A Beautiful Visualization Dashboard For Machine Learning
Project description
ML-Logger makes it easy to:
save data locally and remotely, as binary, in a transparent pickle file, with the same API and zero configuration.
write from 500+ worker containers to a single instrumentation server
visualize matplotlib.pyplot figures from a remote server locally with logger.savefig('my_figure.png?raw=true')
And ml-dash does all of these with minimal configuration — you can use the same logging code-block both locally and remotely with no code-block change.
ML-logger is highly performant – the remote writes are asynchronous. For this reason it doesn’t slow down your training even with 100+ metric keys.
Why did we built this, you might ask? Because we want to make it easy for people in ML to use the same logging code-block in all of they projects, so that it is easy to get started with someone else’s baseline.
Usage
To install ml_dash, do:
pip install ml-dash
Skip this if you just want to log locally. To kickstart a logging server (Instrument Server), run
python -m ml_dash.server
It is the easiest if you setup a long-lived instrument server with a public ip for yourself or the entire lab.
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