Working with Bokeh

Bokeh is a Python library for creating interactive visualizations for modern web browsers.

You can log and display Bokeh charts as interactive HTML in the Neptune app.

See in Neptune  Example script 

Before you start

Tip

To follow the guide without any setup, run the Colab example.

• Set up Neptune. Instructions:
  • Installing Neptune
  • Setting your API token
  • Setting the project name
  • Creating a Neptune project

• Install Bokeh:

  pipconda

  pip install -U bokeh
  

  conda install -c conda-forge bokeh
  

Bokeh logging example

1. Import Neptune and start a run:

   import neptune
   
   run = neptune.init_run()  # (1)!
   

   1. If you haven't set up your credentials, you can log anonymously: neptune.init_run(api_token=neptune.ANONYMOUS_API_TOKEN, project="common/bokeh-support")

2. Create a sample figure:

   import numpy as np
   from bokeh.plotting import figure
   
   N = 500
   x = np.linspace(0, 10, N)
   y = np.linspace(0, 10, N)
   xx, yy = np.meshgrid(x, y)
   d = np.sin(xx) * np.cos(yy)
   
   p = figure(tooltips=[("x", "$x"), ("y", "$y"), ("value", "@image")])
   p.x_range.range_padding = p.y_range.range_padding = 0
   
   # Pass a vector of image data as the 'image' argument
   p.image(
       image=[d], x=0, y=0, dw=10, dh=10, palette="Spectral11", level="image"
   )
   p.grid.grid_line_width = 0.5
   

3. Upload the interactive figure:

   run["interactive_img"].upload(p)
   

4. To stop the connection to Neptune and sync all data, call the stop() method:

   run.stop()
   

   Using stop() is especially important in Jupyter Notebook or other interactive sessions, as the connection otherwise remains open until the session ends completely.

5. To open the run, click the Neptune link that appears in the console output.

   Example link: https://app.neptune.ai/common/bokeh-support/e/BOK-9

6. Find the logged images in the All Metadata section.