Supercharging Machine Learning
Project description
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
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for comet_ml-1.0.36-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 68d45d0f4d8443312415176cb64d8a487942f32cce0d36ea25e1cb83ef9c58db |
|
MD5 | 6f3328b7663f35aba67c08d6b538fd96 |
|
BLAKE2b-256 | 8f4a4d8c849edf3842162bfcedd30c76f968788eb794f31b7fb339c272cc7f30 |