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.
DiRe accelerated by PyTorch, PyKeOps and cuVS
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
Graph embedding for influence maximization in networks
Objective of the repository to play around with different tools (keepsake, MLflow etc) with basic projects.
Scaleable 2-stage Credit Risk pipeline using XGBoost. Analyzes 28M+ Lending Club loan records (2007-2018) to automate underwriting and predict default probability.
Reproducibility suite for arXiv:2503.03156
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
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