---
id: tensorflow-cpu-aws
version: "2.15.1"
license: Apache 2.0
license_treatment: permissive
maintenance: active
---

# tensorflow-cpu-aws — TensorFlow is an open source machine learning framework for everyone.

License: permissive · Maintenance: active · Downloads: 159.0K/mo

## 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 above — verify before relying.

TensorFlow CPU for AWS is a machine learning framework optimized for numerical computation on CPU-based AWS infrastructure, supporting deployment across diverse platforms and devices.

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

pip install tensorflow-cpu-aws
uv add tensorflow-cpu-aws
poetry add tensorflow-cpu-aws

## Installing tensorflow-cpu-aws

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.

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)])

Requires Python 3.9 or later; large dependency tree may require significant disk space and installation time.

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_current
- Install friction: medium
- Maintenance: active
- Downloads: 159.0K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

machine learning framework, deep learning library, numerical computation, tensorflow cpu aws, neural network training, scientific computing, model deployment, machine-learning, deep-learning, aws-optimized

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