Microsoft.MachineLearningServices 작업 영역/서비스 2020-09-01-preview

Bicep 리소스 정의

작업 영역/서비스 리소스 종류는 다음을 대상으로 하는 작업으로 배포할 수 있습니다.

• 리소스 그룹 - 리소스 그룹 배포 명령 참조

각 API 버전에서 변경된 속성 목록은 변경 로그참조하세요.

리소스 형식

Microsoft.MachineLearningServices/workspaces/services 리소스를 만들려면 템플릿에 다음 Bicep을 추가합니다.

resource symbolicname 'Microsoft.MachineLearningServices/workspaces/services@2020-09-01-preview' = {
  name: 'string'
  location: 'string'
  parent: resourceSymbolicName
  description: 'string'
  environmentImageRequest: {
    assets: [
      {
        id: 'string'
        mimeType: 'string'
        unpack: bool
        url: 'string'
      }
    ]
    driverProgram: 'string'
    environment: {
      docker: {
        baseDockerfile: 'string'
        baseImage: 'string'
        baseImageRegistry: {
          address: 'string'
          password: 'string'
          username: 'string'
        }
      }
      environmentVariables: {
        {customized property}: 'string'
      }
      inferencingStackVersion: 'string'
      name: 'string'
      python: {
        baseCondaEnvironment: 'string'
        condaDependencies: any()
        interpreterPath: 'string'
        userManagedDependencies: bool
      }
      r: {
        bioConductorPackages: [
          'string'
        ]
        cranPackages: [
          {
            name: 'string'
            repository: 'string'
          }
        ]
        customUrlPackages: [
          'string'
        ]
        gitHubPackages: [
          {
            authToken: 'string'
            repository: 'string'
          }
        ]
        rscriptPath: 'string'
        rVersion: 'string'
        snapshotDate: 'string'
        userManaged: bool
      }
      spark: {
        packages: [
          {
            artifact: 'string'
            group: 'string'
            version: 'string'
          }
        ]
        precachePackages: bool
        repositories: [
          'string'
        ]
      }
      version: 'string'
    }
    environmentReference: {
      name: 'string'
      version: 'string'
    }
    modelIds: [
      'string'
    ]
    models: [
      {
        createdTime: 'string'
        datasets: [
          {
            id: 'string'
            name: 'string'
          }
        ]
        derivedModelIds: [
          'string'
        ]
        description: 'string'
        experimentName: 'string'
        framework: 'string'
        frameworkVersion: 'string'
        id: 'string'
        kvTags: {}
        mimeType: 'string'
        modifiedTime: 'string'
        name: 'string'
        parentModelId: 'string'
        properties: {
          {customized property}: 'string'
        }
        resourceRequirements: {
          cpu: int
          fpga: int
          gpu: int
          memoryInGB: int
        }
        runId: 'string'
        sampleInputData: 'string'
        sampleOutputData: 'string'
        unpack: bool
        url: 'string'
        version: int
      }
    ]
  }
  keys: {
    primaryKey: 'string'
    secondaryKey: 'string'
  }
  kvTags: {}
  properties: {
    {customized property}: 'string'
  }
  computeType: 'string'
  // For remaining properties, see workspaces/services objects
}

workspaces/services 개체

computeType 속성을 설정하여 개체 유형을 지정합니다.

ACI경우 다음을 사용합니다.

  computeType: 'ACI'
  appInsightsEnabled: bool
  authEnabled: bool
  cname: 'string'
  containerResourceRequirements: {
    cpu: int
    fpga: int
    gpu: int
    memoryInGB: int
  }
  dataCollection: {
    eventHubEnabled: bool
    storageEnabled: bool
  }
  dnsNameLabel: 'string'
  encryptionProperties: {
    keyName: 'string'
    keyVersion: 'string'
    vaultBaseUrl: 'string'
  }
  sslCertificate: 'string'
  sslEnabled: bool
  sslKey: 'string'
  vnetConfiguration: {
    subnetName: 'string'
    vnetName: 'string'
  }

AKS경우 다음을 사용합니다.

  computeType: 'AKS'
  aadAuthEnabled: bool
  appInsightsEnabled: bool
  authEnabled: bool
  autoScaler: {
    autoscaleEnabled: bool
    maxReplicas: int
    minReplicas: int
    refreshPeriodInSeconds: int
    targetUtilization: int
  }
  computeName: 'string'
  containerResourceRequirements: {
    cpu: int
    fpga: int
    gpu: int
    memoryInGB: int
  }
  dataCollection: {
    eventHubEnabled: bool
    storageEnabled: bool
  }
  isDefault: bool
  livenessProbeRequirements: {
    failureThreshold: int
    initialDelaySeconds: int
    periodSeconds: int
    successThreshold: int
    timeoutSeconds: int
  }
  maxConcurrentRequestsPerContainer: int
  maxQueueWaitMs: int
  namespace: 'string'
  numReplicas: int
  scoringTimeoutMs: int
  trafficPercentile: int
  type: 'string'

