Continuous Machine Learning vs DeepSpeed

DeepSpeed vs Continuous Machine Learning: 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 Continuous Machine Learning? CI/CD for Machine Learning Projects. Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.

DeepSpeed and Continuous Machine Learning 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, Continuous Machine Learning provides the following key features:

• GitFlow for data science
• Auto reports for ML experiments
• No additional services

DeepSpeed is an open source tool with 2.7K GitHub stars and 234 GitHub forks. Here's a link to DeepSpeed's open source repository on GitHub.