There are 0 repository under cudf topic.
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.
:truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark
Lightweight and extensible compatibility layer between dataframe libraries!
🚕 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.
Lightweight, engine-agnostic dataframe validation
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.
Python from an HPC viewpoint, the most practical tools, and various indispensable libraries for HPC use cases.
GPU-based ML to classify Higgs boson signal from background in particle physics using RAPIDS framework
A compilation of various ML and DL models and ways to optimize the their inferences.
This project predicts the "Scariest Monster" using a dataset of 12 million entries and 106 features. Utilizing GPU-accelerated processing and the Random Forest Regressor using the Nvidia Rapids API. The goal is to minimize RMSE for accurate predictions
Códigos PYTHON. Pandas, Scikit-Learning, Rapids, Cuml, Cudf.
Supercharge Your Polars Code with RAPIDS cuDF on a GPU
Supercharge Your Pandas Code with RAPIDS cuDF & pandas accelerator mode | RAPIDS cuDF 23.10
Analysis of Bitcoin transactions with Pandas, PySpark, cuDF, cuGraph: trends, classification, and clustering.
Compared the performance of cuDF-Off vs cuDF-On
RAPIDS cuDF Instantly Accelerates pandas up to 50x on Google Colab With Demo
How to use GPU-accelerated tools to conduct data science faster, leading to more scalable, reliable, and cost-effective results.