Gpu-computing Libraries

5 Components & Libraries

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Gpu-computing Libraries

A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.

Install CatBoost by following the guide for the Next you may want to investigate: If you cannot open documentation in your browser try adding yastatic.net and yastat.net to the list of allowed domain…

General purpose GPU compute framework built on Vulkan to support 1000s of cross vendor graphics cards (AMD, Qualcomm, NVIDIA & friends). Blazing fast, mobile-enabled, asynchronous and optimized for advanced GPU data processing usecases. Backed by the Linux Foundation.

Below you can find a GPU multiplication example using the C++ and Python Kompute interfaces. You are able to try out the interactive Colab Notebooks which allow you to use a free GPU. The available e…

A framework for high-performance medical image processing, neural network inference and visualization

FAST is an open-source framework with the main goal of making it easier to do high-performance processing, neural network inference, and visualization of medical images utilizing multi-core CPUs and …

A lightweight (serverless) native python parallel processing framework based on simple decorators and call graphs.

With Entangle you can run simple, hardware parallelized code with conditional logic that looks like this. or train two AI models in parallel using tensorflow container utilizing dedicated CPU and GPU…

System monitoring program for Raspberry pi single board computers written in Python 3.

Latest version 2.4 (12-2023) The package is also archived on github and can be manually download and installed via python and setup.py Program is a python 3 package. Run in a terminal by typing rpi_t…

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differential-equations