# KerasHub: Multi-framework Pretrained Models [![](https://github.com/keras-team/keras-hub/workflows/Tests/badge.svg?branch=master)](https://github.com/keras-team/keras-hub/actions?query=workflow%3ATests+branch%3Amaster) ![Python](https://img.shields.io/badge/python-v3.11.0+-success.svg) [![Kaggle Models](https://img.shields.io/badge/Kaggle-Models-brightgreen?colorA=0099ff)](https://www.kaggle.com/organizations/keras/models) [![contributions welcome](https://img.shields.io/badge/contributions-welcome-brightgreen.svg?style=flat)](https://github.com/keras-team/keras-hub/issues) > [!IMPORTANT] > 📢 KerasNLP is now KerasHub! 📢 Read > [the announcement](https://github.com/keras-team/keras-hub/issues/1831). **KerasHub** is a pretrained modeling library that aims to be simple, flexible, and fast. The library provides [Keras 3](https://keras.io/keras_3/) implementations of popular model architectures, paired with a collection of pretrained checkpoints available on [Kaggle Models](https://www.kaggle.com/organizations/keras/models). Models can be used with text, image, and audio data for generation, classification, and many other built in tasks. KerasHub is an extension of the core Keras API; KerasHub components are provided as `Layer` and `Model` implementations. If you are familiar with Keras, congratulations! You already understand most of KerasHub. All models support JAX, TensorFlow, and PyTorch from a single model definition and can be fine-tuned on GPUs and TPUs out of the box. Models can be trained on individual accelerators with built-in PEFT techniques, or fine-tuned at scale with model and data parallel training. See our [Getting Started guide](https://keras.io/guides/keras_hub/getting_started) to start learning our API. ## Quick Links ### For everyone - [Home page](https://keras.io/keras_hub) - [Getting started](https://keras.io/keras_hub/getting_started) - [Guides](https://keras.io/keras_hub/guides) - [API documentation](https://keras.io/keras_hub/api) - [Pre-trained models](https://keras.io/keras_hub/presets/) ### For contributors - [Call for Contributions](https://github.com/keras-team/keras-hub/issues/1835) - [Roadmap](https://github.com/keras-team/keras-hub/issues/1836) - [Contributing Guide](CONTRIBUTING.md) - [Style Guide](STYLE_GUIDE.md) - [API Design Guide](API_DESIGN_GUIDE.md) ## Quickstart Choose a backend: ```python import os os.environ["KERAS_BACKEND"] = "jax" # Or "tensorflow" or "torch"! ``` Import KerasHub and other libraries: ```python import keras import keras_hub import numpy as np import tensorflow_datasets as tfds ``` Load a resnet model and use it to predict a label for an image: ```python classifier = keras_hub.models.ImageClassifier.from_preset( "resnet_50_imagenet", activation="softmax", ) url = "https://upload.wikimedia.org/wikipedia/commons/a/aa/California_quail.jpg" path = keras.utils.get_file(origin=url) image = keras.utils.load_img(path) preds = classifier.predict(np.array([image])) print(keras_hub.utils.decode_imagenet_predictions(preds)) ``` Load a Bert model and fine-tune it on IMDb movie reviews: ```python classifier = keras_hub.models.TextClassifier.from_preset( "bert_base_en_uncased", activation="softmax", num_classes=2, ) imdb_train, imdb_test = tfds.load( "imdb_reviews", split=["train", "test"], as_supervised=True, batch_size=16, ) classifier.fit(imdb_train, validation_data=imdb_test) preds = classifier.predict(["What an amazing movie!", "A total waste of time."]) print(preds) ``` ## Installation To install the latest KerasHub release with Keras 3, simply run: ``` pip install --upgrade keras-hub ``` To install the latest nightly changes for both KerasHub and Keras, you can use our nightly package. ``` pip install --upgrade keras-hub-nightly ``` Currently, installing KerasHub will always pull in TensorFlow for use of the `tf.data` API for preprocessing. When pre-processing with `tf.data`, training can still happen on any backend. Visit the [core Keras getting started page](https://keras.io/getting_started/) for more information on installing Keras 3, accelerator support, and compatibility with different frameworks. ## Configuring your backend If you have Keras 3 installed in your environment (see installation above), you can use KerasHub with any of JAX, TensorFlow and PyTorch. To do so, set the `KERAS_BACKEND` environment variable. For example: ```shell export KERAS_BACKEND=jax ``` Or in Colab, with: ```python import os os.environ["KERAS_BACKEND"] = "jax" import keras_hub ``` > [!IMPORTANT] > Make sure to set the `KERAS_BACKEND` **before** importing any Keras libraries; > it will be used to set up Keras when it is first imported. ## Compatibility We follow [Semantic Versioning](https://semver.org/), and plan to provide backwards compatibility guarantees both for code and saved models built with our components. While we continue with pre-release `0.y.z` development, we may break compatibility at any time and APIs should not be considered stable. ## Disclaimer KerasHub provides access to pre-trained models via the `keras_hub.models` API. These pre-trained models are provided on an "as is" basis, without warranties or conditions of any kind. The following underlying models are provided by third parties, and subject to separate licenses: BART, BLOOM, DeBERTa, DistilBERT, GPT-2, Llama, Mistral, OPT, RoBERTa, Whisper, and XLM-RoBERTa. ## Citing KerasHub If KerasHub helps your research, we appreciate your citations. Here is the BibTeX entry: ```bibtex @misc{kerashub2024, title={KerasHub}, author={Watson, Matthew, and Chollet, Fran\c{c}ois and Sreepathihalli, Divyashree, and Saadat, Samaneh and Sampath, Ramesh, and Rasskin, Gabriel and and Zhu, Scott and Singh, Varun and Wood, Luke and Tan, Zhenyu and Stenbit, Ian and Qian, Chen, and Bischof, Jonathan and others}, year={2024}, howpublished={\url{https://github.com/keras-team/keras-hub}}, } ``` ## Acknowledgements Thank you to all of our wonderful contributors!