Aurora on Azure AI Foundry

Aurora is available as a model on Azure AI Foundry! This part of the documentation describes how you can produce predictions with Aurora running on a Foundry endpoint.

Managing Secrets

In order to access the endpoint on Azure AI Foundry, you will need the endpoint URL and endpoint access token. These can be found in the Azure interface. As will be explained later, you will also need to create an URL to a Azure blob storage folder with a SAS token appended that has both read and write rights. (In a nutshell, this blob storage folder is necessary to and retrieve data from the endpoint.) Instead of storing these values in files, we recommend to store them in the environment variables FOUNDRY_ENDPOINT, FOUNDRY_TOKEN, and BLOB_URL_WITH_SAS. This is what this documentation will assume.

Accessing Environment Variables in a Jupyter Notebook

If your usual workflow is in a Jupyter notebook and you are having trouble setting and accessing environment variables, one alternative is to set the environment variables in a file called .env and load them from there. The layout of the .env file should follow this:

FOUNDRY_ENDPOINT=<foundry_endpoint>
FOUNDRY_TOKEN=<foundry_token>
BLOB_URL_WITH_SAS=<blob_url_with_sas>

Once this .env file has been created and populated, you can load it into a notebook environment with

!pip install python-dotenv
%load_ext dotenv
%dotenv path/to/.env

Warning

Do not accidentally commit .env to version control.