DeepSpeed vs baikal

DeepSpeed vs baikal: 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, baikal is detailed as "A graph-based functional API for building complex scikit-learn pipelines". It is a graph-based, functional API for building complex machine learning pipelines of objects that implement the scikit-learn API. It is mostly inspired on the excellent Keras API for Deep Learning, and borrows a few concepts from the TensorFlow framework and the (perhaps lesser known) graphkit package. It aims to provide an API that allows to build complex, non-linear machine learning pipelines.

DeepSpeed and baikal 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, baikal provides the following key features:

• Build non-linear pipelines effortlessly
• Handle multiple inputs and outputs
• Add steps that operate on targets as part of the pipeline

DeepSpeed and baikal are both open source tools. It seems that DeepSpeed with 2.22K GitHub stars and 166 forks on GitHub has more adoption than baikal with 553 GitHub stars and 23 GitHub forks.