Mila’s mission is to be a global pole for scientific advances that inspires innovation and the progress of AI for the benefit of all. As part of this mission, Mila recognizes the significant potential of AI and the importance of making research more open, interdisciplinary and accessible.
Explore a selection of notable open source software efforts led by or co-developed with Mila researchers over the years.
Theano, one of the earliest programming frameworks for deep learning, originated at Mila and Université de Montréal. Theano is a Python library and optimizing compiler for manipulating and evaluating mathematical expressions. The development of Theano was completed in 2017.
Myia (follow-up to Theano) is a differentiable programming language capable of supporting large scale high-performance computations (e.g. linear algebra) and their gradients.
Minimalistic Gridworld Environment (MiniGrid) gym is maintained by the Farama Foundation.
BabyAI is a testbed for training agents to understand and execute language commands.
SpeechBrain is an open-source, general-purpose PyTorch speech processing toolkit designed to make the research and development of neural speech processing technologies easier by being simple, flexible, user-friendly, and well-rounded.
DEAP is a novel evolutionary computation framework for rapid prototyping and testing of ideas, seeking to make algorithms explicit and data structures transparent.
TorchDrug is an open-source machine learning platform for drug discovery, covering techniques ranging from graph machine learning, deep generative models to reinforcement learning.
Available as part of TorchDrug, TorchProtein is a ML library for protein science, providing representation learning models for both protein sequences and structures, as well as fundamental protein tasks like function prediction and structure prediction.
A collaboration between Mila and IBM, Oríon is a black-box function optimization library with a key focus on usability and integrability for its users.
Ivadomed is an integrated framework for medical image analysis with deep learning based on PyTorch.
A research framework for fast prototyping of reinforcement learning algorithms. Dopamine was co-developed by Professor Marc G. Bellemare at Google.
ALE is a reinforcement learning benchmark and a framework allowing researchers to develop AI agents for Atari 2600 games. It continues to be maintained by Mila researchers.
Mila PhD student Scott Fujimoto, co-supervised by Doina Precup and David Meger, holds the open source code for TD3, one of the best performing current deep reinforcement learning methods.
The AxonDeepSeg framework is a segmentation software for microscopy data of nerve fibers based on a convolutional neural network.
MilaBench is a repository of training benchmarks.
Ptera allows you to instrument code from the outside by specifying a set of variables to watch in an arbitrary Python call graph and manipulate a stream of their values.
A software framework to unify continual learning research
Paperoni allows users to search for scientific papers from the command line.
Jurigged lets you update your code while it runs.
HTML representation for Python objects.
Academic Torrents is scalable platform using BitTorrent which distributes the cost of hosting data to prevent the rise and fall of dataset hosting providers and the erasure of the data they host.
Chester is a free and accessible prototype system that can be used by medical professionals to understand the reality of deep learning tools for chest X-ray diagnostics.