Microsoft.MachineLearningServices workspaces/schedules 2024-04-01-preview
Bicep resource definition
The workspaces/schedules 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/schedules resource, add the following Bicep to your template.
resource symbolicname 'Microsoft.MachineLearningServices/workspaces/schedules@2024-04-01-preview' = {
parent: resourceSymbolicName
name: 'string'
properties: {
action: {
actionType: 'string'
// For remaining properties, see ScheduleActionBase objects
}
description: 'string'
displayName: 'string'
isEnabled: bool
properties: {
{customized property}: 'string'
}
tags: {
{customized property}: 'string'
}
trigger: {
endTime: 'string'
startTime: 'string'
timeZone: 'string'
triggerType: 'string'
// For remaining properties, see TriggerBase objects
}
}
}
JobBaseProperties objects
Set the jobType property to specify the type of object.
For AutoML, use:
{
environmentId: 'string'
environmentVariables: {
{customized property}: 'string'
}
jobType: 'AutoML'
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
queueSettings: {
jobTier: 'string'
priority: int
}
resources: {
dockerArgs: 'string'
instanceCount: int
instanceType: 'string'
locations: [
'string'
]
maxInstanceCount: int
properties: {
{customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
}
shmSize: 'string'
}
taskDetails: {
logVerbosity: 'string'
targetColumnName: 'string'
trainingData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
taskType: 'string'
// For remaining properties, see AutoMLVertical objects
}
}
For Command, use:
{
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
}
}
jobType: 'Command'
limits: {
jobLimitsType: 'string'
timeout: 'string'
}
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
queueSettings: {
jobTier: 'string'
priority: int
}
resources: {
dockerArgs: 'string'
instanceCount: int
instanceType: 'string'
locations: [
'string'
]
maxInstanceCount: int
properties: {
{customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
}
shmSize: 'string'
}
}
For FineTuning, use:
{
fineTuningDetails: {
model: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
taskType: 'string'
trainingData: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
validationData: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
modelProvider: 'string'
// For remaining properties, see FineTuningVertical objects
}
jobType: 'FineTuning'
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
}
For Labeling, use:
{
dataConfiguration: {
dataId: 'string'
incrementalDataRefresh: 'string'
}
jobInstructions: {
uri: 'string'
}
jobType: 'Labeling'
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:
{
inputs: {
{customized property}: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
}
jobs: {
{customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
}
jobType: 'Pipeline'
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
settings: any(Azure.Bicep.Types.Concrete.AnyType)
sourceJobId: 'string'
}
For Spark, use:
{
archives: [
'string'
]
args: 'string'
codeId: 'string'
conf: {
{customized property}: 'string'
}
entry: {
sparkJobEntryType: 'string'
// For remaining properties, see SparkJobEntry objects
}
environmentId: 'string'
environmentVariables: {
{customized property}: 'string'
}
files: [
'string'
]
inputs: {
{customized property}: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
}
jars: [
'string'
]
jobType: 'Spark'
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
pyFiles: [
'string'
]
queueSettings: {
jobTier: 'string'
priority: int
}
resources: {
instanceType: 'string'
runtimeVersion: 'string'
}
}
For Sweep, use:
{
componentConfiguration: {
pipelineSettings: any(Azure.Bicep.Types.Concrete.AnyType)
}
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
}
}
jobType: 'Sweep'
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
}
}
queueSettings: {
jobTier: 'string'
priority: int
}
resources: {
dockerArgs: 'string'
instanceCount: int
instanceType: 'string'
locations: [
'string'
]
maxInstanceCount: int
properties: {
{customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
}
shmSize: 'string'
}
samplingAlgorithm: {
samplingAlgorithmType: 'string'
// For remaining properties, see SamplingAlgorithm objects
}
searchSpace: any(Azure.Bicep.Types.Concrete.AnyType)
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'
locations: [
'string'
]
maxInstanceCount: int
properties: {
{customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
}
shmSize: 'string'
}
}
}
MLAssistConfiguration objects
Set the mlAssist property to specify the type of object.
For Disabled, use:
{
mlAssist: 'Disabled'
}
For Enabled, use:
{
inferencingComputeBinding: 'string'
mlAssist: 'Enabled'
trainingComputeBinding: 'string'
}
AutoMLVertical objects
Set the taskType property to specify the type of object.
For Classification, use:
{
cvSplitColumnNames: [
'string'
]
featurizationSettings: {
blockedTransformers: [
'string'
]
columnNameAndTypes: {
{customized property}: 'string'
}
datasetLanguage: 'string'
enableDnnFeaturization: bool
mode: 'string'
transformerParams: {
{customized property}: [
{
fields: [
'string'
]
parameters: any(Azure.Bicep.Types.Concrete.AnyType)
}
]
}
}
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'
}
taskType: 'Classification'
testData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: '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(Azure.Bicep.Types.Concrete.AnyType)
stackMetaLearnerTrainPercentage: int
stackMetaLearnerType: 'string'
}
trainingMode: 'string'
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
validationDataSize: int
weightColumnName: 'string'
}
For Forecasting, use:
{
cvSplitColumnNames: [
'string'
]
featurizationSettings: {
blockedTransformers: [
'string'
]
columnNameAndTypes: {
{customized property}: 'string'
}
datasetLanguage: 'string'
enableDnnFeaturization: bool
mode: 'string'
transformerParams: {
{customized property}: [
{
fields: [
'string'
]
parameters: any(Azure.Bicep.Types.Concrete.AnyType)
}
]
}
}
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'
featuresUnknownAtForecastTime: [
'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'
}
taskType: 'Forecasting'
testData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: '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(Azure.Bicep.Types.Concrete.AnyType)
stackMetaLearnerTrainPercentage: int
stackMetaLearnerType: 'string'
}
trainingMode: 'string'
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
validationDataSize: int
weightColumnName: 'string'
}
For ImageClassification, use:
{
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'
pathOnCompute: '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'
}
taskType: 'ImageClassification'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
validationDataSize: int
}
For ImageClassificationMultilabel, use:
{
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'
pathOnCompute: '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'
}
taskType: 'ImageClassificationMultilabel'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
validationDataSize: int
}
For ImageInstanceSegmentation, use:
{
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'
pathOnCompute: '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'
logTrainingMetrics: 'string'
logValidationLoss: '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: 'string'
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'
}
taskType: 'ImageInstanceSegmentation'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
validationDataSize: int
}
For ImageObjectDetection, use:
{
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'
pathOnCompute: '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'
logTrainingMetrics: 'string'
logValidationLoss: '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: 'string'
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'
}
taskType: 'ImageObjectDetection'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
validationDataSize: int
}
For Regression, use:
{
cvSplitColumnNames: [
'string'
]
featurizationSettings: {
blockedTransformers: [
'string'
]
columnNameAndTypes: {
{customized property}: 'string'
}
datasetLanguage: 'string'
enableDnnFeaturization: bool
mode: 'string'
transformerParams: {
{customized property}: [
{
fields: [
'string'
]
parameters: any(Azure.Bicep.Types.Concrete.AnyType)
}
]
}
}
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'
}
taskType: 'Regression'
testData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: '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(Azure.Bicep.Types.Concrete.AnyType)
stackMetaLearnerTrainPercentage: int
stackMetaLearnerType: 'string'
}
trainingMode: 'string'
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
validationDataSize: int
weightColumnName: 'string'
}
For TextClassification, use:
{
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'
}
taskType: 'TextClassification'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
}
For TextClassificationMultilabel, use:
{
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'
}
taskType: 'TextClassificationMultilabel'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
}
For TextNER, use:
{
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'
}
taskType: 'TextNER'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
}
LabelingJobMediaProperties objects
Set the mediaType property to specify the type of object.
For Image, use:
{
annotationType: 'string'
mediaType: 'Image'
}
For Text, use:
{
annotationType: 'string'
mediaType: 'Text'
}
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 Ray, use:
{
address: 'string'
dashboardPort: int
distributionType: 'Ray'
headNodeAdditionalArgs: 'string'
includeDashboard: bool
port: int
workerNodeAdditionalArgs: 'string'
}
For TensorFlow, use:
{
distributionType: 'TensorFlow'
parameterServerCount: int
workerCount: int
}
NCrossValidations 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
]
}
MonitorComputeIdentityBase objects
Set the computeIdentityType property to specify the type of object.
For AmlToken, use:
{
computeIdentityType: 'AmlToken'
}
For ManagedIdentity, use:
{
computeIdentityType: 'ManagedIdentity'
identity: {
type: 'string'
userAssignedIdentities: {
{customized property}: {}
}
}
}
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
}
JobOutput objects
Set the jobOutputType property to specify the type of object.
For custom_model, use:
{
assetName: 'string'
assetVersion: 'string'
autoDeleteSetting: {
condition: 'string'
value: 'string'
}
jobOutputType: 'custom_model'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
For mlflow_model, use:
{
assetName: 'string'
assetVersion: 'string'
autoDeleteSetting: {
condition: 'string'
value: 'string'
}
jobOutputType: 'mlflow_model'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
For mltable, use:
{
assetName: 'string'
assetVersion: 'string'
autoDeleteSetting: {
condition: 'string'
value: 'string'
}
jobOutputType: 'mltable'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
For triton_model, use:
{
assetName: 'string'
assetVersion: 'string'
autoDeleteSetting: {
condition: 'string'
value: 'string'
}
jobOutputType: 'triton_model'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
For uri_file, use:
{
assetName: 'string'
assetVersion: 'string'
autoDeleteSetting: {
condition: 'string'
value: 'string'
}
jobOutputType: 'uri_file'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
For uri_folder, use:
{
assetName: 'string'
assetVersion: 'string'
autoDeleteSetting: {
condition: 'string'
value: 'string'
}
jobOutputType: 'uri_folder'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
MonitoringInputDataBase objects
Set the inputDataType property to specify the type of object.
For Fixed, use:
{
inputDataType: 'Fixed'
}
For Rolling, use:
{
inputDataType: 'Rolling'
preprocessingComponentId: 'string'
windowOffset: 'string'
windowSize: 'string'
}
For Static, use:
{
inputDataType: 'Static'
preprocessingComponentId: 'string'
windowEnd: 'string'
windowStart: 'string'
}
SparkJobEntry objects
Set the sparkJobEntryType property to specify the type of object.
For SparkJobPythonEntry, use:
{
file: 'string'
sparkJobEntryType: 'SparkJobPythonEntry'
}
For SparkJobScalaEntry, use:
{
className: 'string'
sparkJobEntryType: 'SparkJobScalaEntry'
}
Webhook objects
Set the webhookType property to specify the type of object.
For AzureDevOps, use:
{
webhookType: 'AzureDevOps'
}
IdentityConfiguration objects
Set the identityType property to specify the type of object.
For AMLToken, use:
{
identityType: 'AMLToken'
}
For Managed, use:
{
clientId: 'string'
identityType: 'Managed'
objectId: 'string'
resourceId: 'string'
}
For UserIdentity, use:
{
identityType: 'UserIdentity'
}
ScheduleActionBase objects
Set the actionType property to specify the type of object.
