Managing Engines in
Cloudera AI
Managing Engines
Creating Resource profiles
Configuring the engine environment
Set up a custom repository location
Burstable CPUs
Installing additional packages
Using Conda to manage dependencies
Engine environment variables
Engine environment variables
Accessing environmental variables from projects
Customized engine images
Creating a customized engine image
Create a Dockerfile for the custom image
Build the new Docker image
Distribute the image
Including images in allowlist for Cloudera AI projects
Add Docker registry credentials
Limitations with customized engines
End-to-end example: MeCab
Pre-Installed Packages in engines
Base Engine 15-cml-2021.09-1
Base Engine 14-cml-2021.05-1
Base Engine 13-cml-2020.08-1
Base Engine 12-cml-2020.06-2
Base Engine 11-cml1.4
Base Engine 10-cml1.3
Base Engine 9-cml1.2