Content Overview Who should use the Core APIs
Core API applications
Build models and workflows
Build frameworks and tools
Build for scientific computing
Core API components
Next steps Who should use the Core APIs Core API applications Build models and workflows Build frameworks and tools Build for scientific computing Core API components Next steps The TensorFlow Core APIs provide a set of comprehensive, composable, and extensible low-level APIs for high-performance (distributed and accelerated) computation, primarily aimed at building machine learning (ML) models as well as authoring ML workflow tools and frameworks within the TensorFlow platform. These APIs provide a foundation for creating highly configurable models with fine-grained control and new frameworks from the ground up. The Core APIs can be used as an alternative to high-level machine learning APIs like Keras. These high-level APIs are best suited for general machine learning needs. They offer a variety of modules that abstract away the complexities of ML while also offering functionalities for customization through subclassing. If you are looking for an overview of TensorFlow using Keras, see the Quickstarts and Keras sections in the tutorials. tutorials Who should use the Core APIs The TensorFlow Core low-level APIs are designed with the following ML developers in mind: Researchers building complex models with high levels of configurability
Developers interested in using TensorFlow as a high-performance scientific computing platform
Framework authors building tools on top of the TensorFlow platform
High-level API users interested in:

Adding additional functionalities to their machine learning workflows such as custom layers, losses, models, and optimizers
Learning more about the inner workings of their models Researchers building complex models with high levels of configurability Developers interested in using TensorFlow as a high-performance scientific computing platform Framework authors building tools on top of the TensorFlow platform High-level API users interested in:

Adding additional functionalities to their machine learning workflows such as custom layers, losses, models, and optimizers
Learning more about the inner workings of their models Adding additional functionalities to their machine learning workflows such as custom layers, losses, models, and optimizers
Learning more about the inner workings of their models Adding additional functionalities to their machine learning workflows such as custom layers, losses, models, and optimizers Learning more about the inner workings of their models Core API applications The TensorFlow Core APIs provide access to low level functionality within the TensorFlow ecosystem. This API provides more flexibility and control for building ML models, applications, and tools, compared to high-level APIs, such as Keras. Build models and workflows The Core APIs are most commonly used to build highly customizable and optimized machine learning models and workflows. Here are some of the ways that the TensorFlow Core APIs can improve your machine learning models and workflow development: Building non-traditional models or layers that do not fully fit the structures supported by high-level APIs
Building custom layers, losses, models, and optimizers within Keras
Implementing new optimization techniques to expedite convergence during training
Creating custom metrics for performance evaluation
Designing highly-configurable training loops with support for features like batching, cross-validation, and distribution strategies Building non-traditional models or layers that do not fully fit the structures supported by high-level APIs Building custom layers, losses, models, and optimizers within Keras Implementing new optimization techniques to expedite convergence during training Creating custom metrics for performance evaluation Designing highly-configurable training loops with support for features like batching, cross-validation, and distribution strategies Build frameworks and tools The TensorFlow Core APIs can also serve as the building blocks for new high-level frameworks. Here are some examples of tools and frameworks that are created with the low-level APIs: Keras: deep learning for humans
TensorFlow Model Optimization Toolkit: a suite of tools to optimize ML models for deployment and execution
TensorFlow Graphics: a library for making useful graphics functions widely accessible Keras: deep learning for humans Keras TensorFlow Model Optimization Toolkit: a suite of tools to optimize ML models for deployment and execution TensorFlow Model Optimization Toolkit TensorFlow Graphics: a library for making useful graphics functions widely accessible TensorFlow Graphics Build for scientific computing The TensorFlow Core APIs can also be applied outside the realm of machine learning. Here are a few general-purpose use cases of TensorFlow for scientific computing: Physics simulations for solid mechanics and fluid dynamics problems
Graphics rendering applications like ray tracing
Solving constrained optimization problems Physics simulations for solid mechanics and fluid dynamics problems fluid dynamics Graphics rendering applications like ray tracing ray tracing Solving constrained optimization problems constrained optimization problems Core API components Here are some of the fundamental components that comprise TensorFlow Core’s low- level APIs. Note that this is not an all-encompassing list: Data structures : tf.Tensor, tf.Variable, tf.TensorArray
Primitive APIs: tf.shape, slicing, tf.concat, tf.bitwise
Numerical: tf.math, tf.linalg, tf.random
Functional components: tf.function, tf.GradientTape
Distribution: DTensor
Export: tf.saved_model Data structures : tf.Tensor, tf.Variable, tf.TensorArray tf.Tensor tf.Variable tf.TensorArray Primitive APIs: tf.shape, slicing, tf.concat, tf.bitwise tf.shape slicing tf.concat tf.bitwise Numerical: tf.math, tf.linalg, tf.random tf.math tf.linalg tf.random Functional components: tf.function, tf.GradientTape tf.function tf.GradientTape Distribution: DTensor DTensor Export: tf.saved_model tf.saved_model Next steps The Build with Core documentation provides tutorials of basic machine learning concepts from scratch. The tutorials in this section help you get comfortable with writing low-level code with Core APIs that you can then apply to more complex use cases of your own. Build with Core Note: You should not use the Core APIs to simply re-implement high-level APIs, and it is possible to use high-level APIs, such as Keras, with the Core APIs. Note: You should not use the Core APIs to simply re-implement high-level APIs, and it is possible to use high-level APIs, such as Keras, with the Core APIs. Note: To get started using and learning more about the Core APIs, check out the Quickstart for TensorFlow Core. Quickstart for TensorFlow Core Originally published on the TensorFlow website, this article appears here under a new headline and is licensed under CC BY 4.0. Code samples shared under the Apache 2.0 License. Originally published on the TensorFlow website, this article appears here under a new headline and is licensed under CC BY 4.0. Code samples shared under the Apache 2.0 License. Originally published on the TensorFlow website, this article appears here under a new headline and is licensed under CC BY 4.0. Code samples shared under the Apache 2.0 License. TensorFlow TensorFlow

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A Detailed Overview of TensorFlow Core APIs

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