Managing Engines in Cloudera Machine Learning
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 Machine Learning projects
Add Docker registry credentials
Limitations
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