Avalanche: an End-to-End Library for Continual Learning
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Avalanche is an End-to-End Continual Learning Library based on , born within with the goal of providing a shared and collaborative open-source (MIT licensed) codebase for fast prototyping, training and of continual learning algorithms.
Looking for continual learning baselines? In the sibling project based on Avalanche we reproduce seminal papers results you can directly use in your experiments!
Avalanche can help Continual Learning researchers and practitioners in several ways:
Write less code, prototype faster & reduce errors
Improve reproducibility, modularity and reusability
Increase code efficiency, scalability & portability
Augment impact and usability of your research products
The library is organized in five main modules:
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 modules 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.
Let's make it together 👫 a wonderful ride! 🎈
Check out how your code changes when you start using Avalanche! 👇
We know that learning a new tool may be tough at first. This is why we made Avalanche as easy as possible to learn with a set of resources that will help you along the way.
For example, you may start with our 5-minutes guide that will let you acquire the basics about Avalanche and how you can use it in your research project:
We have also prepared for you a large set of examples & snippets you can plug-in directly into your code and play with:
Having completed these two sections, you will already feel with superpowers ⚡, this is why we have also created an in-depth tutorial that will cover all the aspect of Avalanche in details and make you a true Continual Learner! 👨🎓️
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 ).
Models
: In this module you'll be able to find several model architectures and pre-trained models that can be used for your continual learning experiment (similar to what has been done in ).
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 the first experiment of a End-to-end Library for research & development where you can find benchmarks, algorithms, evaluation metrics and much more, in the same place.
If you use Avalanche in your research project, please remember to cite our JMLR-MLOSS paper . This will help us make Avalanche better known in the machine learning community, ultimately making a better tool for everyone:
you can also cite the previous workshop paper: .
Avalanche is the flagship open-source collaborative project of : a non profit research organization and the largest open community on Continual Learning for AI.
Do you have a question, do you want to report an issue or simply ask for a new feature? Check out the center. Do you want to improve Avalanche yourself? Follow these simple rules on .
The Avalanche project is maintained by the collaborative research team and used extensively by the Units of the consortium, a research network of the major continual learning stakeholders around the world.
We are always looking for new awesome members willing to join the ContinualAI Lab, so check out our if you want to learn more about us and our activities, or .
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