속성 값

작업 영역/서비스

ACIServiceCreateRequest

ContainerResourceRequirements

ACIServiceCreateRequestDataCollection

ACIServiceCreateRequestEncryptionProperties

ACIServiceCreateRequestVnetConfiguration

AKSServiceCreateRequest

AKSServiceCreateRequestAutoScaler

AKSServiceCreateRequestDataCollection

AKSServiceCreateRequestLivenessProbeRequirements

CreateServiceRequestEnvironmentImageRequest

ImageAsset

EnvironmentImageRequestEnvironment

ModelEnvironmentDefinitionDocker

ModelDockerSectionBaseImageRegistry

ModelEnvironmentDefinitionEnvironmentVariables

ModelEnvironmentDefinitionPython

ModelEnvironmentDefinitionR

RCranPackage

RGitHubPackage

ModelEnvironmentDefinitionSpark

SparkMavenPackage

EnvironmentImageRequestEnvironmentReference

모델

DatasetReference

ModelProperties

CreateServiceRequestKeys

CreateServiceRequestProperties

빠른 시작 템플릿

다음 빠른 시작 템플릿은 이 리소스 유형을 배포합니다.

ARM 템플릿 리소스 정의

작업 영역/서비스 리소스 종류는 다음을 대상으로 하는 작업으로 배포할 수 있습니다.

• 리소스 그룹 - 리소스 그룹 배포 명령 참조

각 API 버전에서 변경된 속성 목록은 변경 로그참조하세요.

리소스 형식

Microsoft.MachineLearningServices/workspaces/services 리소스를 만들려면 템플릿에 다음 JSON을 추가합니다.

{
  "type": "Microsoft.MachineLearningServices/workspaces/services",
  "apiVersion": "2020-09-01-preview",
  "name": "string",
  "location": "string",
  "description": "string",
  "environmentImageRequest": {
    "assets": [
      {
        "id": "string",
        "mimeType": "string",
        "unpack": "bool",
        "url": "string"
      }
    ],
    "driverProgram": "string",
    "environment": {
      "docker": {
        "baseDockerfile": "string",
        "baseImage": "string",
        "baseImageRegistry": {
          "address": "string",
          "password": "string",
          "username": "string"
        }
      },
      "environmentVariables": {
        "{customized property}": "string"
      },
      "inferencingStackVersion": "string",
      "name": "string",
      "python": {
        "baseCondaEnvironment": "string",
        "condaDependencies": {},
        "interpreterPath": "string",
        "userManagedDependencies": "bool"
      },
      "r": {
        "bioConductorPackages": [ "string" ],
        "cranPackages": [
          {
            "name": "string",
            "repository": "string"
          }
        ],
        "customUrlPackages": [ "string" ],
        "gitHubPackages": [
          {
            "authToken": "string",
            "repository": "string"
          }
        ],
        "rscriptPath": "string",
        "rVersion": "string",
        "snapshotDate": "string",
        "userManaged": "bool"
      },
      "spark": {
        "packages": [
          {
            "artifact": "string",
            "group": "string",
            "version": "string"
          }
        ],
        "precachePackages": "bool",
        "repositories": [ "string" ]
      },
      "version": "string"
    },
    "environmentReference": {
      "name": "string",
      "version": "string"
    },
    "modelIds": [ "string" ],
    "models": [
      {
        "createdTime": "string",
        "datasets": [
          {
            "id": "string",
            "name": "string"
          }
        ],
        "derivedModelIds": [ "string" ],
        "description": "string",
        "experimentName": "string",
        "framework": "string",
        "frameworkVersion": "string",
        "id": "string",
        "kvTags": {},
        "mimeType": "string",
        "modifiedTime": "string",
        "name": "string",
        "parentModelId": "string",
        "properties": {
          "{customized property}": "string"
        },
        "resourceRequirements": {
          "cpu": "int",
          "fpga": "int",
          "gpu": "int",
          "memoryInGB": "int"
        },
        "runId": "string",
        "sampleInputData": "string",
        "sampleOutputData": "string",
        "unpack": "bool",
        "url": "string",
        "version": "int"
      }
    ]
  },
  "keys": {
    "primaryKey": "string",
    "secondaryKey": "string"
  },
  "kvTags": {},
  "properties": {
    "{customized property}": "string"
  },
  "computeType": "string"
  // For remaining properties, see workspaces/services objects
}

workspaces/services 개체

computeType 속성을 설정하여 개체 유형을 지정합니다.

