Anaconda Enterprise makes it easy for you to create and share interactive data visualizations, live notebooks or machine learning models built using popular libraries such as Bokeh and HoloViews.
To get you started quickly, Anaconda Enterprise provides sample projects of Bokeh applications for clustering and cross filtering data. There are also several examples of AE5 projects that use PyViz here.
You can also watch this video to see how to use Anaconda Enterprise to create a Jupyter Notebook that uses Python 3.6 and matplotlib visualizations:
Follow these steps to create an interactive plot:
From the Projects view, select Create + > New Project and create a project from the
anaconda-project add-packages --env-spec anaconda50_py36 hvplot panel pyct bokeh=1
Watch this video to see how to configure your Anaconda Enterprise project to include the specific environments and conda packages your project depends on:
Select New > Python 3 to create a new notebook, rename it
tips.ipynb, and add the following code to create an interactive plot:
In this example, the data is being read from the Internet. Alternatively, you could download the
.csv and upload it to the project.
Open the project’s
anaconda-project.ymlfile, and add the following lines after the
description. This is the deployment command that Anaconda Enterprise will use when you deploy the notebook:
commands: scatter-plot: unix: panel serve tips.ipynb
Now you’re ready to deploy the project.
To interact with the notebook—executing its cells without making changes to it—click the deployment’s name.
To dive deeper into the world of data visualization, follow this HoloViz tutorial.
To view and monitor the logs for the deployment while it’s running, click Logs in the left menu. The app section records the initialization steps and any messages printed to standard output by the command used in your project.
You can also share the deployment with others.
Watch this short video to see how to deploy a dashboard (created from a sample project) and make it publicly available using Anaconda Enterprise: