Contributing to PlaidML

We welcome contributions to PlaidML from anyone. This document contains:

  • Guidelines for creating successful PRs
  • Outlines the contribution process
  • Lists general areas for contribution
  • Provides resources and context to ease development, where relevant and available

Before starting any work please ensure you are able to build and test PlaidML.


  • Create unit tests for new features and bug fixes. Integration tests are required for larger features.
  • Pre-commit linters will be available soon.


  1. Ensure there is an open issue assigned to you before doing (too much) work:
    • If you’re tackling an open issue that isn’t assigned, please assign it to yourself.
    • If you’d like to solve an issue that is already assigned, please comment on the issue to ensure you’re not duplicating effort.
    • If you’d like to provide a new feature, open a new issue. Please provide a reasonably detailed description of what you’d like to do, and clearly indicate that you’re willing to do the work.
  2. Work on a fork as usual in Github. Please ensure the same tests travis runs will pass before creating a PR
  3. Once you’ve created a PR you’ll be asked to review and sign our Contributor License Agreement. You should review this before doing too much work to ensure the terms are acceptable.
  4. Once tests have passed, a maintainer will assign the issue to themselves and run the PR through the (currently private) performance test suite. If there are issues, we will attempt to resolve them, but we may provide details and ask the author to address.
  5. Once the performance regression suite has passed, we will accept and merge the PR.

Areas for Contribution

  • Ops for Frontends
    • PlaidML welcomes implementations for currently unimplemented operations as well as Tile code for novel operations supported by research.
    • Please read the Tile Tutorial and the PlaidML Op Tutorial
  • ML Framework Frontends (e.g., Keras, Pytorch, etc)
    • PlaidML welcomes integrations with any established ML framework or interop (NNVM, ONNX, etc)
    • Currently this involves duplicating tile operations. We will eventually abstract common NN tile operations into a separate C++ library to ease backend development.
  • HALs for Backend Targets (OpenCL, Vulkan, SPIR-V, HVX, etc)
    • There is no documentation for the HAL currently. The interface is fairly straightforward and the OpenCL HAL provides a good example of a complete HAL.

Please follow the process above before embarking on anything major (like integrating a new frontend or backend).