Projects sharing features with FATE

SecretFlow is a privacy computing framework and platform designed for secure multi-party computation, federated learning, and privacy-preserving data analysis across independent nodes. It provides a management system to coordinate secure workloads and cryptographic tasks across a distributed cluster. The project enables joint data analysis and machine learning on partitioned datasets using cryptographic protocols. It allows for the training of models and the execution of analytical queries across multiple parties without exposing raw source information to any single participant. The framewor

MONAI is a PyTorch-based deep learning framework and library specifically designed for healthcare imaging. It provides a suite of domain-specific neural network architectures, specialized loss functions, and preprocessing pipelines tailored for analyzing multi-dimensional medical data. The project distinguishes itself through a decentralized federated learning system that allows models to learn from datasets across multiple institutions without exchanging raw patient images. It also features AI-assisted medical image annotation tools and a standardized model bundling system to ensure consiste

jetson-inference is a set of libraries and tools for executing optimized deep learning models on embedded GPU hardware. Its primary purpose is to enable real-time computer vision and AI inference at the edge with low latency and high throughput. The project distinguishes itself through high-performance streaming analytics and the ability to execute concurrent AI pipelines on auto-grade silicon. It provides specialized support for multi-sensor stream processing, utilizing zero-copy data transport to load camera frames directly into GPU memory. The codebase covers a broad surface of capabiliti

This project is a mobile-first web interface built with Vue 3 that replicates the layout and interaction patterns of a short-form video platform. It is designed as a responsive web application focused on high-performance mobile rendering and short-video workflows. The application features a vertical video carousel with infinite scrolling and a vertical-slide view orchestration system for seamless content playback. It employs a responsive layout using viewport-relative units to ensure consistent rendering across various mobile screen sizes and aspect ratios. The project incorporates Pinia for

Gollum is a Git-powered wiki engine and content management system that provides a web-based interface for editing and organizing files stored in a Git repository. It functions as a self-hosted documentation tool, using a Git-based storage backend to manage page content and track version history. The system is characterized by a pluggable markup rendering architecture that converts multiple markup languages and specialized notations into HTML. It supports a wide array of rich content, including mathematical typesetting, BibTeX bibliographies, and diagrams rendered via Mermaid. Broad capabilit

Azkaban is a distributed workflow manager and DAG-based job orchestrator designed as an enterprise batch processor. It serves as a Java-based workflow engine that schedules and executes complex job sequences across a cluster of executor servers, with specific functionality for managing big data workloads on Hadoop clusters. The system distinguishes itself through a distributed executor model that coordinates state via a shared database to ensure high availability. It employs a plugin-based architecture that allows for custom job types and system functionality extensions, including the ability

Data engineering practice repository providing tutorials, distributed processing engines, and Python data pipeline automation scripts. The system encompasses automated data validation, distributed compute aggregation, embedded columnar querying, lazy evaluation planning, partitioned storage export, and cloud storage retrieval. The capability surface covers cloud integration and storage, data engineering and pipelines, data processing and analytics, data quality and testing, database and storage, file management, and monitoring and observability.

This project is a research-oriented platform designed for simulating decentralized machine learning environments. It provides a framework for training models across multiple client nodes while keeping raw data localized, enabling the evaluation of model convergence and performance under various distributed network conditions. The system utilizes a parameter-server architecture to coordinate training, where a central coordinator manages the global model state and aggregates weight updates from distributed participants. By decoupling the training orchestration logic from the underlying neural n

Sglang is a high-performance inference engine and serving system designed for large language and multimodal models. It provides a programmable interface for orchestrating complex generation workflows, enabling developers to coordinate multi-turn dialogues, tool invocations, and reasoning chains through a domain-specific language. The platform is built to support production-scale deployments, offering an OpenAI-compatible API that allows for integration with existing application ecosystems. The system distinguishes itself through a disaggregated architecture that separates compute-intensive pr

