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This template creates an Azure Machine Learning service.
Этот шаблон Azure Resource Manager (ARM) был создан участником сообщества, а не Майкрософт. Каждый шаблон ARM лицензирован для вас в соответствии с условиями лицензионного соглашения с владельцем, а не с Майкрософт. Майкрософт не несет ответственности за шаблоны ARM, предоставленные и лицензированные членами сообщества, и не отслеживает их безопасности, совместимости или производительности. Шаблоны ARM, созданные сообществом, не поддерживаются ни в соответствии с какой-либо программой поддержки Майкрософт, ни какими-либо службами поддержки Майкрософт и предоставляются КАК ЕСТЬ без каких-либо гарантий.
Параметры
| Имя параметра | Описание |
|---|---|
| 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. |
Использование шаблона
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 deploymentУстановка и настройка служб 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
Командная строка
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 deploymentУстановка и настройка кросс-платформенного интерфейса командной стройки Azure
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