Avalanche is an open-source, end-to-end continual learning library developed by ContinualAI to accelerate research and development in continual learning. Built on PyTorch, Avalanche provides a comprehensive framework for prototyping, training, and evaluating continual learning algorithms, making it easier for researchers and practitioners to advance their work in the field.
For an in-depth overview of Avalanche, check out the official research paper.
Key Features of Avalanche
- Benchmarks: Access a variety of continual learning benchmarks through a consistent API for streamlined data handling.
- Training: Leverage pre-implemented baselines and state-of-the-art algorithms for quick prototyping and comparison of continual learning strategies.
- Evaluation: Use built-in tools and metrics to assess algorithms across various metrics crucial to continual learning.
- Models: A range of model architectures and pre-trained models suited for continual learning experiments.
- Logging: Advanced logging and plotting tools, including native TensorBoard support, for real-time experiment tracking.
By using Avalanche, continual learning researchers and developers can:
- Write less code and prototype faster while reducing errors.
- Improve reproducibility, modularity, and reusability of their work.
- Increase code efficiency, scalability, and portability.
- Amplify the impact and usability of their research products.
Get Started with Avalanche
To start exploring Avalanche, visit the official website for installation guides, tutorials, and comprehensive documentation. Whether you’re looking for continual learning baselines or a unified framework for your own research, Avalanche is designed to support and streamline your continual learning projects.
