Search Results for "graph" - Page 11
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Showing 1424 open source projects for "graph"
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Gemini 3 and 200+ AI Models on One PlatformBuild, govern, and optimize agents and models with Gemini Enterprise Agent Platform.
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Swinject
Dependency injection framework for Swift with iOS/macOS/Linux
Swinject is a lightweight dependency injection framework for Swift. Dependency injection (DI) is a software design pattern that implements Inversion of Control (IoC) for resolving dependencies. In the pattern, Swinject helps your app split into loosely-coupled components, which can be developed, tested, and maintained more easily. Swinject is powered by the Swift generic type system and first-class functions to define the dependencies of your app simply and fluently. Services must be... -
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LinkChecker
Check links in web documents or full websites
LinkChecker is a free, GPL licensed website validator. LinkChecker checks links in web documents or full websites. It runs on Python 3 systems, requiring Python 3.8 or later. The version in the pip repository may be old, to find out how to get the latest code, plus platform-specific information and other advice see doc/install.txt in the source code archive. If you do not want to install any additional libraries/dependencies you can use the Docker image which is published on GitHub... -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of an easy-to-use mini-batch loader for many small and single giant graphs, a large number of common benchmark datasets (based on simple interfaces to create your own), and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. We have outsourced a lot of... -
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Luigi
Python module that helps you build complex pipelines of batch jobs
Luigi is a Python (3.6, 3.7, 3.8, 3.9 tested) package that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and much more. The purpose of Luigi is to address all the plumbing typically associated with long-running batch processes. You want to chain many tasks, automate them, and failures will happen. These tasks can be anything, but are typically long running things like Hadoop... -
Build Securely on Azure with Proven FrameworksMoving to the cloud brings new challenges. How can you manage a larger attack surface while ensuring great network performance? Turn to Fortinet’s Tested Reference Architectures, blueprints for designing and securing cloud environments built by cybersecurity experts. Learn more and explore use cases in this white paper.
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Spring AI Alibaba Examples
Spring AI Alibaba examples for building and testing AI apps
...It is designed to help developers understand core concepts, explore practical implementations, and follow best practices when building AI-powered systems using the Spring ecosystem. Each module focuses on a specific use case such as chat, image processing, audio handling, graph workflows, and retrieval-augmented generation. The examples highlight how to integrate AI models, manage prompts, handle memory, and build multi-model or multi-agent workflows. Developers can explore individual project folders for detailed instructions and implementation guidance. Spring AI Alibaba Examples also supports experimentation through playground modules and encourages contributions to expand real-world AI use cases and improve development practices. -
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Magic Portfolio
Build your timeless portfolio with Once UI's Magic Portfolio
...Content is managed through a structured configuration and MDX-based content system, so you can define sections, projects, blog posts, gallery items, and about/CV information in code-backed content files. The template emphasizes SEO with auto-generated Open Graph/X images, schemas, and metadata derived from the content configuration, making it more discoverable out of the box. It offers flexible feature toggles to enable or disable sections such as blog, work, gallery, and about, as well as options for automatic social link generation and password-protected URLs. -
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Node Modules Inspector
Interactive UI for local node modules inspection
This is a tool (CLI + interactive UI) for inspecting the node_modules directory of a JavaScript/TypeScript project, created by Anthony Fu. It supports projects using npm, pnpm or bun. The idea is to help developers visualise the dependency graph, see which dependencies are installed, their sizes, types (ESM vs CJS), origins (catalog vs registry), and filter or build a static report of a project’s dependency tree. The project includes a web UI version that you can try at node-modules.dev, and a build command to generate a static output (folder .node-modules-inspector) for hosting. ... -
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Join Monster
A GraphQL to SQL query execution layer for query planning
A GraphQL to SQL query execution layer for query planning and batch data fetching. Efficient query planning and data fetching for SQL. Use JOINs and/or batched requests to retrieve all your data. It takes a GraphQL query and your schema and automatically generates the SQL. Send that SQL to your database and get back all the data needed to resolve with only one or a few round-trips to the database. Since it works with the reference implementation, the API is all very familiar. Join Monster is... -
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The Machine & Deep Learning Compendium
List of references in my private & single document
