Search Results for "processing" - Page 3
Sort By:
Showing 244 open source projects for "processing"
View related business solutions-
Haystack is a modern, engaging, and intuitive intranet platform that employees actually use.With customizable iOS and Android mobile apps, Slack and Microsoft Teams integrations, and an intuitive design employees love, Haystack brings an outstanding digital employee experience to your entire workforce, no matter where their work takes them.
-
SOCRadar Extended Threat Intelligence PlatformSOCRadar Extended Threat Intelligence, a natively single platform from its inception that proactively identifies and analyzes cyber threats with contextual and actionable intelligence.
-
1
django-rest-framework-gis
Geographic add-ons for Django REST Framework
...This field handles GeoDjango geometry fields, providing custom to_native and from_native methods for GeoJSON input/output. While precision and remove_duplicates are designed to reduce the byte size of the API response, they will also increase the processing time required to render the response. This will likely be negligible for small GeoJSON responses but may become an issue for large responses. The primary key of the model (usually the "id" attribute) is automatically used as the id field of each GeoJSON Feature Object. The GeoJSON specification allows a feature to contain a boundingbox of a feature. ... -
2
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. ... -
3
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. ... -
4
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,... -
Striven | All In One Business Management SoftwareStriven is the all-in-one business management software that lowers your costs, improves your operations, and makes work easier. Make your company’s data coherent, connected, and relevant.
-
5
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. -
6
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. ... -
7
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. -
8
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(). ... -
9
MegEngine
Easy-to-use deep learning framework with 3 key features
MegEngine is a fast, scalable and easy-to-use deep learning framework with 3 key features. You can represent quantization/dynamic shape/image pre-processing and even derivation in one model. After training, just put everything into your model and inference it on any platform at ease. Speed and precision problems won't bother you anymore due to the same core inside. In training, GPU memory usage could go down to one-third at the cost of only one additional line, which enables the DTR algorithm. ... -
ACI Learning: Internal Audit, Cybersecurity, and IT TrainingTraditional training doesn't equip employees with the practical skills they need to drive business success. ACI Learning provides hands-on IT and cybersecurity training designed to build real-world, on-the-job skills. Our outcome-based programs empower employees with certification prep, industry-recognized credentials, and flexible learning options. With expert-led video training, labs, and scalable solutions, we help businesses, individuals, governments, and academic institutions develop a skilled workforce, align with business goals, and stay ahead in a rapidly evolving digital world.
-
10
Livewire Best Practices
Laravel Livewire Best Practices
...That's because Livewire serializes/deserializes your component's payload with each request to the server to share the state between the frontend & backend. If you need to work on objects, you can create them inside a method or computed property, and then return the result of the processing. -
11
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.
-
12
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... -
13
TorchQuantum
A PyTorch-based framework for Quantum Classical Simulation
...Researchers on quantum algorithm design, parameterized quantum circuit training, quantum optimal control, quantum machine learning, and quantum neural networks. Dynamic computation graph, automatic gradient computation, fast GPU support, batch model terrorized processing. -
14
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.
-
15
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. -
16
Quil
Main repo, Quil source code
Quil blends Clojure's expressive functional programming with Processing’s creative graphics and animation capabilities, offering a DSL for drawing and interactive visuals in a dynamic, artistic way. In one hand Quil holds Processing, a carefully crafted API for making drawing and animation extremely easy to get your biscuit-loving chops around. In the other she clutches Clojure, an interlocking suite of exquisite language abstractions forged by an army of hammocks and delicately wrapped in flowing silky parens of un-braided joy. In one swift, skilled motion, Quil throws them both high into the air. ... -
17
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... -
18
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.
-
19
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. ... -
20
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. -
21
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. -
22
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. -
23
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. -
24
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. -
25
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. ...
