🚀 Contributing to Keras 🚀

[fchollet]

Keras 3 is a high-velocity open-source project. We welcome contributions! Here's what you can do:

Take up one of these items:

Here are some features that we'd love to see implemented by the Keras community:

• Update FeatureSpace as well as preprocessing layers that have an adapt() method, to make adapt() consume arbitrary Python iterables rather than just tf.data Datasets.
• Add cuDNN support for PyTorch RNNs by implementing keras/backend/torch/rnn.py:cudnn_lstm and cudnn_gru.
• Add support for ONNX in model.export() and ExportArchive. On the TF and JAX side, we can transit via SavedModel. On the torch side, we can do a native export.
• Add rematerialization support to KerasHub LLMs.
• Contribute to the MLX backend. We have a mlx branch on the repo, which does not yet have full operation converge in keras/src/backend/mlx/. Add missing ops and help get the new MLX backend closer to the finish line!

Check out TODOs

There are various TODOs in the codebase. You can do a quick search (e.g. grep "TODO" keras/ -r) and see if anything looks interesting to you. Make sure to ask in this thread before starting work on any item!

Bring new pretrained model implementations to KerasHub

The Keras ecosystem package KerasHub is looking for contributors!
If you'd like to port a pretrained model implementation to the Keras ecosystem, that's a great project to join.

Convert an example from keras.io to Keras 3

keras.io/examples offers many great tutorials, but many of them are still based on Keras 2. We'd like to bring over in the shiny new world of multi-framework ML.

There are two stages of conversion:

1. Converting a Keras 2 example so that it can run on Keras 3 with the TF backend.
2. Converting a Keras 2 example so that it can run on Keras 3 with any backend.

If you see an example marked as "v2" on keras.io/examples, you can open a PR on keras-team/keras-io to convert it to Keras 3. Keep in mind:

• Start by only including the Python file in the PR, not the md and ipynb files, because you may have to regenerate those after review comments come in.
• To convert the code, you can refer to the Keras 3 migration guide.

Add new examples to keras.io

Have a great ML project you'd like to use to teach others about ML? You can add a new tutorial on keras.io/examples! Just follow the instructions at keras-team/keras-io.

Increase test coverage

Keras doesn't have 100% code coverage in unit tests just yet. You can try to run a code coverage tool like codecov and see if there are any unit tests you could add to improve Keras.

[suvadityamuk]

Taking up the Moving Dataset utils task as a priority! Should I make a draft PR?

[fchollet]

Taking up the Moving Dataset utils task as a priority! Should I make a draft PR?

No need for a draft -- you can just send the PR when you have something you want a review on. Thank you!

[asingh9530]

@fchollet regarding dataset utils, since all three frameworks tensorflow , torch and jax have their own data-loading modules or have specific classes to provide this utilities, but currently it is relying on tf.keras can only accept either tf.Dataset or regular python structures as of now, so dropping it means that we either support similar logic for individual framework or we accept generic structures + framework specific data-holder and convert it to a unified keras data-holder and then perform similar operations.

since in first option we import framework specific modules and built on top but these will stay in keras.backend but in option two we we don't need to touch keras.backend' we only made changes to keras.utils`. Can you please let me know your thoughts on this. 😊

@suvadityamuk can please also share your thoughts in which direction you are choosing. 😊

[suvadityamuk]

@asingh9530

I'm currently looking into trying to move the tf.keras-specific data utils from the original keras-team/keras space to this space, with all implementations intact. The only thing I'm adding is support to handle torch.utils.data.Dataset along with it, since that's a major focus.

[asingh9530]

@suvadityamuk Thanks for responding, since this will be a big change let's track it in a new issue with specific changes needed to be done since even in keras the utils are for tf.Dataset let's say Iterators, batched etc. How about I'll add you there and work on this.

[dranaivo]

Hello,
I am rather new w.r.t contributions, so I may ask a rather silly question : are publicly exported functions those decorated with @keras_core_export ?