Avalanche
GitHubAPI DocCL-BaselinesAvalanche-RLOnline CL Repo
Avalanche - v0.6.0
Avalanche - v0.6.0
  • Avalanche: an End-to-End Library for Continual Learning
  • 📌Getting Started
    • Introduction
    • Current Release
    • How to Install
    • Learn Avalanche in 5 Minutes
  • 📙From Zero to Hero Tutorial
    • Introduction
    • Models
    • Benchmarks
    • Training
    • Evaluation
    • Loggers
    • Putting All Together
    • Extending Avalanche
    • Contribute to Avalanche
  • How-Tos
    • AvalancheDataset
      • avalanche-datasets
      • avalanche-transformations
    • Dataloaders, Buffers, and Replay
    • Save and load checkpoints
  • 📝Examples
    • Models
    • Benchmarks
    • Training
    • Evaluation
    • Loggers
  • 💻Code Documentation
    • Avalanche API
  • How to Contribute
    • Guidelines
  • ❓Questions and Issues
    • Ask Your Question
    • Add Your Issue
    • Request a Feature
    • Give Feedback
    • FAQ
  • 👪About Us
    • The People
    • Join Us!
    • Slack
    • Email
    • Twitter
Powered by GitBook
On this page

Was this helpful?

Export as PDF
  1. From Zero to Hero Tutorial

Introduction

Understand the Avalanche Package Structure

PreviousLearn Avalanche in 5 MinutesNextModels

Was this helpful?

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 ).

  • 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 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 ). 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 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!

🤝 Run it on Google Colab

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 guide.

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

📙
torchvision
Tensorboard
torchvision.models
Tensorboard
How to Install
Open In Colab