Introduction
Understand the Avalanche Package Structure
Welcome to the "Introduction" tutorial of the "From Zero to Hero" series. We will start our journey by taking a quick look at the Avalanche main modules to understand its general architecture.
As hinted in the getting started introduction Avalanche is organized in five main modules:
Benchmarks: This module maintains a uniform API for data handling: mostly generating a stream of data from one or more datasets. It contains all the major CL benchmarks (similar to what has been done for torchvision).Training: This module provides all the necessary utilities concerning model training. This includes simple and efficient ways of implement new continual learning strategies as well as a set pre-implemented CL baselines and state-of-the-art algorithms you will be able to use for comparison!Evaluation: This module provides all the utilities and metrics that can help evaluate a CL algorithm with respect to all the factors we believe to be important for a continually learning system. It also includes advanced logging and plotting features, including native Tensorboard support.Models: In this module you'll find several model architectures and pre-trained models that can be used for your continual learning experiment (similar to what has been done in torchvision.models). Furthermore, we provide everything you need to implement architectural strategies, task-aware models, and dynamic model expansion.Logging: It includes advanced logging and plotting features, including native stdout, file and Tensorboard support (How cool it is to have a complete, interactive dashboard, tracking your experiment metrics in real-time with a single line of code?)
In this series of tutorials, you'll get the chance to learn in-depth all the features offered by each module and sub-module of Avalanche, how to put them together and how to master Avalanche, for a stress-free continual learning prototyping experience!
🤝 Run it on Google Colab
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