PyMC's Big Adventure: Lessons Learned from the Development of Open-source Software for Probabilistic Programming

Christopher J. Fonnesbeck

Sep 30, 2018 NIPS 2018 Workshop MLOSS Submission readers: everyone
  • Abstract: The PyMC project is a team of open source developers devoted to the development of software for applied Bayesian statistics and probabilistic machine learning. Broadly, our objective is to produce Python implementations of state-of-the-art methods that can be used by a wide range of non-expert analysts, thereby democratizing probabilistic programming and putting powerful Bayesian methods in the hands of those who need them most: economists, astronomers, epidemiologists, ecologists, and more. Our current product, PyMC3, allows users to implement arbitrary probabilistic models using a high-level API that is analogous to specifying a model on a whiteboard. The first version of PyMC was created over fifteen years ago, as a solo academic project. Its original purpose was to provide an open-source alternative to WinBUGS, a proprietary single-platform software package that dominated applied Bayesian analysis at the time. PyMC2 (Patil et al. 2010), released in 2009, represented a transition to a high-performance code base, using optimized Fortran extensions, and was the first truly collaborative release, based largely on the efforts of a team of three developers. The current major release, PyMC3 (Salvatier et al. 2010), is the product of a complete re-write of the code base, moving to Theano (Bergstra et al. 2010), a deep learning framework, as the computational backend. This allowed for the next generation of Bayesian model fitting methods to be implemented, including gradient-based (Hamiltonian) Monte Carlo and variational inference. Today, PyMC3 is supported by over a dozen core developers, and is used widely to support academic and industry research and decision-making. Most of PyMC’s history was characterized by substantial contributions by a few talented but ephemeral developers. The relative success and stability that the project enjoys today is the result of the hard work of a small but enthusiastic core of users and contributors, the emergence of tools for building software communities online, namely GitHub, StackOverflow, and Slack, and a small dose of good fortune. I will share some of the lessons learned since 2003, and provide a summary of best practices and potential pitfalls associated with growing and sustaining a community-driven scientific software project.
  • TL;DR: I will share some of the lessons learned since 2003, and provide a summary of best practices and potential pitfalls associated with growing and sustaining a community-driven scientific software project.
  • Keywords: pymc, python, open source, sustainability, collaboration, probabilistic programming
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