Cortex.dev vs DeepSpeed

DeepSpeed vs Cortex.dev: What are the differences?

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; Cortex.dev: Deploy machine learning models in production. It is an open source platform that takes machine learning models—trained with nearly any framework—and turns them into production web APIs in one command.

DeepSpeed and Cortex.dev can be categorized 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, Cortex.dev provides the following key features:

• Autoscaling
• Supports TensorFlow, Keras, PyTorch, Scikit-learn, XGBoost, and more
• CPU / GPU support

DeepSpeed and Cortex.dev are both open source tools. Cortex.dev with 3.52K GitHub stars and 252 forks on GitHub appears to be more popular than DeepSpeed with 1.98K GitHub stars and 134 GitHub forks.