ml-dash 0.3.25

A Beautiful Visualization Dashboard For Machine Learning

For detailed codumentation, seeml-dash-tutorial

ML-dash replaces visdom and tensorboard. It allows you to see real-time updates, review 1000+ of experiments quickly, and dive in-depth into individual experiments with minimum mental effort.

• Parallel Coordinates

• Aggregating Over Multiple Runs (with different seeds)

• Preview Videos, ``matplotlib`` figures, and images.

Usage

To make sure you install the newest version of ml_dash:

conda install pycurl
pip install ml-logger ml-dash --upgrade --no-cache

Just doing this would not work. The landscape of python modules is a lot messier than that of javascript. The most up-to-date graphene requires the following versioned dependencies:

yes | pip install graphene==2.1.3
yes | pip install graphql-core==2.1
yes | pip install graphql-relay==0.4.5
yes | pip install graphql-server-core==1.1.1

There are two servers:

1. a server that serves the static web-application files ml_dash.app

   This is just a static server that serves the web application client.

   To run this:

   python -m ml_dash.app

2. the visualization backend ml_dash.server

   This server usually lives on your logging server. It offers a graphQL API backend for the dashboard client.

   python -m ml_dash.server --logdir=my/folder

   Note: the server accepts requests from ``localhost`` only by default for safety reasons. To overwrite this, see the documentation here: ml-dash-tutorial

Implementation Notes

See https://github.com/episodeyang/ml_logger/tree/master/ml-dash-server/notes/README.md