tf-keras-nightly
Deep learning for humans.
Decision gist · record as of 2026-08-14
Yes, if you are actively developing with TensorFlow and want the latest Keras API features; no, if you need stability and predictability—use the stable tf-keras release instead. The nightly build is suitable for developers and researchers who can tolerate frequent changes and want early access to new features, but not for production systems requiring long-term API stability.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later and tf-nightly as a runtime dependency.
- Low install friction with a pure Python wheel.
- Active maintenance with a commit from 2026-08-14 and a release on the same day.
License · maintenance · safety
Apache 2.0 (permissive) — Apache 2.0 is permissive; you can use this in commercial and proprietary projects with minimal restrictions, provided you retain license notices.
last release 2026-08-14 (0 days) · last repo commit 2026-08-14 · 90 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,410,295 downloads/mo, #3,940 on PyPI
Alternatives
Verify before relying
pip install tf-keras-nightly
from tf_keras_nightly import keras
model = keras.Sequential([
keras.layers.Dense(activation='relu'),
keras.layers.Dense(activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')- Whether this nightly build is suitable for production use or intended only for development and testing
- Stability guarantees and API compatibility between nightly releases
- Performance characteristics compared to the stable tf-keras release channel
- Specific usage examples and API surface details beyond what the description excerpt provides
What it is and what it does
TF-Keras is the pure-TensorFlow implementation of Keras, providing a high-level API for defining, training, and evaluating deep learning models. It is built on the legacy tf.keras codebase and runs entirely within TensorFlow, making it tightly integrated with TensorFlow's execution model and optimization pipeline. The package offers familiar Keras abstractions—layers, models, optimizers, and loss functions—while remaining a TensorFlow-native implementation.
This is a nightly development build, meaning it tracks the latest development branch of TF-Keras and receives frequent updates. It depends on tf-nightly, so you get the latest TensorFlow features and fixes alongside the latest Keras API changes. This package is distinct from the multi-backend Keras 3, which supports multiple backends; TF-Keras is TensorFlow-specific and maintains the original Keras API surface for TensorFlow users.
Use it for
- Build neural network models for image classification, NLP, or regression tasks using the Keras API.
- Experiment with cutting-edge TensorFlow and Keras features during development by tracking nightly builds.
- Train models with custom layers, callbacks, and training loops while leveraging TensorFlow's distributed training.
- Migrate existing tf.keras code to a standalone package that decouples Keras from the main TensorFlow release cycle.
- Prototype deep learning architectures using high-level Keras abstractions without writing low-level operations.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are actively developing with TensorFlow and want the latest Keras API features; no, if you need stability and predictability—use the stable tf-keras release instead.
The nightly build is suitable for developers and researchers who can tolerate frequent changes and want early access to new features, but not for production systems requiring long-term API stability.
Install
tf-keras-nightly on PyPI
Before you install
Low install friction with a pure Python wheel. Active maintenance with a commit from 2026-08-14 and a release on the same day. Single runtime dependency on tf-nightly means you inherit that package's stability profile.
Requires Python 3.10 or later and tf-nightly as a runtime dependency.
License in practice
Apache 2.0 is permissive; you can use this in commercial and proprietary projects with minimal restrictions, provided you retain license notices.
Quickstart
pip install tf-keras-nightly
from tf_keras_nightly import keras
model = keras.Sequential([
keras.layers.Dense(activation='relu'),
keras.layers.Dense(activation='softmax')
])
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy')Verify before relying
- Whether this nightly build is suitable for production use or intended only for development and testing
- Stability guarantees and API compatibility between nightly releases
- Performance characteristics compared to the stable tf-keras release channel
- Specific usage examples and API surface details beyond what the description excerpt provides
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 1 packagetf-nightly |
| Maintenance | Actively maintained 0 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,410,295 / month, #3,940 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Science/ResearchLicense :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3 :: OnlyProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: tf_keras_nightly-2.21.0.dev2026081409-py3-none-any.whl
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See also tensorflow-addons · tf-keras · tf-slim · Keras-Preprocessing · keras-nightly · keras · scikeras · tensorflow-estimator · efficientnet · keras-hub