An empirical analysis of Python packages on PyPI and biomedical journals in 2023, with a focus on the quality of dependency declarations.

It has never been easier to write testable and type-safe Python.

Do you regularly wonder where your time went during a week worth of work? We present work-dAIgest, a tool developed during Tweag's GenAI hackathon that aims to use data from standard workplace tools such as GitHub, Google Calendar, ... to create a summary of your work week (or any other time period), powered by open-source large language models (LLM).

FawltyDeps 0.13.0 introduces a brand new mapping strategy. In this post, we'll delve into the mechanics of how dependencies and imports are matched, as well as how you can leverage these new features to boost your Python dependency management workflow.

How to build your Python monorepo from scratch: a simple CI

Commandeering techniques from richly typed, functional languages into Python for fun and profit. In this episode: Testing strategies.

To allow innovation in medical imaging with AI, we need efficient and affordable ways to store and compute at scale.

How to build your Python monorepo from scratch: structure and tooling

FawltyDeps is a new tool to help you identify undeclared and unused dependencies in your Python code, making your projects leaner and more reproducible.

Commandeering techniques from richly typed, functional languages into Python for fun and profit. In this episode: Typeclasses and continuation-passing style.

How to tap into the power of reinforcement learning while specifying and executing open games in Haskell.

Commandeering techniques from richly typed, functional languages into Python for fun and profit. In this episode: Gradual typing and algebraic data types.

How to get reproducible development environments for probabilistic programming packages such as PyMC3, Theano or TensorFlow using Nix.

A discussion and benchmark of an alternative integrator for Hamiltonian Monte Carlo.

In the final post of Tweag's four-part series, we discuss Replica Exchange, a powerful MCMC algorithm designed to improve sampling from multimodal distributions. An illustrative example and, as always, an interactive Python notebook with easy-to-modify code lead to an intuitive understanding and invite experimentation.

Learn how to use Nix to create highly reproducible Python builds that are aware of native dependencies.

Learn about Hamiltonian Monte Carlo, and how to implement it from scratch.

In this second post of Tweag's four-part series, we discuss Gibbs sampling, an important MCMC-related algorithm which can be advantageous when sampling from multivariate distributions. Two different examples and, again, an interactive Python notebook illustrate use cases and the issue of heavily correlated samples.

In this first post of Tweag's four-part series on Markov chain Monte Carlo sampling algorithms, you will learn about why and when to use them and the theoretical underpinnings of this powerful class of sampling methods. We discuss the famous Metropolis-Hastings algorithm and give an intuition on the choice of its free parameters. Interactive Python notebooks invite you to play around with MCMC yourself and thus deepen your understanding of the Metropolis-Hastings algorithm.