Bokeh

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​See code examples on GitHub​
​See results in Neptune​
​Run example in Google Colab​
Video Walkthrough
What will you get with this integration?
​Bokeh is a Python library for creating interactive visualizations for modern web browsers. It helps you build beautiful graphics, ranging from simple plots to complex dashboards with streaming datasets. With Bokeh, you can create JavaScript-powered visualizations without writing any JavaScript yourself. 
This integration lets you log interactive charts generated in bokeh to Neptune.
Introduction
This guide will show you how to:
Initialize Neptune and create a run,
Log bokeh charts to Neptune.
Step 0: Before you start
Before you start, make sure that:
You have Python 3.7+ in your system,
You are already registered user, so that you can log metadata to your private projects.
You have your NEPTUNE_API_TOKEN set to the environment variable: check this docs page.
Install neptune-client and bokeh
Depending on your operating system open a terminal or CMD and run this command. All required libraries are available via pip and conda:
pip
conda
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pip install neptune-client bokeh
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conda install -c conda-forge neptune-client bokeh
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For more help see installing neptune-client.
This integration is tested with neptune-client==0.9.19 and bokeh=2.3.2.
Step 1: Import and initialize neptune
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import neptune.new as neptune
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run = neptune.init(api_token='<your_api_token>', project='<your_project_name>')
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You can use the api_token='ANONYMOUS' and project='common/bokeh-support' to explore without having to create a Neptune account
To create your own private projects, you will need a Neptune account and your personal Neptune API token.
Executing this cell will give you a link looking like:
https://app.neptune.ai/common/bokeh-support/e/BOK-9
with common/bokeh-support replaced by your project, and BOK-9 replaced by your run.
Click on the link to open the run in Neptune. For now, it is empty but keep the tab with the run open to see what happens next.
Step 2: Log Bokeh figures to Neptune
Create a sample figure
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import numpy as np
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from bokeh.plotting import figure
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N = 500
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x = np.linspace(0, 10, N)
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y = np.linspace(0, 10, N)
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xx, yy = np.meshgrid(x, y)
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d = np.sin(xx)*np.cos(yy)
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p = figure(tooltips=[("x", "$x"), ("y", "$y"), ("value", "@image")])
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p.x_range.range_padding = p.y_range.range_padding = 0
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# must give a vector of image data for image parameter
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p.image(image=[d], x=0, y=0, dw=10, dh=10, palette="Spectral11", level="image")
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p.grid.grid_line_width = 0.5
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Log interactive figure
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run['interactive_img'].upload(neptune.types.File.as_html(p))
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Step 3: Stop logging
Once you are done logging, you should stop tracking the run using the stop() method. This is needed only while logging from a notebook environment. While logging through a script, Neptune automatically stops tracking once the script has completed execution.
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run.stop()
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Step 4: Explore the results in the Neptune
The images can be found in the All Metadata section:
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​See code examples on GitHub​
​See results in Neptune​
​Run example in Google Colab​
Conclusion
You have learned how to:
Initialize Neptune and create a run,
Log bokeh charts to Neptune.
What's next
Now that you know how to log charts to Neptune, you can learn:
What can you log and display?
How-to guides

Altair

Dalex

Last modified 2mo ago

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Contents

What will you get with this integration?

Introduction

Step 0: Before you start

Step 1: Import and initialize neptune

Step 2: Log Bokeh figures to Neptune

Create a sample figure

Log interactive figure

Step 3: Stop logging

Step 4: Explore the results in the Neptune

Conclusion

What's next