Awesome GitHub RepositoriesParallel Search Engines

The Silver Searcher is a high-performance text search utility and regex code search tool designed to locate strings and regular expressions within plain text and source code. It functions as a codebase pattern matcher that provides highlighted results with surrounding line context and respects standard ignore files. The utility includes specialized capabilities for searching inside zlib and lzma compressed archives. It implements high-throughput processing via parallel-threaded file scanning and just-in-time regular expression compilation. The tool's search and indexing surface covers output

Stockfish is a high-performance chess engine designed to evaluate board positions and calculate optimal moves. It functions as a command-line tool that utilizes neural network-based search algorithms to assess complex game states and determine strategic advantages. The engine is fully compliant with the Universal Chess Interface, allowing it to exchange commands and move data with external graphical user interfaces and professional analysis software. The engine distinguishes itself through advanced computational strategies that maximize hardware efficiency and search depth. It employs multi-t

NNI is an AutoML toolkit designed to automate machine learning lifecycles. It functions as a hyperparameter optimization framework, a neural architecture search tool, and a model compression suite. The project provides a distributed training orchestrator to manage machine learning workloads across local machines, remote servers, and cloud platforms. It enables the discovery of efficient model structures through reinforcement learning and one-shot optimization methods, while utilizing Bayesian and evolutionary algorithms to automate hyperparameter tuning. Additional capabilities include tools

Optuna is a Python-based hyperparameter optimization framework designed to automate the search for optimal machine learning model configurations. It functions as a Bayesian optimization library that systematically tests parameter combinations to maximize or minimize objective functions, streamlining the model development process through iterative evaluation. The project distinguishes itself through a define-by-run dynamic construction model, which allows users to build complex, conditional search spaces using standard programming logic. Its architecture is highly modular, featuring a pluggabl

gosec is a static analysis security tool designed to scan Go source code for vulnerabilities and common coding flaws. It functions as a security analyzer that inspects the abstract syntax tree to identify insecure function calls, API usage, and potential security risks. The tool distinguishes itself by mapping detected vulnerabilities to Common Weakness Enumeration identifiers for standardized reporting and integrating with external AI models to suggest code fixes for identified issues. Its capabilities cover the detection of injection vulnerabilities, hardcoded credentials, weak cryptograph

This is a scikit-learn automated machine learning framework designed to optimize model selection and hyperparameters. It functions as an automated model selector and hyperparameter optimization tool for classification and regression tasks, utilizing an automated ensemble builder to combine high-performing models for increased predictive accuracy. The system features a distributed search engine that uses Dask for parallel machine learning optimization across CPU cores or clusters. It implements a budget-based evaluation strategy through successive halving to prioritize promising model configur

Hyperopt is a Python library for hyperparameter optimization designed to minimize scalar-valued objective functions. It operates as a stochastic search space engine that finds optimal input parameters by searching through real-valued, discrete, and conditional spaces. The framework distinguishes itself through its support for complex search space configurations, allowing for conditional parameter hierarchies where specific hyperparameters are sampled only if their parent parameters meet certain criteria. It is built as an asynchronous optimization framework, decoupling the generation of searc

Komga is a self-hosted digital comics and eBook server that organizes, serves, and streams CBZ, CBR, PDF, and EPUB files through a web interface. Built as a Java Spring Boot application with SQLite-based metadata storage, it provides multi-user access controls with individual reading progress tracking and supports the OPDS protocol for compatibility with third-party reader applications. The server distinguishes itself through comprehensive authentication options, including OAuth2 and OpenID Connect support with automatic account creation and email verification, alongside traditional cookie an

