ML Runtimes
Managing ML Runtimes
Adding new ML Runtimes
Adding Custom ML Runtimes through the Runtime Catalog
Adding ML Runtimes using Runtime Repo files
ML Runtimes versus Legacy Engine
Using Runtime Catalog
Disabling and Deleting Runtimes
PBJ Workbench
Dockerfile compatible with PBJ Workbench
PBJ Runtimes and Models
Example models with PBJ Runtimes
Using ML Runtimes Addons
Adding Hadoop CLI to ML Runtime Sessions
Adding Spark to ML Runtime Sessions
Turning off ML Runtimes Addons
ML Runtimes NVIDIA GPU Edition
Testing ML Runtime GPU Setup
ML Runtimes NVIDIA RAPIDS Edition
Using Editors for ML Runtimes
Using JupyterLab with ML Runtimes
Installing a Jupyter extension
Installing a Jupyter kernel
Using Conda Runtime
Installing Additional ML Runtimes Packages
Restrictions for upgrading R and Python packages
Custom Runtime Addons with CML
ML Runtimes Environment Variables
ML Runtimes Environment Variables List
Accessing Environmental Variables from Projects
Customized Runtimes
Creating Customized ML Runtimes
Create a Dockerfile for the Custom Runtime Image
Metadata for Custom ML Runtimes
Editor Customization
Build the New Docker Image
Distribute the Image
Add the new ML Runtime
Limitations
Add Docker registry credentials and certificates
Pre-Installed Packages in ML Runtimes