Environments and Dependencies

Introduction

Environments are customized, pre-installed collections of dependencies and packages that can be created by Admins and distributed to Users on the DataScience.com Platform.

Browsing Environments

List page

To learn more about what environments are available in your instance, navigate to the Environments page in the menu bar at the top of the page. On this page, you will see all of the environments in your instance and what tools are available for each of them. For more details, you can click on an environment card.

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Details Page

On the Overview page for each environment, you can see a description, README provided by your Platform Admin, and the list of installed dependencies. This list is searchable.

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On the Build Logs tab, you can see and download the installation logs created during the building process.

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Lastly, the Dockerfile tab displays the commands that were used to create this environment.

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Launching Environments

To run analyses or create outputs on the Platform, you can launch Docker containers to host your work. When spawning containers, you can configure the environment that you want to run. Choose an environment from the dropdown menu. Only environments that are available for your tool will be available to select and run.

Adding Additional Requirements

When configuring your container, you can specify additional requirements to install at runtime by clicking the Add Requirements button on the action modals. Depending on the language selected, you’ll find forms for pip, R (which runs install.packages("...")), and apt dependencies. When you include a list (in text file format) of packages for these installers, the Platform will install them before running your code.

Warning

If you are using the Conda package manager, supplying pip dependencies via Add Requirements is not currently supported. With Conda-based environments, avoid pip-only dependencies where possible. If this is not an option, install the required pip dependencies during environment building.

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Notice that the form above points to a text file called requirements.txt. While you can call that file anything you want, it must be formatted as a different package name on each line. The apt-get and R installers accept only package names and will install the latest stable version. The pip installer accepts either package name or a version-locked name, as in the example below:

# install locked version of plotly
plotly==2.0.12
# install latest version of seaborn
seaborn

For pip only, the comments in the example above are valid syntax.