...The compendium includes explanations of concepts across multiple domains such as natural language processing, computer vision, time-series analysis, anomaly detection, and graph learning. In addition to technical algorithms, the project also covers practical topics related to data science workflows, engineering practices, and product development in AI systems. -
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KG-LLM-Papers
Papers integrating knowledge graphs (KGs) and large language models
...The repository functions as a continuously updated index of scholarly work that investigates how structured knowledge representations can enhance the reasoning, factual accuracy, and interpretability of language models. It includes surveys, benchmark studies, and cutting-edge research that examine topics such as knowledge graph-guided prompting, retrieval-augmented generation, reasoning over structured data, and hybrid architectures combining symbolic and neural systems. By gathering these papers into a single organized repository, the project helps researchers quickly discover relevant literature and track the evolution of the field. -
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ANE Training
Training neural networks on Apple Neural Engine via APIs
ANE Training is an experimental research project that demonstrates how to train neural networks directly on Apple’s Neural Engine by leveraging reverse-engineered private APIs that are normally inaccessible to developers. The repository implements a from-scratch transformer training pipeline capable of running both forward and backward passes on ANE hardware without relying on CoreML, Metal, or GPU acceleration. It explores the internal software stack of the Apple Neural Engine by... -
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Mantic.sh
A structural code search engine for Al agents
...Unlike traditional text-based search tools that mainly match keywords, Mantic.sh understands code structure and meaning by combining syntactic heuristics with neural semantic reranking to produce results that reflect conceptual relevance, which helps find functions, definitions, and patterns that literal search might miss. It uses local embeddings and code graph awareness so that queries like “authentication flow” return not just superficially matching text but contextually related code across multiple repositories. The tool supports navigational commands such as go-to-definition and find-references, simplifying large-scale refactoring, comprehension, and exploration tasks for teams. -
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Christmas Tree
Just a simple Christmas tree, based on reddit story
atree is a lightweight JavaScript library for managing and visualizing hierarchical tree data structures in a flexible and efficient way. It offers core abstractions for creating nodes, building parent–child relationships, and traversing trees with utilities for depth, breadth, and custom iteration strategies. Because tree structures are ubiquitous in computing—from file systems and organizational charts to DOM structures and evolutionary taxonomies—atree provides a reusable foundation for... -
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handson-ml
Teaching you the fundamentals of Machine Learning in python
handson-ml hosts the notebooks for the first edition of the same hands-on ML book, reflecting the tooling and idioms of its time while teaching durable concepts. It walks through supervised and unsupervised learning with scikit-learn, then introduces deep learning using the earlier TensorFlow 1 graph-execution style. The examples underscore fundamentals like bias-variance trade-offs, regularization, and proper validation, grounding learners before they move to deep nets. Even though the deep learning stack evolved, the classical ML sections remain highly relevant for production data problems. The code is crafted to be clear rather than clever, prioritizing readability for newcomers. ... -
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Wireit
Wireit upgrades your npm/pnpm/yarn scripts to make them smarter
...It wraps ordinary npm run commands with a declarative configuration describing inputs, outputs, and script relationships, then skips work when nothing has changed. Wireit can watch files, detect invalidated outputs, and propagate rebuilds through a graph of scripts, improving feedback loops in monorepos and complex apps. It persists caches between runs and across CI so identical steps don’t redo expensive tasks like bundling or type-checking. The system also serializes or parallelizes scripts based on dependencies to avoid race conditions while maximizing throughput. Because it integrates at the script layer, it works with any underlying tool—TypeScript, Rollup, Jest, or bespoke commands—while remaining transparent to developers. -
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targets
Function-oriented Make-like declarative workflows for R
The targets package is a pipeline / workflow management tool in R, designed to coordinate multi‐step computational workflows in data science / statistics. It tracks dependencies between “targets” (computational steps), skips steps whose upstream data or code hasn’t changed, supports parallel computation, branching (dynamic generation of sub‐targets), file format abstractions, and encourages reproducible and efficient analyses. It’s something like GNU Make for R, but more integrated. Skipping... -
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Vald
Vald. A Highly Scalable Distributed Vector Search Engine
