Microsoft.MachineLearningServices workspaces/jobs 2022-12-01-preview

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• 2022-02-01-preview
• 2021-03-01-preview

Bicep resource definition

The workspaces/jobs resource type can be deployed with operations that target:

• Resource groups - See resource group deployment commands

For a list of changed properties in each API version, see change log.

Resource format

To create a Microsoft.MachineLearningServices/workspaces/jobs resource, add the following Bicep to your template.

resource symbolicname 'Microsoft.MachineLearningServices/workspaces/jobs@2022-12-01-preview' = {
  name: 'string'
  parent: resourceSymbolicName
  properties: {
    componentId: 'string'
    computeId: 'string'
    description: 'string'
    displayName: 'string'
    experimentName: 'string'
    identity: {
      identityType: 'string'
      // For remaining properties, see IdentityConfiguration objects
    }
    isArchived: bool
    properties: {
      {customized property}: 'string'
    }
    services: {
      {customized property}: {
        endpoint: 'string'
        jobServiceType: 'string'
        nodes: {
          nodesValueType: 'string'
          // For remaining properties, see Nodes objects
        }
        port: int
        properties: {
          {customized property}: 'string'
        }
      }
    }
    tags: {}
    jobType: 'string'
    // For remaining properties, see JobBaseProperties objects
  }
}

JobBaseProperties objects

Set the jobType property to specify the type of object.

For AutoML, use:

  jobType: 'AutoML'
  environmentId: 'string'
  environmentVariables: {
    {customized property}: 'string'
  }
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  resources: {
    dockerArgs: 'string'
    instanceCount: int
    instanceType: 'string'
    properties: {
      {customized property}: any()
    }
    shmSize: 'string'
  }
  taskDetails: {
    logVerbosity: 'string'
    targetColumnName: 'string'
    trainingData: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    taskType: 'string'
    // For remaining properties, see AutoMLVertical objects
  }

For Command, use:

  jobType: 'Command'
  autologgerSettings: {
    mlflowAutologger: 'string'
  }
  codeId: 'string'
  command: 'string'
  distribution: {
    distributionType: 'string'
    // For remaining properties, see DistributionConfiguration objects
  }
  environmentId: 'string'
  environmentVariables: {
    {customized property}: 'string'
  }
  inputs: {
    {customized property}: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
  }
  limits: {
    jobLimitsType: 'string'
    timeout: 'string'
  }
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  resources: {
    dockerArgs: 'string'
    instanceCount: int
    instanceType: 'string'
    properties: {
      {customized property}: any()
    }
    shmSize: 'string'
  }

For Labeling, use:

  jobType: 'Labeling'
  dataConfiguration: {
    dataId: 'string'
    incrementalDataRefresh: 'string'
  }
  jobInstructions: {
    uri: 'string'
  }
  labelCategories: {
    {customized property}: {
      classes: {
        {customized property}: {
          displayName: 'string'
          subclasses: {
            {customized property}: {}
        }
      }
      displayName: 'string'
      multiSelect: 'string'
    }
  }
  labelingJobMediaProperties: {
    mediaType: 'string'
    // For remaining properties, see LabelingJobMediaProperties objects
  }
  mlAssistConfiguration: {
    mlAssist: 'string'
    // For remaining properties, see MLAssistConfiguration objects
  }

For Pipeline, use:

  jobType: 'Pipeline'
  inputs: {
    {customized property}: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
  }
  jobs: {
    {customized property}: any()
  }
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  settings: any()
  sourceJobId: 'string'

For Spark, use:

  jobType: 'Spark'
  archives: [
    'string'
  ]
  args: 'string'
  codeId: 'string'
  conf: {
    {customized property}: 'string'
  }
  entry: {
    sparkJobEntryType: 'string'
    // For remaining properties, see SparkJobEntry objects
  }
  environmentId: 'string'
  files: [
    'string'
  ]
  inputs: {
    {customized property}: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
  }
  jars: [
    'string'
  ]
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  pyFiles: [
    'string'
  ]
  resources: {
    instanceType: 'string'
    runtimeVersion: 'string'
  }

For Sweep, use:

  jobType: 'Sweep'
  earlyTermination: {
    delayEvaluation: int
    evaluationInterval: int
    policyType: 'string'
    // For remaining properties, see EarlyTerminationPolicy objects
  }
  inputs: {
    {customized property}: {
      description: 'string'
      jobInputType: 'string'
      // For remaining properties, see JobInput objects
    }
  }
  limits: {
    jobLimitsType: 'string'
    maxConcurrentTrials: int
    maxTotalTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  objective: {
    goal: 'string'
    primaryMetric: 'string'
  }
  outputs: {
    {customized property}: {
      description: 'string'
      jobOutputType: 'string'
      // For remaining properties, see JobOutput objects
    }
  }
  samplingAlgorithm: {
    samplingAlgorithmType: 'string'
    // For remaining properties, see SamplingAlgorithm objects
  }
  searchSpace: any()
  trial: {
    codeId: 'string'
    command: 'string'
    distribution: {
      distributionType: 'string'
      // For remaining properties, see DistributionConfiguration objects
    }
    environmentId: 'string'
    environmentVariables: {
      {customized property}: 'string'
    }
    resources: {
      dockerArgs: 'string'
      instanceCount: int
      instanceType: 'string'
      properties: {
        {customized property}: any()
      }
      shmSize: 'string'
    }
  }

IdentityConfiguration objects

Set the identityType property to specify the type of object.

For AMLToken, use:

  identityType: 'AMLToken'

For Managed, use:

  identityType: 'Managed'
  clientId: 'string'
  objectId: 'string'
  resourceId: 'string'

For UserIdentity, use:

  identityType: 'UserIdentity'

Nodes objects

Set the nodesValueType property to specify the type of object.

For All, use:

  nodesValueType: 'All'

JobOutput objects

Set the jobOutputType property to specify the type of object.

For custom_model, use:

  jobOutputType: 'custom_model'
  assetName: 'string'
  assetVersion: 'string'
  mode: 'string'
  uri: 'string'

For mlflow_model, use:

  jobOutputType: 'mlflow_model'
  assetName: 'string'
  assetVersion: 'string'
  mode: 'string'
  uri: 'string'

For mltable, use:

  jobOutputType: 'mltable'
  assetName: 'string'
  assetVersion: 'string'
  mode: 'string'
  uri: 'string'

For triton_model, use:

  jobOutputType: 'triton_model'
  assetName: 'string'
  assetVersion: 'string'
  mode: 'string'
  uri: 'string'

For uri_file, use:

  jobOutputType: 'uri_file'
  assetName: 'string'
  assetVersion: 'string'
  mode: 'string'
  uri: 'string'

For uri_folder, use:

  jobOutputType: 'uri_folder'
  assetName: 'string'
  assetVersion: 'string'
  mode: 'string'
  uri: 'string'

AutoMLVertical objects

Set the taskType property to specify the type of object.

For Classification, use:

  taskType: 'Classification'
  cvSplitColumnNames: [
    'string'
  ]
  featurizationSettings: {
    blockedTransformers: [
      'string'
    ]
    columnNameAndTypes: {
      {customized property}: 'string'
    }
    datasetLanguage: 'string'
    enableDnnFeaturization: bool
    mode: 'string'
    transformerParams: {
      {customized property}: [
        {
          fields: [
            'string'
          ]
          parameters: any()
        }
      ]
    }
  }
  fixedParameters: {
    booster: 'string'
    boostingType: 'string'
    growPolicy: 'string'
    learningRate: int
    maxBin: int
    maxDepth: int
    maxLeaves: int
    minDataInLeaf: int
    minSplitGain: int
    modelName: 'string'
    nEstimators: int
    numLeaves: int
    preprocessorName: 'string'
    regAlpha: int
    regLambda: int
    subsample: int
    subsampleFreq: int
    treeMethod: 'string'
    withMean: bool
    withStd: bool
  }
  limitSettings: {
    enableEarlyTermination: bool
    exitScore: int
    maxConcurrentTrials: int
    maxCoresPerTrial: int
    maxNodes: int
    maxTrials: int
    sweepConcurrentTrials: int
    sweepTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  nCrossValidations: {
    mode: 'string'
    // For remaining properties, see NCrossValidations objects
  }
  positiveLabel: 'string'
  primaryMetric: 'string'
  searchSpace: [
    {
      booster: 'string'
      boostingType: 'string'
      growPolicy: 'string'
      learningRate: 'string'
      maxBin: 'string'
      maxDepth: 'string'
      maxLeaves: 'string'
      minDataInLeaf: 'string'
      minSplitGain: 'string'
      modelName: 'string'
      nEstimators: 'string'
      numLeaves: 'string'
      preprocessorName: 'string'
      regAlpha: 'string'
      regLambda: 'string'
      subsample: 'string'
      subsampleFreq: 'string'
      treeMethod: 'string'
      withMean: 'string'
      withStd: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  testData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  testDataSize: int
  trainingSettings: {
    allowedTrainingAlgorithms: [
      'string'
    ]
    blockedTrainingAlgorithms: [
      'string'
    ]
    enableDnnTraining: bool
    enableModelExplainability: bool
    enableOnnxCompatibleModels: bool
    enableStackEnsemble: bool
    enableVoteEnsemble: bool
    ensembleModelDownloadTimeout: 'string'
    stackEnsembleSettings: {
      stackMetaLearnerKWargs: any()
      stackMetaLearnerTrainPercentage: int
      stackMetaLearnerType: 'string'
    }
    trainingMode: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int
  weightColumnName: 'string'

For Forecasting, use:

  taskType: 'Forecasting'
  cvSplitColumnNames: [
    'string'
  ]
  featurizationSettings: {
    blockedTransformers: [
      'string'
    ]
    columnNameAndTypes: {
      {customized property}: 'string'
    }
    datasetLanguage: 'string'
    enableDnnFeaturization: bool
    mode: 'string'
    transformerParams: {
      {customized property}: [
        {
          fields: [
            'string'
          ]
          parameters: any()
        }
      ]
    }
  }
  fixedParameters: {
    booster: 'string'
    boostingType: 'string'
    growPolicy: 'string'
    learningRate: int
    maxBin: int
    maxDepth: int
    maxLeaves: int
    minDataInLeaf: int
    minSplitGain: int
    modelName: 'string'
    nEstimators: int
    numLeaves: int
    preprocessorName: 'string'
    regAlpha: int
    regLambda: int
    subsample: int
    subsampleFreq: int
    treeMethod: 'string'
    withMean: bool
    withStd: bool
  }
  forecastingSettings: {
    countryOrRegionForHolidays: 'string'
    cvStepSize: int
    featureLags: 'string'
    forecastHorizon: {
      mode: 'string'
      // For remaining properties, see ForecastHorizon objects
    }
    frequency: 'string'
    seasonality: {
      mode: 'string'
      // For remaining properties, see Seasonality objects
    }
    shortSeriesHandlingConfig: 'string'
    targetAggregateFunction: 'string'
    targetLags: {
      mode: 'string'
      // For remaining properties, see TargetLags objects
    }
    targetRollingWindowSize: {
      mode: 'string'
      // For remaining properties, see TargetRollingWindowSize objects
    }
    timeColumnName: 'string'
    timeSeriesIdColumnNames: [
      'string'
    ]
    useStl: 'string'
  }
  limitSettings: {
    enableEarlyTermination: bool
    exitScore: int
    maxConcurrentTrials: int
    maxCoresPerTrial: int
    maxNodes: int
    maxTrials: int
    sweepConcurrentTrials: int
    sweepTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  nCrossValidations: {
    mode: 'string'
    // For remaining properties, see NCrossValidations objects
  }
  primaryMetric: 'string'
  searchSpace: [
    {
      booster: 'string'
      boostingType: 'string'
      growPolicy: 'string'
      learningRate: 'string'
      maxBin: 'string'
      maxDepth: 'string'
      maxLeaves: 'string'
      minDataInLeaf: 'string'
      minSplitGain: 'string'
      modelName: 'string'
      nEstimators: 'string'
      numLeaves: 'string'
      preprocessorName: 'string'
      regAlpha: 'string'
      regLambda: 'string'
      subsample: 'string'
      subsampleFreq: 'string'
      treeMethod: 'string'
      withMean: 'string'
      withStd: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  testData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  testDataSize: int
  trainingSettings: {
    allowedTrainingAlgorithms: [
      'string'
    ]
    blockedTrainingAlgorithms: [
      'string'
    ]
    enableDnnTraining: bool
    enableModelExplainability: bool
    enableOnnxCompatibleModels: bool
    enableStackEnsemble: bool
    enableVoteEnsemble: bool
    ensembleModelDownloadTimeout: 'string'
    stackEnsembleSettings: {
      stackMetaLearnerKWargs: any()
      stackMetaLearnerTrainPercentage: int
      stackMetaLearnerType: 'string'
    }
    trainingMode: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int
  weightColumnName: 'string'

For ImageClassification, use:

  taskType: 'ImageClassification'
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  modelSettings: {
    advancedSettings: 'string'
    amsGradient: bool
    augmentations: 'string'
    beta1: int
    beta2: int
    checkpointFrequency: int
    checkpointModel: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    checkpointRunId: 'string'
    distributed: bool
    earlyStopping: bool
    earlyStoppingDelay: int
    earlyStoppingPatience: int
    enableOnnxNormalization: bool
    evaluationFrequency: int
    gradientAccumulationStep: int
    layersToFreeze: int
    learningRate: int
    learningRateScheduler: 'string'
    modelName: 'string'
    momentum: int
    nesterov: bool
    numberOfEpochs: int
    numberOfWorkers: int
    optimizer: 'string'
    randomSeed: int
    stepLRGamma: int
    stepLRStepSize: int
    trainingBatchSize: int
    trainingCropSize: int
    validationBatchSize: int
    validationCropSize: int
    validationResizeSize: int
    warmupCosineLRCycles: int
    warmupCosineLRWarmupEpochs: int
    weightDecay: int
    weightedLoss: int
  }
  primaryMetric: 'string'
  searchSpace: [
    {
      amsGradient: 'string'
      augmentations: 'string'
      beta1: 'string'
      beta2: 'string'
      distributed: 'string'
      earlyStopping: 'string'
      earlyStoppingDelay: 'string'
      earlyStoppingPatience: 'string'
      enableOnnxNormalization: 'string'
      evaluationFrequency: 'string'
      gradientAccumulationStep: 'string'
      layersToFreeze: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      modelName: 'string'
      momentum: 'string'
      nesterov: 'string'
      numberOfEpochs: 'string'
      numberOfWorkers: 'string'
      optimizer: 'string'
      randomSeed: 'string'
      stepLRGamma: 'string'
      stepLRStepSize: 'string'
      trainingBatchSize: 'string'
      trainingCropSize: 'string'
      validationBatchSize: 'string'
      validationCropSize: 'string'
      validationResizeSize: 'string'
      warmupCosineLRCycles: 'string'
      warmupCosineLRWarmupEpochs: 'string'
      weightDecay: 'string'
      weightedLoss: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int

For ImageClassificationMultilabel, use:

  taskType: 'ImageClassificationMultilabel'
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  modelSettings: {
    advancedSettings: 'string'
    amsGradient: bool
    augmentations: 'string'
    beta1: int
    beta2: int
    checkpointFrequency: int
    checkpointModel: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    checkpointRunId: 'string'
    distributed: bool
    earlyStopping: bool
    earlyStoppingDelay: int
    earlyStoppingPatience: int
    enableOnnxNormalization: bool
    evaluationFrequency: int
    gradientAccumulationStep: int
    layersToFreeze: int
    learningRate: int
    learningRateScheduler: 'string'
    modelName: 'string'
    momentum: int
    nesterov: bool
    numberOfEpochs: int
    numberOfWorkers: int
    optimizer: 'string'
    randomSeed: int
    stepLRGamma: int
    stepLRStepSize: int
    trainingBatchSize: int
    trainingCropSize: int
    validationBatchSize: int
    validationCropSize: int
    validationResizeSize: int
    warmupCosineLRCycles: int
    warmupCosineLRWarmupEpochs: int
    weightDecay: int
    weightedLoss: int
  }
  primaryMetric: 'string'
  searchSpace: [
    {
      amsGradient: 'string'
      augmentations: 'string'
      beta1: 'string'
      beta2: 'string'
      distributed: 'string'
      earlyStopping: 'string'
      earlyStoppingDelay: 'string'
      earlyStoppingPatience: 'string'
      enableOnnxNormalization: 'string'
      evaluationFrequency: 'string'
      gradientAccumulationStep: 'string'
      layersToFreeze: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      modelName: 'string'
      momentum: 'string'
      nesterov: 'string'
      numberOfEpochs: 'string'
      numberOfWorkers: 'string'
      optimizer: 'string'
      randomSeed: 'string'
      stepLRGamma: 'string'
      stepLRStepSize: 'string'
      trainingBatchSize: 'string'
      trainingCropSize: 'string'
      validationBatchSize: 'string'
      validationCropSize: 'string'
      validationResizeSize: 'string'
      warmupCosineLRCycles: 'string'
      warmupCosineLRWarmupEpochs: 'string'
      weightDecay: 'string'
      weightedLoss: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int

For ImageInstanceSegmentation, use:

  taskType: 'ImageInstanceSegmentation'
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  modelSettings: {
    advancedSettings: 'string'
    amsGradient: bool
    augmentations: 'string'
    beta1: int
    beta2: int
    boxDetectionsPerImage: int
    boxScoreThreshold: int
    checkpointFrequency: int
    checkpointModel: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    checkpointRunId: 'string'
    distributed: bool
    earlyStopping: bool
    earlyStoppingDelay: int
    earlyStoppingPatience: int
    enableOnnxNormalization: bool
    evaluationFrequency: int
    gradientAccumulationStep: int
    imageSize: int
    layersToFreeze: int
    learningRate: int
    learningRateScheduler: 'string'
    maxSize: int
    minSize: int
    modelName: 'string'
    modelSize: 'string'
    momentum: int
    multiScale: bool
    nesterov: bool
    nmsIouThreshold: int
    numberOfEpochs: int
    numberOfWorkers: int
    optimizer: 'string'
    randomSeed: int
    stepLRGamma: int
    stepLRStepSize: int
    tileGridSize: 'string'
    tileOverlapRatio: int
    tilePredictionsNmsThreshold: int
    trainingBatchSize: int
    validationBatchSize: int
    validationIouThreshold: int
    validationMetricType: 'string'
    warmupCosineLRCycles: int
    warmupCosineLRWarmupEpochs: int
    weightDecay: int
  }
  primaryMetric: 'MeanAveragePrecision'
  searchSpace: [
    {
      amsGradient: 'string'
      augmentations: 'string'
      beta1: 'string'
      beta2: 'string'
      boxDetectionsPerImage: 'string'
      boxScoreThreshold: 'string'
      distributed: 'string'
      earlyStopping: 'string'
      earlyStoppingDelay: 'string'
      earlyStoppingPatience: 'string'
      enableOnnxNormalization: 'string'
      evaluationFrequency: 'string'
      gradientAccumulationStep: 'string'
      imageSize: 'string'
      layersToFreeze: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      maxSize: 'string'
      minSize: 'string'
      modelName: 'string'
      modelSize: 'string'
      momentum: 'string'
      multiScale: 'string'
      nesterov: 'string'
      nmsIouThreshold: 'string'
      numberOfEpochs: 'string'
      numberOfWorkers: 'string'
      optimizer: 'string'
      randomSeed: 'string'
      stepLRGamma: 'string'
      stepLRStepSize: 'string'
      tileGridSize: 'string'
      tileOverlapRatio: 'string'
      tilePredictionsNmsThreshold: 'string'
      trainingBatchSize: 'string'
      validationBatchSize: 'string'
      validationIouThreshold: 'string'
      validationMetricType: 'string'
      warmupCosineLRCycles: 'string'
      warmupCosineLRWarmupEpochs: 'string'
      weightDecay: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int

For ImageObjectDetection, use:

  taskType: 'ImageObjectDetection'
  limitSettings: {
    maxConcurrentTrials: int
    maxTrials: int
    timeout: 'string'
  }
  modelSettings: {
    advancedSettings: 'string'
    amsGradient: bool
    augmentations: 'string'
    beta1: int
    beta2: int
    boxDetectionsPerImage: int
    boxScoreThreshold: int
    checkpointFrequency: int
    checkpointModel: {
      description: 'string'
      jobInputType: 'string'
      mode: 'string'
      uri: 'string'
    }
    checkpointRunId: 'string'
    distributed: bool
    earlyStopping: bool
    earlyStoppingDelay: int
    earlyStoppingPatience: int
    enableOnnxNormalization: bool
    evaluationFrequency: int
    gradientAccumulationStep: int
    imageSize: int
    layersToFreeze: int
    learningRate: int
    learningRateScheduler: 'string'
    maxSize: int
    minSize: int
    modelName: 'string'
    modelSize: 'string'
    momentum: int
    multiScale: bool
    nesterov: bool
    nmsIouThreshold: int
    numberOfEpochs: int
    numberOfWorkers: int
    optimizer: 'string'
    randomSeed: int
    stepLRGamma: int
    stepLRStepSize: int
    tileGridSize: 'string'
    tileOverlapRatio: int
    tilePredictionsNmsThreshold: int
    trainingBatchSize: int
    validationBatchSize: int
    validationIouThreshold: int
    validationMetricType: 'string'
    warmupCosineLRCycles: int
    warmupCosineLRWarmupEpochs: int
    weightDecay: int
  }
  primaryMetric: 'MeanAveragePrecision'
  searchSpace: [
    {
      amsGradient: 'string'
      augmentations: 'string'
      beta1: 'string'
      beta2: 'string'
      boxDetectionsPerImage: 'string'
      boxScoreThreshold: 'string'
      distributed: 'string'
      earlyStopping: 'string'
      earlyStoppingDelay: 'string'
      earlyStoppingPatience: 'string'
      enableOnnxNormalization: 'string'
      evaluationFrequency: 'string'
      gradientAccumulationStep: 'string'
      imageSize: 'string'
      layersToFreeze: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      maxSize: 'string'
      minSize: 'string'
      modelName: 'string'
      modelSize: 'string'
      momentum: 'string'
      multiScale: 'string'
      nesterov: 'string'
      nmsIouThreshold: 'string'
      numberOfEpochs: 'string'
      numberOfWorkers: 'string'
      optimizer: 'string'
      randomSeed: 'string'
      stepLRGamma: 'string'
      stepLRStepSize: 'string'
      tileGridSize: 'string'
      tileOverlapRatio: 'string'
      tilePredictionsNmsThreshold: 'string'
      trainingBatchSize: 'string'
      validationBatchSize: 'string'
      validationIouThreshold: 'string'
      validationMetricType: 'string'
      warmupCosineLRCycles: 'string'
      warmupCosineLRWarmupEpochs: 'string'
      weightDecay: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int

For Regression, use:

  taskType: 'Regression'
  cvSplitColumnNames: [
    'string'
  ]
  featurizationSettings: {
    blockedTransformers: [
      'string'
    ]
    columnNameAndTypes: {
      {customized property}: 'string'
    }
    datasetLanguage: 'string'
    enableDnnFeaturization: bool
    mode: 'string'
    transformerParams: {
      {customized property}: [
        {
          fields: [
            'string'
          ]
          parameters: any()
        }
      ]
    }
  }
  fixedParameters: {
    booster: 'string'
    boostingType: 'string'
    growPolicy: 'string'
    learningRate: int
    maxBin: int
    maxDepth: int
    maxLeaves: int
    minDataInLeaf: int
    minSplitGain: int
    modelName: 'string'
    nEstimators: int
    numLeaves: int
    preprocessorName: 'string'
    regAlpha: int
    regLambda: int
    subsample: int
    subsampleFreq: int
    treeMethod: 'string'
    withMean: bool
    withStd: bool
  }
  limitSettings: {
    enableEarlyTermination: bool
    exitScore: int
    maxConcurrentTrials: int
    maxCoresPerTrial: int
    maxNodes: int
    maxTrials: int
    sweepConcurrentTrials: int
    sweepTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  nCrossValidations: {
    mode: 'string'
    // For remaining properties, see NCrossValidations objects
  }
  primaryMetric: 'string'
  searchSpace: [
    {
      booster: 'string'
      boostingType: 'string'
      growPolicy: 'string'
      learningRate: 'string'
      maxBin: 'string'
      maxDepth: 'string'
      maxLeaves: 'string'
      minDataInLeaf: 'string'
      minSplitGain: 'string'
      modelName: 'string'
      nEstimators: 'string'
      numLeaves: 'string'
      preprocessorName: 'string'
      regAlpha: 'string'
      regLambda: 'string'
      subsample: 'string'
      subsampleFreq: 'string'
      treeMethod: 'string'
      withMean: 'string'
      withStd: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  testData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  testDataSize: int
  trainingSettings: {
    allowedTrainingAlgorithms: [
      'string'
    ]
    blockedTrainingAlgorithms: [
      'string'
    ]
    enableDnnTraining: bool
    enableModelExplainability: bool
    enableOnnxCompatibleModels: bool
    enableStackEnsemble: bool
    enableVoteEnsemble: bool
    ensembleModelDownloadTimeout: 'string'
    stackEnsembleSettings: {
      stackMetaLearnerKWargs: any()
      stackMetaLearnerTrainPercentage: int
      stackMetaLearnerType: 'string'
    }
    trainingMode: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }
  validationDataSize: int
  weightColumnName: 'string'

For TextClassification, use:

  taskType: 'TextClassification'
  featurizationSettings: {
    datasetLanguage: 'string'
  }
  fixedParameters: {
    gradientAccumulationSteps: int
    learningRate: int
    learningRateScheduler: 'string'
    modelName: 'string'
    numberOfEpochs: int
    trainingBatchSize: int
    validationBatchSize: int
    warmupRatio: int
    weightDecay: int
  }
  limitSettings: {
    maxConcurrentTrials: int
    maxNodes: int
    maxTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  primaryMetric: 'string'
  searchSpace: [
    {
      gradientAccumulationSteps: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      modelName: 'string'
      numberOfEpochs: 'string'
      trainingBatchSize: 'string'
      validationBatchSize: 'string'
      warmupRatio: 'string'
      weightDecay: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }

For TextClassificationMultilabel, use:

  taskType: 'TextClassificationMultilabel'
  featurizationSettings: {
    datasetLanguage: 'string'
  }
  fixedParameters: {
    gradientAccumulationSteps: int
    learningRate: int
    learningRateScheduler: 'string'
    modelName: 'string'
    numberOfEpochs: int
    trainingBatchSize: int
    validationBatchSize: int
    warmupRatio: int
    weightDecay: int
  }
  limitSettings: {
    maxConcurrentTrials: int
    maxNodes: int
    maxTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  searchSpace: [
    {
      gradientAccumulationSteps: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      modelName: 'string'
      numberOfEpochs: 'string'
      trainingBatchSize: 'string'
      validationBatchSize: 'string'
      warmupRatio: 'string'
      weightDecay: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }

For TextNER, use:

  taskType: 'TextNER'
  featurizationSettings: {
    datasetLanguage: 'string'
  }
  fixedParameters: {
    gradientAccumulationSteps: int
    learningRate: int
    learningRateScheduler: 'string'
    modelName: 'string'
    numberOfEpochs: int
    trainingBatchSize: int
    validationBatchSize: int
    warmupRatio: int
    weightDecay: int
  }
  limitSettings: {
    maxConcurrentTrials: int
    maxNodes: int
    maxTrials: int
    timeout: 'string'
    trialTimeout: 'string'
  }
  searchSpace: [
    {
      gradientAccumulationSteps: 'string'
      learningRate: 'string'
      learningRateScheduler: 'string'
      modelName: 'string'
      numberOfEpochs: 'string'
      trainingBatchSize: 'string'
      validationBatchSize: 'string'
      warmupRatio: 'string'
      weightDecay: 'string'
    }
  ]
  sweepSettings: {
    earlyTermination: {
      delayEvaluation: int
      evaluationInterval: int
      policyType: 'string'
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm: 'string'
  }
  validationData: {
    description: 'string'
    jobInputType: 'string'
    mode: 'string'
    uri: 'string'
  }

NCrossValidations objects

Set the mode property to specify the type of object.

