Gluon vs Hummingbird

Gluon vs Hummingbird: What are the differences?

What is Gluon? Deep Learning API from AWS and Microsoft. A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components.

What is Hummingbird? Compile trained ML models into tensor computation (By Microsoft). It is a library for compiling trained traditional ML models into tensor computations. It allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models.

Gluon and Hummingbird can be primarily classified as "Machine Learning" tools.

Some of the features offered by Gluon are:

• Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.
• Flexible, Imperative Structure: Gluon does not require the neural network model to be rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.
• Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.

On the other hand, Hummingbird provides the following key features:

• Current and future optimizations implemented in neural network frameworks
• Native hardware acceleration
• Convert your trained traditional ML models into PyTorch

Gluon is an open source tool with 2.32K GitHub stars and 230 GitHub forks. Here's a link to Gluon's open source repository on GitHub.