CREDIT is an open software platform for training and deploying AI atmospheric prediction models. It offers fast models with flexible configuration options for input data and neural network architecture. The user-friendly interface enables quick setup and iteration. Developed by the MILES group and NSF National Center for Atmospheric Research, CREDIT combines advanced AI/ML with atmospheric science expertise. It provides a stable release with various models, training, and deployment options, with ongoing development. Detailed documentation is available for installation, training, deployment, config file interpretation, and API usage.

Kaapana is an open-source toolkit for state-of-the-art platform provisioning in the field of medical data analysis. The applications comprise AI-based workflows and federated learning scenarios with a focus on radiological and radiotherapeutic imaging. Obtaining large amounts of medical data necessary for developing and training modern machine learning methods is an extremely challenging effort that often fails in a multi-center setting, e.g. due to technical, organizational and legal hurdles. A federated approach where the data remains under the authority of the individual institutions and is only processed on-site is, in contrast, a promising approach ideally suited to overcome these difficulties. Following this federated concept, the goal of Kaapana is to provide a framework and a set of tools for sharing data processing algorithms, for standardized workflow design and execution as well as for performing distributed method development. This will facilitate data analysis in a compliant way enabling researchers and clinicians to perform large-scale multi-center studies. By adhering to established standards and by adopting widely used open technologies for private cloud development and containerized data processing, Kaapana integrates seamlessly with the existing clinical IT infrastructure, such as the Picture Archiving and Communication System (PACS), and ensures modularity and easy extensibility.

OpenSPG is a knowledge graph engine developed by Ant Group in collaboration with OpenKG, based on the SPG (Semantic-enhanced Programmable Graph) framework. It provides explicit semantic representations, logical rule definitions, operator frameworks (construction, inference), and other capabilities for domain knowledge graphs. OpenSPG supports pluggable adaptation of basic engines and algorithmic services by various vendors to build customized solutions.

Dioptra is a software test platform for assessing the trustworthy characteristics of artificial intelligence (AI). It supports the NIST AI Risk Management Framework by providing functionality to assess, analyze, and track identified AI risks. Dioptra provides a REST API and can be controlled via a web interface or Python client for designing, managing, executing, and tracking experiments. It aims to be reproducible, traceable, extensible, interoperable, modular, secure, interactive, shareable, and reusable.

ARK is an agentic runtime for Kubernetes that codifies patterns and practices developed across client projects. It provides a foundation for platform-agnostic operations and standardized deployment approaches. The project is in early access, evolving based on team feedback, and aims to share technical approach with the community for feedback and input in the field of agentic AI systems and Kubernetes orchestration.

This repository extends the LLVM framework to generate code for use with AMD/Xilinx AI Engine processors. AI Engine processors are in-order, exposed-pipeline VLIW processors focused on application acceleration for AI, Machine Learning, and DSP applications. The repository adds LLVM support for specific features like non-power of 2 pointers, operand latencies, resource conflicts, negative operand latencies, slot assignment, relocations, code alignment restrictions, and register allocation. It includes support for Clang, LLD, binutils, Compiler-RT, and LLVM-LIBC.

PARAM Benchmarks is a repository of communication and compute micro-benchmarks as well as full workloads for evaluating training and inference platforms. It complements commonly used benchmarks by focusing on AI training with PyTorch based collective benchmarks, GEMM, embedding lookup, linear layer, and DLRM communication patterns. The tool bridges the gap between stand-alone C++ benchmarks and PyTorch/Tensorflow based application benchmarks, providing deep insights into system architecture and framework-level overheads.

This repository provides a comprehensive collection of methodologies, tools, and step-by-step instructions for successful training of large language models (LLMs) and multi-modal models. It is a technical resource suitable for LLM/VLM training engineers and operators, containing numerous scripts and copy-n-paste commands to facilitate quick problem-solving. The repository is an ongoing compilation of the author's experiences training BLOOM-176B and IDEFICS-80B models, and currently focuses on the development and training of Retrieval Augmented Generation (RAG) models at Contextual.AI. The content is organized into six parts: Insights, Hardware, Orchestration, Training, Development, and Miscellaneous. It includes key comparison tables for high-end accelerators and networks, as well as shortcuts to frequently needed tools and guides. The repository is open to contributions and discussions, and is licensed under Attribution-ShareAlike 4.0 International.

