A modular toolbox for accelerating meta-learning research

A Modular Toolbox for Accelerating Meta-Learning Research :rocket:

WARNING:Repository is under construction. Feel free to star and subscribe for updates, but the code will be unstable and might be changing under the hood until the first beta.

| Coverage Badge

Meta-Blocksis 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 and emerging benchmarks, and add your own meta-learning problems to the suite and benchmark algorithms on them.

Meta-Blockspackage comes with:

• Flexible APIs, detailed documentation, and multiple examples.
• Popular models and algorithms such as MAML [1], Reptile [2], Protonets [3].
• Supervised and unsupervised meta-learning setups compatible with all algorithms.
• Customizable modules and utility functions for quick prototyping on new meta-learning algorithms.

Links and Resources:

• View the latest code on Github
• Execute Interactive Jupyter Notebooks
• Documentation and APIs

Installation

It is recommended to use pip for installation. Please make sure the latest version is installed, as meta-blocks is updated frequently:

$ pip install meta-blocks            # normal install
$ pip install --upgrade meta-blocks  # or update if needed
$ pip install --pre meta-blocks      # or include pre-release version for new features

Alternatively, you could clone and run setup.py file:

$ git clone https://github.com/alshedivat/meta-blocks.git
$ cd meta-blocks
$ pip install .

Required Dependencies :

• albumentations
• hydra-core
• numpy
• Pillow
• scipy
• scikit-learn
• tensorflow>=2.1

Examples

TODO: We should provide a minimal example so people could run immediately. Ideally, the running time should be within a few mins.

Development

For development and contributions, please make sure to install pre-commit hooks to ensure proper code style and formatting:

$ pip install pre-commit      # install pre-commit
$ pre-commit install          # install git hooks $ pre-commit run --all-files  # run pre-commit on all the files ``` 
#### Status

**Meta-Blocks** is currently **under development** as of Apr, 2020.

**Watch & Star** to get the latest update! Also feel free to contact for suggestions and ideas.

----

### Citing Meta-Blocks

TODO: add citation information as soon as available.

----


### Reference

[1] Finn, C., Abbeel, P. and Levine, S. Model-agnostic meta-learning for fast adaptation of deep networks. ICML 2017.

[2] Nichol, A., Achiam, J. and Schulman, J. On first-order meta-learning algorithms. arXiv preprint arXiv:1803.02999.

[3] Snell, J., Swersky, K. and Zemel, R. Prototypical networks for few-shot learning. NeurIPS 2017.