free-threaded-compatibility

This repository serves as a platform for coordinating ecosystem-wide work related to free-threading topics in Python. It aims to track, understand, and provide documentation for common issues across multiple libraries. Specific project-related issues should be reported in the respective project's issue tracker. For detailed documentation on free-threading topics, visit py-free-threading.github.io.

VectorHub is a free and open-sourced learning hub for people interested in adding vector retrieval to their ML stack. On VectorHub you will find practical resources to help you create MVPs with easy-to-follow learning materials, solve use case specific challenges in vector retrieval, get confident in taking their MVPs to production and making them actually useful, and learn about vendors in the space and select the ones that fit their use-case.

This repository is the workshop repository for the Causal Modeling in Machine Learning Workshop on Altdeep.ai. The material is open source and free. The course covers causality in model-based machine learning, Bayesian modeling, interventions, counterfactual reasoning, and deep causal latent variable models. It aims to equip learners with the ability to build causal reasoning algorithms into decision-making systems in data science and machine learning teams within top-tier technology organizations.

AI-Powered Search provides code examples for the book 'AI-Powered Search' by Trey Grainger, Doug Turnbull, and Max Irwin. The book teaches modern machine learning techniques for building search engines that continuously learn from users and content to deliver more intelligent and domain-aware search experiences. It covers semantic search, retrieval augmented generation, question answering, summarization, fine-tuning transformer-based models, personalized search, machine-learned ranking, click models, and more. The code examples are in Python, leveraging PySpark for data processing and Apache Solr as the default search engine. The repository is open source under the Apache License, Version 2.0.

The xLSTM-jax repository contains code for training and evaluating the xLSTM model on language modeling using JAX. xLSTM is a Recurrent Neural Network architecture that improves upon the original LSTM through Exponential Gating, normalization, stabilization techniques, and a Matrix Memory. It is optimized for large-scale distributed systems with performant triton kernels for faster training and inference.

The NeMo Framework Launcher is a cloud-native tool designed for launching end-to-end NeMo Framework training jobs. It focuses on foundation model training for generative AI models, supporting large language model pretraining with techniques like model parallelism, tensor, pipeline, sequence, distributed optimizer, mixed precision training, and more. The tool scales to thousands of GPUs and can be used for training LLMs on trillions of tokens. It simplifies launching training jobs on cloud service providers or on-prem clusters, generating submission scripts, organizing job results, and supporting various model operations like fine-tuning, evaluation, export, and deployment.

A catalog of design patterns for building generative AI applications, capturing current best practices in the field. The repository serves as a living catalog on GitHub to help practitioners navigate through the noise and identify areas for improvement. It is too early for a book due to the evolving nature of generative AI in production and the lack of concrete evidence to support certain claims.

Dewhale is a GitHub-Powered AI tool designed for effortless development. It utilizes prompt engineering techniques under the GPT-4 model to issue commands, allowing users to generate code with lower usage costs and easy customization. The tool seamlessly integrates with GitHub, providing version control, code review, and collaborative features. Users can join discussions on the design philosophy of Dewhale and explore detailed instructions and examples for setting up and using the tool.

The Modular Accelerated Xecution (MAX) platform is an integrated suite of AI libraries, tools, and technologies that unifies commonly fragmented AI deployment workflows. MAX accelerates time to market for the latest innovations by giving AI developers a single toolchain that unlocks full programmability, unparalleled performance, and seamless hardware portability.

RecAI is a project that explores the integration of Large Language Models (LLMs) into recommender systems, addressing the challenges of interactivity, explainability, and controllability. It aims to bridge the gap between general-purpose LLMs and domain-specific recommender systems, providing a holistic perspective on the practical requirements of LLM4Rec. The project investigates various techniques, including Recommender AI agents, selective knowledge injection, fine-tuning language models, evaluation, and LLMs as model explainers, to create more sophisticated, interactive, and user-centric recommender systems.

TagUI is an open-source RPA tool that allows users to automate repetitive tasks on their computer, including tasks on websites, desktop apps, and the command line. It supports multiple languages and offers features like interacting with identifiers, automating data collection, moving data between TagUI and Excel, and sending Telegram notifications. Users can create RPA robots using MS Office Plug-ins or text editors, run TagUI on the cloud, and integrate with other RPA tools. TagUI prioritizes enterprise security by running on users' computers and not storing data. It offers detailed logs, enterprise installation guides, and support for centralised reporting.

Metaflow is a user-friendly library designed to assist scientists and engineers in developing and managing real-world data science projects. Initially created at Netflix, Metaflow aimed to enhance the productivity of data scientists working on diverse projects ranging from traditional statistics to cutting-edge deep learning. For further information, refer to Metaflow's website and documentation.

