Here are
16 public repositories
matching this topic...
cuDF - GPU DataFrame Library
BlazingSQL is a lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF.
Open source guides/codes for mastering deep learning to deploying deep learning in production in PyTorch, Python, C++ and more.
Python client for OmniSci GPU-accelerated SQL engine and analytics platform
Updated
Jul 16, 2020
Python
GPU accelerated cross filtering with cuDF.
Updated
Jul 21, 2020
Jupyter Notebook
Rapid large-scale fractional differencing with RAPIDS to minimize memory loss while making a time series stationary. 6x-400x speed up over CPU implementation.
Updated
Oct 4, 2019
Jupyter Notebook
Template repository for a Python 3-based (data) science project with GPU acceleration using NVIDIA RAPIDS libraries.
Updated
Jul 1, 2020
Dockerfile
GPU accelerated Jupyter dashboard.
Updated
Jun 18, 2020
Jupyter Notebook
The Incredible RAPIDS: a curated list of tutorials, papers, projects, communities and more relating to RAPIDS.
Self-contained demo Notebooks.
Updated
May 4, 2020
Jupyter Notebook
RAPIDS data science. No setup required.
Updated
Jun 29, 2020
Jupyter Notebook
A simple demo of cuDF which is a RAPIDS GPU-Accelerated Dataframe Library!
Updated
Feb 8, 2020
Jupyter Notebook
Easily build a customizable, ready to use Docker Image for some AI/Data Science/Deep Learning experiments.
Updated
Mar 13, 2020
Shell
Example notebooks using BlazingSQL with the RAPIDS AI ecoystem.
Updated
Apr 3, 2020
Jupyter Notebook
Building NVIDIA's RAPIDS (cuDF, cuML...) in Arch Linux
Updated
Jul 4, 2020
Jupyter Notebook
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