DeepSpeed vs Tensor2Tensor

DeepSpeed vs Tensor2Tensor: 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, Tensor2Tensor is detailed as "Library of deep learning models & datasets designed to make deep learning more accessible (by Google Brain)". It is a library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research. It was developed by researchers and engineers in the Google Brain team and a community of users.

DeepSpeed and Tensor2Tensor 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, Tensor2Tensor provides the following key features:

• Many state of the art and baseline models are built-in and new models can be added easily
• Many datasets across modalities - text, audio, image - available for generation and use, and new ones can be added easily
• Models can be used with any dataset and input mode (or even multiple)

DeepSpeed and Tensor2Tensor are both open source tools. Tensor2Tensor with 9.7K GitHub stars and 2.51K forks on GitHub appears to be more popular than DeepSpeed with 2.13K GitHub stars and 154 GitHub forks.