keras-team/keras

Deep Learning for humans

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Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, PyTorch, and OpenVINO (for inference-only). Effortlessly build and train models for computer vision, natural language processing, audio processing, timeseries forecasting, recommender systems, etc. and the availability of easy-to-debug runtimes like PyTorch or JAX eager execution. leverage speedups ranging from 20% to 350% compared to other frameworks. Benchmark here. Join nearly three million developers, from burgeoning startups to global enterprises, in harnessing the power of Keras 3. Keras 3 is available on PyPI as keras. Note that Keras 2 remains available as the tf-keras package. To use keras, you should also install the backend of choice: tensorflow, jax, or torch. Additionally, The openvino backend is available with support for model inference only. To save to and load from Google Cloud Storage (gs:// paths) without TensorFlow, install the gcs extra: Keras 3 is compatible with Linux and macOS systems.…

keras-team/keras on GitHub A short preview, not the full document.

Read the full README ↗ · Preview checked 2026-10-30T17:39:58.149Z

Inside the original README — Document outline
  1. Keras 3: Deep Learning for Humans
  2. Installation
  3. Install with pip
  4. Local installation
  5. Minimal installation
  6. Backend Compatibility Table
  7. Adding GPU support
  8. Configuring your backend
  9. Backwards compatibility
  10. Why use Keras 3?

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Documentation belongs to its respective authors. Reported project/model license: Apache-2.0. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.

What this repository does

Deep Learning for humans

Repository facts

Owner
keras-team
Primary language
Python
Stars
64,405
Forks
19,814
Open issues + pull requests
273
License
Apache-2.0
Archived
No
Default branch
master
Created
2015-03-28T00:35:42.000Z
Last push
2026-10-30T22:56:18.000Z

Topics and intended use

Owner-supplied topics: data-science, deep-learning, jax, machine-learning, neural-networks, python, pytorch, tensorflow

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Source: GitHub. Metadata observed 2026-10-31T12:19:22.904Z. Daily imports are snapshots, not real-time monitoring.

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