mobyware/bokeh-python-visualization

A Bokeh project developed for learning and teaching Bokeh interactive plotting!

Getting Started with Visualizations using Bokeh & Docker

Acknowledgements

Adapted from repo by Will Koehrsen

Goal

Show how you can repeatably and scalability share analysis using Docker and and OSS data science packages in the Python ecosystem.

Setup

Run

Visualization Application

Simpliest way to run the application is to execute the command below for the Bokeh Application. You can then access the application at http://localhost:8080:

docker run -d -p 8080:8080 registry.gitlab.zoll-lifevest.com/research/mli-codebank/ml-viz-sample:1.0.0-app

Jupyter Notebooks

You can also explore with the underlying Jupyter Notebooks by running. You can then access the notebooks at http://localhost:8888:

docker run -d -p 8888:8888 registry.gitlab.zoll-lifevest.com/research/mli-codebank/ml-viz-sample:1.0.0-notebook

Docker

Images

Description	Tag
Base image	registry.gitlab.zoll-lifevest.com/research/mli-codebank/ml-viz-sample:1.0.0-base
Application image	registry.gitlab.zoll-lifevest.com/research/mli-codebank/ml-viz-sample:1.0.0-app
Notebook image	registry.gitlab.zoll-lifevest.com/research/mli-codebank/ml-viz-sample:1.0.0-notebook

Known issue

When running notebook may ask you to enter name because workspace is in use. Just enter arbitrary text in textbox.

References

1. Bokeh Python repo by Will Koehrsen
2. Data Visualizations in Bokeh
3. Jupyter Stacks
4. Bokeh