SeaTunnel is a distributed data integration engine designed to synchronize structured and unstructured data across diverse sources and sinks. It functions as a multi-engine execution framework that can run data integration tasks across different distributed computing backends to optimize workload performance. The project is distinguished by a visual data pipeline designer for configuring workflows without manual code and a specialized change data capture tool for streaming incremental database updates. It also includes an enrichment pipeline that integrates large language models and embedding

Edict is a multi-agent orchestration system and framework designed to coordinate specialized large language model agents. It functions as a workflow designer and orchestrator that decomposes complex objectives into structured plans, using directed acyclic graphs and role-based hierarchies to execute sub-tasks. The system is distinguished by its event-driven architecture, utilizing a publish-subscribe event bus and transactional outbox to manage agent communications and task transitions. It features a dedicated skill management system that allows for the importation, updating, and sandboxed ex

Hatchet is an open-source durable workflow engine and task orchestration platform. It provides a framework for building and executing fault-tolerant, multi-step pipelines as directed acyclic graphs (DAGs), with automatic retries, scheduling, and real-time observability. The system is built around durable task checkpointing, which persists execution state after each step so work can resume from the last checkpoint after a worker crash or restart, and it supports event-driven task resumption that pauses a task until a matching external event arrives. The platform distinguishes itself through it

This is a reference guide for designing, deploying, and maintaining production-ready machine learning systems, grounded in MLOps best practices. It covers the complete machine learning lifecycle, from system design and workflow planning through to deployment and ongoing maintenance, with a focus on reliability, scalability, and maintainability as business requirements evolve. The guide provides an architecture reference for establishing shared ML infrastructure, including model registries and feature stores that standardize asset reuse across teams. It details pipeline automation through conf

SmolLM is a project dedicated to the development of small language models. It focuses on training and fine-tuning compact models that maintain high performance while utilizing fewer parameters. The project emphasizes efficient AI inference and on-device text generation, aiming to enable the deployment of lightweight models on edge devices with limited memory and processing power. It utilizes synthetic data generation to produce artificial datasets that improve the reasoning and training of these AI systems. The system supports a variety of optimization and training capabilities, including we

This project is a comprehensive educational resource and curriculum focused on the design and implementation of the full machine learning software and hardware stack. It serves as a technical reference for architecting machine learning systems, spanning from low-level programming interfaces to large-scale deployment infrastructure. The project provides instructional guidance on several specialized domains, including the development of AI compilers through intermediate representations and graph optimizations. It covers the architectural patterns required for distributed training across GPU clu

Gensim is an unsupervised natural language processing toolkit designed for topic modeling, word embedding training, and the processing of large-scale text corpora. It provides a framework for discovering latent themes and semantic structures in text without the need for labeled data. The toolkit is distinguished by its ability to handle datasets that exceed system memory through iterator-based data streaming from disk. It also supports distributed model training, allowing complex modeling tasks to be executed across computer clusters. The library covers a broad range of analysis capabilities

Devenv is a Nix-based development environment manager that provides declarative definitions for reproducible shells and toolchains. It functions as a declarative task runner for executing dependency-aware pipelines and a service orchestration tool for supervising background processes. The project distinguishes itself by generating OCI container images directly from environment definitions without requiring a separate container engine. It also implements the Model Context Protocol to expose project context and package search to AI agents, and supports AI-assisted scaffolding to generate config

ReAgent is a reinforcement learning platform designed for training, deploying, and evaluating reinforcement learning models and contextual bandit systems for large-scale decision making. It provides a comprehensive suite of tools that spans the entire workflow from initial feasibility analysis to production serving. The system includes a deep reinforcement learning training framework for distributed off-policy algorithms and a specialized model serving layer for high-volume production inference. It distinguishes itself with a counterfactual policy evaluator for estimating performance using hi