MCP Context Forge is a powerful tool for generating context-aware data for machine learning models. It provides functionalities to create diverse datasets with contextual information, enhancing the performance of AI algorithms. The tool supports various data formats and allows users to customize the context generation process easily. With MCP Context Forge, users can efficiently prepare training data for tasks requiring contextual understanding, such as sentiment analysis, recommendation systems, and natural language processing.

This repository contains a Geographic Information System (GIS) tool for performing Minimum Cumulative Path (MCP) analysis. The tool allows users to calculate the optimal path that minimizes cumulative cost between multiple locations on a map. It is particularly useful for urban planning, transportation route optimization, and environmental impact assessment. The tool supports various cost functions such as distance, travel time, and resource consumption, providing flexibility for different applications. Users can visualize the results on interactive maps and export the analysis outputs for further processing. The tool is implemented in Python and leverages popular GIS libraries such as GeoPandas and NetworkX for efficient spatial analysis.

The End-to-End LLM Bootcamp is a comprehensive training program that covers the entire process of developing and deploying large language models. Participants learn to preprocess datasets, train models, optimize performance using NVIDIA technologies, understand guardrail prompts, and deploy AI pipelines using Triton Inference Server. The bootcamp includes labs, challenges, and practical applications, with a total duration of approximately 7.5 hours. It is designed for individuals interested in working with advanced language models and AI technologies.

DeepFlow is an open-source project that provides deep observability for complex cloud-native and AI applications. It offers Zero Code data collection with eBPF for metrics, distributed tracing, request logs, and function profiling. DeepFlow is integrated with SmartEncoding to achieve Full Stack correlation and efficient access to all observability data. With DeepFlow, cloud-native and AI applications automatically gain deep observability, removing the burden of developers continually instrumenting code and providing monitoring and diagnostic capabilities covering everything from code to infrastructure for DevOps/SRE teams.

OpsPilot is an AI-powered operations navigator developed by the WeOps team. It leverages deep learning and LLM technologies to make operations plans interactive and generalize and reason about local operations knowledge. OpsPilot can be integrated with web applications in the form of a chatbot and primarily provides the following capabilities: 1. Operations capability precipitation: By depositing operations knowledge, operations skills, and troubleshooting actions, when solving problems, it acts as a navigator and guides users to solve operations problems through dialogue. 2. Local knowledge Q&A: By indexing local knowledge and Internet knowledge and combining the capabilities of LLM, it answers users' various operations questions. 3. LLM chat: When the problem is beyond the scope of OpsPilot's ability to handle, it uses LLM's capabilities to solve various long-tail problems.

This repository contains a hand-curated list of great machine (deep) learning resources for Natural Language Processing (NLP) with a focus on Generative Pre-trained Transformer (GPT), Bidirectional Encoder Representations from Transformers (BERT), attention mechanism, Transformer architectures/networks, Chatbot, and transfer learning in NLP.

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.

SolarLLMZeroToAll is a comprehensive repository that provides a step-by-step guide and resources for learning and implementing Solar Longitudinal Learning Machines (SolarLLM) from scratch. The repository covers various aspects of SolarLLM, including theory, implementation, and applications, making it suitable for beginners and advanced users interested in solar energy forecasting and machine learning. The materials include detailed explanations, code examples, datasets, and visualization tools to facilitate understanding and practical implementation of SolarLLM models.

CREDIT is an open software platform for training and deploying AI atmospheric prediction models. It offers fast models with flexible configuration options for input data and neural network architecture. The user-friendly interface enables quick setup and iteration. Developed by the MILES group and NSF National Center for Atmospheric Research, CREDIT combines advanced AI/ML with atmospheric science expertise. It provides a stable release with various models, training, and deployment options, with ongoing development. Detailed documentation is available for installation, training, deployment, config file interpretation, and API usage.

