keras-nightly
Multi-backend Keras
Decision gist · record as of 2026-08-14
Yes, if you need a unified deep learning API across multiple backends or want to avoid framework lock-in. The active maintenance, low install friction, permissive license, and zero known vulnerabilities support adoption. Install it alongside your chosen backend (TensorFlow, JAX, PyTorch, or OpenVINO) and set KERAS_BACKEND before importing. Not suitable if you require Python versions below 3.11 or need a single-backend framework.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Backend must be configured via KERAS_BACKEND environment variable before importing keras-nightly, and cannot be changed after import.
- A backend package (tensorflow, jax, torch, or openvino) must be installed separately.
- Low install friction with a pure-Python wheel.
License · maintenance · safety
Apache License 2.0 (permissive) — Apache License 2.0 (permissive) allows use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 64,228 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 256,557 downloads/mo, #8,463 on PyPI
Alternatives
Verify before relying
pip install keras-nightly
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras_nightly
model = keras_nightly.Sequential([keras_nightly.layers.Dense(10)])- Performance speedup claims (20% to 350%) and their applicability to specific model architectures or use cases.
- Compatibility and testing coverage across all four backends for custom layers and training loops.
- Maturity of the OpenVINO inference-only backend relative to the other backends.
What it is and what it does
keras-nightly is a high-level deep learning framework designed to work with multiple compute backends—JAX, TensorFlow, PyTorch, and OpenVINO—so you can write model code once and run it on whichever backend suits your needs. It provides a unified API for building neural networks across computer vision, natural language processing, audio, time-series, and recommender systems, with the goal of letting you switch backends without rewriting your models.
The package includes 8 runtime dependencies (absl-py, numpy, rich, namex, h5py, optree, ml-dtypes, packaging) and is actively maintained with recent releases. It requires Python 3.11 or later and one or more backend packages installed separately. The framework is positioned as a drop-in replacement for tf.keras when using the TensorFlow backend, and supports consuming datasets from tf.data or PyTorch DataLoaders regardless of which backend you choose.
Use it for
- Train a model on JAX for development speed, then switch to TensorFlow for production deployment without rewriting code.
- Build computer vision models (image classification, detection) that run on any of the four supported backends.
- Develop NLP or time-series models that can be deployed via OpenVINO for inference-only optimization.
- Convert existing tf.keras code to run on PyTorch or JAX by changing the backend environment variable.
- Write custom layers or training loops that work across multiple frameworks without framework-specific code.
- Scale model training from a laptop to GPU or TPU clusters while keeping the same high-level API.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you need a unified deep learning API across multiple backends or want to avoid framework lock-in.
The active maintenance, low install friction, permissive license, and zero known vulnerabilities support adoption. Install it alongside your chosen backend (TensorFlow, JAX, PyTorch, or OpenVINO) and set KERAS_BACKEND before importing. Not suitable if you require Python versions below 3.11 or need a single-backend framework.
Install
keras-nightly on PyPI
Before you install
Low install friction with a pure-Python wheel. Active maintenance with a recent release and high repository engagement (64228 stars). Requires Python 3.11 or later and one or more backend packages (TensorFlow, JAX, PyTorch, or OpenVINO) installed separately.
Backend must be configured via KERAS_BACKEND environment variable before importing keras-nightly, and cannot be changed after import. A backend package (tensorflow, jax, torch, or openvino) must be installed separately.
License in practice
Apache License 2.0 (permissive) allows use, modification, and distribution with minimal restrictions, making it suitable for both open-source and commercial projects.
Quickstart
pip install keras-nightly
import os
os.environ["KERAS_BACKEND"] = "jax"
import keras_nightly
model = keras_nightly.Sequential([keras_nightly.layers.Dense(10)])Verify before relying
- Performance speedup claims (20% to 350%) and their applicability to specific model architectures or use cases.
- Compatibility and testing coverage across all four backends for custom layers and training loops.
- Maturity of the OpenVINO inference-only backend relative to the other backends.
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 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 256,557 / month, #8,463 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_nightly-3.16.0.dev2026081404-py3-none-any.whl
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See also keras · keras-nlp · keras-hub · tf-keras · tf-keras-nightly · openvino-dev · tensorly · netron · tensorflow · fastai