Displaying 1 to 6 from 6 results

swoft - ⚡️ Modern High performance AOP and Coroutine PHP Framework, base on Swoole

  •    PHP

The first high-performance PHP coroutine full-stack componentization framework based on Swoole native coroutine, built-in coroutine web server and commonly-used coroutine client, resident memory, which has no dependency on PHP-FPM, asynchronous non-blocking IO implementation, similar to synchronous client style of writing to achieve the use of asynchronous clients, without complex asynchronous callback, no tedious yield, similar Go language coroutines, flexible annotations framework, a powerful global dependency injection container base on annotations, and great service governance , flexible and powerful AOP, PSR specification implementation, etc., could be used to build high-performance Web systems, APIs, middleware, basic services, microservice and so on. Chinese Document English Document Not yet, please help us write it.

spark-nlp - Natural Language Understanding Library for Apache Spark.

  •    Jupyter

John Snow Labs Spark-NLP is a natural language processing library built on top of Apache Spark ML. It provides simple, performant & accurate NLP annotations for machine learning pipelines, that scale easily in a distributed environment. This library has been uploaded to the spark-packages repository https://spark-packages.org/package/JohnSnowLabs/spark-nlp .

neurocurator - Desktop application (GUI) to perform systematic and collaborative curation of neuroscientific literature

  •    Python

Desktop application to perform systematic and collaborative curation of neuroscientific literature. This is a Graphical User Interface (GUI) for the Python package NeuroAnnotation Toolbox (NAT).

swoft-component - 🗂 This is a Repository of centralized management of all Swoft components

  •    PHP

This reposiory is used to manage all swoft components. All components will NOT be modified in the original repository of component, SHOULD ALWAYS be modified in this repository, also commit and push to this repository, and use git subtree push to sync changes to the original repository of component.

VIAME - Video and Image Analytics for Marine Environments

  •    C++

VIAME is a computer vision application designed for do-it-yourself artificial intelligence including object detection, object tracking, image/video annotation, image/video search, image mosaicing, size measurement, rapid model generation, and tools for the evaluation of different algorithms. Originally targetting marine species analytics, it now contains many common algorithms and libraries, and is also useful as a generic computer vision toolkit. It contains a number of standalone tools for accomplishing the above, a pipeline framework which can connect C/C++, python, and matlab nodes together in a multi-threaded fashion, and, lastly, multiple algorithms resting on top of the pipeline infrastructure. Both a desktop and web version exist for deployments in different types of environments. For a full installation guide and description of the various flavors of VIAME, see the quick-start guide, above. The desktop version is provided as either a .msi, .zip or .tar file. Alternatively, docker files are available for both VIAME Desktop and Web (below). A sample instance of VIAME Web is also online, hosted at viame.kitware.com. For desktop installs, extract the binaries (or use the msi Windows installation wizard) and place them in a directory of your choosing, for example /opt/noaa/viame on Linux or C:\Program Files\VIAME on Windows. If using packages built with GPU support, make sure to have sufficient video drivers installed, version 451.82 or higher. The best way to install drivers depends on your operating system, see below. Lastly, run through some of the examples to validate the installation. The binaries are quite large, in terms of disk space, due to the inclusion of multiple default model files and programs, but if just building your desired features from source (e.g. for embedded apps) they are much smaller.

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