Added instructions for creating compute instances with no/SystemAssigned/UserAssigned Managed Identities. (#126)
* Added instructions for creating compute instances with managed identity config. * UX Improvements. * UX Improvements.
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@ -463,7 +463,7 @@ The solution accelerator includes code and data for a sample end-to-end machine
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In this section you will execute an Azure DevOps pipeline that will create and run an Azure Machine Learning pipeline. Together, they perform the following steps:
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* Connect to the Azure Machine Learning workspace created by the infrastructure deployment
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* Create a compute cluster for training in the workspace
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* Create a compute cluster for training in the workspace (refer to section below to create compute instances with or without managed identity)
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* Register the training dataset in the workspace
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* Prepare data for training
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* Registers a custom python environment with the packages required for this model
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@ -471,6 +471,50 @@ In this section you will execute an Azure DevOps pipeline that will create and r
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* Evaluate the model on the test dataset against the performance of any previously-registered models
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* If the new model performs better, register the model as an MLflow model in the workspace for later deployment
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<details>
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<summary><strong>Create Compute Instances with SystemAssigned/UserAssigned/No Managed Identity</strong></summary>
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<br>
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In order to create a compute instance with or without managed identity, you can leverage the `/mlops-templates/templates/python-sdk-v2/create-compute-instance.yml` located within the **mlops-templates** repository.
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If you want to create a **compute instance without a managed identity** reference, you can add the following snippet with your own parameters to the `/mlops/devops-pipelines/deploy-model-training-pipeline.yml` pipeline definition:
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``` yaml
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- template: templates/python-sdk-v2/create-compute-instance.yml@mlops-templates
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parameters:
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instance_name: compute-instance-a
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size: Standard_DS3_v2
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location: canadacentral
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description: compute instance a
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```
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In order to **create a system-assigned managed identity** and assign it your compute instance during creation, the above snippet can be adjusted as follows:
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``` yaml
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- template: templates/python-sdk-v2/create-compute-instance.yml@mlops-templates
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parameters:
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instance_name: compute-instance-a
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size: Standard_DS3_v2
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location: canadacentral
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description: compute instance a
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identity_type: SystemAssigned
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```
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Lastly, to leverage a **user-assigned managed identity** for your compute, the following snippet can be used and adjusted as needed:
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``` yaml
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- template: templates/python-sdk-v2/create-compute-instance.yml@mlops-templates
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parameters:
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instance_name: compute-instance-a
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size: Standard_DS3_v2
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location: canadacentral
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description: compute instance a
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identity_type: UserAssigned
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user_assigned_identity: e12c9326-0618-4036-a0a7-ad3bb396dc97
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```
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</details>
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To deploy the model training pipeline, open the **Pipelines** section again and select **New pipeline** in the upper right of the page
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- Select **Azure Repos Git**
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