Environments and Dependencies¶
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.
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.
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.
On the Build Logs tab, you can see and download the installation logs created during the building process.
Lastly, the Dockerfile tab displays the commands that were used to create this environment.
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
R (which runs
dependencies. When you include a list (in text file format) of packages
for these installers, the Platform will install them before running your
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.
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
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
pip only, the comments in the example above are valid syntax.