Avalanche is an End-to-End Continual Learning Library (now part of the PyTorch Ecosystem!) powered by ContinualAI with the unique goal of providing a shared and collaborative open-source (MIT licensed) codebase for fast prototyping, training and reproducible evaluation of continual learning algorithms. Learning continually from a non-stationary stream of experiences is a challenging task, especially for deep neural networks, where simply fine-tuning a pre-trained model on the new available data often incurs in catastrophic forgetting of previously learned knowledge. Check out how your code changes when you start using Avalanche! Avalanche is the first experiment of a End-to-end Library for reproducible continual learning research & development where you can find benchmarks, algorithms, evaluation metrics and much more, in the same place. Do you want to start using Avalanche right now? Check out the complete "From Zero to Hero" tutorial runnable on google colab!

Albert Einstein once said that "wisdom is not a product of schooling, but the lifelong attempt to acquire it." Centuries of human progress have been built on our brains' ability to continually acquire, fine-tune and transfer knowledge and skills. Such continual learning however remains a long-standing challenge in machine learning (ML), where the ongoing acquisition of incrementally available information from non-stationary data often leads to catastrophic forgetting problems. Gradient-based deep architectures have spurred the development of continual learning in recent years, but continual learning algorithms are often designed and implemented from scratch with different assumptions, settings, and benchmarks, making them difficult to compare, port, or reproduce. Now, a research and development team from ContinualAI with researchers from KU Leuven, ByteDance AI Lab, University of California, New York University and other institutions has proposed Avalanche, an end-to-end library for continual learning based on PyTorch.

In addition to efficient statistical estimators of a treatment's effect, successful application of causal inference requires specifying assumptions about the mechanisms underlying observed data and testing whether they are valid, and to what extent. However, most libraries for causal inference focus only on the task of providing powerful statistical estimators. We describe DoWhy, an open-source Python library that is built with causal assumptions as its first-class citizens, based on the formal framework of causal graphs to specify and test causal assumptions. DoWhy presents an API for the four steps common to any causal analysis---1) modeling the data using a causal graph and structural assumptions, 2) identifying whether the desired effect is estimable under the causal model, 3) estimating the effect using statistical estimators, and finally 4) refuting the obtained estimate through robustness checks and sensitivity analyses. In particular, DoWhy implements a number of robustness checks including placebo tests, bootstrap tests, and tests for unoberved confounding. DoWhy is an extensible library that supports interoperability with other implementations, such as EconML and CausalML for the the estimation step. The library is available at https://github.com/microsoft/dowhy