For Auto, use:

  mode: 'Auto'

For Custom, use:

  mode: 'Custom'
  value: int

EarlyTerminationPolicy objects

Set the policyType property to specify the type of object.

For Bandit, use:

  policyType: 'Bandit'
  slackAmount: int
  slackFactor: int

For MedianStopping, use:

  policyType: 'MedianStopping'

For TruncationSelection, use:

  policyType: 'TruncationSelection'
  truncationPercentage: int

ForecastHorizon objects

Set the mode property to specify the type of object.

For Auto, use:

  mode: 'Auto'

For Custom, use:

  mode: 'Custom'
  value: int

Seasonality objects

Set the mode property to specify the type of object.

For Auto, use:

  mode: 'Auto'

For Custom, use:

  mode: 'Custom'
  value: int

TargetLags objects

Set the mode property to specify the type of object.

For Auto, use:

  mode: 'Auto'

For Custom, use:

  mode: 'Custom'
  values: [
    int
  ]

TargetRollingWindowSize objects

Set the mode property to specify the type of object.

For Auto, use:

  mode: 'Auto'

For Custom, use:

  mode: 'Custom'
  value: int

DistributionConfiguration objects

Set the distributionType property to specify the type of object.

For Mpi, use:

  distributionType: 'Mpi'
  processCountPerInstance: int

For PyTorch, use:

  distributionType: 'PyTorch'
  processCountPerInstance: int

For TensorFlow, use:

  distributionType: 'TensorFlow'
  parameterServerCount: int
  workerCount: int

JobInput objects

Set the jobInputType property to specify the type of object.

For custom_model, use:

  jobInputType: 'custom_model'
  mode: 'string'
  uri: 'string'

For literal, use:

  jobInputType: 'literal'
  value: 'string'

For mlflow_model, use:

  jobInputType: 'mlflow_model'
  mode: 'string'
  uri: 'string'

For mltable, use:

  jobInputType: 'mltable'
  mode: 'string'
  uri: 'string'

For triton_model, use:

  jobInputType: 'triton_model'
  mode: 'string'
  uri: 'string'

For uri_file, use:

  jobInputType: 'uri_file'
  mode: 'string'
  uri: 'string'

For uri_folder, use:

  jobInputType: 'uri_folder'
  mode: 'string'
  uri: 'string'

LabelingJobMediaProperties objects

Set the mediaType property to specify the type of object.

For Image, use:

  mediaType: 'Image'
  annotationType: 'string'

For Text, use:

  mediaType: 'Text'
  annotationType: 'string'

MLAssistConfiguration objects

Set the mlAssist property to specify the type of object.

For Disabled, use:

  mlAssist: 'Disabled'

For Enabled, use:

  mlAssist: 'Enabled'
  inferencingComputeBinding: 'string'
  trainingComputeBinding: 'string'

SparkJobEntry objects

Set the sparkJobEntryType property to specify the type of object.

For SparkJobPythonEntry, use:

  sparkJobEntryType: 'SparkJobPythonEntry'
  file: 'string'

For SparkJobScalaEntry, use:

  sparkJobEntryType: 'SparkJobScalaEntry'
  className: 'string'

SamplingAlgorithm objects

Set the samplingAlgorithmType property to specify the type of object.

For Bayesian, use:

  samplingAlgorithmType: 'Bayesian'

For Grid, use:

  samplingAlgorithmType: 'Grid'

For Random, use:

  samplingAlgorithmType: 'Random'
  logbase: 'string'
  rule: 'string'
  seed: int

Property values

workspaces/jobs

Name	Description	Value
name	The resource name See how to set names and types for child resources in Bicep.	string (required)
parent	In Bicep, you can specify the parent resource for a child resource. You only need to add this property when the child resource is declared outside of the parent resource. For more information, see Child resource outside parent resource.	Symbolic name for resource of type: workspaces
properties	[Required] Additional attributes of the entity.	JobBaseProperties (required)

JobBaseProperties

Name	Description	Value
componentId	ARM resource ID of the component resource.	string
computeId	ARM resource ID of the compute resource.	string
description	The asset description text.	string
displayName	Display name of job.	string
experimentName	The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.	string
identity	Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.	IdentityConfiguration
isArchived	Is the asset archived?	bool
properties	The asset property dictionary.	ResourceBaseProperties
services	List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.	JobBaseServices
tags	Tag dictionary. Tags can be added, removed, and updated.	object
jobType	Set the object type	AutoML Command Labeling Pipeline Spark Sweep (required)

IdentityConfiguration

Name	Description	Value
identityType	Set the object type	AMLToken Managed UserIdentity (required)

AmlToken

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	'AMLToken' (required)

ManagedIdentity

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	'Managed' (required)
clientId	Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.	string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
objectId	Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.	string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
resourceId	Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.	string

UserIdentity

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	'UserIdentity' (required)

ResourceBaseProperties

Name	Description	Value
{customized property}		string

JobBaseServices

Name	Description	Value
{customized property}		JobService

JobService

Name	Description	Value
endpoint	Url for endpoint.	string
jobServiceType	Endpoint type.	string
nodes	Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.	Nodes
port	Port for endpoint set by user.	int
properties	Additional properties to set on the endpoint.	JobServiceProperties

Nodes

Name	Description	Value
nodesValueType	Set the object type	All (required)

AllNodes

Name	Description	Value
nodesValueType	[Required] Type of the Nodes value	'All' (required)

JobServiceProperties

Name	Description	Value
{customized property}		string

AutoMLJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	'AutoML' (required)
environmentId	The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.	string
environmentVariables	Environment variables included in the job.	AutoMLJobEnvironmentVariables
outputs	Mapping of output data bindings used in the job.	AutoMLJobOutputs
resources	Compute Resource configuration for the job.	JobResourceConfiguration
taskDetails	[Required] This represents scenario which can be one of Tables/NLP/Image	AutoMLVertical (required)

AutoMLJobEnvironmentVariables

Name	Description	Value
{customized property}		string

AutoMLJobOutputs

Name	Description	Value
{customized property}		JobOutput

JobOutput

Name	Description	Value
description	Description for the output.	string
jobOutputType	Set the object type	custom_model mlflow_model mltable triton_model uri_file uri_folder (required)

CustomModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'custom_model' (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

MLFlowModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'mlflow_model' (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

MLTableJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'mltable' (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

TritonModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'triton_model' (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

UriFileJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'uri_file' (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

UriFolderJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'uri_folder' (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

JobResourceConfiguration

Name	Description	Value
dockerArgs	Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.	string
instanceCount	Optional number of instances or nodes used by the compute target.	int
instanceType	Optional type of VM used as supported by the compute target.	string
properties	Additional properties bag.	ResourceConfigurationProperties
shmSize	Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).	string Constraints: Pattern = \d+[bBkKmMgG]

ResourceConfigurationProperties

Name	Description	Value
{customized property}		For Bicep, you can use the any() function.

AutoMLVertical

Name	Description	Value
logVerbosity	Log verbosity for the job.	'Critical' 'Debug' 'Error' 'Info' 'NotSet' 'Warning'
targetColumnName	Target column name: This is prediction values column. Also known as label column name in context of classification tasks.	string
trainingData	[Required] Training data input.	MLTableJobInput (required)
taskType	Set the object type	Classification Forecasting ImageClassification ImageClassificationMultilabel ImageInstanceSegmentation ImageObjectDetection Regression TextClassification TextClassificationMultilabel TextNER (required)

MLTableJobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	[Required] Specifies the type of job.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

Classification

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'Classification' (required)
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	TableFixedParameters
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
positiveLabel	Positive label for binary metrics calculation.	string
primaryMetric	Primary metric for the task.	'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	TableParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	TableSweepSettings
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	ClassificationTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

TableVerticalFeaturizationSettings

Name	Description	Value
blockedTransformers	These transformers shall not be used in featurization.	String array containing any of: 'CatTargetEncoder' 'CountVectorizer' 'HashOneHotEncoder' 'LabelEncoder' 'NaiveBayes' 'OneHotEncoder' 'TextTargetEncoder' 'TfIdf' 'WoETargetEncoder' 'WordEmbedding'
columnNameAndTypes	Dictionary of column name and its type (int, float, string, datetime etc).	TableVerticalFeaturizationSettingsColumnNameAndTypes
datasetLanguage	Dataset language, useful for the text data.	string
enableDnnFeaturization	Determines whether to use Dnn based featurizers for data featurization.	bool
mode	Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.	'Auto' 'Custom' 'Off'
transformerParams	User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.	TableVerticalFeaturizationSettingsTransformerParams

TableVerticalFeaturizationSettingsColumnNameAndTypes

Name	Description	Value
{customized property}		string

TableVerticalFeaturizationSettingsTransformerParams

Name	Description	Value
{customized property}		ColumnTransformer[]

ColumnTransformer

Name	Description	Value
fields	Fields to apply transformer logic on.	string[]
parameters	Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.	For Bicep, you can use the any() function.

TableFixedParameters

Name	Description	Value
booster	Specify the boosting type, e.g gbdt for XGBoost.	string
boostingType	Specify the boosting type, e.g gbdt for LightGBM.	string
growPolicy	Specify the grow policy, which controls the way new nodes are added to the tree.	string
learningRate	The learning rate for the training procedure.	int
maxBin	Specify the Maximum number of discrete bins to bucket continuous features .	int
maxDepth	Specify the max depth to limit the tree depth explicitly.	int
maxLeaves	Specify the max leaves to limit the tree leaves explicitly.	int
minDataInLeaf	The minimum number of data per leaf.	int
minSplitGain	Minimum loss reduction required to make a further partition on a leaf node of the tree.	int
modelName	The name of the model to train.	string
nEstimators	Specify the number of trees (or rounds) in an model.	int
numLeaves	Specify the number of leaves.	int
preprocessorName	The name of the preprocessor to use.	string
regAlpha	L1 regularization term on weights.	int
regLambda	L2 regularization term on weights.	int
subsample	Subsample ratio of the training instance.	int
subsampleFreq	Frequency of subsample.	int
treeMethod	Specify the tree method.	string
withMean	If true, center before scaling the data with StandardScalar.	bool
withStd	If true, scaling the data with Unit Variance with StandardScalar.	bool

TableVerticalLimitSettings

Name	Description	Value
enableEarlyTermination	Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.	bool
exitScore	Exit score for the AutoML job.	int
maxConcurrentTrials	Maximum Concurrent iterations.	int
maxCoresPerTrial	Max cores per iteration.	int
maxNodes	Maximum nodes to use for the experiment.	int
maxTrials	Number of iterations.	int
sweepConcurrentTrials	Number of concurrent sweeping runs that user wants to trigger.	int
sweepTrials	Number of sweeping runs that user wants to trigger.	int
timeout	AutoML job timeout.	string
trialTimeout	Iteration timeout.	string

NCrossValidations

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoNCrossValidations

Name	Description	Value
mode	[Required] Mode for determining N-Cross validations.	'Auto' (required)

CustomNCrossValidations

Name	Description	Value
mode	[Required] Mode for determining N-Cross validations.	'Custom' (required)
value	[Required] N-Cross validations value.	int (required)

TableParameterSubspace

Name	Description	Value
booster	Specify the boosting type, e.g gbdt for XGBoost.	string
boostingType	Specify the boosting type, e.g gbdt for LightGBM.	string
growPolicy	Specify the grow policy, which controls the way new nodes are added to the tree.	string
learningRate	The learning rate for the training procedure.	string
maxBin	Specify the Maximum number of discrete bins to bucket continuous features .	string
maxDepth	Specify the max depth to limit the tree depth explicitly.	string
maxLeaves	Specify the max leaves to limit the tree leaves explicitly.	string
minDataInLeaf	The minimum number of data per leaf.	string
minSplitGain	Minimum loss reduction required to make a further partition on a leaf node of the tree.	string
modelName	The name of the model to train.	string
nEstimators	Specify the number of trees (or rounds) in an model.	string
numLeaves	Specify the number of leaves.	string
preprocessorName	The name of the preprocessor to use.	string
regAlpha	L1 regularization term on weights.	string
regLambda	L2 regularization term on weights.	string
subsample	Subsample ratio of the training instance.	string
subsampleFreq	Frequency of subsample	string
treeMethod	Specify the tree method.	string
withMean	If true, center before scaling the data with StandardScalar.	string
withStd	If true, scaling the data with Unit Variance with StandardScalar.	string

TableSweepSettings

Name	Description	Value
earlyTermination	Type of early termination policy for the sweeping job.	EarlyTerminationPolicy
samplingAlgorithm	[Required] Type of sampling algorithm.	'Bayesian' 'Grid' 'Random' (required)

EarlyTerminationPolicy

Name	Description	Value
delayEvaluation	Number of intervals by which to delay the first evaluation.	int
evaluationInterval	Interval (number of runs) between policy evaluations.	int
policyType	Set the object type	Bandit MedianStopping TruncationSelection (required)

BanditPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'Bandit' (required)
slackAmount	Absolute distance allowed from the best performing run.	int
slackFactor	Ratio of the allowed distance from the best performing run.	int

MedianStoppingPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'MedianStopping' (required)

TruncationSelectionPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'TruncationSelection' (required)
truncationPercentage	The percentage of runs to cancel at each evaluation interval.	int

ClassificationTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for classification task.	String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier'
blockedTrainingAlgorithms	Blocked models for classification task.	String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings
trainingMode	TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen.	'Auto' 'Distributed' 'NonDistributed'

StackEnsembleSettings

Name	Description	Value
stackMetaLearnerKWargs	Optional parameters to pass to the initializer of the meta-learner.	For Bicep, you can use the any() function.
stackMetaLearnerTrainPercentage	Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.	int
stackMetaLearnerType	The meta-learner is a model trained on the output of the individual heterogeneous models.	'ElasticNet' 'ElasticNetCV' 'LightGBMClassifier' 'LightGBMRegressor' 'LinearRegression' 'LogisticRegression' 'LogisticRegressionCV' 'None'

Forecasting

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'Forecasting' (required)
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	TableFixedParameters
forecastingSettings	Forecasting task specific inputs.	ForecastingSettings
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
primaryMetric	Primary metric for forecasting task.	'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	TableParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	TableSweepSettings
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	ForecastingTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

ForecastingSettings

Name	Description	Value
countryOrRegionForHolidays	Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.	string
cvStepSize	Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.	int
featureLags	Flag for generating lags for the numeric features with 'auto' or null.	'Auto' 'None'
forecastHorizon	The desired maximum forecast horizon in units of time-series frequency.	ForecastHorizon
frequency	When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.	string
seasonality	Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.	Seasonality
shortSeriesHandlingConfig	The parameter defining how if AutoML should handle short time series.	'Auto' 'Drop' 'None' 'Pad'
targetAggregateFunction	The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".	'Max' 'Mean' 'Min' 'None' 'Sum'
targetLags	The number of past periods to lag from the target column.	TargetLags
targetRollingWindowSize	The number of past periods used to create a rolling window average of the target column.	TargetRollingWindowSize
timeColumnName	The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.	string
timeSeriesIdColumnNames	The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.	string[]
useStl	Configure STL Decomposition of the time-series target column.	'None' 'Season' 'SeasonTrend'

ForecastHorizon

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoForecastHorizon

Name	Description	Value
mode	[Required] Set forecast horizon value selection mode.	'Auto' (required)

CustomForecastHorizon

Name	Description	Value
mode	[Required] Set forecast horizon value selection mode.	'Custom' (required)
value	[Required] Forecast horizon value.	int (required)

Seasonality

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoSeasonality

Name	Description	Value
mode	[Required] Seasonality mode.	'Auto' (required)

CustomSeasonality

Name	Description	Value
mode	[Required] Seasonality mode.	'Custom' (required)
value	[Required] Seasonality value.	int (required)

TargetLags

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoTargetLags

Name	Description	Value
mode	[Required] Set target lags mode - Auto/Custom	'Auto' (required)

CustomTargetLags

Name	Description	Value
mode	[Required] Set target lags mode - Auto/Custom	'Custom' (required)
values	[Required] Set target lags values.	int[] (required)

TargetRollingWindowSize

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoTargetRollingWindowSize

Name	Description	Value
mode	[Required] TargetRollingWindowSiz detection mode.	'Auto' (required)

CustomTargetRollingWindowSize

Name	Description	Value
mode	[Required] TargetRollingWindowSiz detection mode.	'Custom' (required)
value	[Required] TargetRollingWindowSize value.	int (required)

ForecastingTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for forecasting task.	String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SGD' 'SeasonalAverage' 'SeasonalNaive' 'TCNForecaster' 'XGBoostRegressor'
blockedTrainingAlgorithms	Blocked models for forecasting task.	String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SGD' 'SeasonalAverage' 'SeasonalNaive' 'TCNForecaster' 'XGBoostRegressor'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings
trainingMode	TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen.	'Auto' 'Distributed' 'NonDistributed'

ImageClassification

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'ImageClassification' (required)
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsClassification
primaryMetric	Primary metric to optimize for this task.	'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsClassification[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageLimitSettings

Name	Description	Value
maxConcurrentTrials	Maximum number of concurrent AutoML iterations.	int
maxTrials	Maximum number of AutoML iterations.	int
timeout	AutoML job timeout.	string

ImageModelSettingsClassification

Name	Description	Value
advancedSettings	Settings for advanced scenarios.	string
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	bool
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
checkpointFrequency	Frequency to store model checkpoints. Must be a positive integer.	int
checkpointModel	The pretrained checkpoint model for incremental training.	MLFlowModelJobInput
checkpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.	string
distributed	Whether to use distributed training.	bool
earlyStopping	Enable early stopping logic during training.	bool
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	int
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	int
enableOnnxNormalization	Enable normalization when exporting ONNX model.	bool
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	int
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	int
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	int
learningRate	Initial learning rate. Must be a float in the range [0, 1].	int
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	'None' 'Step' 'WarmupCosine'
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	int
nesterov	Enable nesterov when optimizer is 'sgd'.	bool
numberOfEpochs	Number of training epochs. Must be a positive integer.	int
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	int
optimizer	Type of optimizer.	'Adam' 'Adamw' 'None' 'Sgd'
randomSeed	Random seed to be used when using deterministic training.	int
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	int
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	int
trainingBatchSize	Training batch size. Must be a positive integer.	int
trainingCropSize	Image crop size that is input to the neural network for the training dataset. Must be a positive integer.	int
validationBatchSize	Validation batch size. Must be a positive integer.	int
validationCropSize	Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.	int
validationResizeSize	Image size to which to resize before cropping for validation dataset. Must be a positive integer.	int
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	int
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	int
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	int
weightedLoss	Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.	int

MLFlowModelJobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	[Required] Specifies the type of job.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

ImageModelDistributionSettingsClassification

Name	Description	Value
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	string
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
distributed	Whether to use distributer training.	string
earlyStopping	Enable early stopping logic during training.	string
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	string
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	string
enableOnnxNormalization	Enable normalization when exporting ONNX model.	string
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	string
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	string
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	string
learningRate	Initial learning rate. Must be a float in the range [0, 1].	string
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	string
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	string
nesterov	Enable nesterov when optimizer is 'sgd'.	string
numberOfEpochs	Number of training epochs. Must be a positive integer.	string
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	string
optimizer	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.	string
randomSeed	Random seed to be used when using deterministic training.	string
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	string
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	string
trainingBatchSize	Training batch size. Must be a positive integer.	string
trainingCropSize	Image crop size that is input to the neural network for the training dataset. Must be a positive integer.	string
validationBatchSize	Validation batch size. Must be a positive integer.	string
validationCropSize	Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.	string
validationResizeSize	Image size to which to resize before cropping for validation dataset. Must be a positive integer.	string
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	string
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	string
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	string
weightedLoss	Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.	string

ImageSweepSettings

Name	Description	Value
earlyTermination	Type of early termination policy.	EarlyTerminationPolicy
samplingAlgorithm	[Required] Type of the hyperparameter sampling algorithms.	'Bayesian' 'Grid' 'Random' (required)

ImageClassificationMultilabel

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'ImageClassificationMultilabel' (required)
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsClassification
primaryMetric	Primary metric to optimize for this task.	'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'IOU' 'NormMacroRecall' 'PrecisionScoreWeighted'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsClassification[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageInstanceSegmentation

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'ImageInstanceSegmentation' (required)
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsObjectDetection
primaryMetric	Primary metric to optimize for this task.	'MeanAveragePrecision'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsObjectDetection[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageModelSettingsObjectDetection

Name	Description	Value
advancedSettings	Settings for advanced scenarios.	string
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	bool
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
boxDetectionsPerImage	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.	int
boxScoreThreshold	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].	int
checkpointFrequency	Frequency to store model checkpoints. Must be a positive integer.	int
checkpointModel	The pretrained checkpoint model for incremental training.	MLFlowModelJobInput
checkpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.	string
distributed	Whether to use distributed training.	bool
earlyStopping	Enable early stopping logic during training.	bool
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	int
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	int
enableOnnxNormalization	Enable normalization when exporting ONNX model.	bool
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	int
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	int
imageSize	Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	int
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	int
learningRate	Initial learning rate. Must be a float in the range [0, 1].	int
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	'None' 'Step' 'WarmupCosine'
maxSize	Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	int
minSize	Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	int
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
modelSize	Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	'ExtraLarge' 'Large' 'Medium' 'None' 'Small'
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	int
multiScale	Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.	bool
nesterov	Enable nesterov when optimizer is 'sgd'.	bool
nmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].	int
numberOfEpochs	Number of training epochs. Must be a positive integer.	int
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	int
optimizer	Type of optimizer.	'Adam' 'Adamw' 'None' 'Sgd'
randomSeed	Random seed to be used when using deterministic training.	int
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	int
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	int
tileGridSize	The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.	string
tileOverlapRatio	Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.	int
tilePredictionsNmsThreshold	The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.	int
trainingBatchSize	Training batch size. Must be a positive integer.	int
validationBatchSize	Validation batch size. Must be a positive integer.	int
validationIouThreshold	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].	int
validationMetricType	Metric computation method to use for validation metrics.	'Coco' 'CocoVoc' 'None' 'Voc'
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	int
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	int
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	int

