avalanche - Comprehensive Library for Prototyping and Evaluating Continual Learning in PyTorch

Avalanche: An Introduction to Continual Learning with Ease

Avalanche is a comprehensive library aimed at powering continual learning experiments using the strength of the PyTorch framework. Developed within the ContinualAI organization, its main focus is on providing an open-source platform for rapid prototyping, efficient training, and reproducible evaluation of continual learning algorithms.

Avalanche is designed to assist researchers in minimizing coding errors, enhancing experiment reproducibility, and maximizing the impact of their research. It boasts a user-friendly approach that lessens the learning curve and speeds up the experimentation process in the continual learning field.

Key Features of Avalanche

Ease of Use: With Avalanche, users write less code, prototype faster, and minimize errors. This accelerates research and development in continual learning.
Reproducibility: It improves the reproducibility of experiments, contributing to scientific publishing standards.
Efficiency: Avalanche optimizes code efficiency, supports scalability, and ensures compatibility with various platforms.
Impactful Research: By using Avalanche, researchers can increase the usability and impact of their research products, making it accessible to the broader machine learning community.

Core Modules

Avalanche is structured into four main modules:

Benchmarks: Provides a standard interface for data handling, generating data streams from datasets. It includes all major continual learning benchmarks, facilitating easier integration into experiments.
Training: Offers essential tools for model training. Users can easily implement new learning strategies or use pre-implemented continual learning baselines and state-of-the-art algorithms.
Evaluation: Contains tools to evaluate continual learning algorithms. This module focuses on metrics that are crucial for understanding the performance of a continually learning system. It supports advanced logging and visualization features, such as Tensorboard integration.
Models: Supplies utilities for implementing expandable models and includes pre-trained models for continual learning experiments.
Logging: Features advanced logging and plotting capabilities, with support for outputs to the console, file, and Tensorboard, enabling users to track experiment metrics efficiently.

Getting Started with Avalanche

Avalanche is continually evolving, backed by the active and supportive ContinualAI community. The library offers various resources to help new users get started quickly:

Getting Started Guide: A comprehensive guide that introduces users to the basics of Avalanche and how to incorporate it into their projects.
Examples & Snippets: A collection of code samples that users can integrate into their experiments to learn by doing.
Detailed Tutorials: An in-depth tutorial that covers all critical aspects of Avalanche.

Quick Example

Here is a simple example of how to set up a continual learning experiment with Avalanche:

import torch
from torch.nn import CrossEntropyLoss
from torch.optim import SGD
from avalanche.benchmarks.classic import PermutedMNIST
from avalanche.models import SimpleMLP
from avalanche.training import Naive

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = SimpleMLP(num_classes=10)

perm_mnist = PermutedMNIST(n_experiences=3)
train_stream = perm_mnist.train_stream
test_stream = perm_mnist.test_stream

optimizer = SGD(model.parameters(), lr=0.001, momentum=0.9)
criterion = CrossEntropyLoss()

cl_strategy = Naive(
    model, optimizer, criterion, train_mb_size=32, train_epochs=2,
    eval_mb_size=32, device=device)

results = []
for train_exp in train_stream:
    cl_strategy.train(train_exp)
    results.append(cl_strategy.eval(test_stream))

How to Get Involved

Avalanche is maintained by the ContinualAI Lab, a leading open-source collaborative project focusing on continual learning. The lab encourages community participation and offers avenues to ask questions, report issues, or suggest new features.

Those interested in contributing to Avalanche or learning more about continuous learning research are welcome to explore membership opportunities with the ContinualAI Lab. More information about the lab's activities and team can be found on their official website.

Avalanche offers a unique opportunity for researchers and developers to advance their understanding and implementation of continual learning in machine learning, providing a platform that is both powerful and easy to use.