Search Results for "data capture framework" - Page 12
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Showing 2572 open source projects for "data capture framework"
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Vue Json Pretty
A JSON tree view component that is easy to use
A Vue component for rendering JSON data as a tree structure. The CSS file is included separately and needs to be imported manually. You can either import CSS globally in your app (if supported by your framework) or directly from the component. -
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Seal Report
Database Reporting Tool and Tasks (.Net)
Seal Report is an open-source tool for the generation of daily reports from any database. It offers a complete framework for producing dynamic reports and dashboards, with a focus on simplicity and ease of use. -
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JeeSite
A basic enterprise information development platform
...JeeSite is the best study case for you to learn the Java platform. JeeSite is also the best assistant for private work. JeeSite is a Java-based development platform built on the Spring Framework, with Spring MVC as the model view controller, MyBatis as the data access layer, Apache Shiro as the authority authorization layer, Ehcahe for caching common data, and Activit as the workflow engine. It is the best integration in the JavaEE world. JeeSite is mainly positioned in the field of enterprise informatization. It has built-in basic functions of enterprise informatization system and efficient code generation tools. -
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TTRL
Test-Time Reinforcement Learning
TTRL is an open-source framework for test-time reinforcement learning in large language models, with a particular focus on reasoning tasks where ground-truth labels are not available during inference. The project addresses the problem of how to generate useful reward signals from unlabeled test-time data, and its central insight is that common test-time scaling practices such as majority voting can be repurposed into reward estimates for online reinforcement learning. ... -
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CoreNet
CoreNet: A library for training deep neural networks
CoreNet is Apple’s internal deep learning framework for distributed neural network training, designed for high scalability, low-latency communication, and strong hardware efficiency. It focuses on enabling large-scale model training across clusters of GPUs and accelerators by optimizing data flow and parallelism strategies. CoreNet provides abstractions for data, tensor, and pipeline parallelism, allowing models to scale without code duplication or heavy manual configuration. ... -
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CPlusPlusThings
Collection of various C++ code samples, utilities, patterns
CPlusPlusThings is a repository collecting various C++ code samples, utilities, patterns, and small example projects. It is less a polished product and more a learning/reference collection of snippets and usages of C++ idioms, data structures, algorithms, utilities, and perhaps tricks or meta-programming exercises. (No prominent README or detailed docs were available from my quick search.) Example implementations of data structures and algorithms. Organized as a learning repository (rather than a production framework). Encourages reuse and adaptation by developers as a reference. ... -
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Pyper
Concurrent Python made simple
Pyper is a Python-native orchestration and scheduling framework designed for modern data workflows, machine learning pipelines, and any task that benefits from a lightweight DAG-based execution engine. Unlike heavier platforms like Airflow, Pyper aims to remain lean, modular, and developer-friendly, embracing Pythonic conventions and minimizing boilerplate. It focuses on local development ergonomics and seamless transition to production environments, making it ideal for small teams and individuals needing a programmable and flexible orchestration solution without the overhead of enterprise systems. -
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RenderCV
LaTeX CV generator from a YAML/JSON input file
RenderCV is a LaTeX CV/resume framework. It allows you to create a high-quality CV as a PDF from a YAML file with full Markdown syntax support and complete control over the LaTeX code. RenderCV offers built-in LaTeX and Markdown templates ready to produce high-quality CVs. However, the templates are entirely arbitrary and can easily be updated to leverage RenderCV's capabilities with your custom CV themes. -
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UXF
Framework for creating human behaviour experiments in Unity
The Unity Experiment Framework (UXF) is an open-source package for the Unity3D game engine that can be used to assist the development of virtual reality human behavior experiments. Developed by Jack Brookes, Immersive Cognition Group, University of Leeds. Automates the process of collecting the variables you define within your task, and stores them simple CSV files. -
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JAI Workflow
Build programmatically custom agentic workflows, AI Agents, RAG system
JAI-Workflow is a framework for building and managing machine learning workflows, streamlining the process from data ingestion to model deployment. -
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Kimi k1.5
Scaling Reinforcement Learning with LLMs
...Kimi-k1.5 is trained jointly on text and vision data, giving it true multimodal reasoning capabilities where it can interpret and generate content across modalities in a unified way. -
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Acl
A powerful server and network library, including coroutine
The Acl (Advanced C/C++ Library) project a is powerful multi-platform network communication library and service framework, supporting LINUX, WIN32, Solaris, FreeBSD, MacOS, AndroidOS, iOS. Many applications written by Acl run on these devices with Linux, Windows, iPhone and Android and serve billions of users. There are some important modules in Acl project, including network communcation, server framework, application protocols, multiple coders, etc. The common protocols such as... -
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LightGBM
Gradient boosting framework based on decision tree algorithms
LightGBM or Light Gradient Boosting Machine is a high-performance, open source gradient boosting framework based on decision tree algorithms. Compared to other boosting frameworks, LightGBM offers several advantages in terms of speed, efficiency and accuracy. Parallel experiments have shown that LightGBM can attain linear speed-up through multiple machines for training in specific settings, all while consuming less memory. LightGBM supports parallel and GPU learning, and can handle large-scale data. ... -
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Cosmos-RL
