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karllessard merged 2 commits into from
Sep 9, 2020
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Updated README.md to describe our API layers. #108

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Updated README.md to describe our API layers.
deansher Sep 9, 2020
accfa08
Updated README.md to state that `tensorflow-keras` is early WIP; only…
deansher Sep 9, 2020
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25 changes: 19 additions & 6 deletions README.md
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Expand Up @@ -25,16 +25,29 @@ migrated from Bazel to Maven, which is more familiar for most Java developers.
The following describes the layout of the repository and its different artifacts:

* `tensorflow-core`
* All artifacts that build up the core language bindings of TensorFlow for Java.
* Those artifacts provide the minimal support required to use the TensorFlow runtime on a JVM.
* All artifacts that build up the core language bindings of TensorFlow for Java
* Intended audience: projects that provide their own APIs or frameworks on top of
TensorFlow and just want a thin layer to access the TensorFlow runtime from the JVM

* `tensorflow-framework`
* High-level APIs built on top of the core libraries to simplify neural network training and inference
using TensorFlow.
* Complete but fairly primitive API for building and training neural networks with TensorFlow
* Intended audience: expert neural network developers who prefer to make explicit, detailed decisions
about their models and training algorithms

* `tensorflow-keras` (early WIP; only defined in `dev` profile)
* Partially covers the framework API to allow simpler definition of models and training algorithms
* Intended to be familiar if you know the Python Keras API, but prioritizes clean, idiomatic Java
over fidelity to Python
* Provides defaults based on common best practices
* Allows developers to selectively be more explicit by overriding defaults or dipping into the framework API
* Intended audience: neural network developers across the spectrum from beginner to expert who prefer to
rely mostly on best-practice defaults and then selectively fine-tune

* `ndarray`
* Generic utility library for n-dimensional data I/O operations. It is used by TensorFlow without depending
on it, making its usage eligible to any type of projects, using TensorFlow or not.
* Generic utility library for n-dimensional data I/O operations
* Used by TensorFlow but does not depend on TensorFlow
* Intended audience: any developer who needs a Java n-dimensional array implementation, whether or not they
use it with TensorFlow

## Building Sources

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