ML Runtimes Environment Variables
This topic describes how ML Runtimes environmental variables work. It also lists the different scopes at which they can be set and the order of precedence that will be followed in case of conflicts.
ML Runtimes environment variables behave the same way for Legacy Engines and ML Runtimes.
Environmental variables allow you to customize ML Runtimes environments for projects. For example, if you need to configure a particular timezone for a project, or increase the length of the session/job timeout windows, you can use environmental variables to do so. Environmental variables can also be used to assign variable names to secrets such as passwords or authentication tokens to avoid including these directly in the code.
In general, Cloudera recommends that you do not include passwords, tokens, or any other secrets directly in your code because anyone with read access to your project will be able to view this information. A better place to store secrets is in your project's environment variables, where only project collaborators and admins have view access. They can therefore be used to securely store confidential information such as your AWS keys or database credentials.
A site administrator for your Cloudera Data Science Workbench deployment can set environmental variables on a global level. These values will apply to every project on the deployment.
To set global environmental variables, go to.
Project administrators can set project-specific environmental variables to customize the ML Runtimes launched for a project. Variables set here will override the global values set in the site administration panel.
To set environmental variables for a project, go to the project's Overview page and click.
Environments for individual jobs within a project can be customized while creating the job. Variables set per-job will override the project-level and global settings.
To set environmental variables for a job, go to the job's Overview page and click.
ML Runtimes created for execution of experiments are completely isolated from the project. However, these ML Runtimes inherit values from environmental variables set at the project-level and/or global level. Variables set at the project-level will override the global values set in the site administration panel.
Model environments are completely isolated from the project. Environmental variables for these ML Runtimes can be configured during the build stage of the model deployment process. Models will also inherit any environment variables set at the project and global level. However, variables set per-model build will override other settings.