DeepSpeed vs Hummingbird

DeepSpeed vs Hummingbird: 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, Hummingbird is detailed as "Compile trained ML models into tensor computation (By Microsoft)". It is a library for compiling trained traditional ML models into tensor computations. It allows users to seamlessly leverage neural network frameworks (such as PyTorch) to accelerate traditional ML models.

DeepSpeed and Hummingbird 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, Hummingbird provides the following key features:

• Current and future optimizations implemented in neural network frameworks
• Native hardware acceleration
• Convert your trained traditional ML models into PyTorch

DeepSpeed is an open source tool with 2.61K GitHub stars and 221 GitHub forks. Here's a link to DeepSpeed's open source repository on GitHub.