ACI경우 다음을 사용합니다.

  "computeType": "ACI",
  "appInsightsEnabled": "bool",
  "authEnabled": "bool",
  "cname": "string",
  "containerResourceRequirements": {
    "cpu": "int",
    "fpga": "int",
    "gpu": "int",
    "memoryInGB": "int"
  },
  "dataCollection": {
    "eventHubEnabled": "bool",
    "storageEnabled": "bool"
  },
  "dnsNameLabel": "string",
  "encryptionProperties": {
    "keyName": "string",
    "keyVersion": "string",
    "vaultBaseUrl": "string"
  },
  "sslCertificate": "string",
  "sslEnabled": "bool",
  "sslKey": "string",
  "vnetConfiguration": {
    "subnetName": "string",
    "vnetName": "string"
  }

AKS경우 다음을 사용합니다.

  "computeType": "AKS",
  "aadAuthEnabled": "bool",
  "appInsightsEnabled": "bool",
  "authEnabled": "bool",
  "autoScaler": {
    "autoscaleEnabled": "bool",
    "maxReplicas": "int",
    "minReplicas": "int",
    "refreshPeriodInSeconds": "int",
    "targetUtilization": "int"
  },
  "computeName": "string",
  "containerResourceRequirements": {
    "cpu": "int",
    "fpga": "int",
    "gpu": "int",
    "memoryInGB": "int"
  },
  "dataCollection": {
    "eventHubEnabled": "bool",
    "storageEnabled": "bool"
  },
  "isDefault": "bool",
  "livenessProbeRequirements": {
    "failureThreshold": "int",
    "initialDelaySeconds": "int",
    "periodSeconds": "int",
    "successThreshold": "int",
    "timeoutSeconds": "int"
  },
  "maxConcurrentRequestsPerContainer": "int",
  "maxQueueWaitMs": "int",
  "namespace": "string",
  "numReplicas": "int",
  "scoringTimeoutMs": "int",
  "trafficPercentile": "int",
  "type": "string"

속성 값

작업 영역/서비스

ACIServiceCreateRequest

ContainerResourceRequirements

ACIServiceCreateRequestDataCollection

ACIServiceCreateRequestEncryptionProperties

ACIServiceCreateRequestVnetConfiguration

AKSServiceCreateRequest

AKSServiceCreateRequestAutoScaler

AKSServiceCreateRequestDataCollection

AKSServiceCreateRequestLivenessProbeRequirements

CreateServiceRequestEnvironmentImageRequest

ImageAsset

EnvironmentImageRequestEnvironment

ModelEnvironmentDefinitionDocker

ModelDockerSectionBaseImageRegistry

ModelEnvironmentDefinitionEnvironmentVariables

ModelEnvironmentDefinitionPython

ModelEnvironmentDefinitionR

RCranPackage

RGitHubPackage

ModelEnvironmentDefinitionSpark

SparkMavenPackage

EnvironmentImageRequestEnvironmentReference

모델

DatasetReference

ModelProperties

CreateServiceRequestKeys

CreateServiceRequestProperties

빠른 시작 템플릿

다음 빠른 시작 템플릿은 이 리소스 유형을 배포합니다.

Terraform(AzAPI 공급자) 리소스 정의

작업 영역/서비스 리소스 종류는 다음을 대상으로 하는 작업으로 배포할 수 있습니다.

• 리소스 그룹

각 API 버전에서 변경된 속성 목록은 변경 로그참조하세요.

리소스 형식

Microsoft.MachineLearningServices/workspaces/services 리소스를 만들려면 템플릿에 다음 Terraform을 추가합니다.

