SymJAX
A symbolic programming library built on JAX for concise, explicit, and optimized machine learning computations.
What is SymJAX?
SymJAX is a symbolic programming library built on JAX that simplifies graph input/output/updates for machine learning and deep learning applications. It provides an all-in-one environment with diverse functionalities like datasets, signal processing tools, and IO utilities, aiming to offer concise, explicit, and optimized computations. The library supports CPU, GPU, and TPU execution, combining fast graph optimization with a declarative programming style.
Machine learning researchers and developers who prefer symbolic/declarative programming for building and optimizing computational graphs, especially those working with JAX who need additional utilities and hardware support.
Developers choose SymJAX for its all-in-one approach, offering symbolic programming with JAX's performance, versatile tools for signal processing and datasets, and explicit graph control without the overhead of multiple libraries.
Overview
Documentation:
Use Cases
Best For
- Building and optimizing symbolic computational graphs for machine learning models
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