orchestrator_open.md

Create an open sandbox orchestrator

⚠️ This should be used for development purpose only.

Prerequisites

To run these deployment options, you first need:

• an existing Azure ML workspace (see cookbook)
• have permissions to create resources, set permissions, and create identities in this subscription (or at least in one resource group),
  • Note that to set permissions, you typically need Owner role in the subscription or resource group - Contributor role is not enough. This is key for being able to secure the setup.
• Optional: install the Azure CLI.

Create a compute and storage pair for the orchestrator

Note: both orchestrator and silo can be deployed using the same arm/bicep script, changing Pair Base Name accordingly.

Option 1 : one click deployment

1. Click on

2. Adjust parameters, in particular:

   • Region: this will be set by Azure to the region of your resource group.
   • Machine Learning Name: need to match the name of the AzureML workspace in the resource group.
   • Machine Learning Region: the region in which the AzureML workspace was deployed (default: same as resource group).
   • Pair Region: the region where the compute and storage will be deployed (default: same as resource group).
   • Pair Base Name: a unique name for the orchestrator, example orch. This will be used to create all other resources (storage name, compute name, etc.).

Option 2 : deployment using az cli

In the resource group of your AzureML workspace, use the following command with parameters corresponding to your setup:

az deployment group create --template-file ./mlops/bicep/modules/fl_pairs/open_compute_storage_pair.bicep --resource-group <resource group name> --parameters pairBaseName="orch" pairRegion="eastus" machineLearningName="aml-fldemo" machineLearningRegion="eastus"