The Modular Accelerated Xecution (MAX) platform is an integrated suite of AI libraries, tools, and technologies that unifies commonly fragmented AI deployment workflows. MAX accelerates time to market for the latest innovations by giving AI developers a single toolchain that unlocks full programmability, unparalleled performance, and seamless hardware portability.

H2O.ai's AI Tutorials aim to democratize open source, distributed machine learning by providing step-by-step tutorials for individuals of all skill levels. These tutorials are developed and maintained on Github and published on the H2O.ai Self-Paced Courses Landing Page. Users can begin their AI journey by exploring the tutorials available on the landing page and can contribute by fixing issues, updating tutorials, or creating new ones.

YAO is an open-source application engine written in Golang, suitable for developing business systems, website/APP API, admin panel, and self-built low-code platforms. It adopts a flow-based programming model to implement functions by writing YAO DSL or using JavaScript. Yao allows developers to create web services by processes, creating a database model, writing API services, and describing dashboard interfaces just by JSON for web & hardware, and 10x productivity. It is based on the flow-based programming idea, developed in Go language, and supports multiple ways to expand the data stream processor. Yao has a built-in data management system, making it suitable for quickly making various management backgrounds, CRM, ERP, and other internal enterprise systems. It is highly versatile, efficient, and performs better than PHP, JAVA, and other languages.

Comfyui-Aix-NodeMap is a project by the Aix team to organize and annotate the latest nodes in Comfyui. It aims to address the challenge of finding nodes effectively due to the increasing number of nodes. The project is updated every 7 days to provide the most recent node information. Users can provide feedback for any omissions or errors, and corrections will be made promptly. The project respects every developer and values community collaboration in improving node exposure and accessibility.

This repository serves as a platform for coordinating ecosystem-wide work related to free-threading topics in Python. It aims to track, understand, and provide documentation for common issues across multiple libraries. Specific project-related issues should be reported in the respective project's issue tracker. For detailed documentation on free-threading topics, visit py-free-threading.github.io.

Kaito is an operator that automates the AI/ML inference model deployment in a Kubernetes cluster. It manages large model files using container images, avoids tuning deployment parameters to fit GPU hardware by providing preset configurations, auto-provisions GPU nodes based on model requirements, and hosts large model images in the public Microsoft Container Registry (MCR) if the license allows. Using Kaito, the workflow of onboarding large AI inference models in Kubernetes is largely simplified.

This repository contains assets related to AI/ML workloads on Google Kubernetes Engine (GKE). Run optimized AI/ML workloads with Google Kubernetes Engine (GKE) platform orchestration capabilities. A robust AI/ML platform considers the following layers: Infrastructure orchestration that support GPUs and TPUs for training and serving workloads at scale Flexible integration with distributed computing and data processing frameworks Support for multiple teams on the same infrastructure to maximize utilization of resources

TiDB is an open-source distributed SQL database that supports Hybrid Transactional and Analytical Processing (HTAP) workloads. It is MySQL compatible and features horizontal scalability, strong consistency, and high availability.

Nvidia GPU exporter for prometheus, using `nvidia-smi` binary to gather metrics.

Tracecat is an open-source automation platform for security teams. It's designed to be simple but powerful, with a focus on AI features and a practitioner-obsessed UI/UX. Tracecat can be used to automate a variety of tasks, including phishing email investigation, evidence collection, and remediation plan generation.

OpenInference is a set of conventions and plugins that complement OpenTelemetry to enable tracing of AI applications. It provides a way to capture and analyze the performance and behavior of AI models, including their interactions with other components of the application. OpenInference is designed to be language-agnostic and can be used with any OpenTelemetry-compatible backend. It includes a set of instrumentations for popular machine learning SDKs and frameworks, making it easy to add tracing to your AI applications.

BricksLLM is a cloud native AI gateway written in Go. Currently, it provides native support for OpenAI, Anthropic, Azure OpenAI and vLLM. BricksLLM aims to provide enterprise level infrastructure that can power any LLM production use cases. Here are some use cases for BricksLLM: * Set LLM usage limits for users on different pricing tiers * Track LLM usage on a per user and per organization basis * Block or redact requests containing PIIs * Improve LLM reliability with failovers, retries and caching * Distribute API keys with rate limits and cost limits for internal development/production use cases * Distribute API keys with rate limits and cost limits for students

Kong, or Kong API Gateway, is a cloud-native, platform-agnostic, scalable API Gateway distinguished for its high performance and extensibility via plugins. It also provides advanced AI capabilities with multi-LLM support. By providing functionality for proxying, routing, load balancing, health checking, authentication (and more), Kong serves as the central layer for orchestrating microservices or conventional API traffic with ease. Kong runs natively on Kubernetes thanks to its official Kubernetes Ingress Controller.