GSoC 2020 project ideas


This is the Google Summer of Code 2020 (GSoC’20) ideas page for uarray, unumpy and udiff. The uarray library is is a backend mechanism geared towards array computing, but intended for general use. unumpy is an incomplete stub of the NumPy API that can be dispatched by uarray. udiff is a general-purpose automatic differentiation library built on top of unumpy and uarray.

This page lists a number of ideas for Google Summer of Code projects for uarray, plus gives some pointers for potential GSoC students on how to get started with contributing and putting together their application.

Guidelines & requirements

uarray plans to participate in GSoC’20 under the umbrella of Python Software Foundation.

We expect from students that they’re at least comfortable with Python (intermediate level). Some projects may also require C++ or C skills. Knowing how to use Git is also important; this can be learned before the official start of GSoC if needed though.

If you have an idea of what you would like to work on (see below for ideas) and are considering participating:

  1. Read the PSF page carefully, it contains important advice on the process.

  2. Read advice on writing a proposal (written with the Mailman project in mind, but generally applicable)

  3. Make a enhancement/bugfix/documentation fix – it does not have to be big, and it does not need to be related to your proposal. Doing so before applying for the GSoC is a hard requirement for uarray. It helps everyone you get some idea how things would work during GSoC.

  4. Start writing your proposal early, post a draft to the issue tracker and iterate based on the feedback you receive. This will both improve the quality of your proposal and help you find a suitable mentor.


If you have a question after checking all guideline pages above, you can open an issue in the issue tracker, but feel free to chat with us on Gitter if you need clarification regarding any of the projects. Keep in mind that you might not get a response right away, but we will endeavour to respond as early as possible.

uarray project ideas

uarray: Add querying for state

Adding querying for the uarray._BackendState object will allow users of uarray to see what’s inside the opaque object. Some parts can be re-used from the pickling machinery.

It can also help downstream users to access the parameters of the currently set backend, which is a planned feature of uarray. Here is a list of goals for this project:

  • Allow downstream projects to query the list of backends.

  • Allow downstream projects to query the list of parameters for a backend.

This would enable, for example, the following use-cases:

  • Allow a downstream library to detect a backend and run specialised code for it.

  • Allow a downstream library to fail-fast on a known-unsupported backend.

This project has a straightforward design and needs some implementation work, and will require interacting with the mentors to implement and polish. The accepted student will get an outline of the desired API, along with some failing tests and doctests. The student will make a pull request to implement the desired functionality so that the tests pass.

  • Required knowledge: Python C-API and C++

  • Difficulty level: medium

  • Potential mentors: Peter Bell and Hameer Abbasi

uarray: Allow subdomains

This idea would allow a backend to encompass functions from more than one domain.

The primary goal of this project would be:

  • Develop a system that allows, via some kind of matching mechanism, to select which domains it supports, while maintaining backward compatibility.

This would allow a backend targeting NumPy to also target, for example, the numpy.random submodule. Since the domain for functions in numpy.random will be just that: numpy.random, it won’t match backends defined with the numpy domain, since it’s an exact string match.

The second objective here would be to allow backends to target submodules of projects rather than the whole project. For example, targeting just numpy.random or numpy.fft without targeting all of NumPy.

For more detail see this issue.

This project has a somewhat complicated design and needs some involved implementation work, and will require interacting with the mentors to flesh out and work through.

  • Required knowledge: Python C-API and C++

  • Difficulty level: hard

  • Potential mentors: Peter Bell and Hameer Abbasi

unumpy: Expand overall coverage

This project is split into two parts:

  • Adding further coverage of the NumPy API.

  • Adding more backends to unumpy.

We realise this is a large (possibly open-ended) undertaking, and so there will need to be a minimum amount of work done in order to pass (~150 function stubs, if time allows a JAX backend). You may see the existing methods and figure out how they are written using a combination of the documentation for writing multimethods and the already existing multimethods in this file. For writing backends, you can see the documentation for backends in combination with the already existing backends in this directory.

  • Required knowledge: Python (intermediate level)

  • Difficulty level: easy

  • Potential mentors: Prasun Anand and Hameer Abbasi

udiff: Completion and Packaging

This requires completion and packaging of the udiff library. Potential goals include:

  1. Publishing an initial version to PyPI. Here’s a guide on how to do that.

  2. Adding matrix/tensor calculus support.

    • For this, you can see the matrix cookbook. Don’t be intimidated! There will only be five or so equations you have to pull out of the matrix cookbook and implement, most prominently, the equation for matrix multiplication.

    • Here is how derivatives are registered.

    • The second task here will be to add the “separation” between the data dimensions and the differentiation dimensions. For example, the input could be a vector, or an array of scalars, and this might need to be taken into account when doing the differentiation. That will require some work in this file, and possibly this one as well.

  3. Adding tests.

  • This will require calculating a few derivatives by hand and making sure they match up with what udiff computes.

  • We will use the PyTest framework.

  1. Adding documentation on use, which will be fairly minimal. We will learn to set up Sphinx, and add some documentation.

  2. Publishing a final version to PyPI.

This project has a somewhat some minimal design and needs some involved implementation work. It will allow the accepted student to get an idea of what it’s like to actually publish, test and document a small Python package.

  • Required knowledge: Python (intermediate level) and calculus

  • Difficulty level: medium

  • Potential mentors: Prasun Anand and Hameer Abbasi