24 September 2024
Python Packaging in the Real World: Biomedical projects vs. PyPI
An empirical analysis of Python packages on PyPI and biomedical journals in 2023, with a focus on the quality of dependency declarations.
python
6 June 2024
Safe composable Python
It has never been easier to write testable and type-safe Python.
21 May 2024
LLM-based work summaries with work-dAIgest
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).
21 September 2023
Behind the scenes with FawltyDeps v0.13.0: Matching imports with dependencies
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.
13 July 2023
Python Monorepo: an Example. Part 2: A Simple CI
How to build your Python monorepo from scratch: a simple CI
25 May 2023
Functional Python, Part III: The Ghost in the Machine
Commandeering techniques from richly typed, functional languages into Python for fun and profit. In this episode: Testing strategies.
20 April 2023
Processing medical images at scale on the cloud
To allow innovation in medical imaging with AI, we need efficient and affordable ways to store and compute at scale.
4 April 2023
Python Monorepo: an Example. Part 1: Structure and Tooling
How to build your Python monorepo from scratch: structure and tooling
14 March 2023
Announcing FawltyDeps - a dependency checker for your Python code
FawltyDeps is a new tool to help you identify undeclared and unused dependencies in your Python code, making your projects leaner and more reproducible.
19 January 2023
Functional Python, Part II: Dial M for Monoid
Commandeering techniques from richly typed, functional languages into Python for fun and profit. In this episode: Typeclasses and continuation-passing style.
10 November 2022
Reinforcement Learning for Open Games
How to tap into the power of reinforcement learning while specifying and executing open games in Haskell.
8 September 2022
Functional Python, Part I: Typopædia Pythonica
Commandeering techniques from richly typed, functional languages into Python for fun and profit. In this episode: Gradual typing and algebraic data types.
26 May 2022
Reproducible probabilistic programming environments
How to get reproducible development environments for probabilistic programming packages such as PyMC3, Theano or TensorFlow using Nix.
30 September 2021
A higher-order integrator for Hamiltonian Monte Carlo
A discussion and benchmark of an alternative integrator for Hamiltonian Monte Carlo.
28 October 2020
Introduction to Markov chain Monte Carlo (MCMC) Sampling, Part 4: Replica Exchange
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.
12 August 2020
Developing Python with Poetry & Poetry2nix: Reproducible flexible Python environments
Learn how to use Nix to create highly reproducible Python builds that are aware of native dependencies.
6 August 2020
Introduction to Markov chain Monte Carlo (MCMC) Sampling, Part 3: Hamiltonian Monte Carlo
Learn about Hamiltonian Monte Carlo, and how to implement it from scratch.
9 January 2020
Introduction to Markov chain Monte Carlo (MCMC) Sampling, Part 2: Gibbs Sampling
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.
25 October 2019
Markov chain Monte Carlo (MCMC) Sampling, Part 1: The Basics
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.