This template creates an Azure Machine Learning service.
This Azure Resource Manager template was created by a member of the community and not by Microsoft. Each Resource Manager template is licensed to you under a licence agreement by its owner, not Microsoft. Microsoft is not responsible for Resource Manager templates provided and licensed by community members and does not screen for security, compatibility, or performance. Community Resource Manager templates are not supported under any Microsoft support programme or service, and are made available AS IS without warranty of any kind.
Parameters
| Parameter Name | Description |
|---|---|
| webserviceName | The name of the Azure Machine Learning Web Service. This resource will be created in the same resource group as the workspace. |
| workspaceName | The name of the Azure Machine Learning Workspace. |
| location | The location of the Azure Machine Learning Workspace. |
| environmentName | Name of Azure Machine Learning Environment for deployment. See https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments and https://docs.microsoft.com/en-us/azure/machine-learning/resource-curated-environments . |
| environmentVersion | Version of Azure Machine Learning Environment for deployment. |
| cpu | The default number of CPU cores to allocate for this Webservice. Can be a decimal. |
| cpuLimit | The max number of CPU cores this Webservice is allowed to use. Can be a decimal. |
| gpu | The number of gpu cores to allocate for this Webservice |
| memoryInGB | The amount of memory (in GB) to allocate for this Webservice. Can be a decimal. |
| driverProgram | Relative path of a file from storage account that contains the code to run for service. |
| models | Details of the models to be deployed. Each model must have the following properties: 'name'(name of the model), 'path'(relative path of a file from storage account linked to Workspace), 'mimeType'(MIME type of Model content. For more details about MIME type, please open https://www.iana.org/assignments/media-types/media-types.xhtml), 'framework'(framework of the model, use Custom if unsure) and 'frameworkVersion'(framework version of the model). |
| authEnabled | Whether or not to enable key auth for this Webservice. |
| tokenAuthEnabled | Whether or not to enable token auth for this Webservice. |
| primaryKey | A primary auth key to use for this Webservice. |
| secondaryKey | A secondary auth key to use for this Webservice. |
| scoringTimeoutMilliSeconds | A timeout to enforce for scoring calls to this Webservice. |
| appInsightsEnabled | Whether or not to enable AppInsights for this Webservice. |
| computeTarget | Name of compute target. |
| namespace | Kubernetes namespace in which to deploy the service: up to 63 lowercase alphanumeric ('a'-'z', '0'-'9') and hyphen ('-') characters. The first and last characters cannot be hyphens. |
| numReplicas | The number of containers to allocate for this Webservice. No default, if this parameter is not set then the autoscaler is enabled by default. |
| autoScaleEnabled | Whether or not to enable autoscaling for this Webservice. Defaults to True if num_replicas is None. |
| autoScaleMinReplicas | The minimum number of containers to use when autoscaling this Webservice. |
| autoScaleMaxReplicas | The maximum number of containers to use when autoscaling this Webservice. |
| autoscaleTargetUtilization | The target utilization (in percent out of 100) the autoscaler should attempt to maintain for this Webservice. |
| autoscaleRefreshSeconds | How often the autoscaler should attempt to scale this Webservice. |
| periodSeconds | How often (in seconds) to perform the liveness probe. |
| initialDelaySeconds | Number of seconds after the container has started before liveness probes are initiated. |
| timeoutSeconds | Number of seconds after which the liveness probe times out. |
| failureThreshold | When a pod starts and the liveness probe fails, Kubernetes will try --failure-threshold times before giving up. |
| successThreshold | Minimum consecutive successes for the liveness probe to be considered successful after having failed. |
Use the template
PowerShell
New-AzResourceGroup -Name <resource-group-name> -Location <resource-group-location> #use this command when you need to create a new resource group for your deploymentInstall and configure Azure PowerShell
New-AzResourceGroupDeployment -ResourceGroupName <resource-group-name> -TemplateUri https://raw.githubusercontent.com/Azure/azure-quickstart-templates/master/quickstarts/microsoft.machinelearningservices/machine-learning-service-create-aks/azuredeploy.json
Command line
az group create --name <resource-group-name> --location <resource-group-location> #use this command when you need to create a new resource group for your deploymentInstall and Configure the Azure Cross-Platform Command-Line Interface
az group deployment create --resource-group <my-resource-group> --template-uri https://raw.githubusercontent.com/Azure/azure-quickstart-templates/master/quickstarts/microsoft.machinelearningservices/machine-learning-service-create-aks/azuredeploy.json