ImageModelDistributionSettingsObjectDetection

Name	Description	Value
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	string
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
boxDetectionsPerImage	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.	string
boxScoreThreshold	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].	string
distributed	Whether to use distributer training.	string
earlyStopping	Enable early stopping logic during training.	string
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	string
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	string
enableOnnxNormalization	Enable normalization when exporting ONNX model.	string
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	string
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	string
imageSize	Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	string
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	string
learningRate	Initial learning rate. Must be a float in the range [0, 1].	string
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	string
maxSize	Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	string
minSize	Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	string
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
modelSize	Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	string
multiScale	Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.	string
nesterov	Enable nesterov when optimizer is 'sgd'.	string
nmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].	string
numberOfEpochs	Number of training epochs. Must be a positive integer.	string
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	string
optimizer	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.	string
randomSeed	Random seed to be used when using deterministic training.	string
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	string
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	string
tileGridSize	The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.	string
tileOverlapRatio	Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.	string
tilePredictionsNmsThreshold	The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression	string
trainingBatchSize	Training batch size. Must be a positive integer.	string
validationBatchSize	Validation batch size. Must be a positive integer.	string
validationIouThreshold	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].	string
validationMetricType	Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.	string
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	string
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	string
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	string

ImageObjectDetection

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'ImageObjectDetection' (required)
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsObjectDetection
primaryMetric	Primary metric to optimize for this task.	'MeanAveragePrecision'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsObjectDetection[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

Regression

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'Regression' (required)
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	TableFixedParameters
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
primaryMetric	Primary metric for regression task.	'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	TableParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	TableSweepSettings
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	RegressionTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

RegressionTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for regression task.	String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor'
blockedTrainingAlgorithms	Blocked models for regression task.	String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings
trainingMode	TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen.	'Auto' 'Distributed' 'NonDistributed'

TextClassification

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'TextClassification' (required)
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	NlpFixedParameters
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
primaryMetric	Primary metric for Text-Classification task.	'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	NlpParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	NlpSweepSettings
validationData	Validation data inputs.	MLTableJobInput

NlpVerticalFeaturizationSettings

Name	Description	Value
datasetLanguage	Dataset language, useful for the text data.	string

NlpFixedParameters

Name	Description	Value
gradientAccumulationSteps	Number of steps to accumulate gradients over before running a backward pass.	int
learningRate	The learning rate for the training procedure.	int
learningRateScheduler	The type of learning rate schedule to use during the training procedure.	'Constant' 'ConstantWithWarmup' 'Cosine' 'CosineWithRestarts' 'Linear' 'None' 'Polynomial'
modelName	The name of the model to train.	string
numberOfEpochs	Number of training epochs.	int
trainingBatchSize	The batch size for the training procedure.	int
validationBatchSize	The batch size to be used during evaluation.	int
warmupRatio	The warmup ratio, used alongside LrSchedulerType.	int
weightDecay	The weight decay for the training procedure.	int

NlpVerticalLimitSettings

Name	Description	Value
maxConcurrentTrials	Maximum Concurrent AutoML iterations.	int
maxNodes	Maximum nodes to use for the experiment.	int
maxTrials	Number of AutoML iterations.	int
timeout	AutoML job timeout.	string
trialTimeout	Timeout for individual HD trials.	string

NlpParameterSubspace

Name	Description	Value
gradientAccumulationSteps	Number of steps to accumulate gradients over before running a backward pass.	string
learningRate	The learning rate for the training procedure.	string
learningRateScheduler	The type of learning rate schedule to use during the training procedure.	string
modelName	The name of the model to train.	string
numberOfEpochs	Number of training epochs.	string
trainingBatchSize	The batch size for the training procedure.	string
validationBatchSize	The batch size to be used during evaluation.	string
warmupRatio	The warmup ratio, used alongside LrSchedulerType.	string
weightDecay	The weight decay for the training procedure.	string

NlpSweepSettings

Name	Description	Value
earlyTermination	Type of early termination policy for the sweeping job.	EarlyTerminationPolicy
samplingAlgorithm	[Required] Type of sampling algorithm.	'Bayesian' 'Grid' 'Random' (required)

TextClassificationMultilabel

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'TextClassificationMultilabel' (required)
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	NlpFixedParameters
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	NlpParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	NlpSweepSettings
validationData	Validation data inputs.	MLTableJobInput

TextNer

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'TextNER' (required)
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	NlpFixedParameters
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	NlpParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	NlpSweepSettings
validationData	Validation data inputs.	MLTableJobInput

CommandJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	'Command' (required)
autologgerSettings	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	AutologgerSettings
codeId	ARM resource ID of the code asset.	string
command	[Required] The command to execute on startup of the job. eg. "python train.py"	string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_]
distribution	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	DistributionConfiguration
environmentId	[Required] The ARM resource ID of the Environment specification for the job.	string (required) Constraints: Pattern = [a-zA-Z0-9_]
environmentVariables	Environment variables included in the job.	CommandJobEnvironmentVariables
inputs	Mapping of input data bindings used in the job.	CommandJobInputs
limits	Command Job limit.	CommandJobLimits
outputs	Mapping of output data bindings used in the job.	CommandJobOutputs
resources	Compute Resource configuration for the job.	JobResourceConfiguration

AutologgerSettings

Name	Description	Value
mlflowAutologger	[Required] Indicates whether mlflow autologger is enabled.	'Disabled' 'Enabled' (required)

DistributionConfiguration

Name	Description	Value
distributionType	Set the object type	Mpi PyTorch TensorFlow (required)

Mpi

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'Mpi' (required)
processCountPerInstance	Number of processes per MPI node.	int

PyTorch

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'PyTorch' (required)
processCountPerInstance	Number of processes per node.	int

TensorFlow

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'TensorFlow' (required)
parameterServerCount	Number of parameter server tasks.	int
workerCount	Number of workers. If not specified, will default to the instance count.	int

CommandJobEnvironmentVariables

Name	Description	Value
{customized property}		string

CommandJobInputs

Name	Description	Value
{customized property}		JobInput

JobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	Set the object type	custom_model literal mlflow_model mltable triton_model uri_file uri_folder (required)

CustomModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'custom_model' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

LiteralJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'literal' (required)
value	[Required] Literal value for the input.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

TritonModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'triton_model' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

UriFileJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'uri_file' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

UriFolderJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

CommandJobLimits

Name	Description	Value
jobLimitsType	[Required] JobLimit type.	'Command' 'Sweep' (required)
timeout	The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.	string

CommandJobOutputs

Name	Description	Value
{customized property}		JobOutput

LabelingJobProperties

Name	Description	Value
componentId	ARM resource ID of the component resource.	string
computeId	ARM resource ID of the compute resource.	string
dataConfiguration	Configuration of data used in the job.	LabelingDataConfiguration
description	The asset description text.	string
displayName	Display name of job.	string
experimentName	The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.	string
identity	Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.	IdentityConfiguration
isArchived	Is the asset archived?	bool
jobInstructions	Labeling instructions of the job.	LabelingJobInstructions
jobType	[Required] Specifies the type of job.	'AutoML' 'Command' 'Labeling' 'Pipeline' 'Spark' 'Sweep' (required)
labelCategories	Label categories of the job.	LabelingJobLabelCategories
labelingJobMediaProperties	Media type specific properties in the job.	LabelingJobMediaProperties
mlAssistConfiguration	Configuration of MLAssist feature in the job.	MLAssistConfiguration
properties	The asset property dictionary.	ResourceBaseProperties
services	List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.	JobBaseServices
tags	Tag dictionary. Tags can be added, removed, and updated.	object

LabelingDataConfiguration

Name	Description	Value
dataId	Resource Id of the data asset to perform labeling.	string
incrementalDataRefresh	Indicates whether to enable incremental data refresh.	'Disabled' 'Enabled'

LabelingJobInstructions

Name	Description	Value
uri	The link to a page with detailed labeling instructions for labelers.	string

LabelingJobLabelCategories

Name	Description	Value
{customized property}		LabelCategory

LabelCategory

Name	Description	Value
classes	Dictionary of label classes in this category.	LabelCategoryClasses
displayName	Display name of the label category.	string
multiSelect	Indicates whether it is allowed to select multiple classes in this category.	'Disabled' 'Enabled'

LabelCategoryClasses

Name	Description	Value
{customized property}		LabelClass

LabelClass

Name	Description	Value
displayName	Display name of the label class.	string
subclasses	Dictionary of subclasses of the label class.	LabelClassSubclasses

LabelClassSubclasses

Name	Description	Value
{customized property}		LabelClass

LabelingJobMediaProperties

Name	Description	Value
mediaType	Set the object type	Image Text (required)

LabelingJobImageProperties

Name	Description	Value
mediaType	[Required] Media type of the job.	'Image' (required)
annotationType	Annotation type of image labeling job.	'BoundingBox' 'Classification' 'InstanceSegmentation'

LabelingJobTextProperties

Name	Description	Value
mediaType	[Required] Media type of the job.	'Text' (required)
annotationType	Annotation type of text labeling job.	'Classification' 'NamedEntityRecognition'

MLAssistConfiguration

Name	Description	Value
mlAssist	Set the object type	Disabled Enabled (required)

MLAssistConfigurationDisabled

Name	Description	Value
mlAssist	[Required] Indicates whether MLAssist feature is enabled.	'Disabled' (required)

MLAssistConfigurationEnabled

Name	Description	Value
mlAssist	[Required] Indicates whether MLAssist feature is enabled.	'Enabled' (required)
inferencingComputeBinding	[Required] AML compute binding used in inferencing.	string (required) Constraints: Pattern = [a-zA-Z0-9_]
trainingComputeBinding	[Required] AML compute binding used in training.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

PipelineJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	'Pipeline' (required)
inputs	Inputs for the pipeline job.	PipelineJobInputs
jobs	Jobs construct the Pipeline Job.	PipelineJobJobs
outputs	Outputs for the pipeline job	PipelineJobOutputs
settings	Pipeline settings, for things like ContinueRunOnStepFailure etc.	For Bicep, you can use the any() function.
sourceJobId	ARM resource ID of source job.	string

PipelineJobInputs

Name	Description	Value
{customized property}		JobInput

PipelineJobJobs

Name	Description	Value
{customized property}		For Bicep, you can use the any() function.

PipelineJobOutputs

Name	Description	Value
{customized property}		JobOutput

SparkJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	'Spark' (required)
archives	Archive files used in the job.	string[]
args	Arguments for the job.	string
codeId	[Required] ARM resource ID of the code asset.	string (required) Constraints: Pattern = [a-zA-Z0-9_]
conf	Spark configured properties.	SparkJobConf
entry	[Required] The entry to execute on startup of the job.	SparkJobEntry (required)
environmentId	The ARM resource ID of the Environment specification for the job.	string
files	Files used in the job.	string[]
inputs	Mapping of input data bindings used in the job.	SparkJobInputs
jars	Jar files used in the job.	string[]
outputs	Mapping of output data bindings used in the job.	SparkJobOutputs
pyFiles	Python files used in the job.	string[]
resources	Compute Resource configuration for the job.	SparkResourceConfiguration

SparkJobConf

Name	Description	Value
{customized property}		string

SparkJobEntry

Name	Description	Value
sparkJobEntryType	Set the object type	SparkJobPythonEntry SparkJobScalaEntry (required)

SparkJobPythonEntry

Name	Description	Value
sparkJobEntryType	[Required] Type of the job's entry point.	'SparkJobPythonEntry' (required)
file	[Required] Relative python file path for job entry point.	string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_]

SparkJobScalaEntry

Name	Description	Value
sparkJobEntryType	[Required] Type of the job's entry point.	'SparkJobScalaEntry' (required)
className	[Required] Scala class name used as entry point.	string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_]

SparkJobInputs

Name	Description	Value
{customized property}		JobInput

SparkJobOutputs

Name	Description	Value
{customized property}		JobOutput

SparkResourceConfiguration

Name	Description	Value
instanceType	Optional type of VM used as supported by the compute target.	string
runtimeVersion	Version of spark runtime used for the job.	string

SweepJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	'Sweep' (required)
earlyTermination	Early termination policies enable canceling poor-performing runs before they complete	EarlyTerminationPolicy
inputs	Mapping of input data bindings used in the job.	SweepJobInputs
limits	Sweep Job limit.	SweepJobLimits
objective	[Required] Optimization objective.	Objective (required)
outputs	Mapping of output data bindings used in the job.	SweepJobOutputs
samplingAlgorithm	[Required] The hyperparameter sampling algorithm	SamplingAlgorithm (required)
searchSpace	[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter	For Bicep, you can use the any() function.(required)
trial	[Required] Trial component definition.	TrialComponent (required)

SweepJobInputs

Name	Description	Value
{customized property}		JobInput

SweepJobLimits

Name	Description	Value
jobLimitsType	[Required] JobLimit type.	'Command' 'Sweep' (required)
maxConcurrentTrials	Sweep Job max concurrent trials.	int
maxTotalTrials	Sweep Job max total trials.	int
timeout	The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.	string
trialTimeout	Sweep Job Trial timeout value.	string

Objective

Name	Description	Value
goal	[Required] Defines supported metric goals for hyperparameter tuning	'Maximize' 'Minimize' (required)
primaryMetric	[Required] Name of the metric to optimize.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

SweepJobOutputs

Name	Description	Value
{customized property}		JobOutput

SamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	Set the object type	Bayesian Grid Random (required)

BayesianSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Bayesian' (required)

GridSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Grid' (required)

RandomSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Random' (required)
logbase	An optional positive number or e in string format to be used as base for log based random sampling	string
rule	The specific type of random algorithm	'Random' 'Sobol'
seed	An optional integer to use as the seed for random number generation	int

TrialComponent

Name	Description	Value
codeId	ARM resource ID of the code asset.	string
command	[Required] The command to execute on startup of the job. eg. "python train.py"	string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_]
distribution	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	DistributionConfiguration
environmentId	[Required] The ARM resource ID of the Environment specification for the job.	string (required) Constraints: Pattern = [a-zA-Z0-9_]
environmentVariables	Environment variables included in the job.	TrialComponentEnvironmentVariables
resources	Compute Resource configuration for the job.	JobResourceConfiguration

TrialComponentEnvironmentVariables

Name	Description	Value
{customized property}		string

Quickstart templates

The following quickstart templates deploy this resource type.

Template	Description
Create an Azure Machine Learning AutoML classification job	This template creates an Azure Machine Learning AutoML classification job to find out the best model for predicting if a client will subscribe to a fixed term deposit with a financial institution.
Create an Azure Machine Learning Command job	This template creates an Azure Machine Learning Command job with a basic hello_world script
Create an Azure Machine Learning Sweep job	This template creates an Azure Machine Learning Sweep job for hyperparameter tuning.

ARM template resource definition

The workspaces/jobs resource type can be deployed with operations that target:

• Resource groups - See resource group deployment commands

For a list of changed properties in each API version, see change log.

Resource format

To create a Microsoft.MachineLearningServices/workspaces/jobs resource, add the following JSON to your template.

{
  "type": "Microsoft.MachineLearningServices/workspaces/jobs",
  "apiVersion": "2022-12-01-preview",
  "name": "string",
  "properties": {
    "componentId": "string",
    "computeId": "string",
    "description": "string",
    "displayName": "string",
    "experimentName": "string",
    "identity": {
      "identityType": "string"
      // For remaining properties, see IdentityConfiguration objects
    },
    "isArchived": "bool",
    "properties": {
      "{customized property}": "string"
    },
    "services": {
      "{customized property}": {
        "endpoint": "string",
        "jobServiceType": "string",
        "nodes": {
          "nodesValueType": "string"
          // For remaining properties, see Nodes objects
        },
        "port": "int",
        "properties": {
          "{customized property}": "string"
        }
      }
    },
    "tags": {},
    "jobType": "string"
    // For remaining properties, see JobBaseProperties objects
  }
}

JobBaseProperties objects

Set the jobType property to specify the type of object.

For AutoML, use:

  "jobType": "AutoML",
  "environmentId": "string",
  "environmentVariables": {
    "{customized property}": "string"
  },
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "resources": {
    "dockerArgs": "string",
    "instanceCount": "int",
    "instanceType": "string",
    "properties": {
      "{customized property}": {}
    },
    "shmSize": "string"
  },
  "taskDetails": {
    "logVerbosity": "string",
    "targetColumnName": "string",
    "trainingData": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "taskType": "string"
    // For remaining properties, see AutoMLVertical objects
  }

For Command, use:

  "jobType": "Command",
  "autologgerSettings": {
    "mlflowAutologger": "string"
  },
  "codeId": "string",
  "command": "string",
  "distribution": {
    "distributionType": "string"
    // For remaining properties, see DistributionConfiguration objects
  },
  "environmentId": "string",
  "environmentVariables": {
    "{customized property}": "string"
  },
  "inputs": {
    "{customized property}": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    }
  },
  "limits": {
    "jobLimitsType": "string",
    "timeout": "string"
  },
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "resources": {
    "dockerArgs": "string",
    "instanceCount": "int",
    "instanceType": "string",
    "properties": {
      "{customized property}": {}
    },
    "shmSize": "string"
  }

For Labeling, use:

  "jobType": "Labeling",
  "dataConfiguration": {
    "dataId": "string",
    "incrementalDataRefresh": "string"
  },
  "jobInstructions": {
    "uri": "string"
  },
  "labelCategories": {
    "{customized property}": {
      "classes": {
        "{customized property}": {
          "displayName": "string",
          "subclasses": {
            "{customized property}": {}
        }
      },
      "displayName": "string",
      "multiSelect": "string"
    }
  },
  "labelingJobMediaProperties": {
    "mediaType": "string"
    // For remaining properties, see LabelingJobMediaProperties objects
  },
  "mlAssistConfiguration": {
    "mlAssist": "string"
    // For remaining properties, see MLAssistConfiguration objects
  }

For Pipeline, use:

  "jobType": "Pipeline",
  "inputs": {
    "{customized property}": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    }
  },
  "jobs": {
    "{customized property}": {}
  },
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "settings": {},
  "sourceJobId": "string"

For Spark, use:

  "jobType": "Spark",
  "archives": [ "string" ],
  "args": "string",
  "codeId": "string",
  "conf": {
    "{customized property}": "string"
  },
  "entry": {
    "sparkJobEntryType": "string"
    // For remaining properties, see SparkJobEntry objects
  },
  "environmentId": "string",
  "files": [ "string" ],
  "inputs": {
    "{customized property}": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    }
  },
  "jars": [ "string" ],
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "pyFiles": [ "string" ],
  "resources": {
    "instanceType": "string",
    "runtimeVersion": "string"
  }

For Sweep, use:

  "jobType": "Sweep",
  "earlyTermination": {
    "delayEvaluation": "int",
    "evaluationInterval": "int",
    "policyType": "string"
    // For remaining properties, see EarlyTerminationPolicy objects
  },
  "inputs": {
    "{customized property}": {
      "description": "string",
      "jobInputType": "string"
      // For remaining properties, see JobInput objects
    }
  },
  "limits": {
    "jobLimitsType": "string",
    "maxConcurrentTrials": "int",
    "maxTotalTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "objective": {
    "goal": "string",
    "primaryMetric": "string"
  },
  "outputs": {
    "{customized property}": {
      "description": "string",
      "jobOutputType": "string"
      // For remaining properties, see JobOutput objects
    }
  },
  "samplingAlgorithm": {
    "samplingAlgorithmType": "string"
    // For remaining properties, see SamplingAlgorithm objects
  },
  "searchSpace": {},
  "trial": {
    "codeId": "string",
    "command": "string",
    "distribution": {
      "distributionType": "string"
      // For remaining properties, see DistributionConfiguration objects
    },
    "environmentId": "string",
    "environmentVariables": {
      "{customized property}": "string"
    },
    "resources": {
      "dockerArgs": "string",
      "instanceCount": "int",
      "instanceType": "string",
      "properties": {
        "{customized property}": {}
      },
      "shmSize": "string"
    }
  }

IdentityConfiguration objects

Set the identityType property to specify the type of object.

For AMLToken, use:

  "identityType": "AMLToken"

For Managed, use:

  "identityType": "Managed",
  "clientId": "string",
  "objectId": "string",
  "resourceId": "string"

For UserIdentity, use:

  "identityType": "UserIdentity"

Nodes objects

Set the nodesValueType property to specify the type of object.

For All, use:

  "nodesValueType": "All"

JobOutput objects

Set the jobOutputType property to specify the type of object.

For custom_model, use:

  "jobOutputType": "custom_model",
  "assetName": "string",
  "assetVersion": "string",
  "mode": "string",
  "uri": "string"

For mlflow_model, use:

  "jobOutputType": "mlflow_model",
  "assetName": "string",
  "assetVersion": "string",
  "mode": "string",
  "uri": "string"

For mltable, use:

  "jobOutputType": "mltable",
  "assetName": "string",
  "assetVersion": "string",
  "mode": "string",
  "uri": "string"

For triton_model, use:

  "jobOutputType": "triton_model",
  "assetName": "string",
  "assetVersion": "string",
  "mode": "string",
  "uri": "string"

For uri_file, use:

  "jobOutputType": "uri_file",
  "assetName": "string",
  "assetVersion": "string",
  "mode": "string",
  "uri": "string"

For uri_folder, use:

  "jobOutputType": "uri_folder",
  "assetName": "string",
  "assetVersion": "string",
  "mode": "string",
  "uri": "string"

AutoMLVertical objects

Set the taskType property to specify the type of object.

For Classification, use:

  "taskType": "Classification",
  "cvSplitColumnNames": [ "string" ],
  "featurizationSettings": {
    "blockedTransformers": [ "string" ],
    "columnNameAndTypes": {
      "{customized property}": "string"
    },
    "datasetLanguage": "string",
    "enableDnnFeaturization": "bool",
    "mode": "string",
    "transformerParams": {
      "{customized property}": [
        {
          "fields": [ "string" ],
          "parameters": {}
        }
      ]
    }
  },
  "fixedParameters": {
    "booster": "string",
    "boostingType": "string",
    "growPolicy": "string",
    "learningRate": "int",
    "maxBin": "int",
    "maxDepth": "int",
    "maxLeaves": "int",
    "minDataInLeaf": "int",
    "minSplitGain": "int",
    "modelName": "string",
    "nEstimators": "int",
    "numLeaves": "int",
    "preprocessorName": "string",
    "regAlpha": "int",
    "regLambda": "int",
    "subsample": "int",
    "subsampleFreq": "int",
    "treeMethod": "string",
    "withMean": "bool",
    "withStd": "bool"
  },
  "limitSettings": {
    "enableEarlyTermination": "bool",
    "exitScore": "int",
    "maxConcurrentTrials": "int",
    "maxCoresPerTrial": "int",
    "maxNodes": "int",
    "maxTrials": "int",
    "sweepConcurrentTrials": "int",
    "sweepTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "nCrossValidations": {
    "mode": "string"
    // For remaining properties, see NCrossValidations objects
  },
  "positiveLabel": "string",
  "primaryMetric": "string",
  "searchSpace": [
    {
      "booster": "string",
      "boostingType": "string",
      "growPolicy": "string",
      "learningRate": "string",
      "maxBin": "string",
      "maxDepth": "string",
      "maxLeaves": "string",
      "minDataInLeaf": "string",
      "minSplitGain": "string",
      "modelName": "string",
      "nEstimators": "string",
      "numLeaves": "string",
      "preprocessorName": "string",
      "regAlpha": "string",
      "regLambda": "string",
      "subsample": "string",
      "subsampleFreq": "string",
      "treeMethod": "string",
      "withMean": "string",
      "withStd": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "testData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "testDataSize": "int",
  "trainingSettings": {
    "allowedTrainingAlgorithms": [ "string" ],
    "blockedTrainingAlgorithms": [ "string" ],
    "enableDnnTraining": "bool",
    "enableModelExplainability": "bool",
    "enableOnnxCompatibleModels": "bool",
    "enableStackEnsemble": "bool",
    "enableVoteEnsemble": "bool",
    "ensembleModelDownloadTimeout": "string",
    "stackEnsembleSettings": {
      "stackMetaLearnerKWargs": {},
      "stackMetaLearnerTrainPercentage": "int",
      "stackMetaLearnerType": "string"
    },
    "trainingMode": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int",
  "weightColumnName": "string"

For Forecasting, use:

