Customizing environment templates

You can change the name, the description, and the hardware configuration of an environment template that you created. You can provide a list of conda packages, a list of pip packages, or a combination of both. When using conda packages, you can provide a list of additional conda channel locations through which the packages can be obtained.

Required permissions
You must be have the Admin or Editor role in the project to customize an environment template.
Restrictions
You cannot change the language of an existing environment template.
You can’t customize the software configuration of a Spark environment template that you created.

To customize an environment template that you created:

  1. Under your project's Manage tab, click the Environments page.
  2. In the Active Runtimes section, check that no runtime is active for the environment template that you want to change.
  3. In the Environment Templates section, click the environment template that you want to customize.
  4. If you didn't add any package customizations, close the window to apply your changes. If you added new packages, you must click Apply.

Important when customizing packages:

  • Runtime 25.1 doesn't use conda. If you customize packages for an environment template that is based on Runtime 25.1, you must use pip. If you have custom environment templates that are based on Runtime 24.1 and use conda, you must migrate them before 9th July 2026. For more information, see Migrating custom environment templates from Runtime 24.1 to Runtime 25.1.
  • Libraries that you add to an environment template through customization are automatically installed each time that the environment runtime is started. Other project members who use this template are able to use this package, too.
  • Before you customize a package, verify that the changes that you are planning have the intended effect.
    • conda can report the changes required for installing a given package, without actually installing it.
    • pip does install the package. However, restarting the runtime again after verification will remove the package.
  • If you can get a package through conda from the default channels and through pip from PyPI, the preferred method is through conda from the default channels.
  • The conda package manager checks dependencies when installing packages. This can be memory intensive if you add many packages to the customization. Ensure that you select an environment with sufficient RAM to enable dependency checking at the time that the runtime is started.
  • To prevent unnecessary dependency checking if you only want packages from one conda channel, exclude the default channels by removing defaults from the channels list in the template and adding nodefaults.
  • If you add packages only through pip or only through conda to the customization template, you must make sure that dependencies is not commented out in the template.
  • When you specify a package version, use a single = for conda packages and == for pip packages. Wherever possible, specify a version number as this reduces the installation time and memory consumption significantly. If you don't specify a version, the package manager might pick the latest version available, or keep the version that is available in the package.

Learn more