FAST_LIO ist ein Echtzeit-SLAM-System und ein LiDAR-Inertial-Odometrie-Paket für Simultaneous Localization and Mapping. Es fungiert als State-Estimation-Engine und 3D-Mapping-Tool, das LiDAR-Punktwolken mit Daten von Inertial Measurement Units (IMU) fusioniert, um eine robuste Schätzung des Roboterzustands zu ermöglichen. Das System nutzt einen eng gekoppelten Sensor-Fusionsansatz mit einem iterativen Kalman-Filter zur Schätzung von Position und Orientierung. Es zeichnet sich durch direktes Point-to-Plane-Matching aus, das die Odometrie berechnet, indem rohe Lidar-Punkte mit der Kartenoberfläche abgeglichen werden, ohne manuelle geometrische Merkmalsextraktion. Um hohe Verarbeitungsgeschwindigkeiten beizubehalten, verwendet es inkrementelles KD-Tree-Mapping und parallele räumliche Suchbäume. Das Framework deckt ein breites Spektrum an Funktionen ab, darunter Motion-Undistortion zur Korrektur räumlicher Verzerrungen und Synchronisierung von Sensor-Zeitstempeln. Es bietet zudem Hilfsmittel für die Kalibrierung von Sensor-Extrinsics, die Initialisierung der Sensorausrichtung und den Export akkumulierter globaler Punktwolken. Das Projekt ist in C++ implementiert und bietet Interfaces zur Integration externer IMU- und LiDAR-Datenströme.

FLAML ist ein automatisiertes Machine-Learning-Framework, ein Tool zur Hyperparameter-Optimierung und ein Orchestrator für Large-Language-Model-Agenten. Es bietet ein System zur Modellauswahl und -abstimmung über verschiedene Lerner und Datensätze hinweg und stellt gleichzeitig ein Toolkit zur Optimierung der Inferenzparameter und Fine-Tuning-Einstellungen von Large Language Models bereit. Das Projekt verfügt über ein Meta-Learning-Tuning-System, das historische Aufgabendaten analysiert, um datenabhängige Standardkonfigurationen zu generieren und die Modellkonvergenz zu beschleunigen. Es ermöglicht zudem das Design kollaborativer Multi-Agenten-Systeme durch konversationelle Workflows und ereignisgesteuerte Orchestrierung. Die Funktionen decken eine ressourceneffiziente Hyperparametersuche für Machine-Learning-Modelle und beliebige Python-Funktionen ab und unterstützen hierarchische Suchräume sowie lexikografische Zieloptimierung. Das Framework enthält zudem Dienstprogramme für automatisierte Modellauswahl, gestapelte Ensemble-Konstruktion, Zero-Shot-Konfiguration und die Durchsetzung von Fairness-Beschränkungen. Das System unterstützt die Skalierung verteilter Abstimmungen und die gleichzeitige Ausführung von Versuchen über Compute-Cluster hinweg, um die Gesamtsuchdauer zu reduzieren.

Memories is a self-hosted photo and video management system designed for organizing, indexing, and sharing media libraries from a private server. It functions as an AI-powered media organizer that uses artificial intelligence for face recognition and object tagging to automatically categorize large collections. The system distinguishes itself through deep metadata integration and specialized processing, featuring a geographic photo viewer that plots media on a map using GPS data and reverse geocoding. It also includes a self-hosted video transcoder that converts files into adaptive HLS stream

Minigo is a TensorFlow-based reinforcement learning engine designed to master the game of Go. It functions as a comprehensive system for training neural networks to predict board policies and game outcomes, utilizing a model trainer to generate self-play data and optimize weights. The project is distinguished by its ability to perform large-scale game simulations using Kubernetes to distribute worker nodes across CPU, GPU, and TPU hardware. It employs a Monte Carlo Tree Search implementation to identify optimal moves and supports specialized hardware acceleration, including inference on Edge

ScanCode Toolkit is a software composition analysis tool and scanning framework designed to identify open-source licenses and copyright statements in source code and binary files. It functions as an open-source license detector, a dependency vulnerability scanner, and a generator for standardized software bills of materials in SPDX and CycloneDX formats. The project is built as a plugin-based scanning framework, allowing the integration of custom detection logic, specialized analyzers, and modified scanning behaviors at runtime. It distinguishes itself through the ability to produce formal le