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Description

SHARK is a versatile and high-performance open-source library for machine learning, developed in C++. It encompasses a variety of techniques, including both linear and nonlinear optimization, kernel methods, neural networks, and more. This library serves as an essential resource for both practical applications and academic research endeavors. Built on top of Boost and CMake, SHARK is designed to be cross-platform, supporting operating systems such as Windows, Solaris, MacOS X, and Linux. It operates under the flexible GNU Lesser General Public License, allowing for broad usage and distribution. With a strong balance between flexibility, user-friendliness, and computational performance, SHARK includes a wide array of algorithms from diverse fields of machine learning and computational intelligence, facilitating easy integration and extension. Moreover, it boasts unique algorithms that, to the best of our knowledge, are not available in any other competing frameworks. This makes SHARK a particularly valuable tool for developers and researchers alike.

Description

QikkDB is a high-performance, GPU-accelerated columnar database designed to excel in complex polygon computations and large-scale data analytics. If you're managing billions of data points and require immediate insights, qikkDB is the solution you need. It is compatible with both Windows and Linux operating systems, ensuring flexibility for developers. The project employs Google Tests for its testing framework, featuring hundreds of unit tests alongside numerous integration tests to maintain robust quality. For those developing on Windows, it is advisable to use Microsoft Visual Studio 2019, with essential dependencies that include at least CUDA version 10.2, CMake 3.15 or a more recent version, vcpkg, and Boost libraries. Meanwhile, Linux developers will also require a minimum of CUDA version 10.2, CMake 3.15 or newer, and Boost for optimal operation. This software is distributed under the Apache License, Version 2.0, allowing for a wide range of usage. To simplify the installation process, users can opt for either an installation script or a Dockerfile to get qikkDB up and running seamlessly. Additionally, this versatility makes it an appealing choice for various development environments.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AWS Marketplace

Integrations

AWS Marketplace

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

SHARK

Founded

2018

Website

image.diku.dk/shark/sphinx_pages/build/html/index.html

Vendor Details

Company Name

qikkDB

Website

github.com/qikkDB

Product Features

Machine Learning

Deep Learning
ML Algorithm Library
Model Training
Natural Language Processing (NLP)
Predictive Modeling
Statistical / Mathematical Tools
Templates
Visualization

Product Features

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