Environments

When you run operational assets in projects, the runtime environment details are specified by environment definitions.

Environment definitions specify the hardware and software configurations for the environment runtimes:

• Hardware resources include the amount of processing power and available RAM.
• Software resources include the Python, R, or Scala programming languages, a set of pre-installed libraries, and optional libraries or packages that you can specify.

Environment definitions can be defined by:

• The default environment definitions that are included with Watson Studio.
• Custom environment definitions that you create.
• Provisioning associated services, such as the IBM Analytics Engine service.

You need to specify an environment definition:

• In projects to:

  • Run operational assets in tools like the notebook editor, Data Refinery, model builder, or the flow editor.
  • Create jobs to run Data Refinery flows, SPSS Modeler flows, notebooks.
  • To launch IDEs like RStudio in Watson Studio in which to run R scripts.

All default and custom environment definitions are listed on the Environments page in the environment definitions list. Clicking an environment definition, displays the environment definition details. An environment runtime is an instantiation of the environment definition. When a runtime becomes active, it is listed on the Environments page in the active environment runtimes list.

The following table lists the default environment definitions or compute power by operational asset type. Note that if you have a Watson Studio Lite plan, you can’t use large environment definitions. See Watson Studio offering plans.

Learn more

• Python and R environments
• Spark environments
• Python GPU environments
• Environments for Satellite locations
• Rstudio environments
• Data Refinery environments
• DataStage environments
• Managing environments