This repository contains Dockerfiles, scripts, yaml files, Helm charts, etc. used to scale out AI containers with versions of TensorFlow and PyTorch optimized for Intel platforms. Scaling is done with python, Docker, kubernetes, kubeflow, cnvrg.io, Helm, and other container orchestration frameworks for use in the cloud and on-premise.

This repository provides Terraform configurations to automatically set up Google Cloud resources and deploy Dify in a highly available configuration. It includes features such as serverless hosting, auto-scaling, and data persistence. Users need a Google Cloud account, Terraform, and gcloud CLI installed to use this tool. The configuration involves setting environment-specific values and creating a GCS bucket for managing Terraform state. The tool allows users to initialize Terraform, create Artifact Registry repository, build and push container images, plan and apply Terraform changes, and cleanup resources when needed.

Steel is an open-source browser API designed for AI agents and applications, simplifying the process of building live web agents and browser automation tools. It serves as a core building block for a production-ready, containerized browser sandbox with features like stealth capabilities, text-to-markdown session management, UI for session viewing/debugging, and full browser control through popular automation frameworks. Steel allows users to control, run, and manage a production-ready browser environment via a REST API, offering features such as full browser control, session management, proxy support, extension support, debugging tools, anti-detection mechanisms, resource management, and various browser tools. It aims to streamline complex browsing tasks programmatically, enabling users to focus on their AI applications while Steel handles the underlying complexity.

Bit is a build system that organizes source code into composable components, enabling the creation of reliable, scalable, and consistent applications. It supports the creation of reusable UI components, standard building blocks, shell applications, and atomic deployments. Bit is compatible with various tools in the JavaScript ecosystem and offers official dev environments for popular frameworks. It can be used in different codebase structures like monorepos or polyrepos, and even without repositories. Users can install Bit, create shell applications, compose components, release and deploy components, and modernize existing projects using Bit Cloud or self-hosted scopes.

This repository provides a Docker image that acts as an AirPrint bridge for local printers, allowing them to be exposed to iOS/macOS devices. It runs a container with CUPS and Avahi to facilitate this functionality. Users must have CUPS drivers available for their printers. The tool requires a Linux host and a dedicated IP for the container to avoid interference with other services. It supports setting up printers through environment variables and offers options for automated configuration via command line, web interface, or files. The repository includes detailed instructions on setting up and testing the AirPrint bridge.

AIO-Switch-Updater is a Nintendo Switch homebrew app that allows users to download and update custom firmware, firmware files, cheat codes, and more. It supports Atmosphère, ReiNX, and SXOS on both unpatched and patched Switches. The app provides features like updating CFW with custom RCM payload, updating Hekate/payload, custom downloads, downloading firmwares and cheats, and various tools like rebooting to specific payload, changing color schemes, consulting cheat codes, and more. Users can contribute by submitting PRs and suggestions, and the app supports localization. It does not host or distribute any files and gives special thanks to contributors and supporters.

Trieve is an advanced relevance API for hybrid search, recommendations, and RAG. It offers a range of features including self-hosting, semantic dense vector search, typo tolerant full-text/neural search, sub-sentence highlighting, recommendations, convenient RAG API routes, the ability to bring your own models, hybrid search with cross-encoder re-ranking, recency biasing, tunable popularity-based ranking, filtering, duplicate detection, and grouping. Trieve is designed to be flexible and customizable, allowing users to tailor it to their specific needs. It is also easy to use, with a simple API and well-documented features.

LangStream is a tool for natural language processing tasks, providing a CLI for easy installation and usage. Users can try sample applications like Chat Completions and create their own applications using the developer documentation. It supports running on Kubernetes for production-ready deployment, with support for various Kubernetes distributions and external components like Apache Kafka or Apache Pulsar cluster. Users can deploy LangStream locally using minikube and manage the cluster with mini-langstream. Development requirements include Docker, Java 17, Git, Python 3.11+, and PIP, with the option to test local code changes using mini-langstream.

SciPIP is a scientific paper idea generation tool powered by a large language model (LLM) designed to assist researchers in quickly generating novel research ideas. It conducts a literature review based on user-provided background information and generates fresh ideas for potential studies. The tool is designed to help researchers in various fields by providing a GUI environment for idea generation, supporting NLP, multimodal, and CV fields, and allowing users to interact with the tool through a web app or terminal. SciPIP uses Neo4j as its database and provides functionalities for generating new ideas, fetching papers, and constructing the database.

