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Comet.ml - 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 experiments results are just lost and forgotten. Comet.ml provides a dead simple way of fixing that. Works with any workflow, any ML task, any machine and any piece of code.

Examples Repository

This repository contains examples of using Comet.ml 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.ml/docs/

Installation

• Sign up (free) on comet.ml and obtain an API key

• Install Comet.ml from PyPI:

pip install comet_ml

Comet.ml python SDK is compatible with: Python 3.5-3.9.

Tutorials + Examples

• caffe
• chainer
• fastai
• keras
• mxnet
• pytorch
• scikit
• tensorflow

We also offer Jupyter notebook examples for fastai and keras

Support

Have questions? We have answers -

• Try checking our FAQ Page
• Email us at [email protected]
• 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 checkout our articles on Medium.