Custom environments

Using custom environment variables at runtime

You can add an env section to your yaml configuration file in order to send environment variables into your runner environment variables table. Variables can be prefixed with a $ sign if you wish to substitute local environment variables into your run configuration. Be aware that all values are stored in clear text. If you wish to exchange secrets you will need to encrypt them into your configuration file and then decrypt your secrets within your python code used during the experiment.

Customization of python environment for the workers

Sometimes your experiment relies on an older / custom version of some python package. For example, the Keras API has changed quite a bit between versions 1 and 2. What if you are using a new environment locally, but would like to re-run old experiments that needed older version of packages? Or, for example, you’d like to see if your code would work with the latest version of a package. Studio gives you this opportunity.

studio run --python-pkg=<package_name>==<package_version> <>

allows you to run <> on a remote / cloud worker with a specific version of a package. You can also omit ==<package_version> to install the latest version of the package (which may not be equal to the version in your environment). Note that if a package with a custom version has dependencies conflicting with the current version, the situation gets tricky. For now, it is up to pip to resolve conflicts. In some cases it may fail and you’ll have to manually specify dependencies versions by adding more --python-pkg arguments.