NekoImageGallery is an online AI image search engine that utilizes the Clip model and Qdrant vector database. It supports keyword search and similar image search. The tool generates 768-dimensional vectors for each image using the Clip model, supports OCR text search using PaddleOCR, and efficiently searches vectors using the Qdrant vector database. Users can deploy the tool locally or via Docker, with options for metadata storage using Qdrant database or local file storage. The tool provides API documentation through FastAPI's built-in Swagger UI and can be used for tasks like image search, text extraction, and vector search.

A discord bot built to interface with the InvokeAI fork of stable-diffusion. It is a work in progress for a major rewrite of the arty project, compatible with `invokeai 5.1.1`. The bot supports various functionalities like building node graphs from job requests, refreshing renders using png metadata, removing backgrounds, job progress tracking, and LLM integration. Users can install custom invokeai nodes for advanced functionality and launch the bot natively or with docker. Patches and pull requests are welcomed.

ProX is a lm-based data refinement framework that automates the process of cleaning and improving data used in pre-training large language models. It offers better performance, domain flexibility, efficiency, and cost-effectiveness compared to traditional methods. The framework has been shown to improve model performance by over 2% and boost accuracy by up to 20% in tasks like math. ProX is designed to refine data at scale without the need for manual adjustments, making it a valuable tool for data preprocessing in natural language processing tasks.

Datalore is a terminal tool for generating structured datasets from local files like PDFs, Word docs, images, and text. It extracts content, uses semantic search to understand context, applies instructions through a generated schema, and outputs clean, structured data. Perfect for converting raw or unstructured local documents into ready-to-use datasets for training, analysis, or experimentation, all without manual formatting.

NoLabs is an open-source biolab that provides easy access to state-of-the-art models for bio research. It supports various tasks, including drug discovery, protein analysis, and small molecule design. NoLabs aims to accelerate bio research by making inference models accessible to everyone.

Lantern is an open-source PostgreSQL database extension designed to store vector data, generate embeddings, and handle vector search operations efficiently. It introduces a new index type called 'lantern_hnsw' for vector columns, which speeds up 'ORDER BY ... LIMIT' queries. Lantern utilizes the state-of-the-art HNSW implementation called usearch. Users can easily install Lantern using Docker, Homebrew, or precompiled binaries. The tool supports various distance functions, index construction parameters, and operator classes for efficient querying. Lantern offers features like embedding generation, interoperability with pgvector, parallel index creation, and external index graph generation. It aims to provide superior performance metrics compared to other similar tools and has a roadmap for future enhancements such as cloud-hosted version, hardware-accelerated distance metrics, industry-specific application templates, and support for version control and A/B testing of embeddings.

ChatOpsLLM is a project designed to empower chatbots with effortless DevOps capabilities. It provides an intuitive interface and streamlined workflows for managing and scaling language models. The project incorporates robust MLOps practices, including CI/CD pipelines with Jenkins and Ansible, monitoring with Prometheus and Grafana, and centralized logging with the ELK stack. Developers can find detailed documentation and instructions on the project's website.

The Spellbook Docker Compose repository contains the Docker Compose files for running the Spellbook AI Assistant stack. It requires ExLlama and a Nvidia Ampere or better GPU for real-time results. The repository provides instructions for installing Docker, building and starting containers with or without GPU, additional workers, Nvidia driver installation, port forwarding, and fresh installation steps. Users can follow the detailed guidelines to set up the Spellbook framework on Ubuntu 22, enabling them to run the UI, middleware, and additional workers for resource access.

This repository contains Dockerfiles, scripts, yaml files, Helm charts, etc. used to scale out AI containers with versions of TensorFlow and PyTorch optimized for Intel platforms. Scaling is done with python, Docker, kubernetes, kubeflow, cnvrg.io, Helm, and other container orchestration frameworks for use in the cloud and on-premise.

A collection of LLM services you can self host via docker or modal labs to support your applications development. The goal is to provide docker containers or modal labs deployments of common patterns when using LLMs and endpoints to integrate easily with existing codebases using the openai api. It supports GPT4all's embedding api, JSONFormer api for chat completion, Cross Encoders based on sentence transformers, and provides documentation using MkDocs.

