Buying snapshot
Scroll sideways to see all columns. With a keyboard, focus this table and use the arrow keys.
| Decision point | What TensorFlow states |
|---|---|
License | Apache License 2.0 for the tensorflow/tensorflow repository. |
Installation | pip install tensorflow installs the current release, with support for CUDA-enabled GPU cards on Ubuntu and Windows. A smaller CPU-only package is pip install tensorflow-cpu. The install guide also covers a Docker container and building from source. Nightly test builds ship as tf-nightly and tf-nightly-cpu on PyPI. |
Official builds | Linux CPU and GPU, macOS, Windows CPU and GPU, Android and Raspberry Pi. The Linux XLA build is marked TBA. Community builds for more platforms are listed in the SIG Build Community Builds Table. |
Where to get help | GitHub Issues for requests and bugs, the TensorFlow Forum for general questions and discussion, and Stack Overflow for specific questions. |
Release and security notices | Release announcements and security updates go out on the announce@tensorflow.org mailing list. |
Our take
Choose TensorFlow if your team writes models in Python or C++ and wants one open-source framework to take them from data pipelines and training to browser, mobile, edge and production pipeline targets. If you only need one of those pieces, you still take on a large framework with many APIs, so weigh that cost against the reach you will actually use.
Evaluate TensorFlow
Comparisons
Where it stands out
Many places to run a model. TensorFlow.js trains and runs models directly in the browser using JavaScript or Node.js. LiteRT deploys ML on mobile and edge devices such as Android, iOS, Raspberry Pi and Edge TPU. A team can keep one framework when a model must leave the server.
Pipelines from data to production. tf.data preprocesses data and builds input pipelines for ML models, and TFX creates production ML pipelines and implements MLOps best practices. The site also lists developer tools to evaluate models, optimize performance and productionize ML workflows.
Starting assets, not a blank page. Kaggle Models offers pre-trained models ready for fine-tuning and deployment, TensorFlow Datasets offers standard datasets for initial training and validation, and the site lists pretrained models and ready-to-use datasets for image, text, audio and video use cases.
A high-level API with tracking. tf.keras is TensorFlow's high-level API for creating models; the home page sample loads MNIST, builds a Sequential model and trains it for 5 epochs in a few lines. TensorBoard visualizes and tracks model development.
Specialised libraries. TensorFlow GNN analyzes relational data with graph neural networks, and TensorFlow Agents builds recommendation systems with reinforcement learning. TensorFlow says Spotify used its ecosystem to design an offline simulator and train RL agents to generate playlists.
Product screenshot
Trade-offs to weigh
Only two languages have stable APIs. TensorFlow provides stable Python and C++ APIs; the API for other languages is not guaranteed to stay backward compatible. Teams building in another language should expect to revisit code on upgrades.
GPU support outside CUDA needs plugins. Other devices, such as DirectX and macOS Metal, are supported through Device Plugins rather than the main package, so Mac and DirectX GPU setups have an extra piece to install and test.
Patching a specific version yourself means rebuilding the package. To fix a bug or security issue in a specific version, the project's guide says to clone the repository, switch to that version's branch (for example r2.8), cherry-pick the fixes, run the tests and build the pip package from source. Plan engineering time for this if you stay on one version.
Many entry points to standardise. With tf.keras, tf.data, TFX, TensorFlow.js, LiteRT and domain libraries in one ecosystem, a team needs its own rules for which parts it adopts, or projects drift apart.
Public signals
About these signals
Verified factual signals from public sources. They indicate observable activity or interest, not total adoption, product quality, or cost.
5.5k GitHub commits 90d201.4k GitHub stars0 vulnerabilities across 2 packagesOpenSSF score 7.5/10
See all signals from 11 sources
npm · @tensorflow/tfjs@4.22.0 · PyPI · tensorflow@2.21.0
Alternatives to TensorFlow
The reviewed substitutes for TensorFlow among the deep learning frameworks, and what would make each one the better answer.
Direct alternatives
Reviewed substitutes: products bought for the same job, where a team picks one.
- PyTorch
- TensorFlow and PyTorch are open-source frameworks for building and training deep learning models. TensorFlow, from Google, offers the Keras API and tools to run models on servers, phones and in the browser, while PyTorch, governed by the PyTorch Foundation, is known for its Pythonic style and its large ecosystem for vision and language work.Applies to: Choosing between two products of the same kind for one job.
Related technologies
Normally used together rather than chosen between, so these are not alternatives.
- MLflow
- A deep learning framework defines and trains the model; a tracker records the runs, parameters and artefacts that training produces. Trackers ship integrations for both frameworks, so they are used together rather than chosen between.Applies to: Whether training code needs a separate tool to record what each run produced.