keras-hub-nightly

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

• Home page
• Getting started
• Guides
• API documentation
• Pre-trained models

For contributors

• Call for Contributions
• Roadmap
• Contributing Guide
• Style Guide
• API Design Guide

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.BertClassifier.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 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_BACKEND before 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!