Supercharging Machine Learning
Full documentation and additional training examples are available on http://www.comet.ml/docs/
- 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/
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|comet_ml-1.0.37-py3-none-any.whl (71.0 kB) Copy SHA256 hash SHA256||Wheel||py3||Nov 14, 2018|
|comet_ml-1.0.37.tar.gz (53.9 kB) Copy SHA256 hash SHA256||Source||None||Nov 14, 2018|