Libraries & extensions

State-of-the-art algorithms for training, serving and interpreting models that use decision forests for classification, regression and ranking.

A library for reusable machine learning. Download and reuse the latest trained models with a minimal amount of code.

The TensorFlow Model Optimization Toolkit is a suite of tools for optimizing ML models for deployment and execution.

A library for building recommender system models.

A library for flexible, controlled and interpretable ML solutions with common-sense shape constraints.

A library of computer graphics functionalities ranging from cameras, lights, and materials to renderers.

An open source framework for machine learning and other computations on decentralized data.

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis.

Tensor2Tensor is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.

A Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy.

A library for reinforcement learning in TensorFlow.

A research framework for fast prototyping of reinforcement learning algorithms.

TRFL (pronounced “truffle”) is a library for reinforcement learning building blocks created by DeepMind.

A language for distributed deep learning, capable of specifying a broad class of distributed tensor computations.

Makes it easy to store and manipulate data with non-uniform shape, including text (words, sentences, characters), and batches of variable length.

Supports working with Unicode text directly in TensorFlow.

TensorFlow Ranking is a library for Learning-to-Rank (LTR) techniques on the TensorFlow platform.

Magenta is a research project exploring the role of machine learning in the process of creating art and music.

Nucleus is a library of Python and C++ code designed to make it easy to read, write and analyze data in common genomics file formats like SAM and VCF.

A library from DeepMind for constructing neural networks.

A learning framework to train neural networks by leveraging structured signals in addition to feature inputs.

Extra functionality for TensorFlow, maintained by SIG Addons.

Dataset, streaming, and file system extensions, maintained by SIG IO.

TensorFlow Quantum is a quantum machine learning library for rapid prototyping of hybrid quantum-classical ML models.

Streamline and generate Model Cards—machine learning documents that provide context and transparency into a model's development and performance.

A library to help create and train models in a way that reduces or eliminates user harm resulting from underlying performance biases.

A library that enables easy computation of commonly-identified fairness metrics for binary and multiclass classifiers.

TensorFlow Cloud is a library to connect your local environment to Google Cloud.

A collection of text- and NLP-related classes and ops ready to use with TensorFlow 2.

A library to analyze training and serving data to compute descriptive statistics, infer schemas, and detect anomalies.

A library for recording and retrieving MLOps metadata associated with machine learning workflows.

A library for deep analysis of model results beyond simple training metrics, to measure edge and corner cases and bias.

A flexible, high-performance serving system for machine learning models, designed for production environments

A library for large-scale feature engineering and eliminating training-serving skew.

A hardware-accelerated library for training and deploying ML models using JavaScript or Node.js.

An end-to-end platform for deploying production ML pipelines.

A collection of community projects to build new components, examples, libraries, and tools for TFX.