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Create an Azure Machine Learning Service.

上次更新时间: 2020/12/16

This template creates an Azure Machine Learning service.

此 Azure Resource Manager (ARM) 模板由社区的某个成员(而不是由 Microsoft)创建。每个 ARM 模板都根据其所有者(不是 Microsoft)的许可协议向你授予许可。Microsoft 不对由社区成员提供并授予许可的 ARM 模板负责,并且不针对安全性、兼容性和性能进行筛选。社区 ARM 模板不由任何 Microsoft 支持计划或服务提供支持,按“原样”提供,没有任何种类的担保。

参数

参数名 说明
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.

使用模板

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
安装和配置 Azure PowerShell

命令行

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
安装和配置 Azure 跨平台命令行界面