Search Results for "processing" - Page 3
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Showing 206 open source projects for "processing"
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Atera all-in-one platform IT management software with AI agentsAtera’s AI agents don’t just assist, they act. From detection to resolution, they handle incidents and requests instantly, taking your IT management from automated to autonomous.
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Outgrown Windows Task Scheduler?Windows Task Scheduler wasn't built for complex, cross-platform automation. Get a free diagnostic that shows exactly where things are failing and provides remediation recommendations. Interactive HTML report delivered in minutes.
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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. ... -
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future
R package: future: Unified Parallel and Distributed Processing in R
The future package in R provides a unified abstraction for asynchronous and/or parallel computation. It allows R expressions to be scheduled for future evaluation, with the result retrieved later, in a way decoupled from the specific backend used. This lets code be written in a way that works with sequential execution, multicore, multisession, cluster, or remote compute backends, without changing the high-level code. It handles automatic exporting of needed global variables/functions,... -
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KubeEdge
Kubernetes Native Edge Computing Framework (project under CNCF)
...It also supports MQTT which enables edge devices to access through edge nodes. With KubeEdge it is easy to get and deploy existing complicated machine learning, image recognition, event processing, and other high-level applications to the Edge. With business logic running at the Edge, much larger volumes of data can be secured & processed locally where the data is produced. With data processed at the Edge, the responsiveness is increased dramatically and data privacy is protected. -
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Apache InLong
Apache InLong - a one-stop integration framework for massive data
Apache InLong is a one-stop integration framework for massive data that provides automatic, secure and reliable data transmission capabilities. InLong supports both batch and stream data processing at the same time, which offers great power to build data analysis, modeling and other real-time applications based on streaming data. InLong (应龙) is a divine beast in Chinese mythology who guides the river into the sea, and it is regarded as a metaphor of the InLong system for reporting data streams. InLong was originally built at Tencent, which has served online businesses for more than 8 years, to support massive data (data scale of more than 80 trillion pieces of data per day) reporting services in big data scenarios. ... -
AI-generated apps that pass security reviewRetool lets you generate dashboards, admin panels, and workflows directly on your data. Type something like “Build me a revenue dashboard on my Stripe data” and get a working app with security, permissions, and compliance built in from day one. Whether on our cloud or self-hosted, create the internal software your team needs without compromising enterprise standards or control.
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GoAWK
A POSIX-compliant AWK interpreter written in Go, with CSV support
GoAWK now uses a bytecode compiler and includes native support for CSV files. AWK is a fascinating text processing language, and The AWK Programming Language is a wonderfully concise book describing it. The A, W, and K in AWK stand for the surnames of the three original creators: Alfred Aho, Peter Weinberger, and Brian Kernighan. Kernighan is also an author of The C Programming Language (“K&R”), and the two books have that same each-page-packs-a-punch feel. -
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graphql-upload
Middleware and an Upload scalar to add support for GraphQL multipart
...Clients implementing the GraphQL multipart request spec upload files as Upload scalar query or mutation variables. Their resolver values are promises that resolve file upload details for processing and storage. Files are typically streamed into cloud storage but may also be stored in the filesystem. Use the middleware graphqlUploadKoa or graphqlUploadExpress just before GraphQL middleware. Alternatively, use the function processRequest to create custom middleware. The process must have both read and write access to the directory identified by os.tmpdir(). ... -
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Zappa - Serverless Python
Serverless Python
...That means infinite scaling, zero downtime, zero maintenance - and at a fraction of the cost of your current deployments! With a traditional HTTP server, the server is online 24/7, processing requests one by one as they come in. If the queue of incoming requests grows too large, some requests will time out. With Zappa, each request is given its own virtual HTTP "server" by Amazon API Gateway. AWS handles the horizontal scaling automatically, so no requests ever time out. Each request then calls your application from a memory cache in AWS Lambda and returns the response via Python's WSGI interface. ... -
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Erupt Framework
Pure Java annotation, single class file, rapid development
Pure Java annotation, single class file, rapid development of Admin management background. Does not generate any code, zero front-end code, zero CURD, automatic table creation, annotated API, custom service logic, supports all mainstream databases, supports custom pages, supports multiple data sources, provides more than 20 types of business components, more than a dozen It supports logical deletion, dynamic timing tasks, front-end and back-end separation, etc. Core technologies: Spring... -
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MMDeploy
OpenMMLab Model Deployment Framework
...It is a part of the OpenMMLab project. Models can be exported and run in several backends, and more will be compatible. All kinds of modules in the SDK can be extended, such as Transform for image processing, Net for Neural Network inference, Module for postprocessing and so on. Install and build your target backend. ONNX Runtime is a cross-platform inference and training accelerator compatible with many popular ML/DNN frameworks. Please read getting_started for the basic usage of MMDeploy. -
Lightspeed golf course management softwareFrom tee sheet management, point of sale and payment processing to marketing, automation, reporting and more—Lightspeed is built for the pro shop, restaurant, back office, beverage cart and beyond.
