AutoMLPipeline vs DeepSpeed

AutoMLPipeline vs DeepSpeed: What are the differences?

AutoMLPipeline: A package that makes it trivial to create and evaluate machine learning pipeline architectures (by IBM). It is a package that makes it trivial to create complex ML pipeline structures using simple expressions. It leverages on the built-in macro programming features of Julia to symbolically process, manipulate pipeline expressions, and automatically discover optimal structures for machine learning prediction and classification; 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.

AutoMLPipeline and DeepSpeed can be categorized as "Machine Learning" tools.

Some of the features offered by AutoMLPipeline are:

• Pipeline API that allows high-level description of processing workflow
• Common API wrappers for ML libs including Scikitlearn, DecisionTree, etc
• Symbolic pipeline parsing for easy expression of complexed pipeline structures

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

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

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.