serves as an index for discovering ready-made flows to automate tasks across multiple services and platforms. 2,057 workflows with meaningful, searchable names. 365 unique integrations across popular platforms. 29,445 total nodes with professional categorization. Quality assurance - All workflows analyzed and categorized. -
6
The Data Engineering Handbook
Links to everything you'd ever want to learn about data engineering
...It includes beginner and intermediate boot camps, interview guides, data cleaning and transformation resources, and curated lists of newsletters and industry communities, making it useful both for self-study and technical interview preparation. The repository is actively maintained and widely starred, reflecting its role as a go-to reference for newcomers and experienced practitioners alike. -
7
Pymunk
Pymunk is a easy-to-use pythonic 2d physics library
...Pymunk has been used with success in many projects, big and small. For example: 3 Pyweek game competition winners, dozens of published scientific papers, and even in a self-driving car simulation. -
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MediaManager
A modern selfhosted media management system for your media library
MediaManager is a modern, self-hosted media management system that unifies and replaces the traditional “ARR” stack with a single, cohesive platform for discovering, organizing, and automating TV and movie libraries. Rather than relying on separate tools patched together, MediaManager offers a streamlined interface and workflow where media metadata, collection insights, and automation policies live side-by-side in one system. -
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LangExtract
A Python library for extracting structured information
LangExtract is a Python library developed by Google that leverages large language models (LLMs) to extract structured information from unstructured text—such as clinical notes, research papers, or literary works—based on user-defined instructions. It is designed to transform free-form text into reliable, schema-constrained data while maintaining traceability back to the source material. Each extracted entity is precisely grounded in its original context, allowing visual inspection and... -
Build Securely on AWS with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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10
Yandex Music API
Non-official Python library for works with API service Index
This library provides Python interface for anyone undocumented and self-made API Yandex Music. It is compatible with Python 3.7 + and supports working with both synchronous and asyncio code. In addition to implementing a clean API, this library has a number of — high-level wrapping classes in order to make the development of customers and scripts simple and understandable. All documentation was written from scratch based on logical analysis during reverse development (reverse engineering) API. -
11
GitHub520
Community-maintained approach to improving access to GitHub services
GitHub520 is a community-maintained approach to improving access to GitHub services from regions with network friction by leveraging host mappings. The repository provides a regularly updated list of domain-to-IP entries meant to be appended to a system’s hosts file so certain GitHub endpoints resolve faster or more reliably. It includes scripts or guidance to automate updates, reducing the need for manual lookups when IPs change. The project’s goal is pragmatic: improve developer productivity by mitigating timeouts and slow asset retrieval during cloning, package installs, or browsing. ... -
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Courses (Anthropic)
Anthropic's educational courses
Anthropic’s courses repository is a growing collection of self-paced learning materials that teach practical AI skills using Claude and the Anthropic API. It’s organized as a sequence of hands-on courses—starting with API fundamentals and prompt engineering—so learners build capability step by step rather than in isolation. Each course mixes short readings with runnable notebooks and exercises, guiding you through concepts like model parameters, streaming, multimodal prompts, structured outputs, and evaluation. ... -
13
Boltons
250+ constructs, recipes, and snippets which extend the Python library
...In many cases the respective boltons module will describe 3rd-party alternatives worth investigating when use cases outgrow boltons. If you’ve found a natural “next-step” library worth mentioning, consider filing an issue! boltons has a minimalist architecture, remain as consistent, and self-contained as possible, with an eye toward maintaining its range of use cases and usage patterns as wide as possible. The boltons package depends on no packages, making it easy for inclusion into a project. -
14
Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before they pass into a neural network (if you use augmentation). ... -
15
Professional Programming
A collection of learning resources for curious software engineers
...Because it has been maintained for many years, it also acts as a kind of “canon” of articles that many engineers reference throughout their careers. The repository is especially helpful for self-taught developers or those transitioning from junior to senior roles who want a structured reading roadmap instead of random blog posts. -
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Homemade Machine Learning
Python examples of popular machine learning algorithms
homemade-machine-learning is a repository by Oleksii Trekhleb containing Python implementations of classic machine-learning algorithms done “from scratch”, meaning you don’t rely heavily on high-level libraries but instead write the logic yourself to deepen understanding. Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is... -
