TF Watcher
Monitor your ML jobs on mobile devices, especially for Google Colab / Kaggle
Overview
TF Watcher is a Python package and web app that allows users to monitor their Machine Learning training or testing process on mobile devices. It is specifically designed for use with Google Colab, Azure ML, and Kaggle. The project includes two subprojects: TF Watcher Python Package and TF Watcher Web App. The Python package is built using TensorFlow and Pyrebase, allowing users to easily monitor the metrics they want and write them to a Firebase realtime database. The web app is built using React, Chakra-UI, Recharts, and Firebase, and it displays the logs using charts.
Features
- Simple to use Python package
- Designed for monitoring Machine Learning training or testing process on mobile devices
- Built for Google Colab, Azure ML, and Kaggle
- TF Watcher Python Package uses TensorFlow and Pyrebase
- TF Watcher Web App uses React, Chakra-UI, Recharts, and Firebase
Summary
- React
React is a widely used JavaScript library for building user interfaces and single-page applications. It follows a component-based architecture and uses a virtual DOM to efficiently update and render UI components
- Chakra UI
Chakra UI is a popular open-source React component library that provides a set of accessible and customizable UI components to help developers create modern web applications.
- Firebase
Firebase offers a comprehensive set of features, including real-time database, authentication, hosting, cloud functions, storage, and more. Firebase provides an easy-to-use interface and allows developers to focus on building features rather than managing infrastructure.
- Recharts
Recharts is a powerful and easy-to-use React library for building customizable and interactive charts. Built on D3.js, it offers a wide range of pre-built chart types, such as line, bar, pie, and scatter charts, all of which can be composed with a declarative syntax.
