MXNet vs Tensor2Tensor

MXNet vs Tensor2Tensor: What are the differences?

MXNet: A flexible and efficient library for deep learning. A deep learning framework designed for both efficiency and flexibility. It allows you to mix symbolic and imperative programming to maximize efficiency and productivity. At its core, it contains a dynamic dependency scheduler that automatically parallelizes both symbolic and imperative operations on the fly; Tensor2Tensor: 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.

MXNet and Tensor2Tensor can be primarily classified as "Machine Learning" tools.

Some of the features offered by MXNet are:

• Lightweight
• Portable
• Flexible distributed/Mobile deep learning

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)

MXNet and Tensor2Tensor are both open source tools. It seems that MXNet with 18.5K GitHub stars and 6.58K forks on GitHub has more adoption than Tensor2Tensor with 9.7K GitHub stars and 2.51K GitHub forks.