For CreateJob, use:
{
actionType: 'CreateJob'
jobDefinition: {
componentId: 'string'
computeId: 'string'
description: 'string'
displayName: 'string'
experimentName: 'string'
identity: {
identityType: 'string'
// For remaining properties, see IdentityConfiguration objects
}
isArchived: bool
notificationSetting: {
emailOn: [
'string'
]
emails: [
'string'
]
webhooks: {
{customized property}: {
eventType: 'string'
webhookType: 'string'
// For remaining properties, see Webhook objects
}
}
}
properties: {
{customized property}: 'string'
}
secretsConfiguration: {
{customized property}: {
uri: 'string'
workspaceSecretName: 'string'
}
}
services: {
{customized property}: {
endpoint: 'string'
jobServiceType: 'string'
nodes: {
nodesValueType: 'string'
// For remaining properties, see Nodes objects
}
port: int
properties: {
{customized property}: 'string'
}
}
}
tags: {
{customized property}: 'string'
}
jobType: 'string'
// For remaining properties, see JobBaseProperties objects
}
}
For CreateMonitor, use:
{
actionType: 'CreateMonitor'
monitorDefinition: {
alertNotificationSettings: {
emailNotificationSettings: {
emails: [
'string'
]
}
}
computeConfiguration: {
computeType: 'string'
// For remaining properties, see MonitorComputeConfigurationBase objects
}
monitoringTarget: {
deploymentId: 'string'
modelId: 'string'
taskType: 'string'
}
signals: {
{customized property}: {
notificationTypes: [
'string'
]
properties: {
{customized property}: 'string'
}
signalType: 'string'
// For remaining properties, see MonitoringSignalBase objects
}
}
}
}
For ImportData, use:
{
actionType: 'ImportData'
dataImportDefinition: {
assetName: 'string'
autoDeleteSetting: {
condition: 'string'
value: 'string'
}
dataType: 'string'
dataUri: 'string'
description: 'string'
intellectualProperty: {
protectionLevel: 'string'
publisher: 'string'
}
isAnonymous: bool
isArchived: bool
properties: {
{customized property}: 'string'
}
source: {
connection: 'string'
sourceType: 'string'
// For remaining properties, see DataImportSource objects
}
stage: 'string'
tags: {
{customized property}: 'string'
}
}
}
For InvokeBatchEndpoint, use:
{
actionType: 'InvokeBatchEndpoint'
endpointInvocationDefinition: any(Azure.Bicep.Types.Concrete.AnyType)
}
ModelPerformanceMetricThresholdBase objects
Set the modelType property to specify the type of object.
For Classification, use:
{
metric: 'string'
modelType: 'Classification'
}
For Regression, use:
{
metric: 'string'
modelType: 'Regression'
}
DataQualityMetricThresholdBase objects
Set the dataType property to specify the type of object.
For Categorical, use:
{
dataType: 'Categorical'
metric: 'string'
}
For Numerical, use:
{
dataType: 'Numerical'
metric: '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:
{
logbase: 'string'
rule: 'string'
samplingAlgorithmType: 'Random'
seed: int
}
FineTuningVertical objects
Set the modelProvider property to specify the type of object.
For AzureOpenAI, use:
{
hyperParameters: {
batchSize: int
learningRateMultiplier: int
nEpochs: int
}
modelProvider: 'AzureOpenAI'
}
For Custom, use:
{
hyperParameters: {
{customized property}: 'string'
}
modelProvider: 'Custom'
}
MonitoringSignalBase objects
Set the signalType property to specify the type of object.
For Custom, use:
{
componentId: 'string'
inputAssets: {
{customized property}: {
columns: {
{customized property}: 'string'
}
dataContext: 'string'
jobInputType: 'string'
uri: 'string'
inputDataType: 'string'
// For remaining properties, see MonitoringInputDataBase objects
}
}
inputs: {
{customized property}: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
}
metricThresholds: [
{
metric: 'string'
threshold: {
value: int
}
}
]
signalType: 'Custom'
workspaceConnection: {
environmentVariables: {
{customized property}: 'string'
}
secrets: {
{customized property}: 'string'
}
}
}
For DataDrift, use:
{
dataSegment: {
feature: 'string'
values: [
'string'
]
}
featureDataTypeOverride: {
{customized property}: 'string'
}
featureImportanceSettings: {
mode: 'string'
targetColumn: 'string'
}
features: {
filterType: 'string'
// For remaining properties, see MonitoringFeatureFilterBase objects
}
metricThresholds: [
{
threshold: {
value: int
}
dataType: 'string'
// For remaining properties, see DataDriftMetricThresholdBase objects
}
]
productionData: {
columns: {
{customized property}: 'string'
}
dataContext: 'string'
jobInputType: 'string'
uri: 'string'
inputDataType: 'string'
// For remaining properties, see MonitoringInputDataBase objects
}
referenceData: {
columns: {
{customized property}: 'string'
}
dataContext: 'string'
jobInputType: 'string'
uri: 'string'
inputDataType: 'string'
// For remaining properties, see MonitoringInputDataBase objects
}
signalType: 'DataDrift'
}
For DataQuality, use:
{
featureDataTypeOverride: {
{customized property}: 'string'
}
featureImportanceSettings: {
mode: 'string'
targetColumn: 'string'
}
features: {
filterType: 'string'
// For remaining properties, see MonitoringFeatureFilterBase objects
}
metricThresholds: [
{
threshold: {
value: int
}
dataType: 'string'
// For remaining properties, see DataQualityMetricThresholdBase objects
}
]
productionData: {
columns: {
{customized property}: 'string'
}
dataContext: 'string'
jobInputType: 'string'
uri: 'string'
inputDataType: 'string'
// For remaining properties, see MonitoringInputDataBase objects
}
referenceData: {
columns: {
{customized property}: 'string'
}
dataContext: 'string'
jobInputType: 'string'
uri: 'string'
inputDataType: 'string'
// For remaining properties, see MonitoringInputDataBase objects
}
signalType: 'DataQuality'
}
For FeatureAttributionDrift, use:
{
featureDataTypeOverride: {
{customized property}: 'string'
}
featureImportanceSettings: {
mode: 'string'
targetColumn: 'string'
}
metricThreshold: {
metric: 'string'
threshold: {
value: int
}
}
productionData: [
{
columns: {
{customized property}: 'string'
}
dataContext: 'string'
jobInputType: 'string'
uri: 'string'
inputDataType: 'string'
// For remaining properties, see MonitoringInputDataBase objects
}
]
referenceData: {
columns: {
{customized property}: 'string'
}
dataContext: 'string'
jobInputType: 'string'
uri: 'string'
inputDataType: 'string'
// For remaining properties, see MonitoringInputDataBase objects
}
signalType: 'FeatureAttributionDrift'
}
For GenerationSafetyQuality, use:
{
metricThresholds: [
{
metric: 'string'
threshold: {
value: int
}
}
]
productionData: [
{
columns: {
{customized property}: 'string'
}
dataContext: 'string'
jobInputType: 'string'
uri: 'string'
inputDataType: 'string'
// For remaining properties, see MonitoringInputDataBase objects
}
]
samplingRate: int
signalType: 'GenerationSafetyQuality'
workspaceConnectionId: 'string'
}
For GenerationTokenStatistics, use:
{
metricThresholds: [
{
metric: 'string'
threshold: {
value: int
}
}
]
productionData: [
{
columns: {
{customized property}: 'string'
}
dataContext: 'string'
jobInputType: 'string'
uri: 'string'
inputDataType: 'string'
// For remaining properties, see MonitoringInputDataBase objects
}
]
samplingRate: int
signalType: 'GenerationTokenStatistics'
}
For ModelPerformance, use:
{
dataSegment: {
feature: 'string'
values: [
'string'
]
}
metricThreshold: {
threshold: {
value: int
}
modelType: 'string'
// For remaining properties, see ModelPerformanceMetricThresholdBase objects
}
productionData: [
{
columns: {
{customized property}: 'string'
}
dataContext: 'string'
jobInputType: 'string'
uri: 'string'
inputDataType: 'string'
// For remaining properties, see MonitoringInputDataBase objects
}
]
referenceData: {
columns: {
{customized property}: 'string'
}
dataContext: 'string'
jobInputType: 'string'
uri: 'string'
inputDataType: 'string'
// For remaining properties, see MonitoringInputDataBase objects
}
signalType: 'ModelPerformance'
}
For PredictionDrift, use:
{
featureDataTypeOverride: {
{customized property}: 'string'
}
metricThresholds: [
{
threshold: {
value: int
}
dataType: 'string'
// For remaining properties, see PredictionDriftMetricThresholdBase objects
}
]
productionData: {
columns: {
{customized property}: 'string'
}
dataContext: 'string'
jobInputType: 'string'
uri: 'string'
inputDataType: 'string'
// For remaining properties, see MonitoringInputDataBase objects
}
referenceData: {
columns: {
{customized property}: 'string'
}
dataContext: 'string'
jobInputType: 'string'
uri: 'string'
inputDataType: 'string'
// For remaining properties, see MonitoringInputDataBase objects
}
signalType: 'PredictionDrift'
}
DataDriftMetricThresholdBase objects
Set the dataType property to specify the type of object.
For Categorical, use:
{
dataType: 'Categorical'
metric: 'string'
}
For Numerical, use:
{
dataType: 'Numerical'
metric: 'string'
}
JobInput objects
Set the jobInputType property to specify the type of object.
For custom_model, use:
{
jobInputType: 'custom_model'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
For literal, use:
{
jobInputType: 'literal'
value: 'string'
}
For mlflow_model, use:
{
jobInputType: 'mlflow_model'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
For mltable, use:
{
jobInputType: 'mltable'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
For triton_model, use:
{
jobInputType: 'triton_model'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
For uri_file, use:
{
jobInputType: 'uri_file'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
For uri_folder, use:
{
jobInputType: 'uri_folder'
mode: 'string'
pathOnCompute: 'string'
uri: 'string'
}
Seasonality objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode: 'Auto'
}
For Custom, use:
{
mode: 'Custom'
value: 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
}
MonitorComputeConfigurationBase objects
Set the computeType property to specify the type of object.
For ServerlessSpark, use:
{
computeIdentity: {
computeIdentityType: 'string'
// For remaining properties, see MonitorComputeIdentityBase objects
}
computeType: 'ServerlessSpark'
instanceType: 'string'
runtimeVersion: 'string'
}
DataImportSource objects
Set the sourceType property to specify the type of object.
For database, use:
{
query: 'string'
sourceType: 'database'
storedProcedure: 'string'
storedProcedureParams: [
{
{customized property}: 'string'
}
]
tableName: 'string'
}
For file_system, use:
{
path: 'string'
sourceType: 'file_system'
}
MonitoringFeatureFilterBase objects
Set the filterType property to specify the type of object.
For AllFeatures, use:
{
filterType: 'AllFeatures'
}
For FeatureSubset, use:
{
features: [
'string'
]
filterType: 'FeatureSubset'
}
For TopNByAttribution, use:
{
filterType: 'TopNByAttribution'
top: int
}
PredictionDriftMetricThresholdBase objects
Set the dataType property to specify the type of object.
For Categorical, use:
{
dataType: 'Categorical'
metric: 'string'
}
For Numerical, use:
{
dataType: 'Numerical'
metric: 'string'
}
TriggerBase objects
Set the triggerType property to specify the type of object.
For Cron, use:
{
expression: 'string'
triggerType: 'Cron'
}
For Recurrence, use:
{
frequency: 'string'
interval: int
schedule: {
hours: [
int
]
minutes: [
int
]
monthDays: [
int
]
weekDays: [
'string'
]
}
triggerType: 'Recurrence'
}
ForecastHorizon objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode: 'Auto'
}
For Custom, use:
{
mode: 'Custom'
value: int
}
Nodes objects
Set the nodesValueType property to specify the type of object.
