Affiliated Projects

NumFOCUS Affiliated Projects are focused on open source data science, make meaningful use of NumFOCUS-sponsored tools, have an active community of contributors, and have a Code of Conduct, either adopted from our own or similar in spirit. Affiliated Projects are not fiscally sponsored by NumFOCUS.

We highlight affiliated projects to encourage the community to contribute to, promote, and support these open source tools!

Affiliated Projects enjoy a number of benefits. If your project is interested in becoming a NumFOCUS Affiliated Project, click here to learn more.

ArviZ

ArviZ is a Python package for exploratory analysis of Bayesian models. Includes functions for posterior analysis, sample diagnostics, model checking, and comparison.

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The goal is to provide backend-agnostic tools for diagnostics and visualizations of Bayesian inference in Python, by first converting inference data into xarray objects. See here for more on xarray and ArviZ.

Chainer

Chainer is a powerful, flexible and intuitive deep learning framework. Chainer supports CUDA computation. It only requires a few lines of code to leverage a GPU. It also runs on multiple GPUs with little effort.

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Chainer supports various network architectures including feed-forward nets, convnets, recurrent nets and recursive nets. It also supports per-batch architectures. Forward computation can include any control flow statements of Python without lacking the ability of backpropagation. It makes code intuitive and easy to debug.

Conda

Conda is an open source package management system and environment management system for installing multiple versions of software packages and their dependencies and switching easily between them.

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It works on Linux, OS X and Windows, and was created for Python programs but can package and distribute any software.

CuPy

CuPy is an open-source matrix library accelerated with NVIDIA CUDA. It also uses CUDA-related libraries including cuBLAS, cuDNN, cuRand, cuSolver, cuSPARSE, cuFFT and NCCL to make full use of the GPU architecture.
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CuPy’s interface is highly compatible with NumPy; in most cases it can be used as a drop-in replacement. All you need to do is just replace numpy with cupy in your Python code. It supports various methods, indexing, data types, broadcasting and more.