Šablony rychlého zprovoznění Azure jsou aktuálně dostupné v angličtině.
This template creates an Azure Machine Learning service.
Tuto šablonu Azure Resource Manageru (ARM) vytvořil člen komunity a ne Microsoft. Licenci na každou šablonu ARM vám na základě licenční smlouvy uděluje její vlastník, ne Microsoft. Za šablony ARM poskytované a licencované členy komunity nenese Microsoft žádnou odpovědnost ani neprověřuje jejich zabezpečení, kompatibilitu nebo výkon. Šablony ARM komunity nejsou podporované v rámci žádné služby nebo programu podpory Microsoftu a jsou dostupné TAK JAK JSOU, bez jakékoliv záruky.
Parametry
Název parametru | Popis |
---|---|
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. |
Použití šablony
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 deploymentInstalace a konfigurace 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
Příkazový řádek
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 deploymentInstalace a konfigurace rozhraní 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