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||
|Python and Decision Optimization||notebook editor||Anaconda Python distribution||
|R||notebook editor||Anaconda R distribution||
|R||RStudio||Anaconda R distribution||
|Data Refinery flow||R||Data Refinery||Spark||
|R||Data Refinery||Spark||Hadoop cluster|
|SPSS modeler flow||SPSS algorithms||Flow editor||Without Spark||IBM SPSS Modeler|
|AutoAI experiment||No coding||AutoAI experiment builder||NA||
* Start running Python notebooks in environments with Python 3.7 as Python 3.6 is deprecated and will be removed in the near future.
- Notebook environment definitions for the notebook editor
- Spark environment definitions
- Environment definitions for RStudio
- GPU environment definitions
- Environment definitions for Data Refinery
- Creating an environment definition
- Customizing environment definitions
- Stopping active runtimes when no longer needed
- Tracking capacity unit consumption of your runtimes