...Vald has automatic vector indexing and index backup, and horizontal scaling which is made for searching from billions of feature vector data. Vald is easy to use, feature-rich and highly customizable as you needed. Usually, the graph requires locking during indexing, which causes stop-the-world. But Vald uses distributed index graphs so it continues to work during indexing. Vald implements it's own highly customizable Ingress/Egress filter. Which can be configured to fit the gRPC interface. Horizontal scalable on memory and cpu for your demand. Vald supports to auto backup feature using Object Storage or Persistent Volume which enables disaster recovery. -
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MercuryMessaging
Framework to facilitate communication in the Unity game engine
...The toolkit contains the Mercury messaging framework, which is a messaging and organizational framework built around the Mercury Protocol. Unity organizes its rendered scene objects (known in Unity as GameObjects) using a standard scene graph (known in Unity as the Scene Hierarchy). While Unity is very powerful, it is fairly difficult to achieve nonspatial communication between scriptable components of GameObjects (in Unity, known as MonoBehaviours). -
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Grafana
Leading open-source visualization and observability platform
Grafana OSS is the leading open-source platform for visualization and observability. It enables teams to query, visualize, alert on, and explore telemetry data from multiple sources in a single interface. With support for 100+ data source plugins—including Prometheus, Loki, Elasticsearch, InfluxDB, SQL/NoSQL databases, and OpenTelemetry—Grafana helps teams correlate metrics, logs, and traces across applications and infrastructure. Users can build interactive dashboards with rich... -
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Minimal Mistakes Jekyll theme
Jekyll theme for building a personal site, blog, project documentation
A flexible two-column Jekyll theme. Perfect for building personal sites, blogs, and portfolios. Everything from the menus, sidebars, comments, and more can be configured or set with YAML Front Matter. Built with HTML5 + CSS3. All layouts are fully responsive with helpers to augment your content. Free to use however you want under the MIT License. Clone it, fork it, customize it, etc. Settings that affect your entire site can be changed in Jekyll’s configuration file: _config.yml, found in... -
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SageMaker Spark Container
Docker image used to run data processing workloads
...It provides high-level APIs in Scala, Java, Python, and R, and an optimized engine that supports general computation graphs for data analysis. It also supports a rich set of higher-level tools including Spark SQL for SQL and DataFrames, MLlib for machine learning, GraphX for graph processing, and Structured Streaming for stream processing. The SageMaker Spark Container is a Docker image used to run batch data processing workloads on Amazon SageMaker using the Apache Spark framework. The container images in this repository are used to build the pre-built container images that are used when running Spark jobs on Amazon SageMaker using the SageMaker Python SDK. ... -
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Agents 2.0
An Open-source Framework for Data-centric Language Agents
Agents is an open-source framework designed to build and train autonomous language agents through a data-centric and learning-oriented architecture. The project introduces a concept known as agent symbolic learning, which treats an agent pipeline similarly to a neural network computational graph. In this framework, each node in the pipeline represents a step in the reasoning or action process, while prompts and tools act as adjustable parameters analogous to neural network weights. During training, the system performs a forward execution where the agent completes a task and records the trajectory of prompts, outputs, and tool usage. ... -
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Anomaly Detection Learning Resources
Anomaly detection related books, papers, videos, and toolboxes
...The repository organizes resources into structured categories such as books, tutorials, academic papers, datasets, benchmark frameworks, and open-source toolkits. It includes materials covering a wide range of anomaly detection domains, including time series data, graph data, tabular datasets, and real-time monitoring systems. By compiling resources from multiple programming ecosystems such as Python, R, and other machine learning platforms, the repository allows users to discover both research papers and practical implementations. -
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Open Model Zoo
Pre-trained Deep Learning models and demos
...In addition to model files, Open Model Zoo provides demo applications that show realistic usage patterns and help developers quickly prototype and understand inference pipelines in C++, Python, or via the OpenCV Graph API. Tools in the repository also help automate model downloads and other tasks, making it easier to incorporate these models into production systems or custom solutions. -
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Visual Blocks
Visual Blocks for ML is a Google visual programming framework
Visual Blocks is a node-based, in-browser environment for building AI and data-processing workflows with drag-and-drop components. It lets you connect sources, transforms, models, and visualizers into a live graph, so changes propagate instantly and results are observable without writing glue code. Under the hood it leans on web-friendly runtimes (e.g., WebGPU/WebGL/WebNN or TensorFlow.js backends) to execute pipelines locally, which is great for demos, teaching, and privacy-sensitive prototypes. The block abstraction encourages modularity: you can package a preprocessor, a model, and a postprocessor as a reusable composite for others to slot into their graphs. ...