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towhee
Framework that is dedicated to making neural data processing
...Towhee provides out-of-the-box integration with your favorite libraries, tools, and frameworks, making development quick and easy. Towhee includes a pythonic method-chaining API for describing custom data processing pipelines. We also support schemas, making processing unstructured data as easy as handling tabular data. -
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Open Match
Flexible, extensible, and scalable video game matchmaking
...Along with implementing the core logic to generate quality matches, it also involves solving challenging problems such as handling massive player populations, effectively searching through them and concurrently processing match generation logic at scale. -
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threeddonut
3D donut. Example of frojasg1.com libraries usage
The application shows a 3D donut, that can be rotated with two sliders in both axis. It is a simple example of what can be done with frojasg1.com platform libraries: - Zoom option for components - Multi language - Dark mode option - Automatic Undo-Redo for text components, with popup menu included - Text Search/Replace window prepared to be used. - Base components for auto-completion windows. - Automatic component relocation after redimensioning a... -
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IM is a toolkit for Digital Imaging. IM is based on 4 concepts: Image Representation, Storage, Processing and Capture. The main goal of the library is to provide a simple API and abstraction of images for scientific applications.
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NOTE THAT THE SOURCE CODE AND ISSUE TRACKER HAVE NOW MOVED TO GITHUB. FIND US AT https://github.com/GateNLP/ GATE (General Architecture for Text Engineering) is an architecture, framework and development environment for developing, evaluating and embedding Human Language Technology. See http://gate.ac.uk for full details.
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Web data extraction (web data mining, web scraping) tool. It leverages well proved XML and text processing techologies in order to easely extract useful data from arbitrary web pages.
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Sasa
Sasa is a collection of organized extensions to the .NET framework.
Sasa is a set of extensions to the .NET framework for networking, generic operator handling, LINQ expression processing, parsing, and more. -
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Pytente
Uma Ferramenta Computacional para Análise e Recuperação de Patentes
O Pytente é uma solução avançada para automatizar o processo de coleta, armazenamento e tratamento de dados bibliográficos de patentes. A ferramenta foi projetada para simplificar a coleta de grandes volumes de dados em repositórios de acesso aberto. O Pytente garante o armazenamento estruturado das informações, além da validação e eliminação de registros duplicados. Dentre as diversas funcionalidades disponibilizadas pela ferramenta, destacam-se a extração personalizada de subconjuntos de... -
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SObjectizer
It's all about in-process message dispatching!
...SObjectizer allows the creation of a concurrent app as a set of agent-objects which interact with each other through asynchronous messages. It handles message dispatching and provides a working context for message processing. And allows to tune those things by supplying various ready-to-use dispatchers. -
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libCIGI
C++ Library for the Common Image Generator Interface
...Currently versions 3.0, 3.2 and 3.3 are supported as well as support for the *draft* V4.0 CIGI standard. Additional functionality to the base packet interfaces is provided through external classes so that to the packet headers have no further dependencies. Packet processing is kept simple and a couple of simple helper functions / classes are provided in CIGIGeneric.h and some other headers to support this. Helpers are provided for version interpretation and conversion of packet parameters to text. Testing is supported using the boost test framework to develop unit tests for each of the packets. ... -
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ElasticJob
Distributed scheduled job framework
...Developers only need code one time and can deploy at will. Support job sharding and high availability in distributed system. Scale out for throughput and efficiency improvement. Job processing capacity is flexible and scalable with the allocation of resources. Execute job on suitable time and assigned resources. Aggregation same job to same job executor. Append resources to newly assigned jobs dynamically. Using ElasticJob can make developers no longer worry about the non-functional requirements such as jobs scale out, so that they can focus more on business coding. -
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Emgu CV
Emgu CV is a cross platform .Net wrapper for OpenCV
Emgu CV is a cross platform .Net wrapper to the OpenCV image processing library. Allowing OpenCV functions to be called from .NET compatible languages such as C#, VB, VC++, IronPython etc. The wrapper can be compiled in Mono and run on Windows, Linux, Mac OS X, iPhone, iPad and Android devices. -
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Horovod
Distributed training framework for TensorFlow, Keras, PyTorch, etc.
...Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. Once Horovod has been configured, the same infrastructure can be used to train models with any framework, making it easy to switch between TensorFlow, PyTorch, MXNet, and future frameworks as machine learning tech stacks continue to evolve. Start scaling your model training with just a few lines of Python code. ... -
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The Related Values Processing Framework helps the integration of Process Control Data Historian Systems.
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DotWeb
Simple and easy go web micro framework
...Support STRING/JSON/JSONP/HTML format output. Integrated Mock Capability. Support custom Context. Integrated Timeout Hook. Global HTTP Error Handling. Global log processing. Support Hijack and websocket. Built-in Cache support. Built-in Session support - supports automatic switching between active and standby redis. Built-in TLS support. Support access to third-party template engines (need to implement dotweb.Renderer interface). -
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MetalPetal
A GPU accelerated image and video processing framework built on Metal
MetalPetal is an image processing framework based on Metal designed to provide real-time processing for still images and video with easy-to-use programming interfaces. This chapter covers the key concepts of MetalPetal, and will help you to get a better understanding of its design, implementation, performance implications, and best practices. A MTIImage object is a representation of an image to be processed or produced.