17
Python-Spider
Python3 web crawler practice
Python-Spider is a repository intended to teach or provide examples for writing web spiders / crawlers in Python — part of a broader learning and resource collection by its author. The code and documentation are oriented toward beginners or intermediate learners who want to learn how to fetch, parse, and extract data from websites programmatically. As part of the author’s public learning-path repositories, python-spider likely includes examples of HTTP requests, HTML parsing, maybe... -
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Google Fonts
Font files available from Google Fonts, and a public issue tracker
...The /catalog subdirectory contains additional metadata, such as profile texts and portrait/avatar images of font designers, and this is open for contributions and corrections from anyone via GitHub. Since all the fonts available here are licensed with permission to redistribute, subject to the license terms, you can self-host using a variety of third-party projects. -
19
Sonnet
TensorFlow-based neural network library
Sonnet is a neural network library built on top of TensorFlow designed to provide simple, composable abstractions for machine learning research. Sonnet can be used to build neural networks for various purposes, including different types of learning. Sonnet’s programming model revolves around a single concept: modules. These modules can hold references to parameters, other modules and methods that apply some function on the user input. There are a number of predefined modules that already... -
20
eCxx
A C++ library for AVR and NodeMCU
...eCxx is a library for AVR and NodeMCU tailored for micro LED displays and lighting effects. eCxx is utilizing Makefile build system. Java and Python based applications/tools are also included to ease the development and debugging process using the host PC. On one side, eCxx supports the original megaAVR series (ATmega328P, ATmega1284P, ATmega2560, ATmega32U4, AT90USB1286, etc.) as well as the new megaAVR 0-Series (ATmega4808, ATmega4809, etc.). On the other side, while eCxx is primarily designed to work in NodeMCU, it should also support other ESP-12 based design as well as ESP-01 based design. ... -
21
iJEPA
Official codebase for I-JEPA
i-JEPA (Image Joint-Embedding Predictive Architecture) is a self-supervised learning framework that predicts missing high-level representations rather than reconstructing pixels. A context encoder sees visible regions of an image and predicts target embeddings for masked regions produced by a slowly updated target encoder, focusing learning on semantics instead of texture. This objective sidesteps generative pixel losses and avoids heavy negative sampling, producing features that transfer strongly with linear probes and minimal fine-tuning. ... -
22
DeepMind Research
Implementations and code to accompany DeepMind publications
This repository collects reference implementations and illustrative code accompanying a wide range of DeepMind publications, making it easier for the research community to reproduce results, inspect algorithms, and build on prior work. The top level organizes many paper-specific directories across domains such as deep reinforcement learning, self-supervised vision, generative modeling, scientific ML, and program synthesis—for example BYOL, Perceiver/Perceiver IO, Enformer for genomics, MeshGraphNets for physics, RL Unplugged, Nowcasting for weather, and more. Each project folder typically includes its own README, scripts, and notebooks so you can run experiments or explore models in isolation, and many link to associated datasets or external environments like DeepMind Lab and StarCraft II. ... -
23
DIG
A library for graph deep learning research
The key difference with current graph deep learning libraries, such as PyTorch Geometric (PyG) and Deep Graph Library (DGL), is that, while PyG and DGL support basic graph deep learning operations, DIG provides a unified testbed for higher level, research-oriented graph deep learning tasks, such as graph generation, self-supervised learning, explainability, 3D graphs, and graph out-of-distribution. If you are working or plan to work on research in graph deep learning, DIG enables you to develop your own methods within our extensible framework, and compare with current baseline methods using common datasets and evaluation metrics without extra efforts. ... -
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albert_zh
Implementation of A Lite Bert For Self-Supervised Learning Language
albert_zh is a Chinese ALBERT pretraining and model release repository. It implements ALBERT with TensorFlow and provides Chinese pretrained models designed to reduce parameter size while preserving strong language understanding performance. The project includes several model variants, such as tiny, small, base, large, and xlarge-style releases, giving users options for speed, size, and accuracy tradeoffs. It also provides guidance for fine-tuning downstream tasks such as sentence-pair... -
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TensorFlow Examples
TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)
...For clarity and educational value, each example is accompanied by explanatory comments or markdown cells to illustrate what the code does and why — a design that makes it especially suitable for self-learners or students following along with real data. Besides raw implementations, the repo often shows best practices using higher-level constructs (e.g. dataset pipelines, estimators, layers) which reflect modern TensorFlow workflows rather than only textbook-style code.
