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Creating environment templates
Creating environment templates

Creating environment templates

You can create custom environment templates if you do not want to use the defaults provided by Watson Studio.

To create an environment template, you must have the Admin or Editor role within the project.

You can create environment templates for the following assets:

  • Watson Studio
    • Notebooks in the Notebook editor
    • Notebooks in RStudio
    • Models created in the Model builder
    • Model flows in the Flow editor
    • Data Refinery flows
    • Jobs that run operational assets, such as Data Refinery flows, or Notebooks in a project
  • DataStage
    • DataStage Flows

To create an environment template:

  1. Select the Environments page on the Manage tab.
  2. Click New template under Templates.
  3. Enter a name and a description.
  4. Select one of the following engine types:
    • Default: Select for Python, R, and RStudio runtimes for Watson Studio.
    • Spark: Select for Spark with Python, R, or Scala runtimes for Watson Studio.
    • GPU: Select for more computing power to improve model training performance for Watson Studio.
    • DataStage: Select for data integration with parallel engine runtime for DataStage
  5. Select the hardware configuration from the Hardware configuration drop-down menu.
  6. Select the software version if you selected a runtime of "Default," "Spark," or "GPU."
  7. If you selected "DataStage" for the runtime type, define the location:
    • IBM if the runtime is on IBM Cloud.
    • AWS (via Satellite) if the runtime is in a Satellite location. The region is automatically completed for you.

Your new environment template is listed under Environment templates on the Environments page of your project. From this page, you can update an environment template, see which runtimes are active, and track the number of capacity units per hour your runtimes have consumed so far. You can also stop runtimes from here.

Limitations

Notebook environments (Anaconda Python or R distributions):

  • You can't add a software customization to the default Python and R environment templates included in Watson Studio. You can only add a customization to an environment template that you create.
  • If you add a software customization using conda, your environment must have at least 2 GB RAM.
  • You can't customize an R environment for a notebook by installing R packages directly from CRAN or GitHub. You can check if the CRAN package you want is available only from conda channels and, if the package is available, add that package name in the customization list as r-<package-name>.
  • After you have started a notebook in an Watson Studio environment, you can't create another conda environment from inside that notebook and use it. Watson Studio environments do not behave like a Conda environment manager.

Spark environments:

  • You can't customize the software configuration of a Spark environment template.

Next steps

Learn more

Parent topic: Managing compute resources