Trax vs Continuous Machine Learning: What are the differences?
Developers describe Trax as "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.. On the other hand, Continuous Machine Learning is detailed as "CI/CD for Machine Learning Projects". Continuous Machine Learning (CML) is an open-source library for implementing continuous integration & delivery (CI/CD) in machine learning projects. Use it to automate parts of your development workflow, including model training and evaluation, comparing ML experiments across your project history, and monitoring changing datasets.
Trax and Continuous Machine Learning 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, Continuous Machine Learning provides the following key features:
- GitFlow for data science
- Auto reports for ML experiments
- No additional services
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.