Sandbox is an open-source cloud-based code editing environment with custom AI code autocompletion and real-time collaboration. It consists of a frontend built with Next.js, TailwindCSS, Shadcn UI, Clerk, Monaco, and Liveblocks, and a backend with Express, Socket.io, Cloudflare Workers, D1 database, R2 storage, Workers AI, and Drizzle ORM. The backend includes microservices for database, storage, and AI functionalities. Users can run the project locally by setting up environment variables and deploying the containers. Contributions are welcome following the commit convention and structure provided in the repository.

OpenOrch is a daemon that transforms servers into a powerful development environment, running AI models, containers, and microservices. It serves as a blend of Kubernetes and a language-agnostic backend framework for building applications on fixed-resource setups. Users can deploy AI models and build microservices, managing applications while retaining control over infrastructure and data.

Airo is a tool designed to simplify the process of deploying containers to self-hosted servers. It allows users to focus on building their products without the complexity of Kubernetes or CI/CD pipelines. With Airo, users can easily build and push Docker images, deploy instantly with a single command, update configurations securely using SSH, and set up HTTPS and reverse proxy automatically using Caddy.

TPI-LLM (Tensor Parallelism Inference for Large Language Models) is a system designed to bring LLM functions to low-resource edge devices, addressing privacy concerns by enabling LLM inference on edge devices with limited resources. It leverages multiple edge devices for inference through tensor parallelism and a sliding window memory scheduler to minimize memory usage. TPI-LLM demonstrates significant improvements in TTFT and token latency compared to other models, and plans to support infinitely large models with low token latency in the future.

KAI Scheduler is a robust, efficient, and scalable Kubernetes scheduler optimized for GPU resource allocation in AI and machine learning workloads. It supports batch scheduling, bin packing, spread scheduling, workload priority, hierarchical queues, resource distribution, fairness policies, workload consolidation, elastic workloads, dynamic resource allocation, GPU sharing, and works in both cloud and on-premise environments.

This repository contains Dockerfiles, scripts, yaml files, Helm charts, etc. used to scale out AI containers with versions of TensorFlow and PyTorch optimized for Intel platforms. Scaling is done with python, Docker, kubernetes, kubeflow, cnvrg.io, Helm, and other container orchestration frameworks for use in the cloud and on-premise.

AirGo is a front and rear end separation, multi user, multi protocol proxy service management system, simple and easy to use. It supports vless, vmess, shadowsocks, and hysteria2.

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API. * **Highly performant** : web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O * **Ease of use** : user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing * **Dynamic batching** : aggregate requests from different users for batched inference and distribute results back * **Pipelined stages** : spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads * **Cloud friendly** : designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems * **Do one thing well** : focus on the online serving part, users can pay attention to the model optimization and business logic

The 'llm-code-interpreter' repository is a deprecated plugin that provides a code interpreter on steroids for ChatGPT by E2B. It gives ChatGPT access to a sandboxed cloud environment with capabilities like running any code, accessing Linux OS, installing programs, using filesystem, running processes, and accessing the internet. The plugin exposes commands to run shell commands, read files, and write files, enabling various possibilities such as running different languages, installing programs, starting servers, deploying websites, and more. It is powered by the E2B API and is designed for agents to freely experiment within a sandboxed environment.

Pezzo is a fully cloud-native and open-source LLMOps platform that allows users to observe and monitor AI operations, troubleshoot issues, save costs and latency, collaborate, manage prompts, and deliver AI changes instantly. It supports various clients for prompt management, observability, and caching. Users can run the full Pezzo stack locally using Docker Compose, with prerequisites including Node.js 18+, Docker, and a GraphQL Language Feature Support VSCode Extension. Contributions are welcome, and the source code is available under the Apache 2.0 License.

Learn Cloud Applied Generative AI Engineering (GenEng) is a course focusing on the application of generative AI technologies in various industries. The course covers topics such as the economic impact of generative AI, the role of developers in adopting and integrating generative AI technologies, and the future trends in generative AI. Students will learn about tools like OpenAI API, LangChain, and Pinecone, and how to build and deploy Large Language Models (LLMs) for different applications. The course also explores the convergence of generative AI with Web 3.0 and its potential implications for decentralized intelligence.