cuDF - GPU DataFrame Library

PyGraphistry is a Python library to quickly load, shape, embed, and explore big graphs with the GPU-accelerated Graphistry visual graph analyzer

BlazingSQL is a lightweight, GPU accelerated, SQL engine for Python. Built on RAPIDS cuDF.

🚚 Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark

Lightweight and extensible compatibility layer between dataframe libraries!

GPU accelerated cross filtering with cuDF.

🚕 A spreadsheet-like data preparation web app that works over Optimus (Pandas, Dask, cuDF, Dask-cuDF, Spark and Vaex)

머신러닝/딥러닝(PyTorch, TensorFlow) 전용 도커입니다. 한글 폰트, 한글 자연어처리 패키지(konlpy), 형태소 분석기, Timezone 등의 설정 등을 추가 하였습니다.

Rapid large-scale fractional differencing with NVIDIA RAPIDS and GPU to minimize memory loss while making a time series stationary. 6x-400x speed up over CPU implementation.

Awesome list of alternative dataframe libraries in Python.

For when your data won't fit in your dataframe

Lightweight, engine-agnostic dataframe validation

H.E.I.M.D.A.L.L looks at fleet telemetry and gives you natural-language insights. GPU data loading (cuDF), local LLM inference (Gemma 2), and production NIM on GKE. Open the notebooks, run cells, get answers! Quick start should not take longer than 10 minutes and the T4 path is completely free!

Rapidsai_Machine_learnring_on_GPU

Python library to process and classify remote sensing imagery by means of GPUs and ML.

Unlimited Data-Science Benchmarks for Numeric, Tabular and Graph Workloads

The Incredible RAPIDS: a curated list of tutorials, papers, projects, communities and more relating to RAPIDS.

Building NVIDIA's RAPIDS (cuDF, cuML...) in Arch Linux

A simple demo of cuDF which is a RAPIDS GPU-Accelerated Dataframe Library!

jiboia-gpu is a Python package to normalize and optimize DataFrames automatically efficiently using the Nvidia GPU in the RAPIDS ecosystem.