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

This repository consists for gpu bootcamp material for HPC and AI

A full pipeline AutoML tool for tabular data

Merlin Models is a collection of deep learning recommender system model reference implementations

Awesome list of alternative dataframe libraries in Python.

This container is no longer supported, and has been deprecated in favor of: https://github.com/joehoeller/NVIDIA-GPU-Tensor-Core-Accelerator-PyTorch-OpenCV

Colab notebooks exploring different Machine Learning topics.

A Kedro plugin that provides pandas dropin replacements for the pandas datasets (e.g modin and cuDF)

Framework for computing Machine Learning algorithms in Python using Dask and RAPIDS AI.

Comparison of Dataframe libraries for parallel processing of large tabular files on CPU and GPU.

This repositorty will contain the code and slides for PyBay2020 talk: Scalable Hyper-parameter Optimization using RAPIDS and AWS

Multi-Objective Recommender System

Objective of the repository to play around with different tools (keepsake, MLflow etc) with basic projects.

This repository demonstrates how to significantly accelerate Apache Spark 3 workloads using NVIDIA GPUs with the [RAPIDS Accelerator for Apache Spark](https://nvidia.github.io/spark-rapids/). It showcases real-world use cases in ETL processing, Spark SQL, ML pipelines, and XGBoost training

Running KNN algorithm much faster on GPU for free using RAPIDS packages like cuML and cuDF

combination of EvalML with Rapids for the WiDS 2021 competition

Accelerated vector search using RAPIDS cuVS.

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