tensorflow
TensorFlow is an open source machine learning framework for everyone.
Decision gist · record as of 2026-08-14
Yes. TensorFlow is a mature, actively maintained framework with no known vulnerabilities, strong community support, and permissive licensing. Install friction is moderate due to compiled dependencies, but prebuilt wheels for modern Python versions (3.10–3.13) and multiple platforms reduce setup complexity. Suitable for research, prototyping, and production machine learning workloads.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python >=3.10.
- GPU support requires NVIDIA CUDA 12 or compatible drivers; CPU-only installation is available but slower for large workloads.
- Medium install friction due to compiled dependencies and platform-specific wheels.
License · maintenance · safety
Apache 2.0 (permissive) — Licensed under Apache 2.0 (permissive), allowing commercial and private use with minimal restrictions. You may use, modify, and distribute TensorFlow freely provided you include a copy of the license and any notices.
last release 2026-03-06 (161 days) · last repo commit 2026-08-14 · 197,024 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 20,761,191 downloads/mo, #1,026 on PyPI
Alternatives
Verify before relying
pip install tensorflow
import tensorflow as tf
model = tf.keras.Sequential([tf.keras.layers.Dense(10)])- Whether pre-built wheels include GPU support by default or if CUDA/cuDNN must be installed separately.
- Performance characteristics and memory footprint for typical model training scenarios.
- Compatibility with specific CUDA versions beyond 12.2 mentioned in classifiers.
What it is and what it does
TensorFlow is a production-grade machine learning framework that provides a flexible architecture for numerical computation and deep learning. It abstracts low-level tensor operations and automatic differentiation, allowing you to define models declaratively and train them on diverse hardware—from laptops to distributed clusters—without rewriting code. The framework includes Keras as its high-level API for building neural networks, and supports both eager execution for research and graph-based execution for deployment.
The package ships with 20 runtime dependencies including numpy for array operations, keras for model building, protobuf for serialization, and grpcio for distributed training. Installation requires Python 3.10 or later and platform-specific compiled wheels; GPU acceleration is available for NVIDIA hardware via CUDA 12. It is actively maintained and widely used in production systems for computer vision, natural language processing, reinforcement learning, and scientific computing.
Use it for
- Build and train convolutional neural networks for image classification or object detection tasks.
- Develop recurrent neural networks or transformer models for sequence modeling and NLP applications.
- Deploy trained models to mobile devices, edge hardware, or cloud infrastructure for inference.
- Perform distributed training across multiple GPUs or TPUs for large-scale model development.
- Conduct numerical experiments and scientific simulations using automatic differentiation.
- Prototype machine learning pipelines with Keras high-level APIs before production deployment.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
TensorFlow is a mature, actively maintained framework with no known vulnerabilities, strong community support, and permissive licensing. Install friction is moderate due to compiled dependencies, but prebuilt wheels for modern Python versions (3.10–3.13) and multiple platforms reduce setup complexity. Suitable for research, prototyping, and production machine learning workloads.
Install
tensorflow on PyPI
Before you install
Medium install friction due to compiled dependencies and platform-specific wheels. Active maintenance with recent commits and strong community backing (197024 stars). Supports Python 3.10–3.13 with prebuilt wheels for macOS ARM, Linux x86_64/aarch64, and Windows.
Requires Python >=3.10. GPU support requires NVIDIA CUDA 12 or compatible drivers; CPU-only installation is available but slower for large workloads.
License in practice
Licensed under Apache 2.0 (permissive), allowing commercial and private use with minimal restrictions. You may use, modify, and distribute TensorFlow freely provided you include a copy of the license and any notices.
Quickstart
pip install tensorflow
import tensorflow as tf
model = tf.keras.Sequential([tf.keras.layers.Dense(10)])Verify before relying
- Whether pre-built wheels include GPU support by default or if CUDA/cuDNN must be installed separately.
- Performance characteristics and memory footprint for typical model training scenarios.
- Compatibility with specific CUDA versions beyond 12.2 mentioned in classifiers.
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_extensionswraptgrpciokerasnumpyh5pyml_dtypes |
| Maintenance | Actively maintained 161 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 20,761,191 / month, #1,026 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 :: 12.2Intended 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: tensorflow-2.21.0-cp310-cp310-macosx_12_0_arm64.whl; tensorflow-2.21.0-cp310-cp310-manylinux_2_27_aarch64.whl; tensorflow-2.21.0-cp310-cp310-manylinux_2_27_x86_64.whl; tensorflow-2.21.0-cp310-cp310-win_amd64.whl; tensorflow-2.21.0-cp311-cp311-macosx_12_0_arm64.whl; tensorflow-2.21.0-cp311-cp311-manylinux_2_27_aarch64.whl; tensorflow-2.21.0-cp311-cp311-manylinux_2_27_x86_64.whl; tensorflow-2.21.0-cp311-cp311-win_amd64.whl; tensorflow-2.21.0-cp312-cp312-macosx_12_0_arm64.whl; tensorflow-2.21.0-cp312-cp312-manylinux_2_27_aarch64.whl; tensorflow-2.21.0-cp312-cp312-manylinux_2_27_x86_64.whl; tensorflow-2.21.0-cp312-cp312-win_amd64.whl; tensorflow-2.21.0-cp313-cp313-macosx_12_0_arm64.whl; tensorflow-2.21.0-cp313-cp313-manylinux_2_27_aarch64.whl; tensorflow-2.21.0-cp313-cp313-manylinux_2_27_x86_64.whl; tensorflow-2.21.0-cp313-cp313-win_amd64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
An agent finds packages by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language. Give your agent the search over MCP.
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 tensorflow-aarch64 · tensorflow-cpu · tensorflow-intel · tensorflow-cpu-aws · tf-nightly · tf-slim · tf-nightly-cpu · tf-models-nightly · paddlepaddle · thinc