Skip to main content

Supercharging Machine Learning

Project description

Latest PyPI version

Documentation

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

Installation

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.com/docs/

License

Copyright (C) 2015-2020 Comet ML INC.

This package can not be copied and/or distributed without the express permission of Comet ML Inc.

Project details


Release history Release notifications | RSS feed

Download files

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

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

comet_ml-3.32.7-py3-none-any.whl (484.6 kB view details)

Uploaded Python 3

File details

Details for the file comet_ml-3.32.7-py3-none-any.whl.

File metadata

  • Download URL: comet_ml-3.32.7-py3-none-any.whl
  • Upload date:
  • Size: 484.6 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.10.6

File hashes

Hashes for comet_ml-3.32.7-py3-none-any.whl
Algorithm Hash digest
SHA256 3257daa7e39f03798ab37e05af386857774e156de5103e0742444f301a2609ba
MD5 fa56cdebb42f720917af0135a5428662
BLAKE2b-256 035bf2756f4bba6c9902c93bd49bba1fe39d0e977976a120aee9dbb9cf37efe2

See more details on using hashes here.

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