Trax vs cnvrg.io

Trax vs cnvrg.io: 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 cnvrg.io? An end-to-end machine learning platform to build and deploy AI models at scale. It is an AI OS, transforming the way enterprises manage, scale and accelerate AI and data science development from research to production. The code-first platform is built by data scientists, for data scientists and offers unrivaled flexibility to run on-premise or cloud.

Trax and cnvrg.io belong to "Machine Learning Tools" category of the tech stack.

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, cnvrg.io provides the following key features:

• Machine Learning Pipelines
• AI Library
• Open Compute

Trax is an open source tool with 4.54K GitHub stars and 354 GitHub forks. Here's a link to Trax's open source repository on GitHub.