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:
-
Install Bokeh:
Bokeh logging example#
-
Import Neptune and start a run:
- 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")
- If you haven't set up your credentials, you can log anonymously:
-
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
-
Upload the interactive figure:
-
To stop the connection to Neptune and sync all data, call the
stop()
method:Using
stop()
is especially important in Jupyter Notebook or other interactive sessions, as the connection otherwise remains open until the session ends completely. -
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
-
Find the logged images in the All Metadata section.