DeepSpeed vs Gluon

DeepSpeed vs Gluon: What are the differences?

What is DeepSpeed? A deep learning optimization library that makes distributed training easy, efficient, and effective (By Microsoft). It is a deep learning optimization library that makes distributed training easy, efficient, and effective. It can train DL models with over a hundred billion parameters on the current generation of GPU clusters while achieving over 5x in system performance compared to the state-of-art. Early adopters of DeepSpeed have already produced a language model (LM) with over 17B parameters called Turing-NLG, establishing a new SOTA in the LM category.

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.

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

Some of the features offered by DeepSpeed are:

• Distributed Training with Mixed Precision
• Model Parallelism
• Memory and Bandwidth Optimizations

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

• 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.

DeepSpeed is an open source tool with 1.98K GitHub stars and 134 GitHub forks. Here's a link to DeepSpeed's open source repository on GitHub.