Skip to main content

Supercharging Machine Learning

Project description

Comet.ml
=======

.. image:: https://img.shields.io/pypi/v/comet_ml.svg
:target: https://pypi.python.org/pypi/comet_ml
:alt: Latest PyPI version


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 at https://www.comet.ml


Getting started: 30 seconds to Comet.ml
---------------------------------------

The core class of Comet.ml is an **Experiment**, a specific run of a
script that generated a result such as training a model on a single set
of hyper parameters. An Experiment will automatically log scripts output (stdout/stderr), code, and command
line arguments on **any** script and for the supported libraries will
also log hyper parameters, metrics and model configuration.

Here is the Experiment object:


from comet_ml import Experiment
experiment = Experiment(api_key="YOUR_API_KEY")

# Your code.



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.

For a more in-depth tutorial about Comet.ml, you can check out or docs http:/www.comet.ml/docs/
--------------


Project details


Release history Release notifications | RSS feed

This version

1.0.4

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

comet_ml-1.0.4.tar.gz (24.2 kB view hashes)

Uploaded Source

Built Distribution

comet_ml-1.0.4-py3-none-any.whl (33.7 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page