MXNet vs Trax

Trax vs MXNet: What are the differences?

What is Trax? Your path to advanced deep learning (By Google). It helps you understand and explore advanced deep learning. It is actively used and maintained in the Google Brain team You can use It either as a library from your own python scripts and notebooks or as a binary from the shell, which can be more convenient for training large models. It includes a number of deep learning models (ResNet, Transformer, RNNs, ...) and has bindings to a large number of deep learning datasets, including Tensor2Tensor and TensorFlow datasets. It runs without any changes on CPUs, GPUs and TPUs..

What is 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.

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

Some of the features offered by Trax are:

• Advanced deep learning
• Actively used and maintained in the Google Brain team
• Runs without any changes on CPUs, GPUs and TPUs

On the other hand, MXNet provides the following key features:

• Lightweight
• Portable
• Flexible distributed/Mobile deep learning

MXNet is an open source tool with 18.4K GitHub stars and 6.56K GitHub forks. Here's a link to MXNet's open source repository on GitHub.