mmccarty

notebooks

Jupyter notebooks

dask-ml

Scalable Machine Learning with Dask

azure-sdk-for-python

This repository is for active development of the Azure SDK for Python. For consumers of the SDK we recommend visiting our public developer docs at https://docs.microsoft.com/python/azure/ or our versioned developer docs at https://azure.github.io/azure-sdk-for-python.

cloud-ml-examples

A collection of Machine Learning examples to get started with deploying RAPIDS in the Cloud

cudf

cuDF - GPU DataFrame Library

cugraph

cuGraph - RAPIDS Graph Analytics Library

cuml

cuML - RAPIDS Machine Learning Library

cupy

NumPy & SciPy for GPU

cuxfilter

GPU accelerated cross filtering with cuDF.

dask

Parallel computing with task scheduling

dask-kubernetes

Native Kubernetes integration for Dask

deployment

distributed

A distributed task scheduler for Dask

docker

Dockerfile templates for creating RAPIDS Docker Images

docs

RAPIDS Documentation Site

fastparquet

python implementation of the parquet columnar file format.

gpu-python-tutorial

GPU Development in Python 101 tutorial

ibis

Expressive analytics in Python at any scale.

integration

RAPIDS - combined conda package & integration tests for all of RAPIDS libraries

jobs-board

Minimalist jobs board implementation

mmccarty.github.io

Repo for website: https://mikemccarty.io/

notebooks-contrib

RAPIDS Community Notebooks

numpy

The fundamental package for scientific computing with Python.

nvidia-cuda-tutorial

Nvidia contributed CUDA tutorial for Numba

pandas

Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

pytorch

Tensors and Dynamic neural networks in Python with strong GPU acceleration

rapids-notebooks

RAPIDS Sample Notebooks

scikit-learn

scikit-learn: machine learning in Python

staged-recipes

A place to submit conda recipes before they become fully fledged conda-forge feedstocks

xgboost

Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow