Projects in
Cloudera Machine Learning
Collaboration Models
Sharing Job and Session Console Outputs
Managing Projects
Creating a Project with Legacy Engine Variants
Creating a Project with ML Runtimes variants
Creating a project from a password-protected Git repo
Configuring Project-level Runtimes
Adding Project Collaborators
Modifying Project Settings
Managing Project Files
Custom Template Projects
Deleting a Project
Native Workbench Console and Editor
Launch a Session
Run Code
Access the Terminal
Stop a Session
Workbench editor file types
Environmental Variables
Third-party Editors
Modes of configuration
Configure a browser-based IDE as an Editor
Testing a browser-based IDE in a Session
Configure a browser-based IDE at the Project level
Legacy Engine level configuration
Configure a local IDE using an SSH gateway
Configure PyCharm as a local IDE
Add Cloudera Machine Learning as an Interpreter for PyCharm
Configure PyCharm to use Cloudera Machine Learning as the remote console
(Optional) Configure the Sync between Cloudera Machine Learning and PyCharm
Configure VS Code as a local IDE
Download cdswctl and add an SSH Key
Initialize an SSH connection to Cloudera Machine Learning for VS code
Setting up VS Code
(Optional) Using VS Code with Python
(Optional) Using VS Code with R
(Optional) Using VS Code with Jupyter
(Optional) Using VS Code with Git integration
Limiting files in Explorer view
Git for Collaboration
Linking an existing Project to a Git remote
Embedded Web Applications
Example: A Shiny Application