Welcome to meta-blocks’s documentation!

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Meta-Blocks is a modular toolbox for research, experimentation, and reproducible benchmarking of learning-to-learn algorithms. The toolbox provides flexible APIs for working with MetaDatasets, TaskDistributions, and MetaLearners (see the figure below). The APIs make it easy to implement a variety of meta-learning algorithms, run them on well-established benchmarks, or add your own meta-learning problems to the suite and benchmark algorithms on them.

System Illustration

Meta-Blocks package comes with:

  • Flexible APIs, detailed documentation, and multiple examples.

  • Popular models and algorithms such as MAML [FAL17], Reptile [NAS18], Protonets [SSZ17].

  • Supervised and unsupervised meta-learning setups compatible with all algorithms.

  • Customizable modules and utility functions for quick prototyping on new meta-learning algorithms.

Key Links and Resources:

Getting Started



Chelsea Finn, Pieter Abbeel, and Sergey Levine. Model-agnostic meta-learning for fast adaptation of deep networks. In ICML, 1126–1135. JMLR. org, 2017.


Alex Nichol, Joshua Achiam, and John Schulman. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999, 2018.


Jake Snell, Kevin Swersky, and Richard Zemel. Prototypical networks for few-shot learning. In NeurIPS, 4077–4087. 2017.

Indices and Tables