tensorflow-aarch64
TensorFlow is an open source machine learning framework for everyone.
Decision gist · record as of 2026-08-14
Yes, if you are developing or deploying machine learning on aarch64 systems. The package is production-stable, actively maintained, permissively licensed, and has no known vulnerabilities. Install friction is moderate due to dependencies, but unavoidable for serious ML work. Not necessary if you only target other architectures or do not require ARM-specific optimization.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; aarch64 architecture required; GPU support requires NVIDIA CUDA 12 or 12.2 (optional but recommended for performance).
- Medium install friction due to 22 runtime dependencies including heavy packages like numpy, keras, and tensorboard.
- The package is actively maintained with recent commits and production-stable status, though the last release was 883 days ago.
License · maintenance · safety
Apache 2.0 (permissive) — Licensed under Apache 2.0 (permissive), allowing commercial and private use with minimal restrictions—suitable for most projects.
last release 2024-03-14 (883 days) · last repo commit 2026-08-14 · 197,024 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 143,548 downloads/mo, #11,173 on PyPI
Alternatives
Verify before relying
pip install tensorflow-aarch64==2.16.1
import tensorflow as tf
model = tf.keras.Sequential([tf.keras.layers.Dense(10)])- Whether this aarch64-specific build offers performance or compatibility advantages over the generic tensorflow package on ARM systems.
- Whether all 22 runtime dependencies are required for basic usage or if some are optional for specific features.
- Compatibility with non-NVIDIA accelerators despite CUDA classifiers.
What it is and what it does
TensorFlow-aarch64 is a specialized build of the TensorFlow machine learning framework optimized for ARM processors. It provides the same core functionality as standard TensorFlow—building and training neural networks, numerical computation, and inference—but compiled and packaged specifically for aarch64 systems, making it suitable for deployment on ARM-based servers, edge devices, and mobile platforms.
The package brings TensorFlow's full ecosystem to aarch64 platforms, including support for GPU acceleration via NVIDIA CUDA (versions 12 and 12.2), integration with keras for high-level model building, and tensorboard for visualization. With 22 runtime dependencies spanning numerical libraries (numpy, h5py), protocol buffers, and grpcio, it is a heavyweight framework designed for serious machine learning workloads rather than lightweight inference.
Use it for
- Train and deploy deep learning models on ARM-based servers or data center hardware.
- Build machine learning pipelines on edge devices or systems running aarch64 Linux.
- Develop computer vision or NLP applications targeting ARM processors with optional GPU acceleration.
- Perform numerical research and scientific computing on aarch64 clusters or cloud instances.
- Deploy inference models on ARM-based mobile or embedded systems.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are developing or deploying machine learning on aarch64 systems.
The package is production-stable, actively maintained, permissively licensed, and has no known vulnerabilities. Install friction is moderate due to dependencies, but unavoidable for serious ML work. Not necessary if you only target other architectures or do not require ARM-specific optimization.
Install
tensorflow-aarch64 on PyPI
Before you install
Medium install friction due to 22 runtime dependencies including heavy packages like numpy, keras, and tensorboard. The package is actively maintained with recent commits and production-stable status, though the last release was 883 days ago.
Requires Python 3.9 or later; aarch64 architecture required; GPU support requires NVIDIA CUDA 12 or 12.2 (optional but recommended for performance).
License in practice
Licensed under Apache 2.0 (permissive), allowing commercial and private use with minimal restrictions—suitable for most projects.
Quickstart
pip install tensorflow-aarch64==2.16.1
import tensorflow as tf
model = tf.keras.Sequential([tf.keras.layers.Dense(10)])Verify before relying
- Whether this aarch64-specific build offers performance or compatibility advantages over the generic tensorflow package on ARM systems.
- Whether all 22 runtime dependencies are required for basic usage or if some are optional for specific features.
- Compatibility with non-NVIDIA accelerators despite CUDA classifiers.
Package facts
| License | Apache 2.0 permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 22 packagesabsl-pyastunparseflatbuffersgastgoogle-pastah5pylibclangml-dtypesopt-einsumpackagingprotobufrequestssetuptoolssixtermcolortyping-extensionswraptgrpciotensorboardkerastensorflow-io-gcs-filesystemnumpy |
| Maintenance | Actively maintained 883 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 143,548 / month, #11,173 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 :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: tensorflow_aarch64-2.16.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; tensorflow_aarch64-2.16.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; tensorflow_aarch64-2.16.1-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; tensorflow_aarch64-2.16.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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See also tensorflow · tensorflow-cpu · tf-nightly-cpu · tensorflow-cpu-aws · tf-nightly · tensorflow-intel · torch · nvidia-cudnn-cu12 · nvidia-cudnn-cu11 · nvidia-cudnn-cu13