A Beautiful Visualization Dashboard For Machine Learning


Keywords
ml_logger, ml-logger, ml, dash, ml-dash, ml_dash, dashboard, machine, learning, vis_server, logging, debug, debugging
License
Other
Install
pip install ml-dash==0.3.25

Documentation

ML-Dash, A Beautiful Visualization Dashboard for ML

Downloads

Dashboard that has super power

For detailed codumentation, see ml-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 ./notes/README.md