For All, use:
{
nodesValueType: 'All'
}
Property values
AllFeatures
| Name | Description | Value |
|---|---|---|
| filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | 'AllFeatures' (required) |
AllNodes
| Name | Description | Value |
|---|---|---|
| nodesValueType | [Required] Type of the Nodes value | 'All' (required) |
AmlToken
| Name | Description | Value |
|---|---|---|
| identityType | [Required] Specifies the type of identity framework. | 'AMLToken' (required) |
AmlTokenComputeIdentity
| Name | Description | Value |
|---|---|---|
| computeIdentityType | [Required] Monitor compute identity type enum. | 'AmlToken' (required) |
AutoDeleteSetting
| Name | Description | Value |
|---|---|---|
| condition | When to check if an asset is expired | 'CreatedGreaterThan' 'LastAccessedGreaterThan' |
| value | Expiration condition value. | string |
AutoForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set forecast horizon value selection mode. | 'Auto' (required) |
AutologgerSettings
| Name | Description | Value |
|---|---|---|
| mlflowAutologger | [Required] Indicates whether mlflow autologger is enabled. | 'Disabled' 'Enabled' (required) |
AutoMLJob
| Name | Description | Value |
|---|---|---|
| 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 |
| jobType | [Required] Specifies the type of job. | 'AutoML' (required) |
| outputs | Mapping of output data bindings used in the job. | AutoMLJobOutputs |
| queueSettings | Queue settings for the job | QueueSettings |
| 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 |
|---|
AutoMLJobOutputs
| Name | Description | Value |
|---|
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 |
| taskType | Set to 'Classification' for type Classification. Set to 'Forecasting' for type Forecasting. Set to 'ImageClassification' for type ImageClassification. Set to 'ImageClassificationMultilabel' for type ImageClassificationMultilabel. Set to 'ImageInstanceSegmentation' for type ImageInstanceSegmentation. Set to 'ImageObjectDetection' for type ImageObjectDetection. Set to 'Regression' for type Regression. Set to 'TextClassification' for type TextClassification. Set to 'TextClassificationMultilabel' for type TextClassificationMultilabel. Set to 'TextNER' for type TextNer. | 'Classification' 'Forecasting' 'ImageClassification' 'ImageClassificationMultilabel' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'Regression' 'TextClassification' 'TextClassificationMultilabel' 'TextNER' (required) |
| trainingData | [Required] Training data input. | MLTableJobInput (required) |
AutoNCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | [Required] Mode for determining N-Cross validations. | 'Auto' (required) |
AutoSeasonality
| Name | Description | Value |
|---|---|---|
| mode | [Required] Seasonality mode. | 'Auto' (required) |
AutoTargetLags
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set target lags mode - Auto/Custom | 'Auto' (required) |
AutoTargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | [Required] TargetRollingWindowSiz detection mode. | 'Auto' (required) |
AzureDevOpsWebhook
| Name | Description | Value |
|---|---|---|
| webhookType | [Required] Specifies the type of service to send a callback | 'AzureDevOps' (required) |
AzureOpenAiFineTuning
| Name | Description | Value |
|---|---|---|
| hyperParameters | HyperParameters for fine tuning Azure Open AI model. | AzureOpenAiHyperParameters |
| modelProvider | [Required] Enum to determine the type of fine tuning. | 'AzureOpenAI' (required) |
AzureOpenAiHyperParameters
| Name | Description | Value |
|---|---|---|
| batchSize | Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. | int |
| learningRateMultiplier | Scaling factor for the learning rate. A smaller learning rate may be useful to avoid over fitting. | int |
| nEpochs | The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. | int |
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 |
BayesianSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Bayesian' (required) |
CategoricalDataDriftMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Categorical' (required) |
| metric | [Required] The categorical data drift metric to calculate. | 'JensenShannonDistance' 'PearsonsChiSquaredTest' 'PopulationStabilityIndex' (required) |
CategoricalDataQualityMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Categorical' (required) |
| metric | [Required] The categorical data quality metric to calculate. | 'DataTypeErrorRate' 'NullValueRate' 'OutOfBoundsRate' (required) |
CategoricalPredictionDriftMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Categorical' (required) |
| metric | [Required] The categorical prediction drift metric to calculate. | 'JensenShannonDistance' 'PearsonsChiSquaredTest' 'PopulationStabilityIndex' (required) |
Classification
| Name | Description | Value |
|---|---|---|
| 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. | 'Accuracy' 'AUCWeighted' '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 |
| taskType | [Required] Task type for AutoMLJob. | 'Classification' (required) |
| 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 |
ClassificationModelPerformanceMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] The classification model performance to calculate. | 'Accuracy' 'Precision' 'Recall' (required) |
| modelType | [Required] Specifies the data type of the metric threshold. | 'Classification' (required) |
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' |
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. |
any |
CommandJob
| Name | Description | Value |
|---|---|---|
| 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 Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, Ray, or null. | DistributionConfiguration |
| environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| environmentVariables | Environment variables included in the job. | CommandJobEnvironmentVariables |
| inputs | Mapping of input data bindings used in the job. | CommandJobInputs |
| jobType | [Required] Specifies the type of job. | 'Command' (required) |
| limits | Command Job limit. | CommandJobLimits |
| outputs | Mapping of output data bindings used in the job. | CommandJobOutputs |
| queueSettings | Queue settings for the job | QueueSettings |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
CommandJobEnvironmentVariables
| Name | Description | Value |
|---|
CommandJobInputs
| Name | Description | Value |
|---|
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 |
|---|
ComponentConfiguration
| Name | Description | Value |
|---|---|---|
| pipelineSettings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | any |
CreateMonitorAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'CreateMonitor' (required) |
| monitorDefinition | [Required] Defines the monitor. | MonitorDefinition (required) |
CronTrigger
| Name | Description | Value |
|---|---|---|
| expression | [Required] Specifies cron expression of schedule. The expression should follow NCronTab format. |
string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| triggerType | [Required] | 'Cron' (required) |
CustomForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set forecast horizon value selection mode. | 'Custom' (required) |
| value | [Required] Forecast horizon value. | int (required) |
CustomMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] The user-defined metric to calculate. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
CustomModelFineTuning
| Name | Description | Value |
|---|---|---|
| hyperParameters | HyperParameters for fine tuning custom model. | CustomModelFineTuningHyperParameters |
| modelProvider | [Required] Enum to determine the type of fine tuning. | 'Custom' (required) |
CustomModelFineTuningHyperParameters
| Name | Description | Value |
|---|
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
CustomModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'custom_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
CustomMonitoringSignal
| Name | Description | Value |
|---|---|---|
| componentId | [Required] ARM resource ID of the component resource used to calculate the custom metrics. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| inputAssets | Monitoring assets to take as input. Key is the component input port name, value is the data asset. | CustomMonitoringSignalInputAssets |
| inputs | Extra component parameters to take as input. Key is the component literal input port name, value is the parameter value. | CustomMonitoringSignalInputs |
| metricThresholds | [Required] A list of metrics to calculate and their associated thresholds. | CustomMetricThreshold[] (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'Custom' (required) |
| workspaceConnection | A list of metrics to calculate and their associated thresholds. | MonitoringWorkspaceConnection |
CustomMonitoringSignalInputAssets
| Name | Description | Value |
|---|
CustomMonitoringSignalInputs
| Name | Description | Value |
|---|
CustomNCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | [Required] Mode for determining N-Cross validations. | 'Custom' (required) |
| value | [Required] N-Cross validations value. | int (required) |
CustomSeasonality
| Name | Description | Value |
|---|---|---|
| mode | [Required] Seasonality mode. | 'Custom' (required) |
| value | [Required] Seasonality value. | int (required) |
CustomTargetLags
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set target lags mode - Auto/Custom | 'Custom' (required) |
| values | [Required] Set target lags values. | int[] (required) |
CustomTargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | [Required] TargetRollingWindowSiz detection mode. | 'Custom' (required) |
| value | [Required] TargetRollingWindowSize value. | int (required) |
DatabaseSource
| Name | Description | Value |
|---|---|---|
| query | SQL Query statement for data import Database source | string |
| sourceType | [Required] Specifies the type of data. | 'database' (required) |
| storedProcedure | SQL StoredProcedure on data import Database source | string |
| storedProcedureParams | SQL StoredProcedure parameters | DatabaseSourceStoredProcedureParamsItem[] |
| tableName | Name of the table on data import Database source | string |
DatabaseSourceStoredProcedureParamsItem
| Name | Description | Value |
|---|
DataDriftMetricThresholdBase
| Name | Description | Value |
|---|---|---|
| dataType | Set to 'Categorical' for type CategoricalDataDriftMetricThreshold. Set to 'Numerical' for type NumericalDataDriftMetricThreshold. | 'Categorical' 'Numerical' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
DataDriftMonitoringSignal
| Name | Description | Value |
|---|---|---|
| dataSegment | The data segment used for scoping on a subset of the data population. | MonitoringDataSegment |
| featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | DataDriftMonitoringSignalFeatureDataTypeOverride |
| featureImportanceSettings | The settings for computing feature importance. | FeatureImportanceSettings |
| features | The feature filter which identifies which feature to calculate drift over. | MonitoringFeatureFilterBase |
| metricThresholds | [Required] A list of metrics to calculate and their associated thresholds. | DataDriftMetricThresholdBase[] (required) |
| productionData | [Required] The data which drift will be calculated for. | MonitoringInputDataBase (required) |
| referenceData | [Required] The data to calculate drift against. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'DataDrift' (required) |
DataDriftMonitoringSignalFeatureDataTypeOverride
| Name | Description | Value |
|---|
DataImport
| Name | Description | Value |
|---|---|---|
| assetName | Name of the asset for data import job to create | string |
| autoDeleteSetting | Specifies the lifecycle setting of managed data asset. | AutoDeleteSetting |
| dataType | [Required] Specifies the type of data. | 'mltable' 'uri_file' 'uri_folder' (required) |
| dataUri | [Required] Uri of the data. Example: https://go.microsoft.com/fwlink/?linkid=2202330 | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| description | The asset description text. | string |
| intellectualProperty | Intellectual Property details. Used if data is an Intellectual Property. | IntellectualProperty |
| isAnonymous | If the name version are system generated (anonymous registration). For types where Stage is defined, when Stage is provided it will be used to populate IsAnonymous | bool |
| isArchived | Is the asset archived? For types where Stage is defined, when Stage is provided it will be used to populate IsArchived | bool |
| properties | The asset property dictionary. | ResourceBaseProperties |
| source | Source data of the asset to import from | DataImportSource |
| stage | Stage in the data lifecycle assigned to this data asset | string |
| tags | Tag dictionary. Tags can be added, removed, and updated. | ResourceBaseTags |
DataImportSource
| Name | Description | Value |
|---|---|---|
| connection | Workspace connection for data import source storage | string |
| sourceType | Set to 'database' for type DatabaseSource. Set to 'file_system' for type FileSystemSource. | 'database' 'file_system' (required) |
DataQualityMetricThresholdBase
| Name | Description | Value |
|---|---|---|
| dataType | Set to 'Categorical' for type CategoricalDataQualityMetricThreshold. Set to 'Numerical' for type NumericalDataQualityMetricThreshold. | 'Categorical' 'Numerical' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
DataQualityMonitoringSignal
| Name | Description | Value |
|---|---|---|
| featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | DataQualityMonitoringSignalFeatureDataTypeOverride |
| featureImportanceSettings | The settings for computing feature importance. | FeatureImportanceSettings |
| features | The features to calculate drift over. | MonitoringFeatureFilterBase |
| metricThresholds | [Required] A list of metrics to calculate and their associated thresholds. | DataQualityMetricThresholdBase[] (required) |
| productionData | [Required] The data produced by the production service which drift will be calculated for. | MonitoringInputDataBase (required) |
| referenceData | [Required] The data to calculate drift against. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'DataQuality' (required) |
DataQualityMonitoringSignalFeatureDataTypeOverride
| Name | Description | Value |
|---|
DistributionConfiguration
| Name | Description | Value |
|---|---|---|
| distributionType | Set to 'Mpi' for type Mpi. Set to 'PyTorch' for type PyTorch. Set to 'Ray' for type Ray. Set to 'TensorFlow' for type TensorFlow. | 'Mpi' 'PyTorch' 'Ray' 'TensorFlow' (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 to 'Bandit' for type BanditPolicy. Set to 'MedianStopping' for type MedianStoppingPolicy. Set to 'TruncationSelection' for type TruncationSelectionPolicy. | 'Bandit' 'MedianStopping' 'TruncationSelection' (required) |
EndpointScheduleAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'InvokeBatchEndpoint' (required) |
| endpointInvocationDefinition | [Required] Defines Schedule action definition details. <see href="TBD" /> |
any (required) |
FeatureAttributionDriftMonitoringSignal
| Name | Description | Value |
