DeepSpeed vs Neptune: What are the differences?

Developers describe DeepSpeed as "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. On the other hand, Neptune is detailed as "The most lightweight experiment tracking tool for machine learning". It brings organization and collaboration to data science projects. All the experiement-related objects are backed-up and organized ready to be analyzed, reproduced and shared with others. Works with all common technologies and integrates with other tools.

DeepSpeed and Neptune 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, Neptune provides the following key features:

• Experiment tracking
• Experiment versioning
• Experiment comparison

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.