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

Ray is a unified framework for scaling AI and Python applications. It consists of a core distributed runtime and a set of AI libraries for simplifying ML compute, including Data, Train, Tune, RLlib, and Serve. Ray runs on any machine, cluster, cloud provider, and Kubernetes, and features a growing ecosystem of community integrations. With Ray, you can seamlessly scale the same code from a laptop to a cluster, making it easy to meet the compute-intensive demands of modern ML workloads.

Labelbox is a data-centric AI platform for enterprises to develop, optimize, and use AI to solve problems and power new products and services. Enterprises use Labelbox to curate data, generate high-quality human feedback data for computer vision and LLMs, evaluate model performance, and automate tasks by combining AI and human-centric workflows. The academic & research community uses Labelbox for cutting-edge AI research.

Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. It is designed to be easy to get started with and simple to use for Java developers. DJL provides a native Java development experience and allows users to integrate machine learning and deep learning models with their Java applications. The framework is deep learning engine agnostic, enabling users to switch engines at any point for optimal performance. DJL's ergonomic API interface guides users with best practices to accomplish deep learning tasks, such as running inference and training neural networks.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are: * `MLflow Tracking `_: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI. * `MLflow Projects `_: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others. * `MLflow Models `_: A model packaging format and tools that let you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as Docker, Apache Spark, Azure ML and AWS SageMaker. * `MLflow Model Registry `_: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.

TT-NN is a python & C++ Neural Network OP library. It provides a low-level programming model, TT-Metalium, enabling kernel development for Tenstorrent hardware.

Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.

Sweep is an AI junior developer that turns bugs and feature requests into code changes. It automatically handles developer experience improvements like adding type hints and improving test coverage.

The Teams AI Library is a software development kit (SDK) that helps developers create bots that can interact with Teams and Microsoft 365 applications. It is built on top of the Bot Framework SDK and simplifies the process of developing bots that interact with Teams' artificial intelligence capabilities. The SDK is available for JavaScript/TypeScript, .NET, and Python.

This guide is dedicated to Large Language Models (LLMs) that you can run on your home computer. It assumes your PC is a lower-end, non-gaming setup.

Supercharge WordPress Content Workflows and Engagement with Artificial Intelligence. Tap into leading cloud-based services like OpenAI, Microsoft Azure AI, Google Gemini and IBM Watson to augment your WordPress-powered websites. Publish content faster while improving SEO performance and increasing audience engagement. ClassifAI integrates Artificial Intelligence and Machine Learning technologies to lighten your workload and eliminate tedious tasks, giving you more time to create original content that matters.

Chatbot UI is an open-source AI chat app that allows users to create and deploy their own AI chatbots. It is easy to use and can be customized to fit any need. Chatbot UI is perfect for businesses, developers, and anyone who wants to create a chatbot.

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

uAgents is a Python library developed by Fetch.ai that allows for the creation of autonomous AI agents. These agents can perform various tasks on a schedule or take action on various events. uAgents are easy to create and manage, and they are connected to a fast-growing network of other uAgents. They are also secure, with cryptographically secured messages and wallets.

Griptape is a modular Python framework for building AI-powered applications that securely connect to your enterprise data and APIs. It offers developers the ability to maintain control and flexibility at every step. Griptape's core components include Structures (Agents, Pipelines, and Workflows), Tasks, Tools, Memory (Conversation Memory, Task Memory, and Meta Memory), Drivers (Prompt and Embedding Drivers, Vector Store Drivers, Image Generation Drivers, Image Query Drivers, SQL Drivers, Web Scraper Drivers, and Conversation Memory Drivers), Engines (Query Engines, Extraction Engines, Summary Engines, Image Generation Engines, and Image Query Engines), and additional components (Rulesets, Loaders, Artifacts, Chunkers, and Tokenizers). Griptape enables developers to create AI-powered applications with ease and efficiency.