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cuDF - GPU DataFrame Library

cuGraph - RAPIDS Graph Analytics Library

cuML - RAPIDS Machine Learning Library

NumPy & SciPy for GPU

cuVS - a library for vector search and clustering on the GPU

Parallel computing with task scheduling

Jupyter notebooks

Contributions to the Intake blog

Land of Lisp

Dynamic Scheduling System for the GBT

Repo for website: https://mikemccarty.io/

RAFT contains fundamental widely-used algorithms and primitives for machine learning and information retrieval. The algorithms are CUDA-accelerated and form building blocks for more easily writing high performance applications.

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Scalable Machine Learning with Dask

GPU accelerated decision optimization

A logging interface for RAPIDS built on spdlog

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GPU Accelerated Backend for NetworkX

KvikIO - High Performance File IO

Utilities for Dask and CUDA interactions

GPU accelerated cross filtering with cuDF.

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RAPIDS Memory Manager

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User tools for RAPIDS GitHub Actions

scikit-learn: machine learning in Python

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The fundamental package for scientific computing with Python.

Dataframes powered by a multithreaded, vectorized query engine, written in Rust

NVIDIA curated collection of educational resources related to general purpose GPU programming.

Conda package download data

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RAPIDS Documentation Site

CUDA-accelerated GIS and spatiotemporal algorithms

rapids.ai web site

GPU Development in Python 101 tutorial

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Dynamic Scheduling System for the GBT - Server Code

Tensors and Dynamic neural networks in Python with strong GPU acceleration

Nvidia contributed CUDA tutorial for Numba

A collection of Machine Learning examples to get started with deploying RAPIDS in the Cloud

A place to submit conda recipes before they become fully fledged conda-forge feedstocks

Expressive analytics in Python at any scale.

python implementation of the parquet columnar file format.

RAPIDS Sample Notebooks

This repository is for active development of the Azure SDK for Python. For consumers of the SDK we recommend visiting our public developer docs at https://docs.microsoft.com/python/azure/ or our versioned developer docs at https://azure.github.io/azure-sdk-for-python.

Dockerfile templates for creating RAPIDS Docker Images

RAPIDS Community Notebooks

Native Kubernetes integration for Dask

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

A distributed task scheduler for Dask

Tools to provide a control plane for managing the lifecycle of Dask clusters.

RAPIDS GPU-BDB

RAPIDS - combined conda package & integration tests for all of RAPIDS libraries

A conda-smithy repository for cuda-python.

Minimalist jobs board implementation

Python bindings for UCX

N-D labeled arrays and datasets in Python

Interface for using cupy in xarray, providing convenience accessors.

The NumPy home page

Deep Learning for Satellite Imagery

The Legion Parallel Programming System

CUDA Python Low-level Bindings

This repository consists for gpu bootcamp material for HPC and AI

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Example projects for getting started with Saturn Cloud

Dask tutorial

Data Analysis Baseline Library

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GBT Vegas Realtime Data Display - Server

Cluster for the Dask Tutorial.

Kubernetes application example tutorials

Python API client for LibraryH3lp.

Repository for the Astropy core package

Integrates Zoomdata visualizations and data into Jupyter notebooks

https://golang.org/doc/articles/wiki/

Ruby-language bindings for the LibraryH3lp API.

GBT Vegas Realtime Data Display - User Interface

A demo of how to use Vagrant to stand up a database server

Data Mining Homework Assignment 3 - Decision Tree Classifier

A virtual machine for Ruby on Rails core development

User interface for the GBT Sensitivity calculator

Dynamic Scheduling System for the GBT - Scheduler's Interface

Library a la Carte, Phoenix Edition