DeepSpeed vs Yellowbrick

DeepSpeed vs Yellowbrick: 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 Yellowbrick? Visual analysis and diagnostic tools to facilitate machine learning model selection. It is a suite of visual diagnostic tools called "Visualizers" that extend the scikit-learn API to allow human steering of the model selection process. In a nutshell, it combines scikit-learn with matplotlib in the best tradition of the scikit-learn documentation, but to produce visualizations for your machine learning workflow.

DeepSpeed and Yellowbrick belong to "Machine Learning Tools" category of the tech stack.

Some of the features offered by DeepSpeed are:

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

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

• Evaluate the stability and predictive value of machine learning models and improve the speed of the experimental workflow
• Provide visual tools for monitoring model performance in real-world applications
• Provide visual interpretation of the behavior of the model in high dimensional feature space.

DeepSpeed is an open source tool with 2.36K GitHub stars and 180 GitHub forks. Here's a link to DeepSpeed's open source repository on GitHub.