|---|---|---|
| featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | FeatureAttributionDriftMonitoringSignalFeatureDataTypeOverride |
| featureImportanceSettings | [Required] The settings for computing feature importance. | FeatureImportanceSettings (required) |
| metricThreshold | [Required] A list of metrics to calculate and their associated thresholds. | FeatureAttributionMetricThreshold (required) |
| productionData | [Required] The data which drift will be calculated for. | MonitoringInputDataBase[] (required) |
| referenceData | [Required] The data to calculate drift against. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'FeatureAttributionDrift' (required) |
FeatureAttributionDriftMonitoringSignalFeatureDataTypeOverride
| Name | Description | Value |
|---|
FeatureAttributionMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] The feature attribution metric to calculate. | 'NormalizedDiscountedCumulativeGain' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
FeatureImportanceSettings
| Name | Description | Value |
|---|---|---|
| mode | The mode of operation for computing feature importance. | 'Disabled' 'Enabled' |
| targetColumn | The name of the target column within the input data asset. | string |
FeatureSubset
| Name | Description | Value |
|---|---|---|
| features | [Required] The list of features to include. | string[] (required) |
| filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | 'FeatureSubset' (required) |
FileSystemSource
| Name | Description | Value |
|---|---|---|
| path | Path on data import FileSystem source | string |
| sourceType | [Required] Specifies the type of data. | 'file_system' (required) |
FineTuningJob
| Name | Description | Value |
|---|---|---|
| fineTuningDetails | [Required] | FineTuningVertical (required) |
| jobType | [Required] Specifies the type of job. | 'FineTuning' (required) |
| outputs | [Required] | FineTuningJobOutputs (required) |
FineTuningJobOutputs
| Name | Description | Value |
|---|
FineTuningVertical
| Name | Description | Value |
|---|---|---|
| model | [Required] Input model for fine tuning. | MLFlowModelJobInput (required) |
| modelProvider | Set to 'AzureOpenAI' for type AzureOpenAiFineTuning. Set to 'Custom' for type CustomModelFineTuning. | 'AzureOpenAI' 'Custom' (required) |
| taskType | [Required] Fine tuning task type. | 'ChatCompletion' 'ImageClassification' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'QuestionAnswering' 'TextClassification' 'TextCompletion' 'TextSummarization' 'TextTranslation' 'TokenClassification' 'VideoMultiObjectTracking' (required) |
| trainingData | [Required] Training data for fine tuning. | JobInput (required) |
| validationData | Validation data for fine tuning. | JobInput |
FixedInputData
| Name | Description | Value |
|---|---|---|
| inputDataType | [Required] Specifies the type of signal to monitor. | 'Fixed' (required) |
ForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoForecastHorizon. Set to 'Custom' for type CustomForecastHorizon. | 'Auto' 'Custom' (required) |
Forecasting
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'Forecasting' (required) |
| 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 bethree days apart. |
int |
| featureLags | Flag for generating lags for the numeric features with 'auto' or null. | 'Auto' 'None' |
| featuresUnknownAtForecastTime | The feature columns that are available for training but unknown at the time of forecast/inference. If features_unknown_at_forecast_time is not set, it is assumed that all the feature columns in the dataset are known at inference time. |
string[] |
| 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' |
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' 'SeasonalAverage' 'SeasonalNaive' 'SGD' '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' 'SeasonalAverage' 'SeasonalNaive' 'SGD' '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' |
GenerationSafetyQualityMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] Gets or sets the feature attribution metric to calculate. | 'AcceptableCoherenceScorePerInstance' 'AcceptableFluencyScorePerInstance' 'AcceptableGroundednessScorePerInstance' 'AcceptableRelevanceScorePerInstance' 'AcceptableSimilarityScorePerInstance' 'AggregatedCoherencePassRate' 'AggregatedFluencyPassRate' 'AggregatedGroundednessPassRate' 'AggregatedRelevancePassRate' 'AggregatedSimilarityPassRate' (required) |
| threshold | Gets or sets the threshold value. If null, a default value will be set depending on the selected metric. |
MonitoringThreshold |
GenerationSafetyQualityMonitoringSignal
| Name | Description | Value |
|---|---|---|
| metricThresholds | [Required] Gets or sets the metrics to calculate and the corresponding thresholds. | GenerationSafetyQualityMetricThreshold[] (required) |
| productionData | Gets or sets the production data for computing metrics. | MonitoringInputDataBase[] |
| samplingRate | [Required] The sample rate of the production data, should be greater than 0 and at most 1. | int (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'GenerationSafetyQuality' (required) |
| workspaceConnectionId | Gets or sets the workspace connection ID used to connect to the content generation endpoint. | string |
GenerationTokenUsageMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] Gets or sets the feature attribution metric to calculate. | 'TotalTokenCount' 'TotalTokenCountPerGroup' (required) |
| threshold | Gets or sets the threshold value. If null, a default value will be set depending on the selected metric. |
MonitoringThreshold |
GenerationTokenUsageSignal
| Name | Description | Value |
|---|---|---|
| metricThresholds | [Required] Gets or sets the metrics to calculate and the corresponding thresholds. | GenerationTokenUsageMetricThreshold[] (required) |
| productionData | Gets or sets the production data for computing metrics. | MonitoringInputDataBase[] |
| samplingRate | [Required] The sample rate of the production data, should be greater than 0 and at most 1. | int (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'GenerationTokenStatistics' (required) |
GridSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Grid' (required) |
IdentityConfiguration
| Name | Description | Value |
|---|---|---|
| identityType | Set to 'AMLToken' for type AmlToken. Set to 'Managed' for type ManagedIdentity. Set to 'UserIdentity' for type UserIdentity. | 'AMLToken' 'Managed' 'UserIdentity' (required) |
ImageClassification
| Name | Description | Value |
|---|---|---|
| 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. | 'Accuracy' 'AUCWeighted' '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 |
| taskType | [Required] Task type for AutoMLJob. | 'ImageClassification' (required) |
| 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 |
ImageClassificationMultilabel
| Name | Description | Value |
|---|---|---|
| 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. | 'Accuracy' 'AUCWeighted' '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 |
| taskType | [Required] Task type for AutoMLJob. | 'ImageClassificationMultilabel' (required) |
| 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 |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'ImageInstanceSegmentation' (required) |
| 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 |
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 |
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 |
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 |
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' |
| logTrainingMetrics | Enable computing and logging training metrics. | 'Disable' 'Enable' |
| logValidationLoss | Enable computing and logging validation loss. | 'Disable' 'Enable' |
| 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 |
ImageObjectDetection
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'ImageObjectDetection' (required) |
| 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 |
ImageSweepSettings
| Name | Description | Value |
|---|---|---|
| earlyTermination | Type of early termination policy. | EarlyTerminationPolicy |
| samplingAlgorithm | [Required] Type of the hyperparameter sampling algorithms. | 'Bayesian' 'Grid' 'Random' (required) |
ImportDataAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'ImportData' (required) |
| dataImportDefinition | [Required] Defines Schedule action definition details. | DataImport (required) |
IntellectualProperty
| Name | Description | Value |
|---|---|---|
| protectionLevel | Protection level of the Intellectual Property. | 'All' 'None' |
| publisher | [Required] Publisher of the Intellectual Property. Must be the same as Registry publisher name. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (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 |
| jobType | Set to 'AutoML' for type AutoMLJob. Set to 'Command' for type CommandJob. Set to 'FineTuning' for type FineTuningJob. Set to 'Labeling' for type LabelingJobProperties. Set to 'Pipeline' for type PipelineJob. Set to 'Spark' for type SparkJob. Set to 'Sweep' for type SweepJob. | 'AutoML' 'Command' 'FineTuning' 'Labeling' 'Pipeline' 'Spark' 'Sweep' (required) |
| notificationSetting | Notification setting for the job | NotificationSetting |
| properties | The asset property dictionary. | ResourceBaseProperties |
| secretsConfiguration | Configuration for secrets to be made available during runtime. | JobBaseSecretsConfiguration |
| 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. | ResourceBaseTags |
JobBaseSecretsConfiguration
| Name | Description | Value |
|---|
JobBaseServices
| Name | Description | Value |
|---|
JobInput
| Name | Description | Value |
|---|---|---|
| description | Description for the input. | string |
| jobInputType | Set to 'custom_model' for type CustomModelJobInput. Set to 'literal' for type LiteralJobInput. Set to 'mlflow_model' for type MLFlowModelJobInput. Set to 'mltable' for type MLTableJobInput. Set to 'triton_model' for type TritonModelJobInput. Set to 'uri_file' for type UriFileJobInput. Set to 'uri_folder' for type UriFolderJobInput. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
JobOutput
| Name | Description | Value |
|---|---|---|
| description | Description for the output. | string |
| jobOutputType | Set to 'custom_model' for type CustomModelJobOutput. Set to 'mlflow_model' for type MLFlowModelJobOutput. Set to 'mltable' for type MLTableJobOutput. Set to 'triton_model' for type TritonModelJobOutput. Set to 'uri_file' for type UriFileJobOutput. Set to 'uri_folder' for type UriFolderJobOutput. | 'custom_model' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
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 |
| locations | Locations where the job can run. | string[] |
| maxInstanceCount | Optional max allowed number of instances or nodes to be used by the compute target. For use with elastic training, currently supported by PyTorch distribution type only. |
int |
| 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] |
JobScheduleAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'CreateJob' (required) |
| jobDefinition | [Required] Defines Schedule action definition details. | JobBaseProperties (required) |
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 |
JobServiceProperties
| Name | Description | Value |
|---|
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 |
|---|
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 |
|---|
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' |
LabelingJobImageProperties
| Name | Description | Value |
|---|---|---|
| annotationType | Annotation type of image labeling job. | 'BoundingBox' 'Classification' 'InstanceSegmentation' |
| mediaType | [Required] Media type of the job. | 'Image' (required) |
LabelingJobInstructions
| Name | Description | Value |
|---|---|---|
| uri | The link to a page with detailed labeling instructions for labelers. | string |
LabelingJobLabelCategories
| Name | Description | Value |
|---|
LabelingJobMediaProperties
| Name | Description | Value |
|---|---|---|
| mediaType | Set to 'Image' for type LabelingJobImageProperties. Set to 'Text' for type LabelingJobTextProperties. | 'Image' 'Text' (required) |
LabelingJobProperties
| Name | Description | Value |
|---|---|---|
| dataConfiguration | Configuration of data used in the job. | LabelingDataConfiguration |
| jobInstructions | Labeling instructions of the job. | LabelingJobInstructions |
| jobType | [Required] Specifies the type of job. | 'Labeling' (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 |
LabelingJobTextProperties
| Name | Description | Value |
|---|---|---|
| annotationType | Annotation type of text labeling job. | 'Classification' 'NamedEntityRecognition' |
| mediaType | [Required] Media type of the job. | 'Text' (required) |
LiteralJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'literal' (required) |
| value | [Required] Literal value for the input. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
ManagedComputeIdentity
| Name | Description | Value |
|---|---|---|
| computeIdentityType | [Required] Monitor compute identity type enum. | 'ManagedIdentity' (required) |
| identity | Managed service identity (system assigned and/or user assigned identities) | ManagedServiceIdentity |
ManagedIdentity
| Name | Description | Value |
|---|---|---|
| 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}$ |
| identityType | [Required] Specifies the type of identity framework. | 'Managed' (required) |
| 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 |
ManagedServiceIdentity
| Name | Description | Value |
|---|---|---|
| type | Type of managed service identity (where both SystemAssigned and UserAssigned types are allowed). | 'None' 'SystemAssigned' 'SystemAssigned,UserAssigned' 'UserAssigned' (required) |
| userAssignedIdentities | The set of user assigned identities associated with the resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form: '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}. The dictionary values can be empty objects ({}) in requests. | UserAssignedIdentities |
MedianStoppingPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'MedianStopping' (required) |
Microsoft.MachineLearningServices/workspaces/schedules
| Name | Description | Value |
|---|---|---|
| name | The resource name | string Constraints: Pattern = ^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$ (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. | ScheduleProperties (required) |
MLAssistConfiguration
| Name | Description | Value |
|---|---|---|
| mlAssist | Set to 'Disabled' for type MLAssistConfigurationDisabled. Set to 'Enabled' for type MLAssistConfigurationEnabled. | 'Disabled' 'Enabled' (required) |
MLAssistConfigurationDisabled
| Name | Description | Value |
|---|---|---|
| mlAssist | [Required] Indicates whether MLAssist feature is enabled. | 'Disabled' (required) |
MLAssistConfigurationEnabled
| Name | Description | Value |
|---|---|---|
| inferencingComputeBinding | [Required] AML compute binding used in inferencing. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| mlAssist | [Required] Indicates whether MLAssist feature is enabled. | 'Enabled' (required) |
| trainingComputeBinding | [Required] AML compute binding used in training. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'mlflow_model' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'mlflow_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLTableJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'mltable' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLTableJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'mltable' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
ModelPerformanceMetricThresholdBase
| Name | Description | Value |
|---|---|---|
| modelType | Set to 'Classification' for type ClassificationModelPerformanceMetricThreshold. Set to 'Regression' for type RegressionModelPerformanceMetricThreshold. | 'Classification' 'Regression' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
ModelPerformanceSignal
| Name | Description | Value |
|---|---|---|
| dataSegment | The data segment. | MonitoringDataSegment |
| metricThreshold | [Required] A list of metrics to calculate and their associated thresholds. | ModelPerformanceMetricThresholdBase (required) |
| productionData | [Required] The data produced by the production service which performance will be calculated for. | MonitoringInputDataBase[] (required) |
| referenceData | [Required] The reference data used as the basis to calculate model performance. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'ModelPerformance' (required) |
MonitorComputeConfigurationBase
| Name | Description | Value |
|---|---|---|
| computeType | Set to 'ServerlessSpark' for type MonitorServerlessSparkCompute. | 'ServerlessSpark' (required) |
MonitorComputeIdentityBase
| Name | Description | Value |
|---|---|---|
| computeIdentityType | Set to 'AmlToken' for type AmlTokenComputeIdentity. Set to 'ManagedIdentity' for type ManagedComputeIdentity. | 'AmlToken' 'ManagedIdentity' (required) |
MonitorDefinition
| Name | Description | Value |
|---|---|---|
| alertNotificationSettings | The monitor's notification settings. | MonitorNotificationSettings |
| computeConfiguration | [Required] The ARM resource ID of the compute resource to run the monitoring job on. | MonitorComputeConfigurationBase (required) |
| monitoringTarget | The ARM resource ID of either the model or deployment targeted by this monitor. | MonitoringTarget |
| signals | [Required] The signals to monitor. | MonitorDefinitionSignals (required) |
MonitorDefinitionSignals
| Name | Description | Value |
|---|
MonitorEmailNotificationSettings
| Name | Description | Value |
|---|---|---|
| emails | This is the email recipient list which has a limitation of 499 characters in total. | string[] |
MonitoringDataSegment
| Name | Description | Value |
|---|---|---|
| feature | The feature to segment the data on. | string |
| values | Filters for only the specified values of the given segmented feature. | string[] |
MonitoringFeatureFilterBase
| Name | Description | Value |
|---|---|---|
| filterType | Set to 'AllFeatures' for type AllFeatures. Set to 'FeatureSubset' for type FeatureSubset. Set to 'TopNByAttribution' for type TopNFeaturesByAttribution. | 'AllFeatures' 'FeatureSubset' 'TopNByAttribution' (required) |
MonitoringInputDataBase
| Name | Description | Value |
|---|---|---|
| columns | Mapping of column names to special uses. | MonitoringInputDataBaseColumns |
| dataContext | The context metadata of the data source. | string |
| inputDataType | Set to 'Fixed' for type FixedInputData. Set to 'Rolling' for type RollingInputData. Set to 'Static' for type StaticInputData. | 'Fixed' 'Rolling' 'Static' (required) |
| jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MonitoringInputDataBaseColumns
| Name | Description | Value |
|---|
MonitoringSignalBase
| Name | Description | Value |
|---|---|---|
| notificationTypes | The current notification mode for this signal. | String array containing any of: 'AmlNotification' 'AzureMonitor' |
| properties | Property dictionary. Properties can be added, but not removed or altered. | MonitoringSignalBaseProperties |
| signalType | Set to 'Custom' for type CustomMonitoringSignal. Set to 'DataDrift' for type DataDriftMonitoringSignal. Set to 'DataQuality' for type DataQualityMonitoringSignal. Set to 'FeatureAttributionDrift' for type FeatureAttributionDriftMonitoringSignal. Set to 'GenerationSafetyQuality' for type GenerationSafetyQualityMonitoringSignal. Set to 'GenerationTokenStatistics' for type GenerationTokenUsageSignal. Set to 'ModelPerformance' for type ModelPerformanceSignal. Set to 'PredictionDrift' for type PredictionDriftMonitoringSignal. | 'Custom' 'DataDrift' 'DataQuality' 'FeatureAttributionDrift' 'GenerationSafetyQuality' 'GenerationTokenStatistics' 'ModelPerformance' 'PredictionDrift' (required) |
MonitoringSignalBaseProperties
| Name | Description | Value |
|---|
MonitoringTarget
| Name | Description | Value |
|---|---|---|
| deploymentId | The ARM resource ID of either the deployment targeted by this monitor. | string |
| modelId | The ARM resource ID of either the model targeted by this monitor. | string |
| taskType | [Required] The machine learning task type of the model. | 'Classification' 'QuestionAnswering' 'Regression' (required) |
MonitoringThreshold
| Name | Description | Value |
|---|---|---|
| value | The threshold value. If null, the set default is dependent on the metric type. | int |
MonitoringWorkspaceConnection
| Name | Description | Value |
|---|---|---|
| environmentVariables | The properties of a workspace service connection to store as environment variables in the submitted jobs. Key is workspace connection property path, name is environment variable key. |
MonitoringWorkspaceConnectionEnvironmentVariables |
| secrets | The properties of a workspace service connection to store as secrets in the submitted jobs. Key is workspace connection property path, name is secret key. |
MonitoringWorkspaceConnectionSecrets |
MonitoringWorkspaceConnectionEnvironmentVariables
| Name | Description | Value |
|---|
MonitoringWorkspaceConnectionSecrets
| Name | Description | Value |
|---|
MonitorNotificationSettings
| Name | Description | Value |
|---|---|---|
| emailNotificationSettings | The AML notification email settings. | MonitorEmailNotificationSettings |
MonitorServerlessSparkCompute
| Name | Description | Value |
|---|---|---|
| computeIdentity | [Required] The identity scheme leveraged to by the spark jobs running on serverless Spark. | MonitorComputeIdentityBase (required) |
| computeType | [Required] Specifies the type of signal to monitor. | 'ServerlessSpark' (required) |
| instanceType | [Required] The instance type running the Spark job. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| runtimeVersion | [Required] The Spark runtime version. | string Constraints: Min length = 1 Pattern = ^[0-9]+\.[0-9]+$ (required) |
Mpi
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'Mpi' (required) |
| processCountPerInstance | Number of processes per MPI node. | int |
NCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoNCrossValidations. Set to 'Custom' for type CustomNCrossValidations. | 'Auto' 'Custom' (required) |
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 |
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) |
NlpVerticalFeaturizationSettings
| Name | Description | Value |
|---|---|---|
| datasetLanguage | Dataset language, useful for the text data. | string |
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 |
Nodes
| Name | Description | Value |
|---|---|---|
| nodesValueType | Set to 'All' for type AllNodes. | 'All' (required) |
NotificationSetting
| Name | Description | Value |
|---|---|---|
| emailOn | Send email notification to user on specified notification type | String array containing any of: 'JobCancelled' 'JobCompleted' 'JobFailed' |
| emails | This is the email recipient list which has a limitation of 499 characters in total concat with comma separator | string[] |
| webhooks | Send webhook callback to a service. Key is a user-provided name for the webhook. | NotificationSettingWebhooks |
NotificationSettingWebhooks
| Name | Description | Value |
|---|
NumericalDataDriftMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Numerical' (required) |
| metric | [Required] The numerical data drift metric to calculate. | 'JensenShannonDistance' 'NormalizedWassersteinDistance' 'PopulationStabilityIndex' 'TwoSampleKolmogorovSmirnovTest' (required) |
NumericalDataQualityMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Numerical' (required) |
| metric | [Required] The numerical data quality metric to calculate. | 'DataTypeErrorRate' 'NullValueRate' 'OutOfBoundsRate' (required) |
NumericalPredictionDriftMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Numerical' (required) |
| metric | [Required] The numerical prediction drift metric to calculate. | 'JensenShannonDistance' 'NormalizedWassersteinDistance' 'PopulationStabilityIndex' 'TwoSampleKolmogorovSmirnovTest' (required) |
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 Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
PipelineJob
| Name | Description | Value |
|---|---|---|
| inputs | Inputs for the pipeline job. | PipelineJobInputs |
| jobs | Jobs construct the Pipeline Job. | PipelineJobJobs |
| jobType | [Required] Specifies the type of job. | 'Pipeline' (required) |
| outputs | Outputs for the pipeline job | PipelineJobOutputs |
| settings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | any |
| sourceJobId | ARM resource ID of source job. | string |
PipelineJobInputs
| Name | Description | Value |
|---|
PipelineJobJobs
| Name | Description | Value |
|---|
PipelineJobOutputs
| Name | Description | Value |
|---|
PredictionDriftMetricThresholdBase
| Name | Description | Value |
|---|---|---|
| dataType | Set to 'Categorical' for type CategoricalPredictionDriftMetricThreshold. Set to 'Numerical' for type NumericalPredictionDriftMetricThreshold. | 'Categorical' 'Numerical' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
PredictionDriftMonitoringSignal
| Name | Description | Value |
|---|---|---|
| featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | PredictionDriftMonitoringSignalFeatureDataTypeOverride |
| metricThresholds | [Required] A list of metrics to calculate and their associated thresholds. | PredictionDriftMetricThresholdBase[] (required) |
| productionData | [Required] The data which drift will be calculated for. | MonitoringInputDataBase (required) |
| referenceData | [Required] The data to calculate drift against. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'PredictionDrift' (required) |
PredictionDriftMonitoringSignalFeatureDataTypeOverride
| Name | Description | Value |
|---|
PyTorch
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'PyTorch' (required) |
| processCountPerInstance | Number of processes per node. | int |
QueueSettings
| Name | Description | Value |
|---|---|---|
| jobTier | Controls the compute job tier | 'Basic' 'Null' 'Premium' 'Spot' 'Standard' |
| priority | Controls the priority of the job on a compute. | int |
RandomSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| 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' |
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Random' (required) |
| seed | An optional integer to use as the seed for random number generation | int |
Ray
| Name | Description | Value |
|---|---|---|
| address | The address of Ray head node. | string |
| dashboardPort | The port to bind the dashboard server to. | int |
| distributionType | [Required] Specifies the type of distribution framework. | 'Ray' (required) |
| headNodeAdditionalArgs | Additional arguments passed to ray start in head node. | string |
| includeDashboard | Provide this argument to start the Ray dashboard GUI. | bool |
| port | The port of the head ray process. | int |
| workerNodeAdditionalArgs | Additional arguments passed to ray start in worker node. | string |
RecurrenceSchedule
| Name | Description | Value |
|---|---|---|
| hours | [Required] List of hours for the schedule. | int[] (required) |
| minutes | [Required] List of minutes for the schedule. | int[] (required) |
| monthDays | List of month days for the schedule | int[] |
| weekDays | List of days for the schedule. | String array containing any of: 'Friday' 'Monday' 'Saturday' 'Sunday' 'Thursday' 'Tuesday' 'Wednesday' |
RecurrenceTrigger
| Name | Description | Value |
|---|---|---|
| frequency | [Required] The frequency to trigger schedule. | 'Day' 'Hour' 'Minute' 'Month' 'Week' (required) |
| interval | [Required] Specifies schedule interval in conjunction with frequency | int (required) |
| schedule | The recurrence schedule. | RecurrenceSchedule |
| triggerType | [Required] | 'Recurrence' (required) |
Regression
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'Regression' (required) |
| 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 |
RegressionModelPerformanceMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] The regression model performance metric to calculate. | 'MeanAbsoluteError' 'MeanSquaredError' 'RootMeanSquaredError' (required) |
| modelType | [Required] Specifies the data type of the metric threshold. | 'Regression' (required) |
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' |
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceConfigurationProperties
| Name | Description | Value |
|---|
RollingInputData
| Name | Description | Value |
|---|---|---|
| inputDataType | [Required] Specifies the type of signal to monitor. | 'Rolling' (required) |
| preprocessingComponentId | The ARM resource ID of the component resource used to preprocess the data. | string |
| windowOffset | [Required] The time offset between the end of the data window and the monitor's current run time. | string (required) |
| windowSize | [Required] The size of the trailing data window. | string (required) |
SamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | Set to 'Bayesian' for type BayesianSamplingAlgorithm. Set to 'Grid' for type GridSamplingAlgorithm. Set to 'Random' for type RandomSamplingAlgorithm. | 'Bayesian' 'Grid' 'Random' (required) |
ScheduleActionBase
| Name | Description | Value |
|---|---|---|
| actionType | Set to 'CreateJob' for type JobScheduleAction. Set to 'CreateMonitor' for type CreateMonitorAction. Set to 'ImportData' for type ImportDataAction. Set to 'InvokeBatchEndpoint' for type EndpointScheduleAction. | 'CreateJob' 'CreateMonitor' 'ImportData' 'InvokeBatchEndpoint' (required) |
ScheduleProperties
| Name | Description | Value |
|---|---|---|
| action | [Required] Specifies the action of the schedule | ScheduleActionBase (required) |
| description | The asset description text. | string |
| displayName | Display name of schedule. | string |
| isEnabled | Is the schedule enabled? | bool |
| properties | The asset property dictionary. | ResourceBaseProperties |
| tags | Tag dictionary. Tags can be added, removed, and updated. | ResourceBaseTags |
| trigger | [Required] Specifies the trigger details | TriggerBase (required) |
Seasonality
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoSeasonality. Set to 'Custom' for type CustomSeasonality. | 'Auto' 'Custom' (required) |
SecretConfiguration
| Name | Description | Value |
|---|---|---|
