This release is a pre-release and may not be stable for production use.
KerasHub: Multi-framework Pretrained Models
[!IMPORTANT] 📢 KerasNLP is now KerasHub! 📢 Read the announcement.
KerasHub is a pretrained modeling library that aims to be simple, flexible, and fast. The library provides Keras 3 implementations of popular model architectures, paired with a collection of pretrained checkpoints available on Kaggle 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 to start learning our API.
Quick Links
For everyone
For contributors
Quickstart
Choose a backend:
import os
os.environ["KERAS_BACKEND"] = "jax" # Or "tensorflow" or "torch"!
Import KerasHub and other libraries:
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:
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:
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
Our text tokenizers are based on TensorFlow Text. Hence, if you are using any model which has language as a modality, you will have to run:
pip install --upgrade keras-hub[nlp]
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 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:
export KERAS_BACKEND=jax
Or in Colab, with:
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras_hub
[!IMPORTANT] Make sure to set the
KERAS_BACKENDbefore importing any Keras libraries; it will be used to set up Keras when it is first imported.
Compatibility
We follow Semantic Versioning, 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:
@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!
Release files for keras-hub-nightly 0.24.0.dev202512130428
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| keras_hub_nightly-0.24.0.dev202512130428.tar.gz | 810.8 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| keras_hub_nightly-0.24.0.dev202512130428-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 2.0 MB
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