  "taskType": "Forecasting",
  "cvSplitColumnNames": [ "string" ],
  "featurizationSettings": {
    "blockedTransformers": [ "string" ],
    "columnNameAndTypes": {
      "{customized property}": "string"
    },
    "datasetLanguage": "string",
    "enableDnnFeaturization": "bool",
    "mode": "string",
    "transformerParams": {
      "{customized property}": [
        {
          "fields": [ "string" ],
          "parameters": {}
        }
      ]
    }
  },
  "fixedParameters": {
    "booster": "string",
    "boostingType": "string",
    "growPolicy": "string",
    "learningRate": "int",
    "maxBin": "int",
    "maxDepth": "int",
    "maxLeaves": "int",
    "minDataInLeaf": "int",
    "minSplitGain": "int",
    "modelName": "string",
    "nEstimators": "int",
    "numLeaves": "int",
    "preprocessorName": "string",
    "regAlpha": "int",
    "regLambda": "int",
    "subsample": "int",
    "subsampleFreq": "int",
    "treeMethod": "string",
    "withMean": "bool",
    "withStd": "bool"
  },
  "forecastingSettings": {
    "countryOrRegionForHolidays": "string",
    "cvStepSize": "int",
    "featureLags": "string",
    "forecastHorizon": {
      "mode": "string"
      // For remaining properties, see ForecastHorizon objects
    },
    "frequency": "string",
    "seasonality": {
      "mode": "string"
      // For remaining properties, see Seasonality objects
    },
    "shortSeriesHandlingConfig": "string",
    "targetAggregateFunction": "string",
    "targetLags": {
      "mode": "string"
      // For remaining properties, see TargetLags objects
    },
    "targetRollingWindowSize": {
      "mode": "string"
      // For remaining properties, see TargetRollingWindowSize objects
    },
    "timeColumnName": "string",
    "timeSeriesIdColumnNames": [ "string" ],
    "useStl": "string"
  },
  "limitSettings": {
    "enableEarlyTermination": "bool",
    "exitScore": "int",
    "maxConcurrentTrials": "int",
    "maxCoresPerTrial": "int",
    "maxNodes": "int",
    "maxTrials": "int",
    "sweepConcurrentTrials": "int",
    "sweepTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "nCrossValidations": {
    "mode": "string"
    // For remaining properties, see NCrossValidations objects
  },
  "primaryMetric": "string",
  "searchSpace": [
    {
      "booster": "string",
      "boostingType": "string",
      "growPolicy": "string",
      "learningRate": "string",
      "maxBin": "string",
      "maxDepth": "string",
      "maxLeaves": "string",
      "minDataInLeaf": "string",
      "minSplitGain": "string",
      "modelName": "string",
      "nEstimators": "string",
      "numLeaves": "string",
      "preprocessorName": "string",
      "regAlpha": "string",
      "regLambda": "string",
      "subsample": "string",
      "subsampleFreq": "string",
      "treeMethod": "string",
      "withMean": "string",
      "withStd": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "testData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "testDataSize": "int",
  "trainingSettings": {
    "allowedTrainingAlgorithms": [ "string" ],
    "blockedTrainingAlgorithms": [ "string" ],
    "enableDnnTraining": "bool",
    "enableModelExplainability": "bool",
    "enableOnnxCompatibleModels": "bool",
    "enableStackEnsemble": "bool",
    "enableVoteEnsemble": "bool",
    "ensembleModelDownloadTimeout": "string",
    "stackEnsembleSettings": {
      "stackMetaLearnerKWargs": {},
      "stackMetaLearnerTrainPercentage": "int",
      "stackMetaLearnerType": "string"
    },
    "trainingMode": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int",
  "weightColumnName": "string"

For ImageClassification, use:

  "taskType": "ImageClassification",
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "modelSettings": {
    "advancedSettings": "string",
    "amsGradient": "bool",
    "augmentations": "string",
    "beta1": "int",
    "beta2": "int",
    "checkpointFrequency": "int",
    "checkpointModel": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "checkpointRunId": "string",
    "distributed": "bool",
    "earlyStopping": "bool",
    "earlyStoppingDelay": "int",
    "earlyStoppingPatience": "int",
    "enableOnnxNormalization": "bool",
    "evaluationFrequency": "int",
    "gradientAccumulationStep": "int",
    "layersToFreeze": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "modelName": "string",
    "momentum": "int",
    "nesterov": "bool",
    "numberOfEpochs": "int",
    "numberOfWorkers": "int",
    "optimizer": "string",
    "randomSeed": "int",
    "stepLRGamma": "int",
    "stepLRStepSize": "int",
    "trainingBatchSize": "int",
    "trainingCropSize": "int",
    "validationBatchSize": "int",
    "validationCropSize": "int",
    "validationResizeSize": "int",
    "warmupCosineLRCycles": "int",
    "warmupCosineLRWarmupEpochs": "int",
    "weightDecay": "int",
    "weightedLoss": "int"
  },
  "primaryMetric": "string",
  "searchSpace": [
    {
      "amsGradient": "string",
      "augmentations": "string",
      "beta1": "string",
      "beta2": "string",
      "distributed": "string",
      "earlyStopping": "string",
      "earlyStoppingDelay": "string",
      "earlyStoppingPatience": "string",
      "enableOnnxNormalization": "string",
      "evaluationFrequency": "string",
      "gradientAccumulationStep": "string",
      "layersToFreeze": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "modelName": "string",
      "momentum": "string",
      "nesterov": "string",
      "numberOfEpochs": "string",
      "numberOfWorkers": "string",
      "optimizer": "string",
      "randomSeed": "string",
      "stepLRGamma": "string",
      "stepLRStepSize": "string",
      "trainingBatchSize": "string",
      "trainingCropSize": "string",
      "validationBatchSize": "string",
      "validationCropSize": "string",
      "validationResizeSize": "string",
      "warmupCosineLRCycles": "string",
      "warmupCosineLRWarmupEpochs": "string",
      "weightDecay": "string",
      "weightedLoss": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int"

For ImageClassificationMultilabel, use:

  "taskType": "ImageClassificationMultilabel",
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "modelSettings": {
    "advancedSettings": "string",
    "amsGradient": "bool",
    "augmentations": "string",
    "beta1": "int",
    "beta2": "int",
    "checkpointFrequency": "int",
    "checkpointModel": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "checkpointRunId": "string",
    "distributed": "bool",
    "earlyStopping": "bool",
    "earlyStoppingDelay": "int",
    "earlyStoppingPatience": "int",
    "enableOnnxNormalization": "bool",
    "evaluationFrequency": "int",
    "gradientAccumulationStep": "int",
    "layersToFreeze": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "modelName": "string",
    "momentum": "int",
    "nesterov": "bool",
    "numberOfEpochs": "int",
    "numberOfWorkers": "int",
    "optimizer": "string",
    "randomSeed": "int",
    "stepLRGamma": "int",
    "stepLRStepSize": "int",
    "trainingBatchSize": "int",
    "trainingCropSize": "int",
    "validationBatchSize": "int",
    "validationCropSize": "int",
    "validationResizeSize": "int",
    "warmupCosineLRCycles": "int",
    "warmupCosineLRWarmupEpochs": "int",
    "weightDecay": "int",
    "weightedLoss": "int"
  },
  "primaryMetric": "string",
  "searchSpace": [
    {
      "amsGradient": "string",
      "augmentations": "string",
      "beta1": "string",
      "beta2": "string",
      "distributed": "string",
      "earlyStopping": "string",
      "earlyStoppingDelay": "string",
      "earlyStoppingPatience": "string",
      "enableOnnxNormalization": "string",
      "evaluationFrequency": "string",
      "gradientAccumulationStep": "string",
      "layersToFreeze": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "modelName": "string",
      "momentum": "string",
      "nesterov": "string",
      "numberOfEpochs": "string",
      "numberOfWorkers": "string",
      "optimizer": "string",
      "randomSeed": "string",
      "stepLRGamma": "string",
      "stepLRStepSize": "string",
      "trainingBatchSize": "string",
      "trainingCropSize": "string",
      "validationBatchSize": "string",
      "validationCropSize": "string",
      "validationResizeSize": "string",
      "warmupCosineLRCycles": "string",
      "warmupCosineLRWarmupEpochs": "string",
      "weightDecay": "string",
      "weightedLoss": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int"

For ImageInstanceSegmentation, use:

  "taskType": "ImageInstanceSegmentation",
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "modelSettings": {
    "advancedSettings": "string",
    "amsGradient": "bool",
    "augmentations": "string",
    "beta1": "int",
    "beta2": "int",
    "boxDetectionsPerImage": "int",
    "boxScoreThreshold": "int",
    "checkpointFrequency": "int",
    "checkpointModel": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "checkpointRunId": "string",
    "distributed": "bool",
    "earlyStopping": "bool",
    "earlyStoppingDelay": "int",
    "earlyStoppingPatience": "int",
    "enableOnnxNormalization": "bool",
    "evaluationFrequency": "int",
    "gradientAccumulationStep": "int",
    "imageSize": "int",
    "layersToFreeze": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "maxSize": "int",
    "minSize": "int",
    "modelName": "string",
    "modelSize": "string",
    "momentum": "int",
    "multiScale": "bool",
    "nesterov": "bool",
    "nmsIouThreshold": "int",
    "numberOfEpochs": "int",
    "numberOfWorkers": "int",
    "optimizer": "string",
    "randomSeed": "int",
    "stepLRGamma": "int",
    "stepLRStepSize": "int",
    "tileGridSize": "string",
    "tileOverlapRatio": "int",
    "tilePredictionsNmsThreshold": "int",
    "trainingBatchSize": "int",
    "validationBatchSize": "int",
    "validationIouThreshold": "int",
    "validationMetricType": "string",
    "warmupCosineLRCycles": "int",
    "warmupCosineLRWarmupEpochs": "int",
    "weightDecay": "int"
  },
  "primaryMetric": "MeanAveragePrecision",
  "searchSpace": [
    {
      "amsGradient": "string",
      "augmentations": "string",
      "beta1": "string",
      "beta2": "string",
      "boxDetectionsPerImage": "string",
      "boxScoreThreshold": "string",
      "distributed": "string",
      "earlyStopping": "string",
      "earlyStoppingDelay": "string",
      "earlyStoppingPatience": "string",
      "enableOnnxNormalization": "string",
      "evaluationFrequency": "string",
      "gradientAccumulationStep": "string",
      "imageSize": "string",
      "layersToFreeze": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "maxSize": "string",
      "minSize": "string",
      "modelName": "string",
      "modelSize": "string",
      "momentum": "string",
      "multiScale": "string",
      "nesterov": "string",
      "nmsIouThreshold": "string",
      "numberOfEpochs": "string",
      "numberOfWorkers": "string",
      "optimizer": "string",
      "randomSeed": "string",
      "stepLRGamma": "string",
      "stepLRStepSize": "string",
      "tileGridSize": "string",
      "tileOverlapRatio": "string",
      "tilePredictionsNmsThreshold": "string",
      "trainingBatchSize": "string",
      "validationBatchSize": "string",
      "validationIouThreshold": "string",
      "validationMetricType": "string",
      "warmupCosineLRCycles": "string",
      "warmupCosineLRWarmupEpochs": "string",
      "weightDecay": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int"

For ImageObjectDetection, use:

  "taskType": "ImageObjectDetection",
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxTrials": "int",
    "timeout": "string"
  },
  "modelSettings": {
    "advancedSettings": "string",
    "amsGradient": "bool",
    "augmentations": "string",
    "beta1": "int",
    "beta2": "int",
    "boxDetectionsPerImage": "int",
    "boxScoreThreshold": "int",
    "checkpointFrequency": "int",
    "checkpointModel": {
      "description": "string",
      "jobInputType": "string",
      "mode": "string",
      "uri": "string"
    },
    "checkpointRunId": "string",
    "distributed": "bool",
    "earlyStopping": "bool",
    "earlyStoppingDelay": "int",
    "earlyStoppingPatience": "int",
    "enableOnnxNormalization": "bool",
    "evaluationFrequency": "int",
    "gradientAccumulationStep": "int",
    "imageSize": "int",
    "layersToFreeze": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "maxSize": "int",
    "minSize": "int",
    "modelName": "string",
    "modelSize": "string",
    "momentum": "int",
    "multiScale": "bool",
    "nesterov": "bool",
    "nmsIouThreshold": "int",
    "numberOfEpochs": "int",
    "numberOfWorkers": "int",
    "optimizer": "string",
    "randomSeed": "int",
    "stepLRGamma": "int",
    "stepLRStepSize": "int",
    "tileGridSize": "string",
    "tileOverlapRatio": "int",
    "tilePredictionsNmsThreshold": "int",
    "trainingBatchSize": "int",
    "validationBatchSize": "int",
    "validationIouThreshold": "int",
    "validationMetricType": "string",
    "warmupCosineLRCycles": "int",
    "warmupCosineLRWarmupEpochs": "int",
    "weightDecay": "int"
  },
  "primaryMetric": "MeanAveragePrecision",
  "searchSpace": [
    {
      "amsGradient": "string",
      "augmentations": "string",
      "beta1": "string",
      "beta2": "string",
      "boxDetectionsPerImage": "string",
      "boxScoreThreshold": "string",
      "distributed": "string",
      "earlyStopping": "string",
      "earlyStoppingDelay": "string",
      "earlyStoppingPatience": "string",
      "enableOnnxNormalization": "string",
      "evaluationFrequency": "string",
      "gradientAccumulationStep": "string",
      "imageSize": "string",
      "layersToFreeze": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "maxSize": "string",
      "minSize": "string",
      "modelName": "string",
      "modelSize": "string",
      "momentum": "string",
      "multiScale": "string",
      "nesterov": "string",
      "nmsIouThreshold": "string",
      "numberOfEpochs": "string",
      "numberOfWorkers": "string",
      "optimizer": "string",
      "randomSeed": "string",
      "stepLRGamma": "string",
      "stepLRStepSize": "string",
      "tileGridSize": "string",
      "tileOverlapRatio": "string",
      "tilePredictionsNmsThreshold": "string",
      "trainingBatchSize": "string",
      "validationBatchSize": "string",
      "validationIouThreshold": "string",
      "validationMetricType": "string",
      "warmupCosineLRCycles": "string",
      "warmupCosineLRWarmupEpochs": "string",
      "weightDecay": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int"

For Regression, use:

  "taskType": "Regression",
  "cvSplitColumnNames": [ "string" ],
  "featurizationSettings": {
    "blockedTransformers": [ "string" ],
    "columnNameAndTypes": {
      "{customized property}": "string"
    },
    "datasetLanguage": "string",
    "enableDnnFeaturization": "bool",
    "mode": "string",
    "transformerParams": {
      "{customized property}": [
        {
          "fields": [ "string" ],
          "parameters": {}
        }
      ]
    }
  },
  "fixedParameters": {
    "booster": "string",
    "boostingType": "string",
    "growPolicy": "string",
    "learningRate": "int",
    "maxBin": "int",
    "maxDepth": "int",
    "maxLeaves": "int",
    "minDataInLeaf": "int",
    "minSplitGain": "int",
    "modelName": "string",
    "nEstimators": "int",
    "numLeaves": "int",
    "preprocessorName": "string",
    "regAlpha": "int",
    "regLambda": "int",
    "subsample": "int",
    "subsampleFreq": "int",
    "treeMethod": "string",
    "withMean": "bool",
    "withStd": "bool"
  },
  "limitSettings": {
    "enableEarlyTermination": "bool",
    "exitScore": "int",
    "maxConcurrentTrials": "int",
    "maxCoresPerTrial": "int",
    "maxNodes": "int",
    "maxTrials": "int",
    "sweepConcurrentTrials": "int",
    "sweepTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "nCrossValidations": {
    "mode": "string"
    // For remaining properties, see NCrossValidations objects
  },
  "primaryMetric": "string",
  "searchSpace": [
    {
      "booster": "string",
      "boostingType": "string",
      "growPolicy": "string",
      "learningRate": "string",
      "maxBin": "string",
      "maxDepth": "string",
      "maxLeaves": "string",
      "minDataInLeaf": "string",
      "minSplitGain": "string",
      "modelName": "string",
      "nEstimators": "string",
      "numLeaves": "string",
      "preprocessorName": "string",
      "regAlpha": "string",
      "regLambda": "string",
      "subsample": "string",
      "subsampleFreq": "string",
      "treeMethod": "string",
      "withMean": "string",
      "withStd": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "testData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "testDataSize": "int",
  "trainingSettings": {
    "allowedTrainingAlgorithms": [ "string" ],
    "blockedTrainingAlgorithms": [ "string" ],
    "enableDnnTraining": "bool",
    "enableModelExplainability": "bool",
    "enableOnnxCompatibleModels": "bool",
    "enableStackEnsemble": "bool",
    "enableVoteEnsemble": "bool",
    "ensembleModelDownloadTimeout": "string",
    "stackEnsembleSettings": {
      "stackMetaLearnerKWargs": {},
      "stackMetaLearnerTrainPercentage": "int",
      "stackMetaLearnerType": "string"
    },
    "trainingMode": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  },
  "validationDataSize": "int",
  "weightColumnName": "string"

For TextClassification, use:

  "taskType": "TextClassification",
  "featurizationSettings": {
    "datasetLanguage": "string"
  },
  "fixedParameters": {
    "gradientAccumulationSteps": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "modelName": "string",
    "numberOfEpochs": "int",
    "trainingBatchSize": "int",
    "validationBatchSize": "int",
    "warmupRatio": "int",
    "weightDecay": "int"
  },
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxNodes": "int",
    "maxTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "primaryMetric": "string",
  "searchSpace": [
    {
      "gradientAccumulationSteps": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "modelName": "string",
      "numberOfEpochs": "string",
      "trainingBatchSize": "string",
      "validationBatchSize": "string",
      "warmupRatio": "string",
      "weightDecay": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  }

For TextClassificationMultilabel, use:

  "taskType": "TextClassificationMultilabel",
  "featurizationSettings": {
    "datasetLanguage": "string"
  },
  "fixedParameters": {
    "gradientAccumulationSteps": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "modelName": "string",
    "numberOfEpochs": "int",
    "trainingBatchSize": "int",
    "validationBatchSize": "int",
    "warmupRatio": "int",
    "weightDecay": "int"
  },
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxNodes": "int",
    "maxTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "searchSpace": [
    {
      "gradientAccumulationSteps": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "modelName": "string",
      "numberOfEpochs": "string",
      "trainingBatchSize": "string",
      "validationBatchSize": "string",
      "warmupRatio": "string",
      "weightDecay": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  }

For TextNER, use:

  "taskType": "TextNER",
  "featurizationSettings": {
    "datasetLanguage": "string"
  },
  "fixedParameters": {
    "gradientAccumulationSteps": "int",
    "learningRate": "int",
    "learningRateScheduler": "string",
    "modelName": "string",
    "numberOfEpochs": "int",
    "trainingBatchSize": "int",
    "validationBatchSize": "int",
    "warmupRatio": "int",
    "weightDecay": "int"
  },
  "limitSettings": {
    "maxConcurrentTrials": "int",
    "maxNodes": "int",
    "maxTrials": "int",
    "timeout": "string",
    "trialTimeout": "string"
  },
  "searchSpace": [
    {
      "gradientAccumulationSteps": "string",
      "learningRate": "string",
      "learningRateScheduler": "string",
      "modelName": "string",
      "numberOfEpochs": "string",
      "trainingBatchSize": "string",
      "validationBatchSize": "string",
      "warmupRatio": "string",
      "weightDecay": "string"
    }
  ],
  "sweepSettings": {
    "earlyTermination": {
      "delayEvaluation": "int",
      "evaluationInterval": "int",
      "policyType": "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    },
    "samplingAlgorithm": "string"
  },
  "validationData": {
    "description": "string",
    "jobInputType": "string",
    "mode": "string",
    "uri": "string"
  }

NCrossValidations objects

Set the mode property to specify the type of object.

For Auto, use:

  "mode": "Auto"

For Custom, use:

  "mode": "Custom",
  "value": "int"

EarlyTerminationPolicy objects

Set the policyType property to specify the type of object.

For Bandit, use:

  "policyType": "Bandit",
  "slackAmount": "int",
  "slackFactor": "int"

For MedianStopping, use:

  "policyType": "MedianStopping"

For TruncationSelection, use:

  "policyType": "TruncationSelection",
  "truncationPercentage": "int"

ForecastHorizon objects

Set the mode property to specify the type of object.

For Auto, use:

  "mode": "Auto"

For Custom, use:

  "mode": "Custom",
  "value": "int"

Seasonality objects

Set the mode property to specify the type of object.

For Auto, use:

  "mode": "Auto"

For Custom, use:

  "mode": "Custom",
  "value": "int"

TargetLags objects

Set the mode property to specify the type of object.

For Auto, use:

  "mode": "Auto"

For Custom, use:

  "mode": "Custom",
  "values": [ "int" ]

TargetRollingWindowSize objects

Set the mode property to specify the type of object.

For Auto, use:

  "mode": "Auto"

For Custom, use:

  "mode": "Custom",
  "value": "int"

DistributionConfiguration objects

Set the distributionType property to specify the type of object.

For Mpi, use:

  "distributionType": "Mpi",
  "processCountPerInstance": "int"

For PyTorch, use:

  "distributionType": "PyTorch",
  "processCountPerInstance": "int"

For TensorFlow, use:

  "distributionType": "TensorFlow",
  "parameterServerCount": "int",
  "workerCount": "int"

JobInput objects

Set the jobInputType property to specify the type of object.

For custom_model, use:

  "jobInputType": "custom_model",
  "mode": "string",
  "uri": "string"

For literal, use:

  "jobInputType": "literal",
  "value": "string"

For mlflow_model, use:

  "jobInputType": "mlflow_model",
  "mode": "string",
  "uri": "string"

For mltable, use:

  "jobInputType": "mltable",
  "mode": "string",
  "uri": "string"

For triton_model, use:

  "jobInputType": "triton_model",
  "mode": "string",
  "uri": "string"

For uri_file, use:

  "jobInputType": "uri_file",
  "mode": "string",
  "uri": "string"

For uri_folder, use:

  "jobInputType": "uri_folder",
  "mode": "string",
  "uri": "string"

LabelingJobMediaProperties objects

Set the mediaType property to specify the type of object.

For Image, use:

  "mediaType": "Image",
  "annotationType": "string"

For Text, use:

  "mediaType": "Text",
  "annotationType": "string"

MLAssistConfiguration objects

Set the mlAssist property to specify the type of object.

For Disabled, use:

  "mlAssist": "Disabled"

For Enabled, use:

  "mlAssist": "Enabled",
  "inferencingComputeBinding": "string",
  "trainingComputeBinding": "string"

SparkJobEntry objects

Set the sparkJobEntryType property to specify the type of object.

For SparkJobPythonEntry, use:

  "sparkJobEntryType": "SparkJobPythonEntry",
  "file": "string"

For SparkJobScalaEntry, use:

  "sparkJobEntryType": "SparkJobScalaEntry",
  "className": "string"

SamplingAlgorithm objects

Set the samplingAlgorithmType property to specify the type of object.

For Bayesian, use:

  "samplingAlgorithmType": "Bayesian"

For Grid, use:

  "samplingAlgorithmType": "Grid"

For Random, use:

  "samplingAlgorithmType": "Random",
  "logbase": "string",
  "rule": "string",
  "seed": "int"

Property values

workspaces/jobs

Name	Description	Value
type	The resource type	'Microsoft.MachineLearningServices/workspaces/jobs'
apiVersion	The resource api version	'2022-12-01-preview'
name	The resource name See how to set names and types for child resources in JSON ARM templates.	string (required)
properties	[Required] Additional attributes of the entity.	JobBaseProperties (required)

JobBaseProperties

Name	Description	Value
componentId	ARM resource ID of the component resource.	string
computeId	ARM resource ID of the compute resource.	string
description	The asset description text.	string
displayName	Display name of job.	string
experimentName	The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.	string
identity	Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.	IdentityConfiguration
isArchived	Is the asset archived?	bool
properties	The asset property dictionary.	ResourceBaseProperties
services	List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.	JobBaseServices
tags	Tag dictionary. Tags can be added, removed, and updated.	object
jobType	Set the object type	AutoML Command Labeling Pipeline Spark Sweep (required)

IdentityConfiguration

Name	Description	Value
identityType	Set the object type	AMLToken Managed UserIdentity (required)

AmlToken

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	'AMLToken' (required)

ManagedIdentity

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	'Managed' (required)
clientId	Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.	string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
objectId	Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.	string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
resourceId	Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.	string

UserIdentity

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	'UserIdentity' (required)

ResourceBaseProperties

Name	Description	Value
{customized property}		string

JobBaseServices

Name	Description	Value
{customized property}		JobService

JobService

Name	Description	Value
endpoint	Url for endpoint.	string
jobServiceType	Endpoint type.	string
nodes	Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.	Nodes
port	Port for endpoint set by user.	int
properties	Additional properties to set on the endpoint.	JobServiceProperties

Nodes

Name	Description	Value
nodesValueType	Set the object type	All (required)

AllNodes

Name	Description	Value
nodesValueType	[Required] Type of the Nodes value	'All' (required)

JobServiceProperties

Name	Description	Value
{customized property}		string

AutoMLJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	'AutoML' (required)
environmentId	The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.	string
environmentVariables	Environment variables included in the job.	AutoMLJobEnvironmentVariables
outputs	Mapping of output data bindings used in the job.	AutoMLJobOutputs
resources	Compute Resource configuration for the job.	JobResourceConfiguration
taskDetails	[Required] This represents scenario which can be one of Tables/NLP/Image	AutoMLVertical (required)

AutoMLJobEnvironmentVariables

Name	Description	Value
{customized property}		string

AutoMLJobOutputs

Name	Description	Value
{customized property}		JobOutput

JobOutput

Name	Description	Value
description	Description for the output.	string
jobOutputType	Set the object type	custom_model mlflow_model mltable triton_model uri_file uri_folder (required)

CustomModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'custom_model' (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

MLFlowModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'mlflow_model' (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

MLTableJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'mltable' (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

TritonModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'triton_model' (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

UriFileJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'uri_file' (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

UriFolderJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	'uri_folder' (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	'Direct' 'ReadWriteMount' 'Upload'
uri	Output Asset URI.	string

JobResourceConfiguration

Name	Description	Value
dockerArgs	Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.	string
instanceCount	Optional number of instances or nodes used by the compute target.	int
instanceType	Optional type of VM used as supported by the compute target.	string
properties	Additional properties bag.	ResourceConfigurationProperties
shmSize	Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).	string Constraints: Pattern = \d+[bBkKmMgG]

ResourceConfigurationProperties

Name	Description	Value
{customized property}

AutoMLVertical

Name	Description	Value
logVerbosity	Log verbosity for the job.	'Critical' 'Debug' 'Error' 'Info' 'NotSet' 'Warning'
targetColumnName	Target column name: This is prediction values column. Also known as label column name in context of classification tasks.	string
trainingData	[Required] Training data input.	MLTableJobInput (required)
taskType	Set the object type	Classification Forecasting ImageClassification ImageClassificationMultilabel ImageInstanceSegmentation ImageObjectDetection Regression TextClassification TextClassificationMultilabel TextNER (required)

MLTableJobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	[Required] Specifies the type of job.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

Classification

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'Classification' (required)
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	TableFixedParameters
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
positiveLabel	Positive label for binary metrics calculation.	string
primaryMetric	Primary metric for the task.	'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	TableParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	TableSweepSettings
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	ClassificationTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

TableVerticalFeaturizationSettings

Name	Description	Value
blockedTransformers	These transformers shall not be used in featurization.	String array containing any of: 'CatTargetEncoder' 'CountVectorizer' 'HashOneHotEncoder' 'LabelEncoder' 'NaiveBayes' 'OneHotEncoder' 'TextTargetEncoder' 'TfIdf' 'WoETargetEncoder' 'WordEmbedding'
columnNameAndTypes	Dictionary of column name and its type (int, float, string, datetime etc).	TableVerticalFeaturizationSettingsColumnNameAndTypes
datasetLanguage	Dataset language, useful for the text data.	string
enableDnnFeaturization	Determines whether to use Dnn based featurizers for data featurization.	bool
mode	Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.	'Auto' 'Custom' 'Off'
transformerParams	User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.	TableVerticalFeaturizationSettingsTransformerParams

TableVerticalFeaturizationSettingsColumnNameAndTypes

Name	Description	Value
{customized property}		string

TableVerticalFeaturizationSettingsTransformerParams

Name	Description	Value
{customized property}		ColumnTransformer[]

ColumnTransformer

Name	Description	Value
fields	Fields to apply transformer logic on.	string[]
parameters	Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.