resource "azapi_resource" "symbolicname" {
  type = "Microsoft.MachineLearningServices/workspaces/services@2020-09-01-preview"
  name = "string"
  location = "string"
  parent_id = "string"
  // For remaining properties, see workspaces/services objects
  body = jsonencode({
    description = "string"
    environmentImageRequest = {
      assets = [
        {
          id = "string"
          mimeType = "string"
          unpack = bool
          url = "string"
        }
      ]
      driverProgram = "string"
      environment = {
        docker = {
          baseDockerfile = "string"
          baseImage = "string"
          baseImageRegistry = {
            address = "string"
            password = "string"
            username = "string"
          }
        }
        environmentVariables = {
          {customized property} = "string"
        }
        inferencingStackVersion = "string"
        name = "string"
        python = {
          baseCondaEnvironment = "string"
          interpreterPath = "string"
          userManagedDependencies = bool
        }
        r = {
          bioConductorPackages = [
            "string"
          ]
          cranPackages = [
            {
              name = "string"
              repository = "string"
            }
          ]
          customUrlPackages = [
            "string"
          ]
          gitHubPackages = [
            {
              authToken = "string"
              repository = "string"
            }
          ]
          rscriptPath = "string"
          rVersion = "string"
          snapshotDate = "string"
          userManaged = bool
        }
        spark = {
          packages = [
            {
              artifact = "string"
              group = "string"
              version = "string"
            }
          ]
          precachePackages = bool
          repositories = [
            "string"
          ]
        }
        version = "string"
      }
      environmentReference = {
        name = "string"
        version = "string"
      }
      modelIds = [
        "string"
      ]
      models = [
        {
          createdTime = "string"
          datasets = [
            {
              id = "string"
              name = "string"
            }
          ]
          derivedModelIds = [
            "string"
          ]
          description = "string"
          experimentName = "string"
          framework = "string"
          frameworkVersion = "string"
          id = "string"
          kvTags = {}
          mimeType = "string"
          modifiedTime = "string"
          name = "string"
          parentModelId = "string"
          properties = {
            {customized property} = "string"
          }
          resourceRequirements = {
            cpu = int
            fpga = int
            gpu = int
            memoryInGB = int
          }
          runId = "string"
          sampleInputData = "string"
          sampleOutputData = "string"
          unpack = bool
          url = "string"
          version = int
        }
      ]
    }
    keys = {
      primaryKey = "string"
      secondaryKey = "string"
    }
    kvTags = {}
    body = jsonencode({
    properties = {
      {customized property} = "string"
    }
    computeType = "string"
  })
}

workspaces/services 개체

computeType 속성을 설정하여 개체 유형을 지정합니다.

ACI경우 다음을 사용합니다.

  computeType = "ACI"
  appInsightsEnabled = bool
  authEnabled = bool
  cname = "string"
  containerResourceRequirements = {
    cpu = int
    fpga = int
    gpu = int
    memoryInGB = int
  }
  dataCollection = {
    eventHubEnabled = bool
    storageEnabled = bool
  }
  dnsNameLabel = "string"
  encryptionProperties = {
    keyName = "string"
    keyVersion = "string"
    vaultBaseUrl = "string"
  }
  sslCertificate = "string"
  sslEnabled = bool
  sslKey = "string"
  vnetConfiguration = {
    subnetName = "string"
    vnetName = "string"
  }

AKS경우 다음을 사용합니다.

  computeType = "AKS"
  aadAuthEnabled = bool
  appInsightsEnabled = bool
  authEnabled = bool
  autoScaler = {
    autoscaleEnabled = bool
    maxReplicas = int
    minReplicas = int
    refreshPeriodInSeconds = int
    targetUtilization = int
  }
  computeName = "string"
  containerResourceRequirements = {
    cpu = int
    fpga = int
    gpu = int
    memoryInGB = int
  }
  dataCollection = {
    eventHubEnabled = bool
    storageEnabled = bool
  }
  isDefault = bool
  livenessProbeRequirements = {
    failureThreshold = int
    initialDelaySeconds = int
    periodSeconds = int
    successThreshold = int
    timeoutSeconds = int
  }
  maxConcurrentRequestsPerContainer = int
  maxQueueWaitMs = int
  namespace = "string"
  numReplicas = int
  scoringTimeoutMs = int
  trafficPercentile = int
  type = "string"

속성 값

작업 영역/서비스

ACIServiceCreateRequest

ContainerResourceRequirements

ACIServiceCreateRequestDataCollection

ACIServiceCreateRequestEncryptionProperties

ACIServiceCreateRequestVnetConfiguration

AKSServiceCreateRequest

AKSServiceCreateRequestAutoScaler

AKSServiceCreateRequestDataCollection

AKSServiceCreateRequestLivenessProbeRequirements

CreateServiceRequestEnvironmentImageRequest

ImageAsset

EnvironmentImageRequestEnvironment

ModelEnvironmentDefinitionDocker

ModelDockerSectionBaseImageRegistry

ModelEnvironmentDefinitionEnvironmentVariables

ModelEnvironmentDefinitionPython

ModelEnvironmentDefinitionR

RCranPackage

RGitHubPackage

ModelEnvironmentDefinitionSpark

SparkMavenPackage

EnvironmentImageRequestEnvironmentReference

모델

DatasetReference

ModelProperties

CreateServiceRequestKeys

CreateServiceRequestProperties