| uri | Secret Uri. Sample Uri : https://myvault.vault.azure.net/secrets/mysecretname/secretversion |
string |
| workspaceSecretName | Name of secret in workspace key vault. | string |
SparkJob
| Name | Description | Value |
|---|---|---|
| archives | Archive files used in the job. | string[] |
| args | Arguments for the job. | string |
| codeId | [Required] ARM resource ID of the code asset. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| 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 |
| environmentVariables | Environment variables included in the job. | SparkJobEnvironmentVariables |
| 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[] |
| jobType | [Required] Specifies the type of job. | 'Spark' (required) |
| outputs | Mapping of output data bindings used in the job. | SparkJobOutputs |
| pyFiles | Python files used in the job. | string[] |
| queueSettings | Queue settings for the job | QueueSettings |
| resources | Compute Resource configuration for the job. | SparkResourceConfiguration |
SparkJobConf
| Name | Description | Value |
|---|
SparkJobEntry
| Name | Description | Value |
|---|---|---|
| sparkJobEntryType | Set to 'SparkJobPythonEntry' for type SparkJobPythonEntry. Set to 'SparkJobScalaEntry' for type SparkJobScalaEntry. | 'SparkJobPythonEntry' 'SparkJobScalaEntry' (required) |
SparkJobEnvironmentVariables
| Name | Description | Value |
|---|
SparkJobInputs
| Name | Description | Value |
|---|
SparkJobOutputs
| Name | Description | Value |
|---|
SparkJobPythonEntry
| Name | Description | Value |
|---|---|---|
| file | [Required] Relative python file path for job entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobPythonEntry' (required) |
SparkJobScalaEntry
| Name | Description | Value |
|---|---|---|
| className | [Required] Scala class name used as entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobScalaEntry' (required) |
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 |
StackEnsembleSettings
| Name | Description | Value |
|---|---|---|
| stackMetaLearnerKWargs | Optional parameters to pass to the initializer of the meta-learner. | any |
| 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' |
StaticInputData
| Name | Description | Value |
|---|---|---|
| inputDataType | [Required] Specifies the type of signal to monitor. | 'Static' (required) |
| preprocessingComponentId | The ARM resource ID of the component resource used to preprocess the data. | string |
| windowEnd | [Required] The end date of the data window. | string (required) |
| windowStart | [Required] The start date of the data window. | string (required) |
SweepJob
| Name | Description | Value |
|---|---|---|
| componentConfiguration | Component Configuration for sweep over component | ComponentConfiguration |
| earlyTermination | Early termination policies enable canceling poor-performing runs before they complete | EarlyTerminationPolicy |
| inputs | Mapping of input data bindings used in the job. | SweepJobInputs |
| jobType | [Required] Specifies the type of job. | 'Sweep' (required) |
| limits | Sweep Job limit. | SweepJobLimits |
| objective | [Required] Optimization objective. | Objective (required) |
| outputs | Mapping of output data bindings used in the job. | SweepJobOutputs |
| queueSettings | Queue settings for the job | QueueSettings |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
| 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 | any (required) |
| trial | [Required] Trial component definition. | TrialComponent (required) |
SweepJobInputs
| Name | Description | Value |
|---|
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 |
SweepJobOutputs
| Name | Description | Value |
|---|
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 |
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) |
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 |
|---|
TableVerticalFeaturizationSettingsTransformerParams
| Name | Description | Value |
|---|
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 |
TargetLags
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoTargetLags. Set to 'Custom' for type CustomTargetLags. | 'Auto' 'Custom' (required) |
TargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoTargetRollingWindowSize. Set to 'Custom' for type CustomTargetRollingWindowSize. | 'Auto' 'Custom' (required) |
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 |
TextClassification
| Name | Description | Value |
|---|---|---|
| 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. | 'Accuracy' 'AUCWeighted' '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 |
| taskType | [Required] Task type for AutoMLJob. | 'TextClassification' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TextClassificationMultilabel
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'TextClassificationMultilabel' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TextNer
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'TextNER' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TopNFeaturesByAttribution
| Name | Description | Value |
|---|---|---|
| filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | 'TopNByAttribution' (required) |
| top | The number of top features to include. | 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 Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| 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 Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| environmentVariables | Environment variables included in the job. | TrialComponentEnvironmentVariables |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
TrialComponentEnvironmentVariables
| Name | Description | Value |
|---|
TriggerBase
| Name | Description | Value |
|---|---|---|
| endTime | Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely |
string |
| startTime | Specifies start time of schedule in ISO 8601 format, but without a UTC offset. | string |
| timeZone | Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones?view=windows-11 |
string |
| triggerType | Set to 'Cron' for type CronTrigger. Set to 'Recurrence' for type RecurrenceTrigger. | 'Cron' 'Recurrence' (required) |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
TritonModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'triton_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
TruncationSelectionPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'TruncationSelection' (required) |
| truncationPercentage | The percentage of runs to cancel at each evaluation interval. | int |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
UriFileJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'uri_file' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
UriFolderJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'uri_folder' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
UserAssignedIdentities
| Name | Description | Value |
|---|
UserAssignedIdentity
| Name | Description | Value |
|---|
UserIdentity
| Name | Description | Value |
|---|---|---|
| identityType | [Required] Specifies the type of identity framework. | 'UserIdentity' (required) |
Webhook
| Name | Description | Value |
|---|---|---|
| eventType | Send callback on a specified notification event | string |
| webhookType | Set to 'AzureDevOps' for type AzureDevOpsWebhook. | 'AzureDevOps' (required) |
ARM template resource definition
The workspaces/schedules 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/schedules resource, add the following JSON to your template.
{
"type": "Microsoft.MachineLearningServices/workspaces/schedules",
"apiVersion": "2024-04-01-preview",
"name": "string",
"properties": {
"action": {
"actionType": "string"
// For remaining properties, see ScheduleActionBase objects
},
"description": "string",
"displayName": "string",
"isEnabled": "bool",
"properties": {
"{customized property}": "string"
},
"tags": {
"{customized property}": "string"
},
"trigger": {
"endTime": "string",
"startTime": "string",
"timeZone": "string",
"triggerType": "string"
// For remaining properties, see TriggerBase objects
}
}
}
JobBaseProperties objects
Set the jobType property to specify the type of object.
For AutoML, use:
{
"environmentId": "string",
"environmentVariables": {
"{customized property}": "string"
},
"jobType": "AutoML",
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"queueSettings": {
"jobTier": "string",
"priority": "int"
},
"resources": {
"dockerArgs": "string",
"instanceCount": "int",
"instanceType": "string",
"locations": [ "string" ],
"maxInstanceCount": "int",
"properties": {
"{customized property}": {}
},
"shmSize": "string"
},
"taskDetails": {
"logVerbosity": "string",
"targetColumnName": "string",
"trainingData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
},
"taskType": "string"
// For remaining properties, see AutoMLVertical objects
}
}
For Command, use:
{
"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
}
},
"jobType": "Command",
"limits": {
"jobLimitsType": "string",
"timeout": "string"
},
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"queueSettings": {
"jobTier": "string",
"priority": "int"
},
"resources": {
"dockerArgs": "string",
"instanceCount": "int",
"instanceType": "string",
"locations": [ "string" ],
"maxInstanceCount": "int",
"properties": {
"{customized property}": {}
},
"shmSize": "string"
}
}
For FineTuning, use:
{
"fineTuningDetails": {
"model": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
},
"taskType": "string",
"trainingData": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
},
"validationData": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
},
"modelProvider": "string"
// For remaining properties, see FineTuningVertical objects
},
"jobType": "FineTuning",
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
}
}
For Labeling, use:
{
"dataConfiguration": {
"dataId": "string",
"incrementalDataRefresh": "string"
},
"jobInstructions": {
"uri": "string"
},
"jobType": "Labeling",
"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:
{
"inputs": {
"{customized property}": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
}
},
"jobs": {
"{customized property}": {}
},
"jobType": "Pipeline",
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"settings": {},
"sourceJobId": "string"
}
For Spark, use:
{
"archives": [ "string" ],
"args": "string",
"codeId": "string",
"conf": {
"{customized property}": "string"
},
"entry": {
"sparkJobEntryType": "string"
// For remaining properties, see SparkJobEntry objects
},
"environmentId": "string",
"environmentVariables": {
"{customized property}": "string"
},
"files": [ "string" ],
"inputs": {
"{customized property}": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
}
},
"jars": [ "string" ],
"jobType": "Spark",
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"pyFiles": [ "string" ],
"queueSettings": {
"jobTier": "string",
"priority": "int"
},
"resources": {
"instanceType": "string",
"runtimeVersion": "string"
}
}
For Sweep, use:
{
"componentConfiguration": {
"pipelineSettings": {}
},
"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
}
},
"jobType": "Sweep",
"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
}
},
"queueSettings": {
"jobTier": "string",
"priority": "int"
},
"resources": {
"dockerArgs": "string",
"instanceCount": "int",
"instanceType": "string",
"locations": [ "string" ],
"maxInstanceCount": "int",
"properties": {
"{customized property}": {}
},
"shmSize": "string"
},
"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",
"locations": [ "string" ],
"maxInstanceCount": "int",
"properties": {
"{customized property}": {}
},
"shmSize": "string"
}
}
}
MLAssistConfiguration objects
Set the mlAssist property to specify the type of object.
For Disabled, use:
{
"mlAssist": "Disabled"
}
For Enabled, use:
{
"inferencingComputeBinding": "string",
"mlAssist": "Enabled",
"trainingComputeBinding": "string"
}
AutoMLVertical objects
Set the taskType property to specify the type of object.
For Classification, use:
{
"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"
},
"taskType": "Classification",
"testData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"pathOnCompute": "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",
"pathOnCompute": "string",
"uri": "string"
},
"validationDataSize": "int",
"weightColumnName": "string"
}
For Forecasting, use:
{
"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",
"featuresUnknownAtForecastTime": [ "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"
},
"taskType": "Forecasting",
"testData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"pathOnCompute": "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",
"pathOnCompute": "string",
"uri": "string"
},
"validationDataSize": "int",
"weightColumnName": "string"
}
For ImageClassification, use:
{
"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",
"pathOnCompute": "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"
},
"taskType": "ImageClassification",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
},
"validationDataSize": "int"
}
For ImageClassificationMultilabel, use:
{
"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",
"pathOnCompute": "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"
},
"taskType": "ImageClassificationMultilabel",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
},
"validationDataSize": "int"
}
For ImageInstanceSegmentation, use:
{
"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",
"pathOnCompute": "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",
"logTrainingMetrics": "string",
"logValidationLoss": "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": "string",
"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"
},
"taskType": "ImageInstanceSegmentation",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
},
"validationDataSize": "int"
}
For ImageObjectDetection, use:
{
"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",
"pathOnCompute": "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",
"logTrainingMetrics": "string",
"logValidationLoss": "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": "string",
"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"
},
"taskType": "ImageObjectDetection",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
},
"validationDataSize": "int"
}
For Regression, use:
{
"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"
},
"taskType": "Regression",
"testData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"pathOnCompute": "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",
"pathOnCompute": "string",
"uri": "string"
},
"validationDataSize": "int",
"weightColumnName": "string"
}
For TextClassification, use:
{
"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"
},
"taskType": "TextClassification",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
}
For TextClassificationMultilabel, use:
{
"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"
},
"taskType": "TextClassificationMultilabel",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
}
For TextNER, use:
{
"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"
},
"taskType": "TextNER",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
}
LabelingJobMediaProperties objects
Set the mediaType property to specify the type of object.
