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Create an Azure Machine Learning Service.

Poslední aktualizace: 16.12.2020

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 deployment
New-AzResourceGroupDeployment -ResourceGroupName <resource-group-name> -TemplateUri https://raw.githubusercontent.com/Azure/azure-quickstart-templates/master/101-machine-learning-service-create-aks/azuredeploy.json
Instalace a konfigurace Azure PowerShell

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 deployment
az group deployment create --resource-group <my-resource-group> --template-uri https://raw.githubusercontent.com/Azure/azure-quickstart-templates/master/101-machine-learning-service-create-aks/azuredeploy.json
Instalace a konfigurace rozhraní Azure Cross-Platform Command-Line Interface