Awesome

Comet for Machine Learning Experiment Management

Our Misson: Comet is doing for ML what GitHub did for code. We allow data science teams to automagically track their datasets, code changes, experimentation history and production models creating efficiency, transparency, and reproducibility.

We all strive to be data driven and yet every day valuable experiment results are lost and forgotten. Comet provides a dead simple way of fixing that. It works with any workflow, any ML task, any machine, and any piece of code.

Examples Repository

This repository contains examples of using Comet in many Machine Learning Python libraries, including fastai, torch, sklearn, chainer, caffe, keras, tensorflow, mxnet, Jupyter notebooks, and with just pre Python.

If you don't see something you need, just let us know! See contact methods below.

Documentation

Full documentation and additional training examples are available on http://www.comet.com/docs/v2

Installation

• Sign up for free!

• Install Comet from PyPI:

pip install comet_ml

Comet Python SDK is compatible with: Python 3.5-3.13.

Tutorials + Examples

• fastai
• keras
• pytorch
• scikit
• tensorflow

Support

Have questions? We have answers -

• Email us at info@comet.com
• For the fastest response, ping us on Slack

Want to request a feature? We take feature requests through github at: https://github.com/comet-ml/issue-tracking

Feature Spotlight

Check out new product features and updates through our Release Notes. Also check out our blog.