Working with projects

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 Python, R, Bokeh and Shiny.

AE uses projects to encapsulate all of the components necessary to use or run an application: the relevant packages, channels, scripts, notebooks and other related files, environment variables, services and commands, along with a configuration file named anaconda-project.yml. For more information, see Configuring project settings.

Project components are all compressed into a .tar.bz2, .tar.gz or .zip file to make the project portable–so it’s easier to store and share with others.

To get you started, Anaconda Enterprise provides several sample projects, including the following:

  • Anaconda Distribution for Python 2.7, 3.5 and 3.6
  • A REST API written in a Jupyter Notebook
  • R notebooks & R Shiny apps
  • Bokeh applications for clustering and cross filtering data
  • TensorFlow apps for Flask, Tornado and MNIST trained data

You can access them by clicking Sample Projects from within your Projects list.

You can copy any of these sample projects to your project list, and open a session icon2 to make changes to the project.

To work with the contents offline, you can download icon3 the compressed file and then upload it to work with it within AE.

You can also create new—or upload existing—projects to add them to the server. To update the server with your changes, you commit your changes to the project.

Watch this short video to see how to use Anaconda Enterprise 5.2 to create a Jupyter Notebook that uses Python 3.6 and matplotlib visualizations:

NOTE: To maintain performance, there is a 1GB file size limit for project files you upload. Anaconda Enterprise projects are versioned using Git, so we recommend you commit only text-based files relevant to a project, and keep them under 100MB. Binary files are difficult for version control systems to manage, so we recommend using storage solutions designed for that type of data, and connecting to those data sources from within your Anaconda Enterprise sessions.

If your organization would prefer to use its own supported external version control repository, your Administrator can configure Anaconda Enterprise to use that repository instead of the internal GitHub server. After they do so, you will be prompted for your personal access token before you create your first project in Anaconda Enterprise. We recommend you create an ever-lasting token, so you can retain permanent access to your files from within Anaconda Enterprise. See Configuring your user settings for the steps to configure connectivity to your version control repository.

Select Projects from the top menu to work with your projects:

  • Use the Activity tab to view a log of all actions performed on the project.
  • Use the Jobs tab to schedule deployment jobs to run on the project.
  • Use the Share tab to share the project with collaborators.
  • Use the Settings tab to change the default editor–Jupyter Notebook–for the project. For example, if you prefer to work with Apache Zeppelin or JupyterLab, choose it as your default editor. You can also select a resource profile that meets your requirements for the project, or delete the project.

NOTE: Deleting a project is irreversible, and therefore can only be done if it is not shared and has no active editing sessions or deployments.