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.
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
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
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
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
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
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.
To get started using and learning more about the Core APIs, check out the Quickstart for TensorFlow Core.