ml-dash 0.0.1

A Beautiful Visualization Dashboard For Machine Learning

Downloads

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.