For Image, use:
{
"annotationType": "string",
"mediaType": "Image"
}
For Text, use:
{
"annotationType": "string",
"mediaType": "Text"
}
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 Ray, use:
{
"address": "string",
"dashboardPort": "int",
"distributionType": "Ray",
"headNodeAdditionalArgs": "string",
"includeDashboard": "bool",
"port": "int",
"workerNodeAdditionalArgs": "string"
}
For TensorFlow, use:
{
"distributionType": "TensorFlow",
"parameterServerCount": "int",
"workerCount": "int"
}
NCrossValidations 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" ]
}
MonitorComputeIdentityBase objects
Set the computeIdentityType property to specify the type of object.
For AmlToken, use:
{
"computeIdentityType": "AmlToken"
}
For ManagedIdentity, use:
{
"computeIdentityType": "ManagedIdentity",
"identity": {
"type": "string",
"userAssignedIdentities": {
"{customized property}": {
}
}
}
}
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"
}
JobOutput objects
Set the jobOutputType property to specify the type of object.
For custom_model, use:
{
"assetName": "string",
"assetVersion": "string",
"autoDeleteSetting": {
"condition": "string",
"value": "string"
},
"jobOutputType": "custom_model",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
For mlflow_model, use:
{
"assetName": "string",
"assetVersion": "string",
"autoDeleteSetting": {
"condition": "string",
"value": "string"
},
"jobOutputType": "mlflow_model",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
For mltable, use:
{
"assetName": "string",
"assetVersion": "string",
"autoDeleteSetting": {
"condition": "string",
"value": "string"
},
"jobOutputType": "mltable",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
For triton_model, use:
{
"assetName": "string",
"assetVersion": "string",
"autoDeleteSetting": {
"condition": "string",
"value": "string"
},
"jobOutputType": "triton_model",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
For uri_file, use:
{
"assetName": "string",
"assetVersion": "string",
"autoDeleteSetting": {
"condition": "string",
"value": "string"
},
"jobOutputType": "uri_file",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
For uri_folder, use:
{
"assetName": "string",
"assetVersion": "string",
"autoDeleteSetting": {
"condition": "string",
"value": "string"
},
"jobOutputType": "uri_folder",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
MonitoringInputDataBase objects
Set the inputDataType property to specify the type of object.
For Fixed, use:
{
"inputDataType": "Fixed"
}
For Rolling, use:
{
"inputDataType": "Rolling",
"preprocessingComponentId": "string",
"windowOffset": "string",
"windowSize": "string"
}
For Static, use:
{
"inputDataType": "Static",
"preprocessingComponentId": "string",
"windowEnd": "string",
"windowStart": "string"
}
SparkJobEntry objects
Set the sparkJobEntryType property to specify the type of object.
For SparkJobPythonEntry, use:
{
"file": "string",
"sparkJobEntryType": "SparkJobPythonEntry"
}
For SparkJobScalaEntry, use:
{
"className": "string",
"sparkJobEntryType": "SparkJobScalaEntry"
}
Webhook objects
Set the webhookType property to specify the type of object.
For AzureDevOps, use:
{
"webhookType": "AzureDevOps"
}
IdentityConfiguration objects
Set the identityType property to specify the type of object.
For AMLToken, use:
{
"identityType": "AMLToken"
}
For Managed, use:
{
"clientId": "string",
"identityType": "Managed",
"objectId": "string",
"resourceId": "string"
}
For UserIdentity, use:
{
"identityType": "UserIdentity"
}
ScheduleActionBase objects
Set the actionType property to specify the type of object.
For CreateJob, use:
{
"actionType": "CreateJob",
"jobDefinition": {
"componentId": "string",
"computeId": "string",
"description": "string",
"displayName": "string",
"experimentName": "string",
"identity": {
"identityType": "string"
// For remaining properties, see IdentityConfiguration objects
},
"isArchived": "bool",
"notificationSetting": {
"emailOn": [ "string" ],
"emails": [ "string" ],
"webhooks": {
"{customized property}": {
"eventType": "string",
"webhookType": "string"
// For remaining properties, see Webhook objects
}
}
},
"properties": {
"{customized property}": "string"
},
"secretsConfiguration": {
"{customized property}": {
"uri": "string",
"workspaceSecretName": "string"
}
},
"services": {
"{customized property}": {
"endpoint": "string",
"jobServiceType": "string",
"nodes": {
"nodesValueType": "string"
// For remaining properties, see Nodes objects
},
"port": "int",
"properties": {
"{customized property}": "string"
}
}
},
"tags": {
"{customized property}": "string"
},
"jobType": "string"
// For remaining properties, see JobBaseProperties objects
}
}
For CreateMonitor, use:
{
"actionType": "CreateMonitor",
"monitorDefinition": {
"alertNotificationSettings": {
"emailNotificationSettings": {
"emails": [ "string" ]
}
},
"computeConfiguration": {
"computeType": "string"
// For remaining properties, see MonitorComputeConfigurationBase objects
},
"monitoringTarget": {
"deploymentId": "string",
"modelId": "string",
"taskType": "string"
},
"signals": {
"{customized property}": {
"notificationTypes": [ "string" ],
"properties": {
"{customized property}": "string"
},
"signalType": "string"
// For remaining properties, see MonitoringSignalBase objects
}
}
}
}
For ImportData, use:
{
"actionType": "ImportData",
"dataImportDefinition": {
"assetName": "string",
"autoDeleteSetting": {
"condition": "string",
"value": "string"
},
"dataType": "string",
"dataUri": "string",
"description": "string",
"intellectualProperty": {
"protectionLevel": "string",
"publisher": "string"
},
"isAnonymous": "bool",
"isArchived": "bool",
"properties": {
"{customized property}": "string"
},
"source": {
"connection": "string",
"sourceType": "string"
// For remaining properties, see DataImportSource objects
},
"stage": "string",
"tags": {
"{customized property}": "string"
}
}
}
For InvokeBatchEndpoint, use:
{
"actionType": "InvokeBatchEndpoint",
"endpointInvocationDefinition": {}
}
ModelPerformanceMetricThresholdBase objects
Set the modelType property to specify the type of object.
For Classification, use:
{
"metric": "string",
"modelType": "Classification"
}
For Regression, use:
{
"metric": "string",
"modelType": "Regression"
}
DataQualityMetricThresholdBase objects
Set the dataType property to specify the type of object.
For Categorical, use:
{
"dataType": "Categorical",
"metric": "string"
}
For Numerical, use:
{
"dataType": "Numerical",
"metric": "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:
{
"logbase": "string",
"rule": "string",
"samplingAlgorithmType": "Random",
"seed": "int"
}
FineTuningVertical objects
Set the modelProvider property to specify the type of object.
For AzureOpenAI, use:
{
"hyperParameters": {
"batchSize": "int",
"learningRateMultiplier": "int",
"nEpochs": "int"
},
"modelProvider": "AzureOpenAI"
}
For Custom, use:
{
"hyperParameters": {
"{customized property}": "string"
},
"modelProvider": "Custom"
}
MonitoringSignalBase objects
Set the signalType property to specify the type of object.
For Custom, use:
{
"componentId": "string",
"inputAssets": {
"{customized property}": {
"columns": {
"{customized property}": "string"
},
"dataContext": "string",
"jobInputType": "string",
"uri": "string",
"inputDataType": "string"
// For remaining properties, see MonitoringInputDataBase objects
}
},
"inputs": {
"{customized property}": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
}
},
"metricThresholds": [
{
"metric": "string",
"threshold": {
"value": "int"
}
}
],
"signalType": "Custom",
"workspaceConnection": {
"environmentVariables": {
"{customized property}": "string"
},
"secrets": {
"{customized property}": "string"
}
}
}
For DataDrift, use:
{
"dataSegment": {
"feature": "string",
"values": [ "string" ]
},
"featureDataTypeOverride": {
"{customized property}": "string"
},
"featureImportanceSettings": {
"mode": "string",
"targetColumn": "string"
},
"features": {
"filterType": "string"
// For remaining properties, see MonitoringFeatureFilterBase objects
},
"metricThresholds": [ {
"threshold": {
"value": "int"
},
"dataType": "string"
// For remaining properties, see DataDriftMetricThresholdBase objects
} ],
"productionData": {
"columns": {
"{customized property}": "string"
},
"dataContext": "string",
"jobInputType": "string",
"uri": "string",
"inputDataType": "string"
// For remaining properties, see MonitoringInputDataBase objects
},
"referenceData": {
"columns": {
"{customized property}": "string"
},
"dataContext": "string",
"jobInputType": "string",
"uri": "string",
"inputDataType": "string"
// For remaining properties, see MonitoringInputDataBase objects
},
"signalType": "DataDrift"
}
For DataQuality, use:
{
"featureDataTypeOverride": {
"{customized property}": "string"
},
"featureImportanceSettings": {
"mode": "string",
"targetColumn": "string"
},
"features": {
"filterType": "string"
// For remaining properties, see MonitoringFeatureFilterBase objects
},
"metricThresholds": [ {
"threshold": {
"value": "int"
},
"dataType": "string"
// For remaining properties, see DataQualityMetricThresholdBase objects
} ],
"productionData": {
"columns": {
"{customized property}": "string"
},
"dataContext": "string",
"jobInputType": "string",
"uri": "string",
"inputDataType": "string"
// For remaining properties, see MonitoringInputDataBase objects
},
"referenceData": {
"columns": {
"{customized property}": "string"
},
"dataContext": "string",
"jobInputType": "string",
"uri": "string",
"inputDataType": "string"
// For remaining properties, see MonitoringInputDataBase objects
},
"signalType": "DataQuality"
}
For FeatureAttributionDrift, use:
{
"featureDataTypeOverride": {
"{customized property}": "string"
},
"featureImportanceSettings": {
"mode": "string",
"targetColumn": "string"
},
"metricThreshold": {
"metric": "string",
"threshold": {
"value": "int"
}
},
"productionData": [ {
"columns": {
"{customized property}": "string"
},
"dataContext": "string",
"jobInputType": "string",
"uri": "string",
"inputDataType": "string"
// For remaining properties, see MonitoringInputDataBase objects
} ],
"referenceData": {
"columns": {
"{customized property}": "string"
},
"dataContext": "string",
"jobInputType": "string",
"uri": "string",
"inputDataType": "string"
// For remaining properties, see MonitoringInputDataBase objects
},
"signalType": "FeatureAttributionDrift"
}
For GenerationSafetyQuality, use:
{
"metricThresholds": [
{
"metric": "string",
"threshold": {
"value": "int"
}
}
],
"productionData": [ {
"columns": {
"{customized property}": "string"
},
"dataContext": "string",
"jobInputType": "string",
"uri": "string",
"inputDataType": "string"
// For remaining properties, see MonitoringInputDataBase objects
} ],
"samplingRate": "int",
"signalType": "GenerationSafetyQuality",
"workspaceConnectionId": "string"
}
For GenerationTokenStatistics, use:
{
"metricThresholds": [
{
"metric": "string",
"threshold": {
"value": "int"
}
}
],
"productionData": [ {
"columns": {
"{customized property}": "string"
},
"dataContext": "string",
"jobInputType": "string",
"uri": "string",
"inputDataType": "string"
// For remaining properties, see MonitoringInputDataBase objects
} ],
"samplingRate": "int",
"signalType": "GenerationTokenStatistics"
}
For ModelPerformance, use:
{
"dataSegment": {
"feature": "string",
"values": [ "string" ]
},
"metricThreshold": {
"threshold": {
"value": "int"
},
"modelType": "string"
// For remaining properties, see ModelPerformanceMetricThresholdBase objects
},
"productionData": [ {
"columns": {
"{customized property}": "string"
},
"dataContext": "string",
"jobInputType": "string",
"uri": "string",
"inputDataType": "string"
// For remaining properties, see MonitoringInputDataBase objects
} ],
"referenceData": {
"columns": {
"{customized property}": "string"
},
"dataContext": "string",
"jobInputType": "string",
"uri": "string",
"inputDataType": "string"
// For remaining properties, see MonitoringInputDataBase objects
},
"signalType": "ModelPerformance"
}
For PredictionDrift, use:
{
"featureDataTypeOverride": {
"{customized property}": "string"
},
"metricThresholds": [ {
"threshold": {
"value": "int"
},
"dataType": "string"
// For remaining properties, see PredictionDriftMetricThresholdBase objects
} ],
"productionData": {
"columns": {
"{customized property}": "string"
},
"dataContext": "string",
"jobInputType": "string",
"uri": "string",
"inputDataType": "string"
// For remaining properties, see MonitoringInputDataBase objects
},
"referenceData": {
"columns": {
"{customized property}": "string"
},
"dataContext": "string",
"jobInputType": "string",
"uri": "string",
"inputDataType": "string"
// For remaining properties, see MonitoringInputDataBase objects
},
"signalType": "PredictionDrift"
}
DataDriftMetricThresholdBase objects
Set the dataType property to specify the type of object.
