keras
Multi-backend Keras
Decision gist · record as of 2026-08-14
Yes. Keras 3 is actively maintained, widely adopted (19.5M monthly downloads), has no known vulnerabilities, and offers genuine value if you want to avoid framework lock-in or need to switch backends. Install it if you're building deep learning models and want flexibility; skip it only if you're committed to a single framework and don't need Keras's high-level API.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- You must install a backend package (tensorflow>=2.16.1, jax>=0.4.20, torch>=2.1.0, or openvino>=2025.3.0) separately, and set KERAS_BACKEND before importing keras.
- Installation is straightforward with low friction—a pure Python wheel with eight runtime dependencies.
- The package is actively maintained with a recent release and high community adoption, though you must separately install at least one backend (TensorFlow, JAX, PyTorch, or OpenVINO) for the framework to function.
License · maintenance · safety
Apache License 2.0 (permissive) — Licensed under Apache License 2.0 (permissive), so you can use, modify, and distribute Keras 3 freely in commercial and open-source projects with minimal restrictions.
last release 2026-07-29 (16 days) · last repo commit 2026-08-14 · 64,227 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 19,542,671 downloads/mo, #1,061 on PyPI
Alternatives
Verify before relying
pip install keras
import os
os.environ["KERAS_BACKEND"] = "jax" # or "tensorflow", "torch", "openvino"
import keras
model = keras.Sequential([keras.layers.Dense(10, activation="relu")])- Actual performance gains (20% to 350% speedup claims) depend on model architecture and backend choice—not independently verified here.
- Compatibility with custom tf.keras components and conversion effort required for non-trivial models.
What it is and what it does
Keras 3 is a high-level deep learning framework that abstracts away backend differences, letting you write model code once and run it on JAX, TensorFlow, PyTorch, or OpenVINO. It provides a familiar API for building and training neural networks across domains—computer vision, NLP, audio, timeseries, and recommendation systems—while letting you choose the backend that best fits your performance or deployment needs.
The framework is designed as a drop-in replacement for tf.keras when using the TensorFlow backend, and supports both high-level Keras workflows and lower-level custom components. It depends on numpy, h5py, rich, absl-py, namex, optree, ml-dtypes, and packaging. You must install and configure a backend separately before use, and the backend cannot be changed after import.
Use it for
- Build computer vision models (CNNs, transformers) that run on JAX for research or on TensorFlow for production.
- Migrate existing tf.keras code to run on PyTorch or JAX without rewriting the model definition.
- Train NLP models (text classification, sequence-to-sequence) with a unified API across multiple frameworks.
- Deploy inference-only models using the OpenVINO backend for edge or embedded systems.
- Write backend-agnostic custom layers and metrics that work across TensorFlow, JAX, and PyTorch.
- Prototype on CPU with PyTorch, then scale to TPUs with JAX using the same model code.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
Keras 3 is actively maintained, widely adopted (19.5M monthly downloads), has no known vulnerabilities, and offers genuine value if you want to avoid framework lock-in or need to switch backends. Install it if you're building deep learning models and want flexibility; skip it only if you're committed to a single framework and don't need Keras's high-level API.
Install
keras on PyPI
Before you install
Installation is straightforward with low friction—a pure Python wheel with eight runtime dependencies. The package is actively maintained with a recent release and high community adoption, though you must separately install at least one backend (TensorFlow, JAX, PyTorch, or OpenVINO) for the framework to function.
You must install a backend package (tensorflow>=2.16.1, jax>=0.4.20, torch>=2.1.0, or openvino>=2025.3.0) separately, and set KERAS_BACKEND before importing keras.
License in practice
Licensed under Apache License 2.0 (permissive), so you can use, modify, and distribute Keras 3 freely in commercial and open-source projects with minimal restrictions.
Quickstart
pip install keras
import os
os.environ["KERAS_BACKEND"] = "jax" # or "tensorflow", "torch", "openvino"
import keras
model = keras.Sequential([keras.layers.Dense(10, activation="relu")])Verify before relying
- Actual performance gains (20% to 350% speedup claims) depend on model architecture and backend choice—not independently verified here.
- Compatibility with custom tf.keras components and conversion effort required for non-trivial models.
Package facts
| License | Apache License 2.0 permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 8 packagesabsl-pynumpyrichnamexh5pyoptreeml-dtypespackaging |
| Maintenance | Actively maintained 16 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 19,542,671 / month, #1,061 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: Science/ResearchOperating System :: MacOSOperating System :: UnixProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Topic :: Scientific/EngineeringTopic :: Software Development |
Evidence: keras-3.15.1-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “multi-backend machine learning”
- kerasKeras 3 is a multi-backend deep learning framework supporting JAX,…
- tinygradtinygrad is a minimal deep learning framework providing a tensor…
- tensorlyTensorLy performs tensor decomposition, tensor learning, and tensor…
Give your agent the search over MCP, or paste the wish link into any chat.
More Software Development packages
Provides backported and experimental type hints for Python 3.9+, allowing use of newer typing features on older Python versions and enabling early experimentation with type system PEPs before they enter the standard library.
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
FastAPI is a Python web framework for building REST APIs using type hints, with automatic request validation, serialization, and interactive API documentation.
Provides a way to document function parameters, class attributes, return types, and variables inline using Python's `Annotated` type hint syntax instead of traditional docstrings.
Typer builds command-line applications from Python functions using type hints, automatically generating help text, argument parsing, and shell completion.
Install it if you are building CLIs in Python.
Distlib provides low-level packaging utilities for building, distributing, and managing Python software—including metadata handling, version specifiers, wheel support, script installation, and dependency resolution.
See also fastai · keras-hub · keras-nightly · keras-nlp · scikeras · tensorflow-recommenders · tf-keras · tf-keras-nightly · Keras-Applications · openvino-dev