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.
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.
Full documentation and additional training examples are available on http://www.comet.com/docs/v2
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Install Comet from PyPI:
pip install comet_mlComet Python SDK is compatible with: Python 3.5-3.13.
Have questions? We have answers -
- 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
Check out new product features and updates through our Release Notes. Also check out our blog.
