TensorFlow 2 focuses on simplicity and ease of use, with updates like eager execution, intuitive higher-level APIs, and flexible model building on any platform.
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Essential documentation
Install TensorFlow
Install the package or build from source. GPU support for CUDA®-enabled cards.TensorFlow 2
TensorFlow 2 best practices and tools to migrate your code.Keras
Keras is a high-level API that's easier for ML beginners, as well as researchers.Customization
TensorFlow for research and experimentation. Write custom layers and models, forward pass, and training loops.Data input pipelines
Thetf.data API enables you to build complex input pipelines from simple, reusable pieces.
Estimators
A high-level API that represents a complete model, designed for scaling and asynchronous training.Save a model
Save a TensorFlow model using checkpoints or the SavedModel format.Accelerators
Distribute training across multiple GPUs, multiple machines or TPUs.Libraries and extensions
Explore additional resources to build advanced models or methods using TensorFlow, and access domain-specific application packages that extend TensorFlow.-
TensorBoard
A suite of visualization tools to understand, debug, and optimize TensorFlow programs. -
TensorFlow Hub
A library for the publication, discovery, and consumption of reusable parts of machine learning models. -
Model Optimization
The TensorFlow Model Optimization Toolkit is a suite of tools for optimizing ML models for deployment and execution. -
TensorFlow Federated
A framework for machine learning and other computations on decentralized data. -
Neural Structured Learning
A learning paradigm to train neural networks by leveraging structured signals in addition to feature inputs. -
TensorFlow Graphics
Computer graphics meets machine learning.
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Datasets
A collection of datasets ready to use with TensorFlow. -
Serving
A TFX serving system for ML models, designed for high-performance in production environments. -
Probability
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis. -
MLIR
MLIR unifies the infrastructure for high-performance ML models in TensorFlow. -
XLA
A domain-specific compiler for linear algebra that accelerates TensorFlow models with potentially no source code changes. -
SIG Addons
Extra functionality for TensorFlow, maintained by SIG Addons. -
SIG IO
Dataset, streaming, and file system extensions, maintained by SIG IO.