tf-nightly
TensorFlow is an open source machine learning framework for everyone.
Decision gist · record as of 2026-08-14
Yes, with conditions. Install if you need early access to new features for research or development and can tolerate potential instability. Active maintenance, permissive Apache 2.0 license, and zero known vulnerabilities are positive signals. Avoid for production systems requiring stability—use a stable release instead. Medium install friction (20 dependencies, platform-specific wheels) is manageable for most environments.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later; GPU support requires NVIDIA CUDA 12; 20 runtime dependencies will be installed automatically.
- Medium install friction due to 20 runtime dependencies and platform-specific wheels (macOS ARM64, Linux x86_64/aarch64, Windows).
- Active maintenance with releases every few days; last commit 2026-08-14.
License · maintenance · safety
Apache 2.0 (permissive) — Licensed under Apache 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions. No license-based barriers to adoption.
last release 2026-08-09 (5 days) · last repo commit 2026-08-14 · 197,026 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 750,954 downloads/mo, #5,158 on PyPI
Alternatives
Verify before relying
pip install tf-nightly
import numpy
import protobuf- Whether nightly builds are suitable for production use versus development/testing only.
- Performance characteristics and stability guarantees compared to stable releases.
- Specific GPU/TPU hardware requirements beyond CUDA 12 compatibility.
- Exact API surface and which features are available in this nightly version.
What it is and what it does
tf-nightly is a nightly development build of TensorFlow, the machine learning framework originally developed by Google Brain. It provides cutting-edge features and bug fixes before they appear in stable releases, making it useful for researchers and developers who want early access to new functionality. The package includes support for distributed computation across CPUs, GPUs, TPUs, and edge devices, with a flexible architecture that abstracts away platform differences.
The framework comes with 20 runtime dependencies including numpy, keras-nightly, protobuf, grpcio, and h5py, which handle numerical operations, deep learning layers, serialization, and data I/O. Installation requires Python 3.10 or later and platform-specific wheels are provided for macOS ARM64, Linux x86_64/aarch64, and Windows. Because this is a nightly build updated frequently, it represents the development version rather than a stable release.
Use it for
- Prototyping new machine learning models and experimenting with unreleased features.
- Training deep neural networks on GPU clusters for large-scale data processing.
- Deploying inference pipelines to mobile, edge, or TPU devices with unified code.
- Numerical computation and scientific research requiring flexible tensor operations.
- Contributing to development or testing compatibility with upcoming releases.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Install if you need early access to new features for research or development and can tolerate potential instability. Active maintenance, permissive Apache 2.0 license, and zero known vulnerabilities are positive signals. Avoid for production systems requiring stability—use a stable release instead. Medium install friction (20 dependencies, platform-specific wheels) is manageable for most environments.
Install
tf-nightly on PyPI
Before you install
Medium install friction due to 20 runtime dependencies and platform-specific wheels (macOS ARM64, Linux x86_64/aarch64, Windows). Active maintenance with releases every few days; last commit 2026-08-14. Requires Python 3.10 or later.
Requires Python 3.10 or later; GPU support requires NVIDIA CUDA 12; 20 runtime dependencies will be installed automatically.
License in practice
Licensed under Apache 2.0 (permissive), allowing commercial use, modification, and distribution with minimal restrictions. No license-based barriers to adoption.
Quickstart
pip install tf-nightly
import numpy
import protobufVerify before relying
- Whether nightly builds are suitable for production use versus development/testing only.
- Performance characteristics and stability guarantees compared to stable releases.
- Specific GPU/TPU hardware requirements beyond CUDA 12 compatibility.
- Exact API surface and which features are available in this nightly version.
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 20 packagesabsl-pyastunparseflatbuffersgastgoogle_pastalibclangopt_einsumpackagingprotobufrequestssetuptoolssixtermcolortyping_extensionswraptgrpciokeras-nightlynumpyh5pyml_dtypes |
| Maintenance | Actively maintained 5 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 750,954 / month, #5,158 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/StableEnvironment :: GPU :: NVIDIA CUDA :: 12Environment :: GPU :: NVIDIA CUDA :: 12 :: 12.0Intended 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 :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: tf_nightly-2.22.0.dev20260809-cp311-cp311-macosx_12_0_arm64.whl; tf_nightly-2.22.0.dev20260809-cp311-cp311-manylinux_2_27_aarch64.whl; tf_nightly-2.22.0.dev20260809-cp311-cp311-manylinux_2_27_x86_64.whl; tf_nightly-2.22.0.dev20260809-cp312-cp312-macosx_12_0_arm64.whl; tf_nightly-2.22.0.dev20260809-cp312-cp312-manylinux_2_27_aarch64.whl; tf_nightly-2.22.0.dev20260809-cp312-cp312-manylinux_2_27_x86_64.whl; tf_nightly-2.22.0.dev20260809-cp313-cp313-macosx_12_0_arm64.whl; tf_nightly-2.22.0.dev20260809-cp313-cp313-manylinux_2_27_aarch64.whl; tf_nightly-2.22.0.dev20260809-cp313-cp313-manylinux_2_27_x86_64.whl; tf_nightly-2.22.0.dev20260809-cp313-cp313-win_amd64.whl; tf_nightly-2.22.0.dev20260809-cp314-cp314-macosx_12_0_arm64.whl; tf_nightly-2.22.0.dev20260809-cp314-cp314-manylinux_2_27_aarch64.whl; tf_nightly-2.22.0.dev20260809-cp314-cp314-manylinux_2_27_x86_64.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 › “tensorflow nightly builds”
- tf-nightlytf-nightly provides nightly builds of TensorFlow, an open-source…
- tf-nightly-cputf-nightly-cpu provides a CPU-optimized nightly build of TensorFlow…
- tf-models-nightlyProvides state-of-the-art TensorFlow model implementations and…
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 tf-models-nightly · tf-nightly-cpu · tfx-bsl · tensorflow · tensorflow-cpu · tensorflow-intel · tensorflow-aarch64 · tensorflow-cpu-aws · tfp-nightly · nvidia-cudnn-cu12