TableFixedParameters

Name	Description	Value
booster	Specify the boosting type, e.g gbdt for XGBoost.	string
boostingType	Specify the boosting type, e.g gbdt for LightGBM.	string
growPolicy	Specify the grow policy, which controls the way new nodes are added to the tree.	string
learningRate	The learning rate for the training procedure.	int
maxBin	Specify the Maximum number of discrete bins to bucket continuous features .	int
maxDepth	Specify the max depth to limit the tree depth explicitly.	int
maxLeaves	Specify the max leaves to limit the tree leaves explicitly.	int
minDataInLeaf	The minimum number of data per leaf.	int
minSplitGain	Minimum loss reduction required to make a further partition on a leaf node of the tree.	int
modelName	The name of the model to train.	string
nEstimators	Specify the number of trees (or rounds) in an model.	int
numLeaves	Specify the number of leaves.	int
preprocessorName	The name of the preprocessor to use.	string
regAlpha	L1 regularization term on weights.	int
regLambda	L2 regularization term on weights.	int
subsample	Subsample ratio of the training instance.	int
subsampleFreq	Frequency of subsample.	int
treeMethod	Specify the tree method.	string
withMean	If true, center before scaling the data with StandardScalar.	bool
withStd	If true, scaling the data with Unit Variance with StandardScalar.	bool

TableVerticalLimitSettings

Name	Description	Value
enableEarlyTermination	Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.	bool
exitScore	Exit score for the AutoML job.	int
maxConcurrentTrials	Maximum Concurrent iterations.	int
maxCoresPerTrial	Max cores per iteration.	int
maxNodes	Maximum nodes to use for the experiment.	int
maxTrials	Number of iterations.	int
sweepConcurrentTrials	Number of concurrent sweeping runs that user wants to trigger.	int
sweepTrials	Number of sweeping runs that user wants to trigger.	int
timeout	AutoML job timeout.	string
trialTimeout	Iteration timeout.	string

NCrossValidations

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoNCrossValidations

Name	Description	Value
mode	[Required] Mode for determining N-Cross validations.	'Auto' (required)

CustomNCrossValidations

Name	Description	Value
mode	[Required] Mode for determining N-Cross validations.	'Custom' (required)
value	[Required] N-Cross validations value.	int (required)

TableParameterSubspace

Name	Description	Value
booster	Specify the boosting type, e.g gbdt for XGBoost.	string
boostingType	Specify the boosting type, e.g gbdt for LightGBM.	string
growPolicy	Specify the grow policy, which controls the way new nodes are added to the tree.	string
learningRate	The learning rate for the training procedure.	string
maxBin	Specify the Maximum number of discrete bins to bucket continuous features .	string
maxDepth	Specify the max depth to limit the tree depth explicitly.	string
maxLeaves	Specify the max leaves to limit the tree leaves explicitly.	string
minDataInLeaf	The minimum number of data per leaf.	string
minSplitGain	Minimum loss reduction required to make a further partition on a leaf node of the tree.	string
modelName	The name of the model to train.	string
nEstimators	Specify the number of trees (or rounds) in an model.	string
numLeaves	Specify the number of leaves.	string
preprocessorName	The name of the preprocessor to use.	string
regAlpha	L1 regularization term on weights.	string
regLambda	L2 regularization term on weights.	string
subsample	Subsample ratio of the training instance.	string
subsampleFreq	Frequency of subsample	string
treeMethod	Specify the tree method.	string
withMean	If true, center before scaling the data with StandardScalar.	string
withStd	If true, scaling the data with Unit Variance with StandardScalar.	string

TableSweepSettings

Name	Description	Value
earlyTermination	Type of early termination policy for the sweeping job.	EarlyTerminationPolicy
samplingAlgorithm	[Required] Type of sampling algorithm.	'Bayesian' 'Grid' 'Random' (required)

EarlyTerminationPolicy

Name	Description	Value
delayEvaluation	Number of intervals by which to delay the first evaluation.	int
evaluationInterval	Interval (number of runs) between policy evaluations.	int
policyType	Set the object type	Bandit MedianStopping TruncationSelection (required)

BanditPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'Bandit' (required)
slackAmount	Absolute distance allowed from the best performing run.	int
slackFactor	Ratio of the allowed distance from the best performing run.	int

MedianStoppingPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'MedianStopping' (required)

TruncationSelectionPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	'TruncationSelection' (required)
truncationPercentage	The percentage of runs to cancel at each evaluation interval.	int

ClassificationTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for classification task.	String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier'
blockedTrainingAlgorithms	Blocked models for classification task.	String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings
trainingMode	TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen.	'Auto' 'Distributed' 'NonDistributed'

StackEnsembleSettings

Name	Description	Value
stackMetaLearnerKWargs	Optional parameters to pass to the initializer of the meta-learner.
stackMetaLearnerTrainPercentage	Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.	int
stackMetaLearnerType	The meta-learner is a model trained on the output of the individual heterogeneous models.	'ElasticNet' 'ElasticNetCV' 'LightGBMClassifier' 'LightGBMRegressor' 'LinearRegression' 'LogisticRegression' 'LogisticRegressionCV' 'None'

Forecasting

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'Forecasting' (required)
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	TableFixedParameters
forecastingSettings	Forecasting task specific inputs.	ForecastingSettings
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
primaryMetric	Primary metric for forecasting task.	'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	TableParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	TableSweepSettings
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	ForecastingTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

ForecastingSettings

Name	Description	Value
countryOrRegionForHolidays	Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.	string
cvStepSize	Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.	int
featureLags	Flag for generating lags for the numeric features with 'auto' or null.	'Auto' 'None'
forecastHorizon	The desired maximum forecast horizon in units of time-series frequency.	ForecastHorizon
frequency	When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.	string
seasonality	Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.	Seasonality
shortSeriesHandlingConfig	The parameter defining how if AutoML should handle short time series.	'Auto' 'Drop' 'None' 'Pad'
targetAggregateFunction	The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".	'Max' 'Mean' 'Min' 'None' 'Sum'
targetLags	The number of past periods to lag from the target column.	TargetLags
targetRollingWindowSize	The number of past periods used to create a rolling window average of the target column.	TargetRollingWindowSize
timeColumnName	The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.	string
timeSeriesIdColumnNames	The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.	string[]
useStl	Configure STL Decomposition of the time-series target column.	'None' 'Season' 'SeasonTrend'

ForecastHorizon

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoForecastHorizon

Name	Description	Value
mode	[Required] Set forecast horizon value selection mode.	'Auto' (required)

CustomForecastHorizon

Name	Description	Value
mode	[Required] Set forecast horizon value selection mode.	'Custom' (required)
value	[Required] Forecast horizon value.	int (required)

Seasonality

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoSeasonality

Name	Description	Value
mode	[Required] Seasonality mode.	'Auto' (required)

CustomSeasonality

Name	Description	Value
mode	[Required] Seasonality mode.	'Custom' (required)
value	[Required] Seasonality value.	int (required)

TargetLags

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoTargetLags

Name	Description	Value
mode	[Required] Set target lags mode - Auto/Custom	'Auto' (required)

CustomTargetLags

Name	Description	Value
mode	[Required] Set target lags mode - Auto/Custom	'Custom' (required)
values	[Required] Set target lags values.	int[] (required)

TargetRollingWindowSize

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoTargetRollingWindowSize

Name	Description	Value
mode	[Required] TargetRollingWindowSiz detection mode.	'Auto' (required)

CustomTargetRollingWindowSize

Name	Description	Value
mode	[Required] TargetRollingWindowSiz detection mode.	'Custom' (required)
value	[Required] TargetRollingWindowSize value.	int (required)

ForecastingTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for forecasting task.	String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SGD' 'SeasonalAverage' 'SeasonalNaive' 'TCNForecaster' 'XGBoostRegressor'
blockedTrainingAlgorithms	Blocked models for forecasting task.	String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SGD' 'SeasonalAverage' 'SeasonalNaive' 'TCNForecaster' 'XGBoostRegressor'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings
trainingMode	TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen.	'Auto' 'Distributed' 'NonDistributed'

ImageClassification

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'ImageClassification' (required)
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsClassification
primaryMetric	Primary metric to optimize for this task.	'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsClassification[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageLimitSettings

Name	Description	Value
maxConcurrentTrials	Maximum number of concurrent AutoML iterations.	int
maxTrials	Maximum number of AutoML iterations.	int
timeout	AutoML job timeout.	string

ImageModelSettingsClassification

Name	Description	Value
advancedSettings	Settings for advanced scenarios.	string
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	bool
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
checkpointFrequency	Frequency to store model checkpoints. Must be a positive integer.	int
checkpointModel	The pretrained checkpoint model for incremental training.	MLFlowModelJobInput
checkpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.	string
distributed	Whether to use distributed training.	bool
earlyStopping	Enable early stopping logic during training.	bool
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	int
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	int
enableOnnxNormalization	Enable normalization when exporting ONNX model.	bool
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	int
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	int
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	int
learningRate	Initial learning rate. Must be a float in the range [0, 1].	int
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	'None' 'Step' 'WarmupCosine'
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	int
nesterov	Enable nesterov when optimizer is 'sgd'.	bool
numberOfEpochs	Number of training epochs. Must be a positive integer.	int
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	int
optimizer	Type of optimizer.	'Adam' 'Adamw' 'None' 'Sgd'
randomSeed	Random seed to be used when using deterministic training.	int
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	int
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	int
trainingBatchSize	Training batch size. Must be a positive integer.	int
trainingCropSize	Image crop size that is input to the neural network for the training dataset. Must be a positive integer.	int
validationBatchSize	Validation batch size. Must be a positive integer.	int
validationCropSize	Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.	int
validationResizeSize	Image size to which to resize before cropping for validation dataset. Must be a positive integer.	int
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	int
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	int
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	int
weightedLoss	Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.	int

MLFlowModelJobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	[Required] Specifies the type of job.	'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

ImageModelDistributionSettingsClassification

Name	Description	Value
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	string
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
distributed	Whether to use distributer training.	string
earlyStopping	Enable early stopping logic during training.	string
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	string
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	string
enableOnnxNormalization	Enable normalization when exporting ONNX model.	string
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	string
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	string
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	string
learningRate	Initial learning rate. Must be a float in the range [0, 1].	string
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	string
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	string
nesterov	Enable nesterov when optimizer is 'sgd'.	string
numberOfEpochs	Number of training epochs. Must be a positive integer.	string
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	string
optimizer	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.	string
randomSeed	Random seed to be used when using deterministic training.	string
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	string
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	string
trainingBatchSize	Training batch size. Must be a positive integer.	string
trainingCropSize	Image crop size that is input to the neural network for the training dataset. Must be a positive integer.	string
validationBatchSize	Validation batch size. Must be a positive integer.	string
validationCropSize	Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.	string
validationResizeSize	Image size to which to resize before cropping for validation dataset. Must be a positive integer.	string
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	string
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	string
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	string
weightedLoss	Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.	string

ImageSweepSettings

Name	Description	Value
earlyTermination	Type of early termination policy.	EarlyTerminationPolicy
samplingAlgorithm	[Required] Type of the hyperparameter sampling algorithms.	'Bayesian' 'Grid' 'Random' (required)

ImageClassificationMultilabel

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'ImageClassificationMultilabel' (required)
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsClassification
primaryMetric	Primary metric to optimize for this task.	'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'IOU' 'NormMacroRecall' 'PrecisionScoreWeighted'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsClassification[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageInstanceSegmentation

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'ImageInstanceSegmentation' (required)
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsObjectDetection
primaryMetric	Primary metric to optimize for this task.	'MeanAveragePrecision'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsObjectDetection[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageModelSettingsObjectDetection

Name	Description	Value
advancedSettings	Settings for advanced scenarios.	string
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	bool
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
boxDetectionsPerImage	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.	int
boxScoreThreshold	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].	int
checkpointFrequency	Frequency to store model checkpoints. Must be a positive integer.	int
checkpointModel	The pretrained checkpoint model for incremental training.	MLFlowModelJobInput
checkpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.	string
distributed	Whether to use distributed training.	bool
earlyStopping	Enable early stopping logic during training.	bool
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	int
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	int
enableOnnxNormalization	Enable normalization when exporting ONNX model.	bool
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	int
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	int
imageSize	Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	int
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	int
learningRate	Initial learning rate. Must be a float in the range [0, 1].	int
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	'None' 'Step' 'WarmupCosine'
maxSize	Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	int
minSize	Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	int
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
modelSize	Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	'ExtraLarge' 'Large' 'Medium' 'None' 'Small'
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	int
multiScale	Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.	bool
nesterov	Enable nesterov when optimizer is 'sgd'.	bool
nmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].	int
numberOfEpochs	Number of training epochs. Must be a positive integer.	int
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	int
optimizer	Type of optimizer.	'Adam' 'Adamw' 'None' 'Sgd'
randomSeed	Random seed to be used when using deterministic training.	int
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	int
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	int
tileGridSize	The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.	string
tileOverlapRatio	Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.	int
tilePredictionsNmsThreshold	The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.	int
trainingBatchSize	Training batch size. Must be a positive integer.	int
validationBatchSize	Validation batch size. Must be a positive integer.	int
validationIouThreshold	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].	int
validationMetricType	Metric computation method to use for validation metrics.	'Coco' 'CocoVoc' 'None' 'Voc'
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	int
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	int
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	int

ImageModelDistributionSettingsObjectDetection

Name	Description	Value
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	string
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
boxDetectionsPerImage	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.	string
boxScoreThreshold	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].	string
distributed	Whether to use distributer training.	string
earlyStopping	Enable early stopping logic during training.	string
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	string
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	string
enableOnnxNormalization	Enable normalization when exporting ONNX model.	string
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	string
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	string
imageSize	Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	string
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	string
learningRate	Initial learning rate. Must be a float in the range [0, 1].	string
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	string
maxSize	Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	string
minSize	Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	string
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
modelSize	Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	string
multiScale	Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.	string
nesterov	Enable nesterov when optimizer is 'sgd'.	string
nmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].	string
numberOfEpochs	Number of training epochs. Must be a positive integer.	string
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	string
optimizer	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.	string
randomSeed	Random seed to be used when using deterministic training.	string
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	string
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	string
tileGridSize	The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.	string
tileOverlapRatio	Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.	string
tilePredictionsNmsThreshold	The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression	string
trainingBatchSize	Training batch size. Must be a positive integer.	string
validationBatchSize	Validation batch size. Must be a positive integer.	string
validationIouThreshold	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].	string
validationMetricType	Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.	string
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	string
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	string
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	string

ImageObjectDetection

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'ImageObjectDetection' (required)
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsObjectDetection
primaryMetric	Primary metric to optimize for this task.	'MeanAveragePrecision'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsObjectDetection[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

Regression

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'Regression' (required)
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	TableFixedParameters
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
primaryMetric	Primary metric for regression task.	'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	TableParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	TableSweepSettings
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	RegressionTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

RegressionTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for regression task.	String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor'
blockedTrainingAlgorithms	Blocked models for regression task.	String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor'
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings
trainingMode	TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen.	'Auto' 'Distributed' 'NonDistributed'

TextClassification

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'TextClassification' (required)
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	NlpFixedParameters
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
primaryMetric	Primary metric for Text-Classification task.	'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted'
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	NlpParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	NlpSweepSettings
validationData	Validation data inputs.	MLTableJobInput

NlpVerticalFeaturizationSettings

Name	Description	Value
datasetLanguage	Dataset language, useful for the text data.	string

NlpFixedParameters

Name	Description	Value
gradientAccumulationSteps	Number of steps to accumulate gradients over before running a backward pass.	int
learningRate	The learning rate for the training procedure.	int
learningRateScheduler	The type of learning rate schedule to use during the training procedure.	'Constant' 'ConstantWithWarmup' 'Cosine' 'CosineWithRestarts' 'Linear' 'None' 'Polynomial'
modelName	The name of the model to train.	string
numberOfEpochs	Number of training epochs.	int
trainingBatchSize	The batch size for the training procedure.	int
validationBatchSize	The batch size to be used during evaluation.	int
warmupRatio	The warmup ratio, used alongside LrSchedulerType.	int
weightDecay	The weight decay for the training procedure.	int

NlpVerticalLimitSettings

Name	Description	Value
maxConcurrentTrials	Maximum Concurrent AutoML iterations.	int
maxNodes	Maximum nodes to use for the experiment.	int
maxTrials	Number of AutoML iterations.	int
timeout	AutoML job timeout.	string
trialTimeout	Timeout for individual HD trials.	string

NlpParameterSubspace

Name	Description	Value
gradientAccumulationSteps	Number of steps to accumulate gradients over before running a backward pass.	string
learningRate	The learning rate for the training procedure.	string
learningRateScheduler	The type of learning rate schedule to use during the training procedure.	string
modelName	The name of the model to train.	string
numberOfEpochs	Number of training epochs.	string
trainingBatchSize	The batch size for the training procedure.	string
validationBatchSize	The batch size to be used during evaluation.	string
warmupRatio	The warmup ratio, used alongside LrSchedulerType.	string
weightDecay	The weight decay for the training procedure.	string

NlpSweepSettings

Name	Description	Value
earlyTermination	Type of early termination policy for the sweeping job.	EarlyTerminationPolicy
samplingAlgorithm	[Required] Type of sampling algorithm.	'Bayesian' 'Grid' 'Random' (required)

TextClassificationMultilabel

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'TextClassificationMultilabel' (required)
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	NlpFixedParameters
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	NlpParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	NlpSweepSettings
validationData	Validation data inputs.	MLTableJobInput

TextNer

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	'TextNER' (required)
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	NlpFixedParameters
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	NlpParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	NlpSweepSettings
validationData	Validation data inputs.	MLTableJobInput

CommandJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	'Command' (required)
autologgerSettings	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	AutologgerSettings
codeId	ARM resource ID of the code asset.	string
command	[Required] The command to execute on startup of the job. eg. "python train.py"	string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_]
distribution	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	DistributionConfiguration
environmentId	[Required] The ARM resource ID of the Environment specification for the job.	string (required) Constraints: Pattern = [a-zA-Z0-9_]
environmentVariables	Environment variables included in the job.	CommandJobEnvironmentVariables
inputs	Mapping of input data bindings used in the job.	CommandJobInputs
limits	Command Job limit.	CommandJobLimits
outputs	Mapping of output data bindings used in the job.	CommandJobOutputs
resources	Compute Resource configuration for the job.	JobResourceConfiguration

AutologgerSettings

Name	Description	Value
mlflowAutologger	[Required] Indicates whether mlflow autologger is enabled.	'Disabled' 'Enabled' (required)

DistributionConfiguration

Name	Description	Value
distributionType	Set the object type	Mpi PyTorch TensorFlow (required)

Mpi

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'Mpi' (required)
processCountPerInstance	Number of processes per MPI node.	int

PyTorch

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'PyTorch' (required)
processCountPerInstance	Number of processes per node.	int

TensorFlow

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	'TensorFlow' (required)
parameterServerCount	Number of parameter server tasks.	int
workerCount	Number of workers. If not specified, will default to the instance count.	int

CommandJobEnvironmentVariables

Name	Description	Value
{customized property}		string

CommandJobInputs

Name	Description	Value
{customized property}		JobInput

JobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	Set the object type	custom_model literal mlflow_model mltable triton_model uri_file uri_folder (required)

CustomModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'custom_model' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

LiteralJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'literal' (required)
value	[Required] Literal value for the input.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

TritonModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'triton_model' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

UriFileJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'uri_file' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

UriFolderJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	'uri_folder' (required)
mode	Input Asset Delivery Mode.	'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount'
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

CommandJobLimits

Name	Description	Value
jobLimitsType	[Required] JobLimit type.	'Command' 'Sweep' (required)
timeout	The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.	string

CommandJobOutputs

Name	Description	Value
{customized property}		JobOutput

LabelingJobProperties

Name	Description	Value
componentId	ARM resource ID of the component resource.	string
computeId	ARM resource ID of the compute resource.	string
dataConfiguration	Configuration of data used in the job.	LabelingDataConfiguration
description	The asset description text.	string
displayName	Display name of job.	string
experimentName	The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.	string
identity	Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.	IdentityConfiguration
isArchived	Is the asset archived?	bool
jobInstructions	Labeling instructions of the job.	LabelingJobInstructions
jobType	[Required] Specifies the type of job.	'AutoML' 'Command' 'Labeling' 'Pipeline' 'Spark' 'Sweep' (required)
labelCategories	Label categories of the job.	LabelingJobLabelCategories
labelingJobMediaProperties	Media type specific properties in the job.	LabelingJobMediaProperties
mlAssistConfiguration	Configuration of MLAssist feature in the job.	MLAssistConfiguration
properties	The asset property dictionary.	ResourceBaseProperties
services	List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.	JobBaseServices
tags	Tag dictionary. Tags can be added, removed, and updated.	object

LabelingDataConfiguration

Name	Description	Value
dataId	Resource Id of the data asset to perform labeling.	string
incrementalDataRefresh	Indicates whether to enable incremental data refresh.	'Disabled' 'Enabled'

LabelingJobInstructions

Name	Description	Value
uri	The link to a page with detailed labeling instructions for labelers.	string

LabelingJobLabelCategories

Name	Description	Value
{customized property}		LabelCategory

LabelCategory

Name	Description	Value
classes	Dictionary of label classes in this category.	LabelCategoryClasses
displayName	Display name of the label category.	string
multiSelect	Indicates whether it is allowed to select multiple classes in this category.	'Disabled' 'Enabled'

LabelCategoryClasses

Name	Description	Value
{customized property}		LabelClass

LabelClass

Name	Description	Value
displayName	Display name of the label class.	string
subclasses	Dictionary of subclasses of the label class.	LabelClassSubclasses

LabelClassSubclasses

Name	Description	Value
{customized property}		LabelClass

LabelingJobMediaProperties

Name	Description	Value
mediaType	Set the object type	Image Text (required)

LabelingJobImageProperties

Name	Description	Value
mediaType	[Required] Media type of the job.	'Image' (required)
annotationType	Annotation type of image labeling job.	'BoundingBox' 'Classification' 'InstanceSegmentation'

LabelingJobTextProperties

Name	Description	Value
mediaType	[Required] Media type of the job.	'Text' (required)
annotationType	Annotation type of text labeling job.	'Classification' 'NamedEntityRecognition'

MLAssistConfiguration

Name	Description	Value
mlAssist	Set the object type	Disabled Enabled (required)

MLAssistConfigurationDisabled

Name	Description	Value
mlAssist	[Required] Indicates whether MLAssist feature is enabled.	'Disabled' (required)

MLAssistConfigurationEnabled

Name	Description	Value
mlAssist	[Required] Indicates whether MLAssist feature is enabled.	'Enabled' (required)
inferencingComputeBinding	[Required] AML compute binding used in inferencing.	string (required) Constraints: Pattern = [a-zA-Z0-9_]
trainingComputeBinding	[Required] AML compute binding used in training.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

PipelineJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	'Pipeline' (required)
inputs	Inputs for the pipeline job.	PipelineJobInputs
jobs	Jobs construct the Pipeline Job.	PipelineJobJobs
outputs	Outputs for the pipeline job	PipelineJobOutputs
settings	Pipeline settings, for things like ContinueRunOnStepFailure etc.
sourceJobId	ARM resource ID of source job.	string

PipelineJobInputs

Name	Description	Value
{customized property}		JobInput

PipelineJobJobs

Name	Description	Value
{customized property}

PipelineJobOutputs

Name	Description	Value
{customized property}		JobOutput

SparkJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	'Spark' (required)
archives	Archive files used in the job.	string[]
args	Arguments for the job.	string
codeId	[Required] ARM resource ID of the code asset.	string (required) Constraints: Pattern = [a-zA-Z0-9_]
conf	Spark configured properties.	SparkJobConf
entry	[Required] The entry to execute on startup of the job.	SparkJobEntry (required)
environmentId	The ARM resource ID of the Environment specification for the job.	string
files	Files used in the job.	string[]
inputs	Mapping of input data bindings used in the job.	SparkJobInputs
jars	Jar files used in the job.	string[]
outputs	Mapping of output data bindings used in the job.	SparkJobOutputs
pyFiles	Python files used in the job.	string[]
resources	Compute Resource configuration for the job.	SparkResourceConfiguration

SparkJobConf

Name	Description	Value
{customized property}		string

SparkJobEntry

Name	Description	Value
sparkJobEntryType	Set the object type	SparkJobPythonEntry SparkJobScalaEntry (required)

SparkJobPythonEntry

Name	Description	Value
sparkJobEntryType	[Required] Type of the job's entry point.	'SparkJobPythonEntry' (required)
file	[Required] Relative python file path for job entry point.	string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_]

SparkJobScalaEntry

Name	Description	Value
sparkJobEntryType	[Required] Type of the job's entry point.	'SparkJobScalaEntry' (required)
className	[Required] Scala class name used as entry point.	string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_]

SparkJobInputs

Name	Description	Value
{customized property}		JobInput

SparkJobOutputs

Name	Description	Value
{customized property}		JobOutput

SparkResourceConfiguration

Name	Description	Value
instanceType	Optional type of VM used as supported by the compute target.	string
runtimeVersion	Version of spark runtime used for the job.	string

SweepJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	'Sweep' (required)
earlyTermination	Early termination policies enable canceling poor-performing runs before they complete	EarlyTerminationPolicy
inputs	Mapping of input data bindings used in the job.	SweepJobInputs
limits	Sweep Job limit.	SweepJobLimits
objective	[Required] Optimization objective.	Objective (required)
outputs	Mapping of output data bindings used in the job.	SweepJobOutputs
samplingAlgorithm	[Required] The hyperparameter sampling algorithm	SamplingAlgorithm (required)
searchSpace	[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
trial	[Required] Trial component definition.	TrialComponent (required)

SweepJobInputs

Name	Description	Value
{customized property}		JobInput

SweepJobLimits

Name	Description	Value
jobLimitsType	[Required] JobLimit type.	'Command' 'Sweep' (required)
maxConcurrentTrials	Sweep Job max concurrent trials.	int
maxTotalTrials	Sweep Job max total trials.	int
timeout	The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.	string
trialTimeout	Sweep Job Trial timeout value.	string

Objective

Name	Description	Value
goal	[Required] Defines supported metric goals for hyperparameter tuning	'Maximize' 'Minimize' (required)
primaryMetric	[Required] Name of the metric to optimize.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

SweepJobOutputs

Name	Description	Value
{customized property}		JobOutput

SamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	Set the object type	Bayesian Grid Random (required)

BayesianSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Bayesian' (required)

GridSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Grid' (required)

RandomSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	'Random' (required)
logbase	An optional positive number or e in string format to be used as base for log based random sampling	string
rule	The specific type of random algorithm	'Random' 'Sobol'
seed	An optional integer to use as the seed for random number generation	int

TrialComponent

Name	Description	Value
codeId	ARM resource ID of the code asset.	string
command	[Required] The command to execute on startup of the job. eg. "python train.py"	string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_]
distribution	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	DistributionConfiguration
environmentId	[Required] The ARM resource ID of the Environment specification for the job.	string (required) Constraints: Pattern = [a-zA-Z0-9_]
environmentVariables	Environment variables included in the job.	TrialComponentEnvironmentVariables
resources	Compute Resource configuration for the job.	JobResourceConfiguration

TrialComponentEnvironmentVariables

Name	Description	Value
{customized property}		string

Quickstart templates

The following quickstart templates deploy this resource type.

Template	Description
Create an Azure Machine Learning AutoML classification job	This template creates an Azure Machine Learning AutoML classification job to find out the best model for predicting if a client will subscribe to a fixed term deposit with a financial institution.
Create an Azure Machine Learning Command job	This template creates an Azure Machine Learning Command job with a basic hello_world script
Create an Azure Machine Learning Sweep job	This template creates an Azure Machine Learning Sweep job for hyperparameter tuning.

Terraform (AzAPI provider) resource definition

The workspaces/jobs resource type can be deployed with operations that target:

• Resource groups

For a list of changed properties in each API version, see change log.

Resource format

To create a Microsoft.MachineLearningServices/workspaces/jobs resource, add the following Terraform to your template.

resource "azapi_resource" "symbolicname" {
  type = "Microsoft.MachineLearningServices/workspaces/jobs@2022-12-01-preview"
  name = "string"
  parent_id = "string"
  body = jsonencode({
    properties = {
      componentId = "string"
      computeId = "string"
      description = "string"
      displayName = "string"
      experimentName = "string"
      identity = {
        identityType = "string"
        // For remaining properties, see IdentityConfiguration objects
      }
      isArchived = bool
      properties = {
        {customized property} = "string"
      }
      services = {
        {customized property} = {
          endpoint = "string"
          jobServiceType = "string"
          nodes = {
            nodesValueType = "string"
            // For remaining properties, see Nodes objects
          }
          port = int
          properties = {
            {customized property} = "string"
          }
        }
      }
      tags = {}
      jobType = "string"
      // For remaining properties, see JobBaseProperties objects
    }
  })
}

JobBaseProperties objects

Set the jobType property to specify the type of object.

For AutoML, use:

  jobType = "AutoML"
  environmentId = "string"
  environmentVariables = {
    {customized property} = "string"
  }
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  resources = {
    dockerArgs = "string"
    instanceCount = int
    instanceType = "string"
    properties = {}
    shmSize = "string"
  }
  taskDetails = {
    logVerbosity = "string"
    targetColumnName = "string"
    trainingData = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    taskType = "string"
    // For remaining properties, see AutoMLVertical objects
  }

For Command, use:

  jobType = "Command"
  autologgerSettings = {
    mlflowAutologger = "string"
  }
  codeId = "string"
  command = "string"
  distribution = {
    distributionType = "string"
    // For remaining properties, see DistributionConfiguration objects
  }
  environmentId = "string"
  environmentVariables = {
    {customized property} = "string"
  }
  inputs = {
    {customized property} = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
  }
  limits = {
    jobLimitsType = "string"
    timeout = "string"
  }
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  resources = {
    dockerArgs = "string"
    instanceCount = int
    instanceType = "string"
    properties = {}
    shmSize = "string"
  }

For Labeling, use:

  jobType = "Labeling"
  dataConfiguration = {
    dataId = "string"
    incrementalDataRefresh = "string"
  }
  jobInstructions = {
    uri = "string"
  }
  labelCategories = {
    {customized property} = {
      classes = {
        {customized property} = {
          displayName = "string"
          subclasses = {
            {customized property} = {}
        }
      }
      displayName = "string"
      multiSelect = "string"
    }
  }
  labelingJobMediaProperties = {
    mediaType = "string"
    // For remaining properties, see LabelingJobMediaProperties objects
  }
  mlAssistConfiguration = {
    mlAssist = "string"
    // For remaining properties, see MLAssistConfiguration objects
  }

For Pipeline, use:

  jobType = "Pipeline"
  inputs = {
    {customized property} = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
  }
  jobs = {}
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  sourceJobId = "string"

For Spark, use:

  jobType = "Spark"
  archives = [
    "string"
  ]
  args = "string"
  codeId = "string"
  conf = {
    {customized property} = "string"
  }
  entry = {
    sparkJobEntryType = "string"
    // For remaining properties, see SparkJobEntry objects
  }
  environmentId = "string"
  files = [
    "string"
  ]
  inputs = {
    {customized property} = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
  }
  jars = [
    "string"
  ]
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  pyFiles = [
    "string"
  ]
  resources = {
    instanceType = "string"
    runtimeVersion = "string"
  }

For Sweep, use:

  jobType = "Sweep"
  earlyTermination = {
    delayEvaluation = int
    evaluationInterval = int
    policyType = "string"
    // For remaining properties, see EarlyTerminationPolicy objects
  }
  inputs = {
    {customized property} = {
      description = "string"
      jobInputType = "string"
      // For remaining properties, see JobInput objects
    }
  }
  limits = {
    jobLimitsType = "string"
    maxConcurrentTrials = int
    maxTotalTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  objective = {
    goal = "string"
    primaryMetric = "string"
  }
  outputs = {
    {customized property} = {
      description = "string"
      jobOutputType = "string"
      // For remaining properties, see JobOutput objects
    }
  }
  samplingAlgorithm = {
    samplingAlgorithmType = "string"
    // For remaining properties, see SamplingAlgorithm objects
  }
  trial = {
    codeId = "string"
    command = "string"
    distribution = {
      distributionType = "string"
      // For remaining properties, see DistributionConfiguration objects
    }
    environmentId = "string"
    environmentVariables = {
      {customized property} = "string"
    }
    resources = {
      dockerArgs = "string"
      instanceCount = int
      instanceType = "string"
      properties = {}
      shmSize = "string"
    }
  }

IdentityConfiguration objects

Set the identityType property to specify the type of object.

For AMLToken, use:

  identityType = "AMLToken"

For Managed, use:

  identityType = "Managed"
  clientId = "string"
  objectId = "string"
  resourceId = "string"

For UserIdentity, use:

  identityType = "UserIdentity"

Nodes objects

Set the nodesValueType property to specify the type of object.

For All, use:

  nodesValueType = "All"

JobOutput objects

Set the jobOutputType property to specify the type of object.

For custom_model, use:

  jobOutputType = "custom_model"
  assetName = "string"
  assetVersion = "string"
  mode = "string"
  uri = "string"

For mlflow_model, use:

  jobOutputType = "mlflow_model"
  assetName = "string"
  assetVersion = "string"
  mode = "string"
  uri = "string"

For mltable, use:

  jobOutputType = "mltable"
  assetName = "string"
  assetVersion = "string"
  mode = "string"
  uri = "string"

For triton_model, use:

  jobOutputType = "triton_model"
  assetName = "string"
  assetVersion = "string"
  mode = "string"
  uri = "string"

For uri_file, use:

  jobOutputType = "uri_file"
  assetName = "string"
  assetVersion = "string"
  mode = "string"
  uri = "string"

For uri_folder, use:

  jobOutputType = "uri_folder"
  assetName = "string"
  assetVersion = "string"
  mode = "string"
  uri = "string"

AutoMLVertical objects

Set the taskType property to specify the type of object.

For Classification, use:

  taskType = "Classification"
  cvSplitColumnNames = [
    "string"
  ]
  featurizationSettings = {
    blockedTransformers = [
      "string"
    ]
    columnNameAndTypes = {
      {customized property} = "string"
    }
    datasetLanguage = "string"
    enableDnnFeaturization = bool
    mode = "string"
    transformerParams = {
      {customized property} = [
        {
          fields = [
            "string"
          ]
        }
      ]
    }
  }
  fixedParameters = {
    booster = "string"
    boostingType = "string"
    growPolicy = "string"
    learningRate = int
    maxBin = int
    maxDepth = int
    maxLeaves = int
    minDataInLeaf = int
    minSplitGain = int
    modelName = "string"
    nEstimators = int
    numLeaves = int
    preprocessorName = "string"
    regAlpha = int
    regLambda = int
    subsample = int
    subsampleFreq = int
    treeMethod = "string"
    withMean = bool
    withStd = bool
  }
  limitSettings = {
    enableEarlyTermination = bool
    exitScore = int
    maxConcurrentTrials = int
    maxCoresPerTrial = int
    maxNodes = int
    maxTrials = int
    sweepConcurrentTrials = int
    sweepTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  nCrossValidations = {
    mode = "string"
    // For remaining properties, see NCrossValidations objects
  }
  positiveLabel = "string"
  primaryMetric = "string"
  searchSpace = [
    {
      booster = "string"
      boostingType = "string"
      growPolicy = "string"
      learningRate = "string"
      maxBin = "string"
      maxDepth = "string"
      maxLeaves = "string"
      minDataInLeaf = "string"
      minSplitGain = "string"
      modelName = "string"
      nEstimators = "string"
      numLeaves = "string"
      preprocessorName = "string"
      regAlpha = "string"
      regLambda = "string"
      subsample = "string"
      subsampleFreq = "string"
      treeMethod = "string"
      withMean = "string"
      withStd = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  testData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  testDataSize = int
  trainingSettings = {
    allowedTrainingAlgorithms = [
      "string"
    ]
    blockedTrainingAlgorithms = [
      "string"
    ]
    enableDnnTraining = bool
    enableModelExplainability = bool
    enableOnnxCompatibleModels = bool
    enableStackEnsemble = bool
    enableVoteEnsemble = bool
    ensembleModelDownloadTimeout = "string"
    stackEnsembleSettings = {
      stackMetaLearnerTrainPercentage = int
      stackMetaLearnerType = "string"
    }
    trainingMode = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int
  weightColumnName = "string"

For Forecasting, use:

  taskType = "Forecasting"
  cvSplitColumnNames = [
    "string"
  ]
  featurizationSettings = {
    blockedTransformers = [
      "string"
    ]
    columnNameAndTypes = {
      {customized property} = "string"
    }
    datasetLanguage = "string"
    enableDnnFeaturization = bool
    mode = "string"
    transformerParams = {
      {customized property} = [
        {
          fields = [
            "string"
          ]
        }
      ]
    }
  }
  fixedParameters = {
    booster = "string"
    boostingType = "string"
    growPolicy = "string"
    learningRate = int
    maxBin = int
    maxDepth = int
    maxLeaves = int
    minDataInLeaf = int
    minSplitGain = int
    modelName = "string"
    nEstimators = int
    numLeaves = int
    preprocessorName = "string"
    regAlpha = int
    regLambda = int
    subsample = int
    subsampleFreq = int
    treeMethod = "string"
    withMean = bool
    withStd = bool
  }
  forecastingSettings = {
    countryOrRegionForHolidays = "string"
    cvStepSize = int
    featureLags = "string"
    forecastHorizon = {
      mode = "string"
      // For remaining properties, see ForecastHorizon objects
    }
    frequency = "string"
    seasonality = {
      mode = "string"
      // For remaining properties, see Seasonality objects
    }
    shortSeriesHandlingConfig = "string"
    targetAggregateFunction = "string"
    targetLags = {
      mode = "string"
      // For remaining properties, see TargetLags objects
    }
    targetRollingWindowSize = {
      mode = "string"
      // For remaining properties, see TargetRollingWindowSize objects
    }
    timeColumnName = "string"
    timeSeriesIdColumnNames = [
      "string"
    ]
    useStl = "string"
  }
  limitSettings = {
    enableEarlyTermination = bool
    exitScore = int
    maxConcurrentTrials = int
    maxCoresPerTrial = int
    maxNodes = int
    maxTrials = int
    sweepConcurrentTrials = int
    sweepTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  nCrossValidations = {
    mode = "string"
    // For remaining properties, see NCrossValidations objects
  }
  primaryMetric = "string"
  searchSpace = [
    {
      booster = "string"
      boostingType = "string"
      growPolicy = "string"
      learningRate = "string"
      maxBin = "string"
      maxDepth = "string"
      maxLeaves = "string"
      minDataInLeaf = "string"
      minSplitGain = "string"
      modelName = "string"
      nEstimators = "string"
      numLeaves = "string"
      preprocessorName = "string"
      regAlpha = "string"
      regLambda = "string"
      subsample = "string"
      subsampleFreq = "string"
      treeMethod = "string"
      withMean = "string"
      withStd = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  testData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  testDataSize = int
  trainingSettings = {
    allowedTrainingAlgorithms = [
      "string"
    ]
    blockedTrainingAlgorithms = [
      "string"
    ]
    enableDnnTraining = bool
    enableModelExplainability = bool
    enableOnnxCompatibleModels = bool
    enableStackEnsemble = bool
    enableVoteEnsemble = bool
    ensembleModelDownloadTimeout = "string"
    stackEnsembleSettings = {
      stackMetaLearnerTrainPercentage = int
      stackMetaLearnerType = "string"
    }
    trainingMode = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int
  weightColumnName = "string"

For ImageClassification, use:

  taskType = "ImageClassification"
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  modelSettings = {
    advancedSettings = "string"
    amsGradient = bool
    augmentations = "string"
    beta1 = int
    beta2 = int
    checkpointFrequency = int
    checkpointModel = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    checkpointRunId = "string"
    distributed = bool
    earlyStopping = bool
    earlyStoppingDelay = int
    earlyStoppingPatience = int
    enableOnnxNormalization = bool
    evaluationFrequency = int
    gradientAccumulationStep = int
    layersToFreeze = int
    learningRate = int
    learningRateScheduler = "string"
    modelName = "string"
    momentum = int
    nesterov = bool
    numberOfEpochs = int
    numberOfWorkers = int
    optimizer = "string"
    randomSeed = int
    stepLRGamma = int
    stepLRStepSize = int
    trainingBatchSize = int
    trainingCropSize = int
    validationBatchSize = int
    validationCropSize = int
    validationResizeSize = int
    warmupCosineLRCycles = int
    warmupCosineLRWarmupEpochs = int
    weightDecay = int
    weightedLoss = int
  }
  primaryMetric = "string"
  searchSpace = [
    {
      amsGradient = "string"
      augmentations = "string"
      beta1 = "string"
      beta2 = "string"
      distributed = "string"
      earlyStopping = "string"
      earlyStoppingDelay = "string"
      earlyStoppingPatience = "string"
      enableOnnxNormalization = "string"
      evaluationFrequency = "string"
      gradientAccumulationStep = "string"
      layersToFreeze = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      modelName = "string"
      momentum = "string"
      nesterov = "string"
      numberOfEpochs = "string"
      numberOfWorkers = "string"
      optimizer = "string"
      randomSeed = "string"
      stepLRGamma = "string"
      stepLRStepSize = "string"
      trainingBatchSize = "string"
      trainingCropSize = "string"
      validationBatchSize = "string"
      validationCropSize = "string"
      validationResizeSize = "string"
      warmupCosineLRCycles = "string"
      warmupCosineLRWarmupEpochs = "string"
      weightDecay = "string"
      weightedLoss = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int

For ImageClassificationMultilabel, use:

  taskType = "ImageClassificationMultilabel"
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  modelSettings = {
    advancedSettings = "string"
    amsGradient = bool
    augmentations = "string"
    beta1 = int
    beta2 = int
    checkpointFrequency = int
    checkpointModel = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    checkpointRunId = "string"
    distributed = bool
    earlyStopping = bool
    earlyStoppingDelay = int
    earlyStoppingPatience = int
    enableOnnxNormalization = bool
    evaluationFrequency = int
    gradientAccumulationStep = int
    layersToFreeze = int
    learningRate = int
    learningRateScheduler = "string"
    modelName = "string"
    momentum = int
    nesterov = bool
    numberOfEpochs = int
    numberOfWorkers = int
    optimizer = "string"
    randomSeed = int
    stepLRGamma = int
    stepLRStepSize = int
    trainingBatchSize = int
    trainingCropSize = int
    validationBatchSize = int
    validationCropSize = int
    validationResizeSize = int
    warmupCosineLRCycles = int
    warmupCosineLRWarmupEpochs = int
    weightDecay = int
    weightedLoss = int
  }
  primaryMetric = "string"
  searchSpace = [
    {
      amsGradient = "string"
      augmentations = "string"
      beta1 = "string"
      beta2 = "string"
      distributed = "string"
      earlyStopping = "string"
      earlyStoppingDelay = "string"
      earlyStoppingPatience = "string"
      enableOnnxNormalization = "string"
      evaluationFrequency = "string"
      gradientAccumulationStep = "string"
      layersToFreeze = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      modelName = "string"
      momentum = "string"
      nesterov = "string"
      numberOfEpochs = "string"
      numberOfWorkers = "string"
      optimizer = "string"
      randomSeed = "string"
      stepLRGamma = "string"
      stepLRStepSize = "string"
      trainingBatchSize = "string"
      trainingCropSize = "string"
      validationBatchSize = "string"
      validationCropSize = "string"
      validationResizeSize = "string"
      warmupCosineLRCycles = "string"
      warmupCosineLRWarmupEpochs = "string"
      weightDecay = "string"
      weightedLoss = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int

For ImageInstanceSegmentation, use:

  taskType = "ImageInstanceSegmentation"
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  modelSettings = {
    advancedSettings = "string"
    amsGradient = bool
    augmentations = "string"
    beta1 = int
    beta2 = int
    boxDetectionsPerImage = int
    boxScoreThreshold = int
    checkpointFrequency = int
    checkpointModel = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    checkpointRunId = "string"
    distributed = bool
    earlyStopping = bool
    earlyStoppingDelay = int
    earlyStoppingPatience = int
    enableOnnxNormalization = bool
    evaluationFrequency = int
    gradientAccumulationStep = int
    imageSize = int
    layersToFreeze = int
    learningRate = int
    learningRateScheduler = "string"
    maxSize = int
    minSize = int
    modelName = "string"
    modelSize = "string"
    momentum = int
    multiScale = bool
    nesterov = bool
    nmsIouThreshold = int
    numberOfEpochs = int
    numberOfWorkers = int
    optimizer = "string"
    randomSeed = int
    stepLRGamma = int
    stepLRStepSize = int
    tileGridSize = "string"
    tileOverlapRatio = int
    tilePredictionsNmsThreshold = int
    trainingBatchSize = int
    validationBatchSize = int
    validationIouThreshold = int
    validationMetricType = "string"
    warmupCosineLRCycles = int
    warmupCosineLRWarmupEpochs = int
    weightDecay = int
  }
  primaryMetric = "MeanAveragePrecision"
  searchSpace = [
    {
      amsGradient = "string"
      augmentations = "string"
      beta1 = "string"
      beta2 = "string"
      boxDetectionsPerImage = "string"
      boxScoreThreshold = "string"
      distributed = "string"
      earlyStopping = "string"
      earlyStoppingDelay = "string"
      earlyStoppingPatience = "string"
      enableOnnxNormalization = "string"
      evaluationFrequency = "string"
      gradientAccumulationStep = "string"
      imageSize = "string"
      layersToFreeze = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      maxSize = "string"
      minSize = "string"
      modelName = "string"
      modelSize = "string"
      momentum = "string"
      multiScale = "string"
      nesterov = "string"
      nmsIouThreshold = "string"
      numberOfEpochs = "string"
      numberOfWorkers = "string"
      optimizer = "string"
      randomSeed = "string"
      stepLRGamma = "string"
      stepLRStepSize = "string"
      tileGridSize = "string"
      tileOverlapRatio = "string"
      tilePredictionsNmsThreshold = "string"
      trainingBatchSize = "string"
      validationBatchSize = "string"
      validationIouThreshold = "string"
      validationMetricType = "string"
      warmupCosineLRCycles = "string"
      warmupCosineLRWarmupEpochs = "string"
      weightDecay = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int

