tensorflow/tensorflow

Project overview

TensorFlow provides an end-to-end open source machine learning platform with tools and libraries for building and training ML models. It supports CPU and GPU execution across Linux, macOS, Windows, Android, and Raspberry Pi, and can be installed locally via a single pip command.

Project type

Data Processing · Infrastructure

Use cases

Coding & Development

Deployment

Refer to project documentation

License

Apache-2.0

Best for

• Researchers and developers who need a comprehensive ecosystem with Python and C++ APIs for building and training ML models locally.

Key capabilities

• Provides core tools and libraries to build and train ML models.
• Offers stable Python and C++ APIs, with non-guaranteed backward compatible APIs for other languages.
• Provides support for CUDA-enabled GPU cards (Ubuntu and Windows), DirectX, and MacOS-metal via Device Plugins.
• Maintains official builds for Linux (CPU/GPU/XLA), macOS, Windows (CPU/GPU), Android, and Raspberry Pi.

Limitations and risks

• APIs for languages outside of Python and C++ do not have guaranteed backward compatibility.
• Telemetry data collection and practices are not documented.

Getting started

• Setup is rated easy because the framework can be installed locally with a single pip command.

Evidence and sources

• README: TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researcher…
• GitHub project description: An Open Source Machine Learning Framework for Everyone
• README: See the [TensorFlow install guide](https://www.tensorflow.org/install) for the [pip package](https://www.tensorflow.org/install/pip), to [enable GPU support](https://www.tensorflo…
• README: Build Type | Status | Artifacts ----------------------------- | -------------------------------------------------------------------------------------------------------------------…
• README: ```shell $ python ``` ```python >>> import tensorflow as tf >>> tf.add(1, 2).numpy() 3 >>> hello = tf.constant('Hello, TensorFlow!') >>> hello.numpy() b'Hello, TensorFlow!' ```

Reviewed at: 2026-07-16T07:10:50Z