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🗺️ Keras Development Roadmap #19519
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I concur with @pure-rgb's observation that the API design has become overly elaborate and intricate for contributors. Previously, it was straightforward and comprehensible, but with the introduction of the Backbone API, it has become more convoluted. |
The person who introduced such API design already left google. :p |
@fchollet I've been going through the features you're planning to implement, and I'm particularly interested in contributing to KerasNLP. Specifically, I'm eager to get involved in the development of dynamic sequence length inference for PyTorch LLMs. |
Here's an overview of the features we intend to work on in the near future, across Core Keras, KerasNLP, and KerasCV.
Core Keras
Saving & export
Distribution
keras.distribution
.keras.distribution
.Performance
ops.flash_attention
ops for Pytorch and JAX (implemented via Pallas).Modeling
inputs
andoutputs
argument inModel
should be flat structured (e.g. flat dicts or lists). However it may be useful to support e.g. dicts of lists, or dicts of dicts.keras.ops
.ops.argpartition
ops.vectorize
ops.select
ops.eigh
tensor.at
operator.keras.ops
:scan
operation in the style of JAX.Ecosystem
.keras
models.mlx
branch).Code health
pip_build.py
to make the Keras package runnable from its sources without any postprocessing.Keras "starter packs"
Keras Models
KerasCV
from_preset
API.channels_first
testing on CI.KerasNLP
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