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The Keras ecosystem

The Keras project isn't limited to the core Keras API for building and training neural networks. It spans a wide range of related initiatives that cover every step of the machine learning workflow.


KerasTuner Documentation - KerasTuner GitHub repository

KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search algorithms.


KerasNLP Documentation - KerasNLP GitHub repository

KerasNLP is a simple and powerful API for building Natural Language Processing (NLP) models. KerasNLP provides modular building blocks following standard Keras interfaces (layers, metrics) that allow you to quickly and flexibly iterate on your task. Engineers working in applied NLP can leverage the library to assemble training and inference pipelines that are both state-of-the-art and production-grade. KerasNLP is maintained directly by the Keras team.


AutoKeras Documentation - AutoKeras GitHub repository

AutoKeras is an AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible for everyone. It provides high-level end-to-end APIs such as ImageClassifier or TextClassifier to solve machine learning problems in a few lines, as well as flexible building blocks to perform architecture search.


KerasCV Documentation - KerasCV GitHub repository

KerasCV is a repository of modular building blocks (layers, metrics, losses, data-augmentation) that applied computer vision engineers can leverage to quickly assemble production-grade, state-of-the-art training and inference pipelines for common use cases such as image classification, object detection, image segmentation, image data augmentation, etc.

KerasCV can be understood as a horizontal extension of the Keras API: the components are new first-party Keras objects (layers, metrics, etc) that are too specialized to be added to core Keras, but that receive the same level of polish and backwards compatibility guarantees as the rest of the Keras API and that are maintained by the Keras team itself (unlike TFAddons).

TensorFlow Cloud

Managed by the Keras team at Google, TensorFlow Cloud is a set of utilities to help you run large-scale Keras training jobs on GCP with very little configuration effort. Running your experiments on 8 or more GPUs in the cloud should be as easy as calling model.fit().


TensorFlow.js is TensorFlow's JavaScript runtime, capable of running TensorFlow models in the browser or on a Node.js server, both for training and inference. It natively supports loading Keras models, including the ability to fine-tune or retrain your Keras models directly in the browser.

TensorFlow Lite

TensorFlow Lite is a runtime for efficient on-device inference that has native support for Keras models. Deploy your models on Android, iOS, or on embedded devices.

Model optimization toolkit

The TensorFlow Model Optimization Toolkit is a set of utilities to make your inference models faster, more memory-efficient, and more power-efficient, by performing post-training weight quantization and pruning-aware training. It has native support for Keras models, and its pruning API is built directly on top on the Keras API.

TFX integration

TFX is an end-to-end platform for deploying and maintaining production machine learning pipelines. TFX has native support for Keras models.