Comet.ml

Comet.ml is meta machine learning platform built to handle the requirements of deep learning. It’s used to track, compare, and optimize the ML and data science experiments. Comet provides tools for logging the process, visualizing results, and collaborating with teams, all within a single interface. The platform supports various machine learning frameworks and allows seamless integration into existing workflows. Simply put, Comet is doing for ML what GitHub does for code.

Examples Repository

For a better understanding of how to work with Comet.ml, you can explore the Example repository on GitHub. This repository is packed with examples demonstrating how to use Comet with a wide array of Machine Learning Python libraries, including Fastai, Torch, Scikit-learn, Chainer, Caffe, Keras, TensorFlow, MXNet and more.

The repository serves as a valuable resource for understanding how to integrate Comet into your ML workflows, regardless of the tools you’re using.

Documentation

For those looking to dive deeper, the full documentation and additional training examples are available on the Comet documentation page. There can be found everything to master Comet.ml. With clear guides, detailed references, and practical examples, it’s ultimate resource for making the most out of this platform.