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?)

Avalanche Main Modules and Sub-Modules

Avalanche
├── Benchmarks
│   ├── Classic
│   ├── Datasets
│   ├── Generators
│   ├── Scenarios
│   └── Utils
├── Evaluation
│   ├── Metrics
│   ├── Tensorboard
|   └── Utils
├── Training
│   ├── Strategies
│   ├── Plugins
|   └── Utils
├── Models
└── Loggers

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!

In the following tutorials we will assume you have already installed Avalanche on your computer or server. If you haven't yet, check out how you can do it following our How to Install guide.

🤝 Run it on Google Colab

You can run this chapter and play with it on Google Colaboratory: