When configuring a compute capsule, there are a number of
starter environments to select.

An environment consists of an operating system, for example Ubuntu Linux 16.04 or 18.04 and a minimal set of accompanying packages.

Some environments are pre-configured to support particular languages; these include a language interpreter, compiler, and/or framework. The 'Python 3.7.0' and the 'R 3.4.4' in the graphic above, environments are pre-configured for specific languages.

You can start from a relatively blank slate, such as Ubuntu Linux 18.04, or 18.04 with GPU support. This is an environment without scientific programming languages pre-installed and is a good choice for users with Linux programming experience.

All environments will have apt-get, a system level package manager, available by default. Language specific environments will have language-specific package managers available for example, CRAN or pip.

Hint: For multiple languages, or a complex workflow, choose a Python environment even if your code isn't, or mostly isn't, Python. As of February 2019, these environments have the Conda installer available, which is the easiest way to install new languages through conda channels.

What's next? 

After selecting an environment, customize it by using the built-in package management system or the PostInstall script.

Switching between different base environments is also how to access a GPU.

Did this answer your question?