Hello /r/ML,

We are pleased to share with you our meta-learning library, that started as a project at the PyTorch hackathon.

learn2learn is a PyTorch library for all things meta-learning. Our goal is to support as many meta-learning algorithms as possible (be it few-shots, meta-descent, or meta-RL) and to enable researchers to develop better methods and easily compare against existing literature.​

Our current features include:​

• Modular API: implement your own training loops with our low-level utilities.
• Provides various meta-learning algorithms (e.g. MAML, FOMAML, MetaSGD, ProtoNets, DiCE)
• Task generator with unified API, compatible with torchvision, torchtext, torchaudio, and cherry
• Provides standardized meta-learning tasks for vision (Omniglot, mini-ImageNet), reinforcement learning (Particles, Mujoco), and even text (news classification).
• 100% compatible with PyTorch — use your own modules, datasets, or libraries!

If this is of interest to you, have a look at the following links:​

• GitHub: learnables/learn2learn
• Examples: learn2learn/examples
• Documentation: learn2learn.net
• Slack: slack.learn2learn.net

​Let us know what you think and how we can help you in your research!

​PS: learn2learn was also accepted as a poster to the PyTorch Dev Conference, so you’ll know all about it there!