tensorflow-cpu-aws
TensorFlow is an open source machine learning framework for everyone.
Decision gist · record as of 2026-08-14
Yes, with conditions. TensorFlow CPU for AWS is production-stable and actively maintained, suitable for machine learning projects on AWS CPU infrastructure. However, verify that this AWS-specific variant offers advantages over the standard TensorFlow CPU distribution, and check whether version 2.15.1 (released 2024-03-14) meets your recency requirements. The medium install friction and large dependency footprint are expected trade-offs for a comprehensive ML framework.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.9 or later; large dependency tree may require significant disk space and installation time.
- Medium install friction due to 22 runtime dependencies including numpy, protobuf, keras, and tensorflow-estimator.
- The package is actively maintained with recent commits and production-stable status, though the dependency footprint is substantial.
License · maintenance · safety
Apache 2.0 (permissive) — Licensed under Apache 2.0 (permissive), allowing commercial use, modification, and redistribution with minimal restrictions—suitable for proprietary 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) · 158,957 downloads/mo, #10,710 on PyPI
Alternatives
Verify before relying
pip install tensorflow-cpu-aws==2.15.1
import tensorflow as tf
model = tf.keras.Sequential([tf.keras.layers.Dense(10)])- Whether this AWS-specific variant offers performance or integration benefits over the standard TensorFlow CPU distribution
- Compatibility and performance characteristics on non-AWS CPU infrastructure
- Whether the 883 days since release (2024-03-14) indicates this version is outdated relative to current TensorFlow releases
What it is and what it does
TensorFlow CPU for AWS is a machine learning and numerical computation library designed for high-performance operations on CPU-based systems, particularly within AWS environments. It provides a flexible architecture for building and training neural networks, supporting deployment from development machines to production clusters and edge devices. The package includes Keras for high-level model building, TensorBoard for visualization, and TensorFlow Estimator for structured workflows.
The framework pulls in a substantial dependency chain (22 runtime packages) covering numerical operations (numpy, h5py), protocol buffers for serialization, and specialized ML components. It targets researchers, engineers, and developers working on machine learning projects and is classified as production-stable. The active maintenance status and large community (197024 GitHub stars) indicate ongoing support, though the 883-day gap since the latest release warrants checking whether newer versions are available.
Use it for
- Build and train deep neural networks for image classification, NLP, or time-series prediction tasks.
- Deploy machine learning models on AWS CPU instances for inference at scale.
- Perform numerical simulations and scientific computing requiring automatic differentiation.
- Prototype machine learning experiments using Keras high-level APIs before production deployment.
- Integrate TensorFlow workflows into data pipelines using TensorFlow Estimator for structured model training.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
TensorFlow CPU for AWS is production-stable and actively maintained, suitable for machine learning projects on AWS CPU infrastructure. However, verify that this AWS-specific variant offers advantages over the standard TensorFlow CPU distribution, and check whether version 2.15.1 (released 2024-03-14) meets your recency requirements. The medium install friction and large dependency footprint are expected trade-offs for a comprehensive ML framework.
Install
tensorflow-cpu-aws on PyPI
Before you install
Medium install friction due to 22 runtime dependencies including numpy, protobuf, keras, and tensorflow-estimator. The package is actively maintained with recent commits and production-stable status, though the dependency footprint is substantial.
Requires Python 3.9 or later; large dependency tree may require significant disk space and installation time.
License in practice
Licensed under Apache 2.0 (permissive), allowing commercial use, modification, and redistribution with minimal restrictions—suitable for proprietary projects.
Quickstart
pip install tensorflow-cpu-aws==2.15.1
import tensorflow as tf
model = tf.keras.Sequential([tf.keras.layers.Dense(10)])Verify before relying
- Whether this AWS-specific variant offers performance or integration benefits over the standard TensorFlow CPU distribution
- Compatibility and performance characteristics on non-AWS CPU infrastructure
- Whether the 883 days since release (2024-03-14) indicates this version is outdated relative to current TensorFlow releases
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-dtypesnumpyopt-einsumpackagingprotobufsetuptoolssixtermcolortyping-extensionswrapttensorflow-io-gcs-filesystemgrpciotensorboardtensorflow-estimatorkeras |
| Maintenance | Actively maintained 883 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 158,957 / month, #10,710 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 :: 11.8Intended 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 :: 3.9Topic :: Scientific/EngineeringTopic :: Scientific/Engineering :: Artificial IntelligenceTopic :: Scientific/Engineering :: MathematicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python Modules |
Evidence: tensorflow_cpu_aws-2.15.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; tensorflow_cpu_aws-2.15.1-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl; tensorflow_cpu_aws-2.15.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl
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See also tensorflow-cpu · tensorflow · tensorflow-intel · tf-nightly-cpu · tensorflow-aarch64 · tf-nightly · tf-models-nightly · torch · paddlepaddle · tflite-runtime