For ImageObjectDetection, use:

  taskType = "ImageObjectDetection"
  limitSettings = {
    maxConcurrentTrials = int
    maxTrials = int
    timeout = "string"
  }
  modelSettings = {
    advancedSettings = "string"
    amsGradient = bool
    augmentations = "string"
    beta1 = int
    beta2 = int
    boxDetectionsPerImage = int
    boxScoreThreshold = int
    checkpointFrequency = int
    checkpointModel = {
      description = "string"
      jobInputType = "string"
      mode = "string"
      uri = "string"
    }
    checkpointRunId = "string"
    distributed = bool
    earlyStopping = bool
    earlyStoppingDelay = int
    earlyStoppingPatience = int
    enableOnnxNormalization = bool
    evaluationFrequency = int
    gradientAccumulationStep = int
    imageSize = int
    layersToFreeze = int
    learningRate = int
    learningRateScheduler = "string"
    maxSize = int
    minSize = int
    modelName = "string"
    modelSize = "string"
    momentum = int
    multiScale = bool
    nesterov = bool
    nmsIouThreshold = int
    numberOfEpochs = int
    numberOfWorkers = int
    optimizer = "string"
    randomSeed = int
    stepLRGamma = int
    stepLRStepSize = int
    tileGridSize = "string"
    tileOverlapRatio = int
    tilePredictionsNmsThreshold = int
    trainingBatchSize = int
    validationBatchSize = int
    validationIouThreshold = int
    validationMetricType = "string"
    warmupCosineLRCycles = int
    warmupCosineLRWarmupEpochs = int
    weightDecay = int
  }
  primaryMetric = "MeanAveragePrecision"
  searchSpace = [
    {
      amsGradient = "string"
      augmentations = "string"
      beta1 = "string"
      beta2 = "string"
      boxDetectionsPerImage = "string"
      boxScoreThreshold = "string"
      distributed = "string"
      earlyStopping = "string"
      earlyStoppingDelay = "string"
      earlyStoppingPatience = "string"
      enableOnnxNormalization = "string"
      evaluationFrequency = "string"
      gradientAccumulationStep = "string"
      imageSize = "string"
      layersToFreeze = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      maxSize = "string"
      minSize = "string"
      modelName = "string"
      modelSize = "string"
      momentum = "string"
      multiScale = "string"
      nesterov = "string"
      nmsIouThreshold = "string"
      numberOfEpochs = "string"
      numberOfWorkers = "string"
      optimizer = "string"
      randomSeed = "string"
      stepLRGamma = "string"
      stepLRStepSize = "string"
      tileGridSize = "string"
      tileOverlapRatio = "string"
      tilePredictionsNmsThreshold = "string"
      trainingBatchSize = "string"
      validationBatchSize = "string"
      validationIouThreshold = "string"
      validationMetricType = "string"
      warmupCosineLRCycles = "string"
      warmupCosineLRWarmupEpochs = "string"
      weightDecay = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int

For Regression, use:

  taskType = "Regression"
  cvSplitColumnNames = [
    "string"
  ]
  featurizationSettings = {
    blockedTransformers = [
      "string"
    ]
    columnNameAndTypes = {
      {customized property} = "string"
    }
    datasetLanguage = "string"
    enableDnnFeaturization = bool
    mode = "string"
    transformerParams = {
      {customized property} = [
        {
          fields = [
            "string"
          ]
        }
      ]
    }
  }
  fixedParameters = {
    booster = "string"
    boostingType = "string"
    growPolicy = "string"
    learningRate = int
    maxBin = int
    maxDepth = int
    maxLeaves = int
    minDataInLeaf = int
    minSplitGain = int
    modelName = "string"
    nEstimators = int
    numLeaves = int
    preprocessorName = "string"
    regAlpha = int
    regLambda = int
    subsample = int
    subsampleFreq = int
    treeMethod = "string"
    withMean = bool
    withStd = bool
  }
  limitSettings = {
    enableEarlyTermination = bool
    exitScore = int
    maxConcurrentTrials = int
    maxCoresPerTrial = int
    maxNodes = int
    maxTrials = int
    sweepConcurrentTrials = int
    sweepTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  nCrossValidations = {
    mode = "string"
    // For remaining properties, see NCrossValidations objects
  }
  primaryMetric = "string"
  searchSpace = [
    {
      booster = "string"
      boostingType = "string"
      growPolicy = "string"
      learningRate = "string"
      maxBin = "string"
      maxDepth = "string"
      maxLeaves = "string"
      minDataInLeaf = "string"
      minSplitGain = "string"
      modelName = "string"
      nEstimators = "string"
      numLeaves = "string"
      preprocessorName = "string"
      regAlpha = "string"
      regLambda = "string"
      subsample = "string"
      subsampleFreq = "string"
      treeMethod = "string"
      withMean = "string"
      withStd = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  testData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  testDataSize = int
  trainingSettings = {
    allowedTrainingAlgorithms = [
      "string"
    ]
    blockedTrainingAlgorithms = [
      "string"
    ]
    enableDnnTraining = bool
    enableModelExplainability = bool
    enableOnnxCompatibleModels = bool
    enableStackEnsemble = bool
    enableVoteEnsemble = bool
    ensembleModelDownloadTimeout = "string"
    stackEnsembleSettings = {
      stackMetaLearnerTrainPercentage = int
      stackMetaLearnerType = "string"
    }
    trainingMode = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }
  validationDataSize = int
  weightColumnName = "string"

For TextClassification, use:

  taskType = "TextClassification"
  featurizationSettings = {
    datasetLanguage = "string"
  }
  fixedParameters = {
    gradientAccumulationSteps = int
    learningRate = int
    learningRateScheduler = "string"
    modelName = "string"
    numberOfEpochs = int
    trainingBatchSize = int
    validationBatchSize = int
    warmupRatio = int
    weightDecay = int
  }
  limitSettings = {
    maxConcurrentTrials = int
    maxNodes = int
    maxTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  primaryMetric = "string"
  searchSpace = [
    {
      gradientAccumulationSteps = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      modelName = "string"
      numberOfEpochs = "string"
      trainingBatchSize = "string"
      validationBatchSize = "string"
      warmupRatio = "string"
      weightDecay = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }

For TextClassificationMultilabel, use:

  taskType = "TextClassificationMultilabel"
  featurizationSettings = {
    datasetLanguage = "string"
  }
  fixedParameters = {
    gradientAccumulationSteps = int
    learningRate = int
    learningRateScheduler = "string"
    modelName = "string"
    numberOfEpochs = int
    trainingBatchSize = int
    validationBatchSize = int
    warmupRatio = int
    weightDecay = int
  }
  limitSettings = {
    maxConcurrentTrials = int
    maxNodes = int
    maxTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  searchSpace = [
    {
      gradientAccumulationSteps = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      modelName = "string"
      numberOfEpochs = "string"
      trainingBatchSize = "string"
      validationBatchSize = "string"
      warmupRatio = "string"
      weightDecay = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }

For TextNER, use:

  taskType = "TextNER"
  featurizationSettings = {
    datasetLanguage = "string"
  }
  fixedParameters = {
    gradientAccumulationSteps = int
    learningRate = int
    learningRateScheduler = "string"
    modelName = "string"
    numberOfEpochs = int
    trainingBatchSize = int
    validationBatchSize = int
    warmupRatio = int
    weightDecay = int
  }
  limitSettings = {
    maxConcurrentTrials = int
    maxNodes = int
    maxTrials = int
    timeout = "string"
    trialTimeout = "string"
  }
  searchSpace = [
    {
      gradientAccumulationSteps = "string"
      learningRate = "string"
      learningRateScheduler = "string"
      modelName = "string"
      numberOfEpochs = "string"
      trainingBatchSize = "string"
      validationBatchSize = "string"
      warmupRatio = "string"
      weightDecay = "string"
    }
  ]
  sweepSettings = {
    earlyTermination = {
      delayEvaluation = int
      evaluationInterval = int
      policyType = "string"
      // For remaining properties, see EarlyTerminationPolicy objects
    }
    samplingAlgorithm = "string"
  }
  validationData = {
    description = "string"
    jobInputType = "string"
    mode = "string"
    uri = "string"
  }

NCrossValidations objects

Set the mode property to specify the type of object.

For Auto, use:

  mode = "Auto"

For Custom, use:

  mode = "Custom"
  value = int

EarlyTerminationPolicy objects

Set the policyType property to specify the type of object.

For Bandit, use:

  policyType = "Bandit"
  slackAmount = int
  slackFactor = int

For MedianStopping, use:

  policyType = "MedianStopping"

For TruncationSelection, use:

  policyType = "TruncationSelection"
  truncationPercentage = int

ForecastHorizon objects

Set the mode property to specify the type of object.

For Auto, use:

  mode = "Auto"

For Custom, use:

  mode = "Custom"
  value = int

Seasonality objects

Set the mode property to specify the type of object.

For Auto, use:

  mode = "Auto"

For Custom, use:

  mode = "Custom"
  value = int

TargetLags objects

Set the mode property to specify the type of object.

For Auto, use:

  mode = "Auto"

For Custom, use:

  mode = "Custom"
  values = [
    int
  ]

TargetRollingWindowSize objects

Set the mode property to specify the type of object.

For Auto, use:

  mode = "Auto"

For Custom, use:

  mode = "Custom"
  value = int

DistributionConfiguration objects

Set the distributionType property to specify the type of object.

For Mpi, use:

  distributionType = "Mpi"
  processCountPerInstance = int

For PyTorch, use:

  distributionType = "PyTorch"
  processCountPerInstance = int

For TensorFlow, use:

  distributionType = "TensorFlow"
  parameterServerCount = int
  workerCount = int

JobInput objects

Set the jobInputType property to specify the type of object.

For custom_model, use:

  jobInputType = "custom_model"
  mode = "string"
  uri = "string"

For literal, use:

  jobInputType = "literal"
  value = "string"

For mlflow_model, use:

  jobInputType = "mlflow_model"
  mode = "string"
  uri = "string"

For mltable, use:

  jobInputType = "mltable"
  mode = "string"
  uri = "string"

For triton_model, use:

  jobInputType = "triton_model"
  mode = "string"
  uri = "string"

For uri_file, use:

  jobInputType = "uri_file"
  mode = "string"
  uri = "string"

For uri_folder, use:

  jobInputType = "uri_folder"
  mode = "string"
  uri = "string"

LabelingJobMediaProperties objects

Set the mediaType property to specify the type of object.

For Image, use:

  mediaType = "Image"
  annotationType = "string"

For Text, use:

  mediaType = "Text"
  annotationType = "string"

MLAssistConfiguration objects

Set the mlAssist property to specify the type of object.

For Disabled, use:

  mlAssist = "Disabled"

For Enabled, use:

  mlAssist = "Enabled"
  inferencingComputeBinding = "string"
  trainingComputeBinding = "string"

SparkJobEntry objects

Set the sparkJobEntryType property to specify the type of object.

For SparkJobPythonEntry, use:

  sparkJobEntryType = "SparkJobPythonEntry"
  file = "string"

For SparkJobScalaEntry, use:

  sparkJobEntryType = "SparkJobScalaEntry"
  className = "string"

SamplingAlgorithm objects

Set the samplingAlgorithmType property to specify the type of object.

For Bayesian, use:

  samplingAlgorithmType = "Bayesian"

For Grid, use:

  samplingAlgorithmType = "Grid"

For Random, use:

  samplingAlgorithmType = "Random"
  logbase = "string"
  rule = "string"
  seed = int

Property values

workspaces/jobs

Name	Description	Value
type	The resource type	"Microsoft.MachineLearningServices/workspaces/jobs@2022-12-01-preview"
name	The resource name	string (required)
parent_id	The ID of the resource that is the parent for this resource.	ID for resource of type: workspaces
properties	[Required] Additional attributes of the entity.	JobBaseProperties (required)

JobBaseProperties

Name	Description	Value
componentId	ARM resource ID of the component resource.	string
computeId	ARM resource ID of the compute resource.	string
description	The asset description text.	string
displayName	Display name of job.	string
experimentName	The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.	string
identity	Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.	IdentityConfiguration
isArchived	Is the asset archived?	bool
properties	The asset property dictionary.	ResourceBaseProperties
services	List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.	JobBaseServices
tags	Tag dictionary. Tags can be added, removed, and updated.	object
jobType	Set the object type	AutoML Command Labeling Pipeline Spark Sweep (required)

IdentityConfiguration

Name	Description	Value
identityType	Set the object type	AMLToken Managed UserIdentity (required)

AmlToken

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	"AMLToken" (required)

ManagedIdentity

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	"Managed" (required)
clientId	Specifies a user-assigned identity by client ID. For system-assigned, do not set this field.	string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
objectId	Specifies a user-assigned identity by object ID. For system-assigned, do not set this field.	string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$
resourceId	Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field.	string

UserIdentity

Name	Description	Value
identityType	[Required] Specifies the type of identity framework.	"UserIdentity" (required)

ResourceBaseProperties

Name	Description	Value
{customized property}		string

JobBaseServices

Name	Description	Value
{customized property}		JobService

JobService

Name	Description	Value
endpoint	Url for endpoint.	string
jobServiceType	Endpoint type.	string
nodes	Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node.	Nodes
port	Port for endpoint set by user.	int
properties	Additional properties to set on the endpoint.	JobServiceProperties

Nodes

Name	Description	Value
nodesValueType	Set the object type	All (required)

AllNodes

Name	Description	Value
nodesValueType	[Required] Type of the Nodes value	"All" (required)

JobServiceProperties

Name	Description	Value
{customized property}		string

AutoMLJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	"AutoML" (required)
environmentId	The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job.	string
environmentVariables	Environment variables included in the job.	AutoMLJobEnvironmentVariables
outputs	Mapping of output data bindings used in the job.	AutoMLJobOutputs
resources	Compute Resource configuration for the job.	JobResourceConfiguration
taskDetails	[Required] This represents scenario which can be one of Tables/NLP/Image	AutoMLVertical (required)

AutoMLJobEnvironmentVariables

Name	Description	Value
{customized property}		string

AutoMLJobOutputs

Name	Description	Value
{customized property}		JobOutput

JobOutput

Name	Description	Value
description	Description for the output.	string
jobOutputType	Set the object type	custom_model mlflow_model mltable triton_model uri_file uri_folder (required)

CustomModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	"custom_model" (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	"Direct" "ReadWriteMount" "Upload"
uri	Output Asset URI.	string

MLFlowModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	"mlflow_model" (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	"Direct" "ReadWriteMount" "Upload"
uri	Output Asset URI.	string

MLTableJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	"mltable" (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	"Direct" "ReadWriteMount" "Upload"
uri	Output Asset URI.	string

TritonModelJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	"triton_model" (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	"Direct" "ReadWriteMount" "Upload"
uri	Output Asset URI.	string

UriFileJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	"uri_file" (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	"Direct" "ReadWriteMount" "Upload"
uri	Output Asset URI.	string

UriFolderJobOutput

Name	Description	Value
jobOutputType	[Required] Specifies the type of job.	"uri_folder" (required)
assetName	Output Asset Name.	string
assetVersion	Output Asset Version.	string
mode	Output Asset Delivery Mode.	"Direct" "ReadWriteMount" "Upload"
uri	Output Asset URI.	string

JobResourceConfiguration

Name	Description	Value
dockerArgs	Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types.	string
instanceCount	Optional number of instances or nodes used by the compute target.	int
instanceType	Optional type of VM used as supported by the compute target.	string
properties	Additional properties bag.	ResourceConfigurationProperties
shmSize	Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).	string Constraints: Pattern = \d+[bBkKmMgG]

ResourceConfigurationProperties

Name	Description	Value
{customized property}

AutoMLVertical

Name	Description	Value
logVerbosity	Log verbosity for the job.	"Critical" "Debug" "Error" "Info" "NotSet" "Warning"
targetColumnName	Target column name: This is prediction values column. Also known as label column name in context of classification tasks.	string
trainingData	[Required] Training data input.	MLTableJobInput (required)
taskType	Set the object type	Classification Forecasting ImageClassification ImageClassificationMultilabel ImageInstanceSegmentation ImageObjectDetection Regression TextClassification TextClassificationMultilabel TextNER (required)

MLTableJobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	[Required] Specifies the type of job.	"custom_model" "literal" "mlflow_model" "mltable" "triton_model" "uri_file" "uri_folder" (required)
mode	Input Asset Delivery Mode.	"Direct" "Download" "EvalDownload" "EvalMount" "ReadOnlyMount" "ReadWriteMount"
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

Classification

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	"Classification" (required)
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	TableFixedParameters
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
positiveLabel	Positive label for binary metrics calculation.	string
primaryMetric	Primary metric for the task.	"AUCWeighted" "Accuracy" "AveragePrecisionScoreWeighted" "NormMacroRecall" "PrecisionScoreWeighted"
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	TableParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	TableSweepSettings
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	ClassificationTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

TableVerticalFeaturizationSettings

Name	Description	Value
blockedTransformers	These transformers shall not be used in featurization.	String array containing any of: "CatTargetEncoder" "CountVectorizer" "HashOneHotEncoder" "LabelEncoder" "NaiveBayes" "OneHotEncoder" "TextTargetEncoder" "TfIdf" "WoETargetEncoder" "WordEmbedding"
columnNameAndTypes	Dictionary of column name and its type (int, float, string, datetime etc).	TableVerticalFeaturizationSettingsColumnNameAndTypes
datasetLanguage	Dataset language, useful for the text data.	string
enableDnnFeaturization	Determines whether to use Dnn based featurizers for data featurization.	bool
mode	Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done.	"Auto" "Custom" "Off"
transformerParams	User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor.	TableVerticalFeaturizationSettingsTransformerParams

TableVerticalFeaturizationSettingsColumnNameAndTypes

Name	Description	Value
{customized property}		string

TableVerticalFeaturizationSettingsTransformerParams

Name	Description	Value
{customized property}		ColumnTransformer[]

ColumnTransformer

Name	Description	Value
fields	Fields to apply transformer logic on.	string[]
parameters	Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format.

TableFixedParameters

Name	Description	Value
booster	Specify the boosting type, e.g gbdt for XGBoost.	string
boostingType	Specify the boosting type, e.g gbdt for LightGBM.	string
growPolicy	Specify the grow policy, which controls the way new nodes are added to the tree.	string
learningRate	The learning rate for the training procedure.	int
maxBin	Specify the Maximum number of discrete bins to bucket continuous features .	int
maxDepth	Specify the max depth to limit the tree depth explicitly.	int
maxLeaves	Specify the max leaves to limit the tree leaves explicitly.	int
minDataInLeaf	The minimum number of data per leaf.	int
minSplitGain	Minimum loss reduction required to make a further partition on a leaf node of the tree.	int
modelName	The name of the model to train.	string
nEstimators	Specify the number of trees (or rounds) in an model.	int
numLeaves	Specify the number of leaves.	int
preprocessorName	The name of the preprocessor to use.	string
regAlpha	L1 regularization term on weights.	int
regLambda	L2 regularization term on weights.	int
subsample	Subsample ratio of the training instance.	int
subsampleFreq	Frequency of subsample.	int
treeMethod	Specify the tree method.	string
withMean	If true, center before scaling the data with StandardScalar.	bool
withStd	If true, scaling the data with Unit Variance with StandardScalar.	bool

TableVerticalLimitSettings

Name	Description	Value
enableEarlyTermination	Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations.	bool
exitScore	Exit score for the AutoML job.	int
maxConcurrentTrials	Maximum Concurrent iterations.	int
maxCoresPerTrial	Max cores per iteration.	int
maxNodes	Maximum nodes to use for the experiment.	int
maxTrials	Number of iterations.	int
sweepConcurrentTrials	Number of concurrent sweeping runs that user wants to trigger.	int
sweepTrials	Number of sweeping runs that user wants to trigger.	int
timeout	AutoML job timeout.	string
trialTimeout	Iteration timeout.	string

NCrossValidations

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoNCrossValidations

Name	Description	Value
mode	[Required] Mode for determining N-Cross validations.	"Auto" (required)

CustomNCrossValidations

Name	Description	Value
mode	[Required] Mode for determining N-Cross validations.	"Custom" (required)
value	[Required] N-Cross validations value.	int (required)

TableParameterSubspace

Name	Description	Value
booster	Specify the boosting type, e.g gbdt for XGBoost.	string
boostingType	Specify the boosting type, e.g gbdt for LightGBM.	string
growPolicy	Specify the grow policy, which controls the way new nodes are added to the tree.	string
learningRate	The learning rate for the training procedure.	string
maxBin	Specify the Maximum number of discrete bins to bucket continuous features .	string
maxDepth	Specify the max depth to limit the tree depth explicitly.	string
maxLeaves	Specify the max leaves to limit the tree leaves explicitly.	string
minDataInLeaf	The minimum number of data per leaf.	string
minSplitGain	Minimum loss reduction required to make a further partition on a leaf node of the tree.	string
modelName	The name of the model to train.	string
nEstimators	Specify the number of trees (or rounds) in an model.	string
numLeaves	Specify the number of leaves.	string
preprocessorName	The name of the preprocessor to use.	string
regAlpha	L1 regularization term on weights.	string
regLambda	L2 regularization term on weights.	string
subsample	Subsample ratio of the training instance.	string
subsampleFreq	Frequency of subsample	string
treeMethod	Specify the tree method.	string
withMean	If true, center before scaling the data with StandardScalar.	string
withStd	If true, scaling the data with Unit Variance with StandardScalar.	string

TableSweepSettings

Name	Description	Value
earlyTermination	Type of early termination policy for the sweeping job.	EarlyTerminationPolicy
samplingAlgorithm	[Required] Type of sampling algorithm.	"Bayesian" "Grid" "Random" (required)

EarlyTerminationPolicy

Name	Description	Value
delayEvaluation	Number of intervals by which to delay the first evaluation.	int
evaluationInterval	Interval (number of runs) between policy evaluations.	int
policyType	Set the object type	Bandit MedianStopping TruncationSelection (required)

BanditPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	"Bandit" (required)
slackAmount	Absolute distance allowed from the best performing run.	int
slackFactor	Ratio of the allowed distance from the best performing run.	int

MedianStoppingPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	"MedianStopping" (required)

TruncationSelectionPolicy

Name	Description	Value
policyType	[Required] Name of policy configuration	"TruncationSelection" (required)
truncationPercentage	The percentage of runs to cancel at each evaluation interval.	int

ClassificationTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for classification task.	String array containing any of: "BernoulliNaiveBayes" "DecisionTree" "ExtremeRandomTrees" "GradientBoosting" "KNN" "LightGBM" "LinearSVM" "LogisticRegression" "MultinomialNaiveBayes" "RandomForest" "SGD" "SVM" "XGBoostClassifier"
blockedTrainingAlgorithms	Blocked models for classification task.	String array containing any of: "BernoulliNaiveBayes" "DecisionTree" "ExtremeRandomTrees" "GradientBoosting" "KNN" "LightGBM" "LinearSVM" "LogisticRegression" "MultinomialNaiveBayes" "RandomForest" "SGD" "SVM" "XGBoostClassifier"
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings
trainingMode	TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen.	"Auto" "Distributed" "NonDistributed"

StackEnsembleSettings

Name	Description	Value
stackMetaLearnerKWargs	Optional parameters to pass to the initializer of the meta-learner.
stackMetaLearnerTrainPercentage	Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2.	int
stackMetaLearnerType	The meta-learner is a model trained on the output of the individual heterogeneous models.	"ElasticNet" "ElasticNetCV" "LightGBMClassifier" "LightGBMRegressor" "LinearRegression" "LogisticRegression" "LogisticRegressionCV" "None"

Forecasting

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	"Forecasting" (required)
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	TableFixedParameters
forecastingSettings	Forecasting task specific inputs.	ForecastingSettings
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
primaryMetric	Primary metric for forecasting task.	"NormalizedMeanAbsoluteError" "NormalizedRootMeanSquaredError" "R2Score" "SpearmanCorrelation"
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	TableParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	TableSweepSettings
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	ForecastingTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

ForecastingSettings

Name	Description	Value
countryOrRegionForHolidays	Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.	string
cvStepSize	Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will be three days apart.	int
featureLags	Flag for generating lags for the numeric features with 'auto' or null.	"Auto" "None"
forecastHorizon	The desired maximum forecast horizon in units of time-series frequency.	ForecastHorizon
frequency	When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.	string
seasonality	Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred.	Seasonality
shortSeriesHandlingConfig	The parameter defining how if AutoML should handle short time series.	"Auto" "Drop" "None" "Pad"
targetAggregateFunction	The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean".	"Max" "Mean" "Min" "None" "Sum"
targetLags	The number of past periods to lag from the target column.	TargetLags
targetRollingWindowSize	The number of past periods used to create a rolling window average of the target column.	TargetRollingWindowSize
timeColumnName	The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency.	string
timeSeriesIdColumnNames	The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting.	string[]
useStl	Configure STL Decomposition of the time-series target column.	"None" "Season" "SeasonTrend"

ForecastHorizon

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoForecastHorizon

Name	Description	Value
mode	[Required] Set forecast horizon value selection mode.	"Auto" (required)

CustomForecastHorizon

Name	Description	Value
mode	[Required] Set forecast horizon value selection mode.	"Custom" (required)
value	[Required] Forecast horizon value.	int (required)

Seasonality

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoSeasonality

Name	Description	Value
mode	[Required] Seasonality mode.	"Auto" (required)

CustomSeasonality

Name	Description	Value
mode	[Required] Seasonality mode.	"Custom" (required)
value	[Required] Seasonality value.	int (required)

TargetLags

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoTargetLags

Name	Description	Value
mode	[Required] Set target lags mode - Auto/Custom	"Auto" (required)

CustomTargetLags

Name	Description	Value
mode	[Required] Set target lags mode - Auto/Custom	"Custom" (required)
values	[Required] Set target lags values.	int[] (required)

TargetRollingWindowSize

Name	Description	Value
mode	Set the object type	Auto Custom (required)

AutoTargetRollingWindowSize

Name	Description	Value
mode	[Required] TargetRollingWindowSiz detection mode.	"Auto" (required)

CustomTargetRollingWindowSize

Name	Description	Value
mode	[Required] TargetRollingWindowSiz detection mode.	"Custom" (required)
value	[Required] TargetRollingWindowSize value.	int (required)

ForecastingTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for forecasting task.	String array containing any of: "Arimax" "AutoArima" "Average" "DecisionTree" "ElasticNet" "ExponentialSmoothing" "ExtremeRandomTrees" "GradientBoosting" "KNN" "LassoLars" "LightGBM" "Naive" "Prophet" "RandomForest" "SGD" "SeasonalAverage" "SeasonalNaive" "TCNForecaster" "XGBoostRegressor"
blockedTrainingAlgorithms	Blocked models for forecasting task.	String array containing any of: "Arimax" "AutoArima" "Average" "DecisionTree" "ElasticNet" "ExponentialSmoothing" "ExtremeRandomTrees" "GradientBoosting" "KNN" "LassoLars" "LightGBM" "Naive" "Prophet" "RandomForest" "SGD" "SeasonalAverage" "SeasonalNaive" "TCNForecaster" "XGBoostRegressor"
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings
trainingMode	TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen.	"Auto" "Distributed" "NonDistributed"

ImageClassification

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	"ImageClassification" (required)
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsClassification
primaryMetric	Primary metric to optimize for this task.	"AUCWeighted" "Accuracy" "AveragePrecisionScoreWeighted" "NormMacroRecall" "PrecisionScoreWeighted"
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsClassification[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageLimitSettings

Name	Description	Value
maxConcurrentTrials	Maximum number of concurrent AutoML iterations.	int
maxTrials	Maximum number of AutoML iterations.	int
timeout	AutoML job timeout.	string

ImageModelSettingsClassification

Name	Description	Value
advancedSettings	Settings for advanced scenarios.	string
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	bool
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
checkpointFrequency	Frequency to store model checkpoints. Must be a positive integer.	int
checkpointModel	The pretrained checkpoint model for incremental training.	MLFlowModelJobInput
checkpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.	string
distributed	Whether to use distributed training.	bool
earlyStopping	Enable early stopping logic during training.	bool
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	int
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	int
enableOnnxNormalization	Enable normalization when exporting ONNX model.	bool
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	int
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	int
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	int
learningRate	Initial learning rate. Must be a float in the range [0, 1].	int
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	"None" "Step" "WarmupCosine"
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	int
nesterov	Enable nesterov when optimizer is 'sgd'.	bool
numberOfEpochs	Number of training epochs. Must be a positive integer.	int
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	int
optimizer	Type of optimizer.	"Adam" "Adamw" "None" "Sgd"
randomSeed	Random seed to be used when using deterministic training.	int
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	int
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	int
trainingBatchSize	Training batch size. Must be a positive integer.	int
trainingCropSize	Image crop size that is input to the neural network for the training dataset. Must be a positive integer.	int
validationBatchSize	Validation batch size. Must be a positive integer.	int
validationCropSize	Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.	int
validationResizeSize	Image size to which to resize before cropping for validation dataset. Must be a positive integer.	int
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	int
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	int
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	int
weightedLoss	Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.	int

MLFlowModelJobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	[Required] Specifies the type of job.	"custom_model" "literal" "mlflow_model" "mltable" "triton_model" "uri_file" "uri_folder" (required)
mode	Input Asset Delivery Mode.	"Direct" "Download" "EvalDownload" "EvalMount" "ReadOnlyMount" "ReadWriteMount"
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

ImageModelDistributionSettingsClassification

Name	Description	Value
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	string
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
distributed	Whether to use distributer training.	string
earlyStopping	Enable early stopping logic during training.	string
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	string
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	string
enableOnnxNormalization	Enable normalization when exporting ONNX model.	string
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	string
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	string
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	string
learningRate	Initial learning rate. Must be a float in the range [0, 1].	string
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	string
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	string
nesterov	Enable nesterov when optimizer is 'sgd'.	string
numberOfEpochs	Number of training epochs. Must be a positive integer.	string
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	string
optimizer	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.	string
randomSeed	Random seed to be used when using deterministic training.	string
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	string
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	string
trainingBatchSize	Training batch size. Must be a positive integer.	string
trainingCropSize	Image crop size that is input to the neural network for the training dataset. Must be a positive integer.	string
validationBatchSize	Validation batch size. Must be a positive integer.	string
validationCropSize	Image crop size that is input to the neural network for the validation dataset. Must be a positive integer.	string
validationResizeSize	Image size to which to resize before cropping for validation dataset. Must be a positive integer.	string
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	string
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	string
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	string
weightedLoss	Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2.	string

ImageSweepSettings

Name	Description	Value
earlyTermination	Type of early termination policy.	EarlyTerminationPolicy
samplingAlgorithm	[Required] Type of the hyperparameter sampling algorithms.	"Bayesian" "Grid" "Random" (required)

ImageClassificationMultilabel

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	"ImageClassificationMultilabel" (required)
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsClassification
primaryMetric	Primary metric to optimize for this task.	"AUCWeighted" "Accuracy" "AveragePrecisionScoreWeighted" "IOU" "NormMacroRecall" "PrecisionScoreWeighted"
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsClassification[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageInstanceSegmentation

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	"ImageInstanceSegmentation" (required)
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsObjectDetection
primaryMetric	Primary metric to optimize for this task.	"MeanAveragePrecision"
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsObjectDetection[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

ImageModelSettingsObjectDetection

Name	Description	Value
advancedSettings	Settings for advanced scenarios.	string
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	bool
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	int
boxDetectionsPerImage	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.	int
boxScoreThreshold	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].	int
checkpointFrequency	Frequency to store model checkpoints. Must be a positive integer.	int
checkpointModel	The pretrained checkpoint model for incremental training.	MLFlowModelJobInput
checkpointRunId	The id of a previous run that has a pretrained checkpoint for incremental training.	string
distributed	Whether to use distributed training.	bool
earlyStopping	Enable early stopping logic during training.	bool
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	int
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	int
enableOnnxNormalization	Enable normalization when exporting ONNX model.	bool
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	int
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	int
imageSize	Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	int
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	int
learningRate	Initial learning rate. Must be a float in the range [0, 1].	int
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	"None" "Step" "WarmupCosine"
maxSize	Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	int
minSize	Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	int
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
modelSize	Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	"ExtraLarge" "Large" "Medium" "None" "Small"
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	int
multiScale	Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.	bool
nesterov	Enable nesterov when optimizer is 'sgd'.	bool
nmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1].	int
numberOfEpochs	Number of training epochs. Must be a positive integer.	int
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	int
optimizer	Type of optimizer.	"Adam" "Adamw" "None" "Sgd"
randomSeed	Random seed to be used when using deterministic training.	int
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	int
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	int
tileGridSize	The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.	string
tileOverlapRatio	Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.	int
tilePredictionsNmsThreshold	The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm.	int
trainingBatchSize	Training batch size. Must be a positive integer.	int
validationBatchSize	Validation batch size. Must be a positive integer.	int
validationIouThreshold	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].	int
validationMetricType	Metric computation method to use for validation metrics.	"Coco" "CocoVoc" "None" "Voc"
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	int
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	int
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	int

ImageModelDistributionSettingsObjectDetection

Name	Description	Value
amsGradient	Enable AMSGrad when optimizer is 'adam' or 'adamw'.	string
augmentations	Settings for using Augmentations.	string
beta1	Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
beta2	Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1].	string
boxDetectionsPerImage	Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.	string
boxScoreThreshold	During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1].	string
distributed	Whether to use distributer training.	string
earlyStopping	Enable early stopping logic during training.	string
earlyStoppingDelay	Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer.	string
earlyStoppingPatience	Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer.	string
enableOnnxNormalization	Enable normalization when exporting ONNX model.	string
evaluationFrequency	Frequency to evaluate validation dataset to get metric scores. Must be a positive integer.	string
gradientAccumulationStep	Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer.	string
imageSize	Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	string
layersToFreeze	Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models.	string
learningRate	Initial learning rate. Must be a float in the range [0, 1].	string
learningRateScheduler	Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'.	string
maxSize	Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	string
minSize	Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm.	string
modelName	Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models.	string
modelSize	Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm.	string
momentum	Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].	string
multiScale	Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm.	string
nesterov	Enable nesterov when optimizer is 'sgd'.	string
nmsIouThreshold	IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1].	string
numberOfEpochs	Number of training epochs. Must be a positive integer.	string
numberOfWorkers	Number of data loader workers. Must be a non-negative integer.	string
optimizer	Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.	string
randomSeed	Random seed to be used when using deterministic training.	string
stepLRGamma	Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1].	string
stepLRStepSize	Value of step size when learning rate scheduler is 'step'. Must be a positive integer.	string
tileGridSize	The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm.	string
tileOverlapRatio	Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm.	string
tilePredictionsNmsThreshold	The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression	string
trainingBatchSize	Training batch size. Must be a positive integer.	string
validationBatchSize	Validation batch size. Must be a positive integer.	string
validationIouThreshold	IOU threshold to use when computing validation metric. Must be float in the range [0, 1].	string
validationMetricType	Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'.	string
warmupCosineLRCycles	Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1].	string
warmupCosineLRWarmupEpochs	Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer.	string
weightDecay	Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1].	string

ImageObjectDetection

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	"ImageObjectDetection" (required)
limitSettings	[Required] Limit settings for the AutoML job.	ImageLimitSettings (required)
modelSettings	Settings used for training the model.	ImageModelSettingsObjectDetection
primaryMetric	Primary metric to optimize for this task.	"MeanAveragePrecision"
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	ImageModelDistributionSettingsObjectDetection[]
sweepSettings	Model sweeping and hyperparameter sweeping related settings.	ImageSweepSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int

Regression

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	"Regression" (required)
cvSplitColumnNames	Columns to use for CVSplit data.	string[]
featurizationSettings	Featurization inputs needed for AutoML job.	TableVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	TableFixedParameters
limitSettings	Execution constraints for AutoMLJob.	TableVerticalLimitSettings
nCrossValidations	Number of cross validation folds to be applied on training dataset when validation dataset is not provided.	NCrossValidations
primaryMetric	Primary metric for regression task.	"NormalizedMeanAbsoluteError" "NormalizedRootMeanSquaredError" "R2Score" "SpearmanCorrelation"
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	TableParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	TableSweepSettings
testData	Test data input.	MLTableJobInput
testDataSize	The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
trainingSettings	Inputs for training phase for an AutoML Job.	RegressionTrainingSettings
validationData	Validation data inputs.	MLTableJobInput
validationDataSize	The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided.	int
weightColumnName	The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down.	string

RegressionTrainingSettings

Name	Description	Value
allowedTrainingAlgorithms	Allowed models for regression task.	String array containing any of: "DecisionTree" "ElasticNet" "ExtremeRandomTrees" "GradientBoosting" "KNN" "LassoLars" "LightGBM" "RandomForest" "SGD" "XGBoostRegressor"
blockedTrainingAlgorithms	Blocked models for regression task.	String array containing any of: "DecisionTree" "ElasticNet" "ExtremeRandomTrees" "GradientBoosting" "KNN" "LassoLars" "LightGBM" "RandomForest" "SGD" "XGBoostRegressor"
enableDnnTraining	Enable recommendation of DNN models.	bool
enableModelExplainability	Flag to turn on explainability on best model.	bool
enableOnnxCompatibleModels	Flag for enabling onnx compatible models.	bool
enableStackEnsemble	Enable stack ensemble run.	bool
enableVoteEnsemble	Enable voting ensemble run.	bool
ensembleModelDownloadTimeout	During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed.	string
stackEnsembleSettings	Stack ensemble settings for stack ensemble run.	StackEnsembleSettings
trainingMode	TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen.	"Auto" "Distributed" "NonDistributed"

TextClassification

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	"TextClassification" (required)
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	NlpFixedParameters
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
primaryMetric	Primary metric for Text-Classification task.	"AUCWeighted" "Accuracy" "AveragePrecisionScoreWeighted" "NormMacroRecall" "PrecisionScoreWeighted"
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	NlpParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	NlpSweepSettings
validationData	Validation data inputs.	MLTableJobInput

NlpVerticalFeaturizationSettings

Name	Description	Value
datasetLanguage	Dataset language, useful for the text data.	string

NlpFixedParameters

Name	Description	Value
gradientAccumulationSteps	Number of steps to accumulate gradients over before running a backward pass.	int
learningRate	The learning rate for the training procedure.	int
learningRateScheduler	The type of learning rate schedule to use during the training procedure.	"Constant" "ConstantWithWarmup" "Cosine" "CosineWithRestarts" "Linear" "None" "Polynomial"
modelName	The name of the model to train.	string
numberOfEpochs	Number of training epochs.	int
trainingBatchSize	The batch size for the training procedure.	int
validationBatchSize	The batch size to be used during evaluation.	int
warmupRatio	The warmup ratio, used alongside LrSchedulerType.	int
weightDecay	The weight decay for the training procedure.	int

NlpVerticalLimitSettings

Name	Description	Value
maxConcurrentTrials	Maximum Concurrent AutoML iterations.	int
maxNodes	Maximum nodes to use for the experiment.	int
maxTrials	Number of AutoML iterations.	int
timeout	AutoML job timeout.	string
trialTimeout	Timeout for individual HD trials.	string

NlpParameterSubspace

Name	Description	Value
gradientAccumulationSteps	Number of steps to accumulate gradients over before running a backward pass.	string
learningRate	The learning rate for the training procedure.	string
learningRateScheduler	The type of learning rate schedule to use during the training procedure.	string
modelName	The name of the model to train.	string
numberOfEpochs	Number of training epochs.	string
trainingBatchSize	The batch size for the training procedure.	string
validationBatchSize	The batch size to be used during evaluation.	string
warmupRatio	The warmup ratio, used alongside LrSchedulerType.	string
weightDecay	The weight decay for the training procedure.	string

NlpSweepSettings

Name	Description	Value
earlyTermination	Type of early termination policy for the sweeping job.	EarlyTerminationPolicy
samplingAlgorithm	[Required] Type of sampling algorithm.	"Bayesian" "Grid" "Random" (required)

TextClassificationMultilabel

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	"TextClassificationMultilabel" (required)
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	NlpFixedParameters
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	NlpParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	NlpSweepSettings
validationData	Validation data inputs.	MLTableJobInput

TextNer

Name	Description	Value
taskType	[Required] Task type for AutoMLJob.	"TextNER" (required)
featurizationSettings	Featurization inputs needed for AutoML job.	NlpVerticalFeaturizationSettings
fixedParameters	Model/training parameters that will remain constant throughout training.	NlpFixedParameters
limitSettings	Execution constraints for AutoMLJob.	NlpVerticalLimitSettings
searchSpace	Search space for sampling different combinations of models and their hyperparameters.	NlpParameterSubspace[]
sweepSettings	Settings for model sweeping and hyperparameter tuning.	NlpSweepSettings
validationData	Validation data inputs.	MLTableJobInput

CommandJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	"Command" (required)
autologgerSettings	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	AutologgerSettings
codeId	ARM resource ID of the code asset.	string
command	[Required] The command to execute on startup of the job. eg. "python train.py"	string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_]
distribution	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	DistributionConfiguration
environmentId	[Required] The ARM resource ID of the Environment specification for the job.	string (required) Constraints: Pattern = [a-zA-Z0-9_]
environmentVariables	Environment variables included in the job.	CommandJobEnvironmentVariables
inputs	Mapping of input data bindings used in the job.	CommandJobInputs
limits	Command Job limit.	CommandJobLimits
outputs	Mapping of output data bindings used in the job.	CommandJobOutputs
resources	Compute Resource configuration for the job.	JobResourceConfiguration

AutologgerSettings

Name	Description	Value
mlflowAutologger	[Required] Indicates whether mlflow autologger is enabled.	"Disabled" "Enabled" (required)

DistributionConfiguration

Name	Description	Value
distributionType	Set the object type	Mpi PyTorch TensorFlow (required)

Mpi

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	"Mpi" (required)
processCountPerInstance	Number of processes per MPI node.	int

PyTorch

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	"PyTorch" (required)
processCountPerInstance	Number of processes per node.	int

TensorFlow

Name	Description	Value
distributionType	[Required] Specifies the type of distribution framework.	"TensorFlow" (required)
parameterServerCount	Number of parameter server tasks.	int
workerCount	Number of workers. If not specified, will default to the instance count.	int

CommandJobEnvironmentVariables

Name	Description	Value
{customized property}		string

CommandJobInputs

Name	Description	Value
{customized property}		JobInput

JobInput

Name	Description	Value
description	Description for the input.	string
jobInputType	Set the object type	custom_model literal mlflow_model mltable triton_model uri_file uri_folder (required)

CustomModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	"custom_model" (required)
mode	Input Asset Delivery Mode.	"Direct" "Download" "EvalDownload" "EvalMount" "ReadOnlyMount" "ReadWriteMount"
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

LiteralJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	"literal" (required)
value	[Required] Literal value for the input.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

TritonModelJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	"triton_model" (required)
mode	Input Asset Delivery Mode.	"Direct" "Download" "EvalDownload" "EvalMount" "ReadOnlyMount" "ReadWriteMount"
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

UriFileJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	"uri_file" (required)
mode	Input Asset Delivery Mode.	"Direct" "Download" "EvalDownload" "EvalMount" "ReadOnlyMount" "ReadWriteMount"
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

UriFolderJobInput

Name	Description	Value
jobInputType	[Required] Specifies the type of job.	"uri_folder" (required)
mode	Input Asset Delivery Mode.	"Direct" "Download" "EvalDownload" "EvalMount" "ReadOnlyMount" "ReadWriteMount"
uri	[Required] Input Asset URI.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

CommandJobLimits

Name	Description	Value
jobLimitsType	[Required] JobLimit type.	"Command" "Sweep" (required)
timeout	The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.	string

CommandJobOutputs

Name	Description	Value
{customized property}		JobOutput

LabelingJobProperties

Name	Description	Value
componentId	ARM resource ID of the component resource.	string
computeId	ARM resource ID of the compute resource.	string
dataConfiguration	Configuration of data used in the job.	LabelingDataConfiguration
description	The asset description text.	string
displayName	Display name of job.	string
experimentName	The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment.	string
identity	Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null.	IdentityConfiguration
isArchived	Is the asset archived?	bool
jobInstructions	Labeling instructions of the job.	LabelingJobInstructions
jobType	[Required] Specifies the type of job.	"AutoML" "Command" "Labeling" "Pipeline" "Spark" "Sweep" (required)
labelCategories	Label categories of the job.	LabelingJobLabelCategories
labelingJobMediaProperties	Media type specific properties in the job.	LabelingJobMediaProperties
mlAssistConfiguration	Configuration of MLAssist feature in the job.	MLAssistConfiguration
properties	The asset property dictionary.	ResourceBaseProperties
services	List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject.	JobBaseServices
tags	Tag dictionary. Tags can be added, removed, and updated.	object

LabelingDataConfiguration

Name	Description	Value
dataId	Resource Id of the data asset to perform labeling.	string
incrementalDataRefresh	Indicates whether to enable incremental data refresh.	"Disabled" "Enabled"

LabelingJobInstructions

Name	Description	Value
uri	The link to a page with detailed labeling instructions for labelers.	string

LabelingJobLabelCategories

Name	Description	Value
{customized property}		LabelCategory

LabelCategory

Name	Description	Value
classes	Dictionary of label classes in this category.	LabelCategoryClasses
displayName	Display name of the label category.	string
multiSelect	Indicates whether it is allowed to select multiple classes in this category.	"Disabled" "Enabled"

LabelCategoryClasses

Name	Description	Value
{customized property}		LabelClass

LabelClass

Name	Description	Value
displayName	Display name of the label class.	string
subclasses	Dictionary of subclasses of the label class.	LabelClassSubclasses

LabelClassSubclasses

Name	Description	Value
{customized property}		LabelClass

LabelingJobMediaProperties

Name	Description	Value
mediaType	Set the object type	Image Text (required)

LabelingJobImageProperties

Name	Description	Value
mediaType	[Required] Media type of the job.	"Image" (required)
annotationType	Annotation type of image labeling job.	"BoundingBox" "Classification" "InstanceSegmentation"

LabelingJobTextProperties

Name	Description	Value
mediaType	[Required] Media type of the job.	"Text" (required)
annotationType	Annotation type of text labeling job.	"Classification" "NamedEntityRecognition"

MLAssistConfiguration

Name	Description	Value
mlAssist	Set the object type	Disabled Enabled (required)

MLAssistConfigurationDisabled

Name	Description	Value
mlAssist	[Required] Indicates whether MLAssist feature is enabled.	"Disabled" (required)

MLAssistConfigurationEnabled

Name	Description	Value
mlAssist	[Required] Indicates whether MLAssist feature is enabled.	"Enabled" (required)
inferencingComputeBinding	[Required] AML compute binding used in inferencing.	string (required) Constraints: Pattern = [a-zA-Z0-9_]
trainingComputeBinding	[Required] AML compute binding used in training.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

PipelineJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	"Pipeline" (required)
inputs	Inputs for the pipeline job.	PipelineJobInputs
jobs	Jobs construct the Pipeline Job.	PipelineJobJobs
outputs	Outputs for the pipeline job	PipelineJobOutputs
settings	Pipeline settings, for things like ContinueRunOnStepFailure etc.
sourceJobId	ARM resource ID of source job.	string

PipelineJobInputs

Name	Description	Value
{customized property}		JobInput

PipelineJobJobs

Name	Description	Value
{customized property}

PipelineJobOutputs

Name	Description	Value
{customized property}		JobOutput

SparkJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	"Spark" (required)
archives	Archive files used in the job.	string[]
args	Arguments for the job.	string
codeId	[Required] ARM resource ID of the code asset.	string (required) Constraints: Pattern = [a-zA-Z0-9_]
conf	Spark configured properties.	SparkJobConf
entry	[Required] The entry to execute on startup of the job.	SparkJobEntry (required)
environmentId	The ARM resource ID of the Environment specification for the job.	string
files	Files used in the job.	string[]
inputs	Mapping of input data bindings used in the job.	SparkJobInputs
jars	Jar files used in the job.	string[]
outputs	Mapping of output data bindings used in the job.	SparkJobOutputs
pyFiles	Python files used in the job.	string[]
resources	Compute Resource configuration for the job.	SparkResourceConfiguration

SparkJobConf

Name	Description	Value
{customized property}		string

SparkJobEntry

Name	Description	Value
sparkJobEntryType	Set the object type	SparkJobPythonEntry SparkJobScalaEntry (required)

SparkJobPythonEntry

Name	Description	Value
sparkJobEntryType	[Required] Type of the job's entry point.	"SparkJobPythonEntry" (required)
file	[Required] Relative python file path for job entry point.	string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_]

SparkJobScalaEntry

Name	Description	Value
sparkJobEntryType	[Required] Type of the job's entry point.	"SparkJobScalaEntry" (required)
className	[Required] Scala class name used as entry point.	string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_]

SparkJobInputs

Name	Description	Value
{customized property}		JobInput

SparkJobOutputs

Name	Description	Value
{customized property}		JobOutput

SparkResourceConfiguration

Name	Description	Value
instanceType	Optional type of VM used as supported by the compute target.	string
runtimeVersion	Version of spark runtime used for the job.	string

SweepJob

Name	Description	Value
jobType	[Required] Specifies the type of job.	"Sweep" (required)
earlyTermination	Early termination policies enable canceling poor-performing runs before they complete	EarlyTerminationPolicy
inputs	Mapping of input data bindings used in the job.	SweepJobInputs
limits	Sweep Job limit.	SweepJobLimits
objective	[Required] Optimization objective.	Objective (required)
outputs	Mapping of output data bindings used in the job.	SweepJobOutputs
samplingAlgorithm	[Required] The hyperparameter sampling algorithm	SamplingAlgorithm (required)
searchSpace	[Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter
trial	[Required] Trial component definition.	TrialComponent (required)

SweepJobInputs

Name	Description	Value
{customized property}		JobInput

SweepJobLimits

Name	Description	Value
jobLimitsType	[Required] JobLimit type.	"Command" "Sweep" (required)
maxConcurrentTrials	Sweep Job max concurrent trials.	int
maxTotalTrials	Sweep Job max total trials.	int
timeout	The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds.	string
trialTimeout	Sweep Job Trial timeout value.	string

Objective

Name	Description	Value
goal	[Required] Defines supported metric goals for hyperparameter tuning	"Maximize" "Minimize" (required)
primaryMetric	[Required] Name of the metric to optimize.	string (required) Constraints: Pattern = [a-zA-Z0-9_]

SweepJobOutputs

Name	Description	Value
{customized property}		JobOutput

SamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	Set the object type	Bayesian Grid Random (required)

BayesianSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	"Bayesian" (required)

GridSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	"Grid" (required)

RandomSamplingAlgorithm

Name	Description	Value
samplingAlgorithmType	[Required] The algorithm used for generating hyperparameter values, along with configuration properties	"Random" (required)
logbase	An optional positive number or e in string format to be used as base for log based random sampling	string
rule	The specific type of random algorithm	"Random" "Sobol"
seed	An optional integer to use as the seed for random number generation	int

TrialComponent

Name	Description	Value
codeId	ARM resource ID of the code asset.	string
command	[Required] The command to execute on startup of the job. eg. "python train.py"	string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_]
distribution	Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null.	DistributionConfiguration
environmentId	[Required] The ARM resource ID of the Environment specification for the job.	string (required) Constraints: Pattern = [a-zA-Z0-9_]
environmentVariables	Environment variables included in the job.	TrialComponentEnvironmentVariables
resources	Compute Resource configuration for the job.	JobResourceConfiguration

TrialComponentEnvironmentVariables

Name	Description	Value
{customized property}		string