Cosmos-RL is a flexible and scalable Reinforcement Learning framework
Cosmos-RL is a scalable reinforcement learning framework designed specifically for physical AI systems such as robotics, autonomous agents, and multimodal models. It provides a distributed training architecture that separates policy learning and environment rollout processes, enabling efficient and asynchronous reinforcement learning at scale. The framework supports multiple parallelism strategies, including tensor, pipeline, and data parallelism, allowing it to leverage large GPU clusters effectively. ... -
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EasyR1
An Efficient, Scalable, Multi-Modality RL Training Framework
EasyR1 is a streamlined training framework for building “R1-style” reasoning models from open-source LLMs with minimal boilerplate. It focuses on the full reasoning stack—data preparation, supervised fine-tuning, preference or outcome-based optimization, and lightweight evaluation—so you can iterate quickly on chain-of-thought–heavy tasks. The project’s philosophy is practicality: sensible defaults, one-command recipes, and compatibility with popular base models let you stand up experiments without wrestling infrastructure. ... -
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Zerocode
API automation and load testing framework
A community-developed, free, open source, microservices API automation and load testing framework built using JUnit core runners for Http REST, SOAP, Security, Database, Kafka and much more. Zerocode Open Source enables you to create, change, orchestrate and maintain your automated test cases declaratively with absolute ease. Zerocode makes it easy to create and maintain automated tests with absolute minimum overhead for REST,SOAP, Kafka Real Time Data Streams and much more. ... -
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Colly
Elegant Scraper and Crawler Framework for Golang
Colly provides a clean interface to write any kind of crawler/scraper/spider. With Colly you can easily extract structured data from websites, which can be used for a wide range of applications, like data mining, data processing or archiving. Clean API. Fast (>1k request/sec on a single core) Manages request delays and maximum concurrency per domain. Automatic cookie and session handling. Sync/async/parallel scraping. Distributed scraping. Caching, automatic encoding of non-unicode... -
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TNT
A lightweight library for PyTorch training tools and utilities
TNT is a lightweight training framework developed by Meta that simplifies the process of building and managing machine learning training loops using PyTorch. The project focuses on providing a flexible yet structured environment for implementing training pipelines without the complexity of large deep learning frameworks. It introduces modular abstractions that allow developers to organize training logic into reusable components such as trainers, evaluators, and callbacks. This design helps... -
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kimuraframework
AI-first Ruby framework for building fast, flexible web scraping spide
Kimurai is an open source web scraping framework written in Ruby that simplifies the process of building automated data extraction tools. It provides a clean domain-specific language that allows developers to define scraping logic and data schemas with minimal boilerplate code. Kimurai can use AI-assisted extraction to identify where data resides in HTML pages, automatically generating selectors that are cached for future use so subsequent scraping runs operate with pure Ruby performance. ... -
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Smallpond
A lightweight data processing framework built on DuckDB and 3FS
smallpond is a lightweight distributed data processing framework built by DeepSeek, designed to scale DuckDB workloads over clusters using their 3FS (Fire-Flyer File System) backend. The idea is to preserve DuckDB’s fast analytics engine but lift it from single-node to multi-node settings, giving you the ability to operate on large datasets (e.g. petabyte scale) without moving to a heavyweight system like Spark. -
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ip2region
Ip2region is an offline IP-to-Region localization library and IP data
ip2region is an offline IP geolocation library and data-management framework supporting both IPv4 and IPv6. It ships with raw IP datasets and optimized XDB database files for city-level region lookup. Returned records can include country, province, city, ISP, and ISO country-code information. The XDB format supports large datasets, deduplication, compression, and customizable region fields. -
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Guardrails
Framework for validating and controlling LLM outputs in AI apps
...These guards can detect and mitigate specific issues by applying validators that analyze content, enforce rules, or ensure structured output formats. Guardrails also supports generating structured data from language models, allowing developers to enforce schemas or type constraints on responses. A companion ecosystem known as a hub provides reusable validators that can be combined into input and output guards to address different reliability and safety concerns. -
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Spring Batch
Spring Batch is a framework for writing batch applications using Java
A lightweight, comprehensive batch framework designed to enable the development of robust batch applications vital for the daily operations of enterprise systems. Spring Batch provides reusable functions that are essential in processing large volumes of records, including logging/tracing, transaction management, job processing statistics, job restart, skip, and resource management. It also provides more advanced technical services and features that will enable extremely high-volume and high... -
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JetPackMvvm
A Jetpack combined with the MVVM rapid development framework
...The sample format is the data format returned by the Android Api. If the errorCode is equal to 0, the request is successful, otherwise the request fails. From the developer's point of view, we mainly want to get the unpacking data-data, and we don't want to judge errorCode==0 every time. Whether the request is successful or failed At this time, we can inherit BaseResponse in the server return database class and implement related methods. -
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Ludwig AI
Low-code framework for building custom LLMs, neural networks
Declarative deep learning framework built for scale and efficiency. Ludwig is a low-code framework for building custom AI models like LLMs and other deep neural networks. Declarative YAML configuration file is all you need to train a state-of-the-art LLM on your data. Support for multi-task and multi-modality learning. Comprehensive config validation detects invalid parameter combinations and prevents runtime failures.
