Environments

When you run analytical 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 analytical 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 analytical asset type. Note that if you have a Watson Studio Lite plan, you can’t use large environment definitions. See Watson Studio offering plans.

Reviewers: When publishing, will add a row to the table for DataStage. Column entries will be as follows: |DataStage flow | | DataStage | | Default DataStage PX| Need to find out about ‘programming language’ and ‘environment definition type’ columns. What info goes there for DataStage?

Analytical asset Programming language Tool Environment definition type Available environment definitions
Jupyter notebook Python notebook editor Anaconda Python distribution Default Python 3.7 XXS
Default Python 3.7 XS
Default Python 3.7 S
Default Python 3.6 XS *
Default Python 3.6 S *
  Python and Decision Optimization notebook editor Anaconda Python distribution Default Python 3.7 XS + DO
Default Python 3.6 XS + DO *
  Python notebook editor Spark Default Spark 3.0 & Python 3.7
Default Spark 2.4 & Python 3.7
Default Spark 2.4 & Python 3.6 *
  Python notebook editor GPU Default GPU Python 3.7
Default GPU Python 3.6 *
  R notebook editor Anaconda R distribution Default R 3.6 S
  R notebook editor Spark Default Spark 3.0 & R 3.6
Default Spark 2.4 & R 3.6
  Scala notebook editor Spark Default Spark 3.0 & Scala 2.12
Default Spark 2.4 & Scala 2.11
  R RStudio Anaconda R distribution Default RStudio L
Default RStudio M
Default RStudio XS
Data Refinery flow R Data Refinery Spark Default Spark 2.4 & R 3.6
Default Spark 2.3 & R 3.4
  R Data Refinery Spark Hadoop cluster
SPSS modeler flow SPSS algorithms Flow editor Without Spark IBM SPSS Modeler
  R Flow editor Spark Default Spark 2.4 & R 3.6
Default Spark 2.3 & R 3.4
  Scala Flow editor Spark Default Spark 3.0 & Scala 2.12
Default Spark 2.4 & Scala 2.11
Default Spark 2.3 & Scala 2.11
AutoAI experiment No coding AutoAI experiment builder NA 8 vCPU and 32 GB RAM
16 vCPU and 64 GB RAM

* Start running Python notebooks in environments with Python 3.7 as Python 3.6 is deprecated and will be removed in the near future.

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