Customizing environment templates (Watson Studio)
You can customize the software configuration of some environment templates. All environment templates have a standard software configuration of pre-selected libraries and packages that are available when the environment runtime is started.
If certain libraries or packages are missing, you can add those libraries with any of the following methods:
- By using conda, mamba and pip directly in a notebook
- By creating a software customization in the Jupyter notebook environment template that you created
- By building a customized image
- By setting up a CRAN repository. This option is available for RStudio only. See Using libs from CRAN repositories in RStudio.
You can customize the software configuration of environment templates that you use in the following tools:
- Notebook editor (using all methods other than setting up a CRAN repository)
- JuypterLab IDE (using all methods other than setting up a CRAN repository)
- RStudio (by building a customized image or setting up a CRAN repository)
- SPSS Modeler (by building a customized image only)
You can't customize the software configuration of Spark and Hadoop environment templates that you have created.
The following table shows the installation methods that are available for customizing the software configuration of Jupyter notebook environment templates for specific tasks.
| Installation method | Customization type | Description |
|---|---|---|
| conda, mamba, or pip in notebook | Custom libraries and Python files added through notebook | - Libraries can only be used in the notebook - Use conda or mamba rather than pip where possible for better dependency management - Packages installed from anaconda.org or IBM repositories by default - Cloud Pak for Data administrator can specify internal conda and mamba channels or a corporate proxy |
| conda or mamba and pip in environment template | Declarative description of dependencies usable across notebooks within a project; the libraries and files are not persisted but installed when the runtime is started | - Environment template is accessible by all project members - Packages installed from anaconda.org or IBM repositories by default - Cloud Pak for Data administrator can specify internal conda or mamba channels or a corporate proxy |
| conda, mamba, pip, microdnf | New customized image is built by a Cloud Pak for Data administrator | - New custom image is built and uploaded from existing runtime image using Dockerfile - In addition to pip, mamba and conda packages, operating system dependencies can be installed using microdnf - Cloud Pak for Data administrator is responsible for maintaining image updates, including security patches |
The diagram illustrates possible software customization options for Jupyter notebook environment templates. Custom libraries can be added through conda, mamba, pip and microdnf. The diagram shows options for accessing libraries in the public network as well as options for customizing without public access.
Next steps
The default settings for conda, mamba and pip in an environment template require that the environment runtimes have access to the public network at the time they are started. If access to the public network is not available or desired, you can customize the conda, mamba and pip configuration to access libraries by alternate methods.
Create a software customization:
- For environment templates with public network access by:
- For environment templates without public access by:
- For environment templates using custom image by:
Parent topic: Environments