Environments (Watson Studio and IBM Knowledge Catalog)
You run assets, create jobs, and launch IDEs like RStudio or JupyterLab in a runtime environment. The runtime environment details are specified by an environment template.
Environment templates specify the hardware and software configuration of the environment runtimes:
- The hardware configuration specifies the amount of processing power and available RAM.
- The software configuration specifies the programming languages, a set of preinstalled libraries, and optional libraries or packages that you can specify.
Included environment templates
You can use the environment templates that are included in Watson Studio Runtimes to quickly get started, without having to create your own environment templates. The included environment templates are listed on the project's Environments page.
The included environments for notebooks and JupyterLab are added as an affiliate of a runtime release and prefixed with Runtime followed by the release year and release version.
A runtime release specifies a list of key data science libraries and a language version, for example Python 3.10. All environments of a runtime release are built based on the library versions that are defined in the release. This ensures the consistent use of data science libraries across all data science applications.
Runtime releases
One 22.2 Runtime release and one 23.1 Runtime release exist for different versions of Python and R:
While a runtime release is supported, IBM continues to update the library versions to address security requirements. These updates will change only the <Patch> version of the libraries, not the <Major>.<Minor> versions. Therefore, your notebook assets will continue to run.
For example: A runtime release supports TensorFlow 2.12. In Cloud Pak for Data 4.8, the runtime release contains TensorFlow 2.12.0. Although TensorFlow might be updated to version 2.12.1 or 2.12.2 in later Cloud Pak for Data 4.8.x releases, it will not be updated to version 2.13.
Library packages included in Runtimes
For specific versions of popular data science library packages that are included in Watson Studio runtimes refer to these tables:
| Library | Runtime 23.1 on Python 3.10 | Runtime 22.2 on Python 3.10 |
|---|---|---|
| Keras | 2.12 | 2.9 |
| Lale | 0.7.x | 0.6 |
| LightGBM | 3.3.5 | 3.3 |
| NumPy | 1.23.5 | 1.23 |
| ONNX | 1.13 | 1.12 |
| ONNX Runtime | 1.14 | 1.12 |
| OpenCV | 4.7 | 4.6 |
| pandas | 1.5 | 1.4 |
| PyArrow | 11.0 | 8.0 |
| PyTorch | 2.0 | 1.12 |
| scikit-learn | 1.1 | 1.1 |
| SciPy | 1.10 | 1.8 |
| SnapML | 1.13 | 1.8 |
| TensorFlow | 2.12 | 2.9 |
| XGBoost | 1.6 | 1.6 |
| Library | Runtime 23.1 on R 4.2 | Runtime 22.2 on R 4.2 |
|---|---|---|
| arrow | 11.0 | 8.0 |
| car | 3.0 | 3.0 |
| caret | 6.0 | 6.0 |
| catools | 1.18 | 1.18 |
| forecast | 8.16 | 8.16 |
| ggplot2 | 3.3 | 3.3 |
| glmnet | 4.1 | 4.1 |
| hmisc | 4.7 | 4.7 |
| keras | 2.11 | 2.9 |
| lme4 | 1.1 | 1.1 |
| mvtnorm | 1.1 | 1.1 |
| pandoc | 2.12 | 2.12 |
| psych | 2.2 | 2.2 |
| python | 3.10 | 3.10 |
| randomforest | 4.7 | 4.7 |
| reticulate | 1.25 | 1.25 |
| sandwich | 3.0 | 3.0 |
| scikit-learn | 1.1 | 1.1 |
| spatial | 7.3 | 7.3 |
| tensorflow | 2.12 | 2.9 |
| tidyr | 1.2 | 1.2 |
| xgboost | 1.6 | 1.6 |
In addition to the libraries listed in the tables, runtimes include many other useful libraries. To see the full list, select the Manage tab in your project, then click Templates, select the Environments tab, and then click on one of the listed environments.
Getting started
Python with GPU and Execution Engine for Apache Hadoop environments are not available by default.
- For Python with GPU environments, the Jupyter Notebooks with Python for GPU service must be installed.
- For Execution Engine for Apache Hadoop environments, the Execution Engine for Apache Hadoop service must be installed on the IBM Cloud Pak for Data platform.
After these services are installed, you must create your own environment templates to use these environments.
Use the following table to find out more about environment templates by asset type.
| Asset | Programming language | Tool | Environment template type | Available environment templates/compute resources |
|---|---|---|---|---|
| Jupyter notebook | Python | notebook editor | Anaconda Python distribution | Python environments |
| Jupyter notebook | Python | notebook editor | Anaconda Python distribution with GPU | GPU environments |
| Jupyter notebook | Python | notebook editor | Spark | Spark environments |
| Jupyter notebook | Python | notebook editor | Spark | Hadoop cluster |
| Jupyter notebook | R | notebook editor | Anaconda R distribution | R environments |
| Jupyter notebook | R | notebook editor | Spark | Spark environments |
| Jupyter notebook | Python | JupyterLab | Anaconda Python distribution | JupyterLab environments |
| Jupyter notebook | Python | Visual Studio Code editor | Anaconda Python distribution | JupyterLab environments. Spark is not supported. |
| Script | R | RStudio | Anaconda R distribution | RStudio environments |
| Shiny app | R | RStudio | Anaconda R distribution | RStudio environments |
| SPSS Modeler flow | N/A | SPSS Modeler | SPSS Modeler | SPSS Modeler environments |
| Data Refinery flow | R | Data Refinery | Spark | Data Refinery environments |
| Data Refinery flow | R | Data Refinery | Spark | Hadoop cluster |
Learn more
- Environment templates for the notebook editor
- Environment templates for JupyterLab
- Spark environment templates
- GPU environment templates
- Environment templates for RStudio
- Environment templates for Data Refinery
- Refinery data on the Hadoop cluster
- Creating environment templates
- Customizing environment templates
- Setting DataStage environment definitions
- Stopping active runtimes when no longer needed
Parent topic: Projects