Modin is a distributed dataframe library and parallel data processing engine designed to handle large datasets that exceed system memory. It functions as a distributed computing framework that parallelizes data manipulation tasks across multiple CPU cores or clusters to increase throughput and avoid memory errors. The project mirrors the Pandas API, allowing for the distribution of data workflows without changing core code logic. It utilizes a pluggable backend interface, which enables users to switch between different distributed execution engines to optimize performance based on available h

This project provides a collection of architectural blueprints and design patterns for building, deploying, and scaling machine learning systems in production environments. It serves as a comprehensive reference for standardizing the end-to-end lifecycle of machine learning components, including training pipelines, model serving, and system observability. The framework distinguishes itself by offering standardized strategies for managing complex operational requirements such as asynchronous inference, traffic routing, and service decoupling. It covers a wide range of patterns for model servin

Maestro is a distributed job scheduler and containerized data pipeline tool designed to coordinate complex sequences of tasks. It functions as a Kubernetes workflow orchestrator and MLOps automation platform, utilizing directed acyclic graphs to manage task dependencies and execution order across computing clusters. The system distinguishes itself through the use of isolated container environments for each workflow step, ensuring consistent runtime dependencies. It incorporates an asynchronous event bus to coordinate state transitions and provides lifecycle hook integration that dispatches sy

Nuke is a build automation system for defining software compilation and deployment pipelines using a strongly typed C# console application. It functions as a cross-platform build engine and pipeline orchestrator that treats build configurations as standard executable programs rather than static files. By leveraging a compiled language, the system provides type safety and IDE support for build script logic. This approach allows for the definition of automation and CI/CD pipelines using a professional programming language instead of YAML or shell scripts. The engine manages .NET project orches

kro is a Kubernetes resource orchestrator and API abstraction layer that enables the definition of simplified custom API surfaces. It allows users to map high-level inputs to complex templates of underlying Kubernetes objects, effectively grouping interdependent resources into single, manageable units. The project differentiates itself by automating the generation of custom resource definitions and dedicated controllers from resource graph specifications without requiring manual Go code. It employs a dependency manager that uses directed acyclic graphs to coordinate the creation, readiness, a

Flyte is a distributed machine learning pipeline manager and MLOps workflow engine. It functions as a Kubernetes-native orchestrator used to coordinate data, models, and compute resources for executing machine learning pipelines and autonomous agents at scale. The platform provides specialized infrastructure for the full machine learning lifecycle, including a dedicated model serving platform to deploy trained models as scalable production-ready inference services. It also enables the coordination and state management of autonomous AI agents. The system manages scalable pipeline execution th

SparkInternals is a technical reference and architecture guide detailing the internal design and implementation of the Apache Spark distributed computing engine. It serves as a study of big data engine analysis, focusing on how the system manages cluster execution and the interaction between driver nodes, executors, and workers. The project provides a detailed breakdown of how logical plans are converted into physical execution stages. It specifically analyzes the mechanics of data shuffle operations, memory management, and the coordination of distributed job scheduling. The documentation co

KServe is a Kubernetes-native platform for deploying and serving machine learning models as scalable inference services. It supports both generative AI models, including large language models, and traditional predictive models from frameworks such as TensorFlow, PyTorch, Scikit-Learn, XGBoost, and ONNX. The platform manages the full lifecycle of model deployments, including revision tracking, canary rollouts, A/B testing, and automatic rollbacks, and provides serverless scale-to-zero capabilities for cost-efficient resource management. KServe distinguishes itself through a standardized infere

This project is a structured learning curriculum and technical reference for mastering deep learning with TensorFlow. It provides a comprehensive guide for building, training, and deploying neural networks, combining theoretical fundamentals with practical implementation examples. The repository distinguishes itself by covering the end-to-end machine learning workflow, from low-level tensor mathematics and linear algebra to the creation of complex model architectures. It includes specific guidance on developing data pipelines for diverse data types, such as images, text, and time-series seque