For Categorical, use:
{
"dataType": "Categorical",
"metric": "string"
}
For Numerical, use:
{
"dataType": "Numerical",
"metric": "string"
}
JobInput objects
Set the jobInputType property to specify the type of object.
For custom_model, use:
{
"jobInputType": "custom_model",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
For literal, use:
{
"jobInputType": "literal",
"value": "string"
}
For mlflow_model, use:
{
"jobInputType": "mlflow_model",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
For mltable, use:
{
"jobInputType": "mltable",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
For triton_model, use:
{
"jobInputType": "triton_model",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
For uri_file, use:
{
"jobInputType": "uri_file",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
For uri_folder, use:
{
"jobInputType": "uri_folder",
"mode": "string",
"pathOnCompute": "string",
"uri": "string"
}
Seasonality objects
Set the mode property to specify the type of object.
For Auto, use:
{
"mode": "Auto"
}
For Custom, use:
{
"mode": "Custom",
"value": "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"
}
MonitorComputeConfigurationBase objects
Set the computeType property to specify the type of object.
For ServerlessSpark, use:
{
"computeIdentity": {
"computeIdentityType": "string"
// For remaining properties, see MonitorComputeIdentityBase objects
},
"computeType": "ServerlessSpark",
"instanceType": "string",
"runtimeVersion": "string"
}
DataImportSource objects
Set the sourceType property to specify the type of object.
For database, use:
{
"query": "string",
"sourceType": "database",
"storedProcedure": "string",
"storedProcedureParams": [
{
"{customized property}": "string"
}
],
"tableName": "string"
}
For file_system, use:
{
"path": "string",
"sourceType": "file_system"
}
MonitoringFeatureFilterBase objects
Set the filterType property to specify the type of object.
For AllFeatures, use:
{
"filterType": "AllFeatures"
}
For FeatureSubset, use:
{
"features": [ "string" ],
"filterType": "FeatureSubset"
}
For TopNByAttribution, use:
{
"filterType": "TopNByAttribution",
"top": "int"
}
PredictionDriftMetricThresholdBase objects
Set the dataType property to specify the type of object.
For Categorical, use:
{
"dataType": "Categorical",
"metric": "string"
}
For Numerical, use:
{
"dataType": "Numerical",
"metric": "string"
}
TriggerBase objects
Set the triggerType property to specify the type of object.
For Cron, use:
{
"expression": "string",
"triggerType": "Cron"
}
For Recurrence, use:
{
"frequency": "string",
"interval": "int",
"schedule": {
"hours": [ "int" ],
"minutes": [ "int" ],
"monthDays": [ "int" ],
"weekDays": [ "string" ]
},
"triggerType": "Recurrence"
}
ForecastHorizon objects
Set the mode property to specify the type of object.
For Auto, use:
{
"mode": "Auto"
}
For Custom, use:
{
"mode": "Custom",
"value": "int"
}
Nodes objects
Set the nodesValueType property to specify the type of object.
For All, use:
{
"nodesValueType": "All"
}
Property values
AllFeatures
| Name | Description | Value |
|---|---|---|
| filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | 'AllFeatures' (required) |
AllNodes
| Name | Description | Value |
|---|---|---|
| nodesValueType | [Required] Type of the Nodes value | 'All' (required) |
AmlToken
| Name | Description | Value |
|---|---|---|
| identityType | [Required] Specifies the type of identity framework. | 'AMLToken' (required) |
AmlTokenComputeIdentity
| Name | Description | Value |
|---|---|---|
| computeIdentityType | [Required] Monitor compute identity type enum. | 'AmlToken' (required) |
AutoDeleteSetting
| Name | Description | Value |
|---|---|---|
| condition | When to check if an asset is expired | 'CreatedGreaterThan' 'LastAccessedGreaterThan' |
| value | Expiration condition value. | string |
AutoForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set forecast horizon value selection mode. | 'Auto' (required) |
AutologgerSettings
| Name | Description | Value |
|---|---|---|
| mlflowAutologger | [Required] Indicates whether mlflow autologger is enabled. | 'Disabled' 'Enabled' (required) |
AutoMLJob
| Name | Description | Value |
|---|---|---|
| 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 |
| jobType | [Required] Specifies the type of job. | 'AutoML' (required) |
| outputs | Mapping of output data bindings used in the job. | AutoMLJobOutputs |
| queueSettings | Queue settings for the job | QueueSettings |
| 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 |
|---|
AutoMLJobOutputs
| Name | Description | Value |
|---|
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 |
| taskType | Set to 'Classification' for type Classification. Set to 'Forecasting' for type Forecasting. Set to 'ImageClassification' for type ImageClassification. Set to 'ImageClassificationMultilabel' for type ImageClassificationMultilabel. Set to 'ImageInstanceSegmentation' for type ImageInstanceSegmentation. Set to 'ImageObjectDetection' for type ImageObjectDetection. Set to 'Regression' for type Regression. Set to 'TextClassification' for type TextClassification. Set to 'TextClassificationMultilabel' for type TextClassificationMultilabel. Set to 'TextNER' for type TextNer. | 'Classification' 'Forecasting' 'ImageClassification' 'ImageClassificationMultilabel' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'Regression' 'TextClassification' 'TextClassificationMultilabel' 'TextNER' (required) |
| trainingData | [Required] Training data input. | MLTableJobInput (required) |
AutoNCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | [Required] Mode for determining N-Cross validations. | 'Auto' (required) |
AutoSeasonality
| Name | Description | Value |
|---|---|---|
| mode | [Required] Seasonality mode. | 'Auto' (required) |
AutoTargetLags
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set target lags mode - Auto/Custom | 'Auto' (required) |
AutoTargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | [Required] TargetRollingWindowSiz detection mode. | 'Auto' (required) |
AzureDevOpsWebhook
| Name | Description | Value |
|---|---|---|
| webhookType | [Required] Specifies the type of service to send a callback | 'AzureDevOps' (required) |
AzureOpenAiFineTuning
| Name | Description | Value |
|---|---|---|
| hyperParameters | HyperParameters for fine tuning Azure Open AI model. | AzureOpenAiHyperParameters |
| modelProvider | [Required] Enum to determine the type of fine tuning. | 'AzureOpenAI' (required) |
AzureOpenAiHyperParameters
| Name | Description | Value |
|---|---|---|
| batchSize | Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. | int |
| learningRateMultiplier | Scaling factor for the learning rate. A smaller learning rate may be useful to avoid over fitting. | int |
| nEpochs | The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. | int |
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 |
BayesianSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Bayesian' (required) |
CategoricalDataDriftMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Categorical' (required) |
| metric | [Required] The categorical data drift metric to calculate. | 'JensenShannonDistance' 'PearsonsChiSquaredTest' 'PopulationStabilityIndex' (required) |
CategoricalDataQualityMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Categorical' (required) |
| metric | [Required] The categorical data quality metric to calculate. | 'DataTypeErrorRate' 'NullValueRate' 'OutOfBoundsRate' (required) |
CategoricalPredictionDriftMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Categorical' (required) |
| metric | [Required] The categorical prediction drift metric to calculate. | 'JensenShannonDistance' 'PearsonsChiSquaredTest' 'PopulationStabilityIndex' (required) |
Classification
| Name | Description | Value |
|---|---|---|
| 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. | 'Accuracy' 'AUCWeighted' '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 |
| taskType | [Required] Task type for AutoMLJob. | 'Classification' (required) |
| 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 |
ClassificationModelPerformanceMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] The classification model performance to calculate. | 'Accuracy' 'Precision' 'Recall' (required) |
| modelType | [Required] Specifies the data type of the metric threshold. | 'Classification' (required) |
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' |
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. |
any |
CommandJob
| Name | Description | Value |
|---|---|---|
| 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 Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, Ray, or null. | DistributionConfiguration |
| environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| environmentVariables | Environment variables included in the job. | CommandJobEnvironmentVariables |
| inputs | Mapping of input data bindings used in the job. | CommandJobInputs |
| jobType | [Required] Specifies the type of job. | 'Command' (required) |
| limits | Command Job limit. | CommandJobLimits |
| outputs | Mapping of output data bindings used in the job. | CommandJobOutputs |
| queueSettings | Queue settings for the job | QueueSettings |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
CommandJobEnvironmentVariables
| Name | Description | Value |
|---|
CommandJobInputs
| Name | Description | Value |
|---|
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 |
|---|
ComponentConfiguration
| Name | Description | Value |
|---|---|---|
| pipelineSettings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | any |
CreateMonitorAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'CreateMonitor' (required) |
| monitorDefinition | [Required] Defines the monitor. | MonitorDefinition (required) |
CronTrigger
| Name | Description | Value |
|---|---|---|
| expression | [Required] Specifies cron expression of schedule. The expression should follow NCronTab format. |
string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| triggerType | [Required] | 'Cron' (required) |
CustomForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set forecast horizon value selection mode. | 'Custom' (required) |
| value | [Required] Forecast horizon value. | int (required) |
CustomMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] The user-defined metric to calculate. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
CustomModelFineTuning
| Name | Description | Value |
|---|---|---|
| hyperParameters | HyperParameters for fine tuning custom model. | CustomModelFineTuningHyperParameters |
| modelProvider | [Required] Enum to determine the type of fine tuning. | 'Custom' (required) |
CustomModelFineTuningHyperParameters
| Name | Description | Value |
|---|
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
CustomModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'custom_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
CustomMonitoringSignal
| Name | Description | Value |
|---|---|---|
| componentId | [Required] ARM resource ID of the component resource used to calculate the custom metrics. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| inputAssets | Monitoring assets to take as input. Key is the component input port name, value is the data asset. | CustomMonitoringSignalInputAssets |
| inputs | Extra component parameters to take as input. Key is the component literal input port name, value is the parameter value. | CustomMonitoringSignalInputs |
| metricThresholds | [Required] A list of metrics to calculate and their associated thresholds. | CustomMetricThreshold[] (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'Custom' (required) |
| workspaceConnection | A list of metrics to calculate and their associated thresholds. | MonitoringWorkspaceConnection |
CustomMonitoringSignalInputAssets
| Name | Description | Value |
|---|
CustomMonitoringSignalInputs
| Name | Description | Value |
|---|
CustomNCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | [Required] Mode for determining N-Cross validations. | 'Custom' (required) |
| value | [Required] N-Cross validations value. | int (required) |
CustomSeasonality
| Name | Description | Value |
|---|---|---|
| mode | [Required] Seasonality mode. | 'Custom' (required) |
| value | [Required] Seasonality value. | int (required) |
CustomTargetLags
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set target lags mode - Auto/Custom | 'Custom' (required) |
| values | [Required] Set target lags values. | int[] (required) |
CustomTargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | [Required] TargetRollingWindowSiz detection mode. | 'Custom' (required) |
| value | [Required] TargetRollingWindowSize value. | int (required) |
DatabaseSource
| Name | Description | Value |
|---|---|---|
| query | SQL Query statement for data import Database source | string |
| sourceType | [Required] Specifies the type of data. | 'database' (required) |
| storedProcedure | SQL StoredProcedure on data import Database source | string |
| storedProcedureParams | SQL StoredProcedure parameters | DatabaseSourceStoredProcedureParamsItem[] |
| tableName | Name of the table on data import Database source | string |
DatabaseSourceStoredProcedureParamsItem
| Name | Description | Value |
|---|
DataDriftMetricThresholdBase
| Name | Description | Value |
|---|---|---|
| dataType | Set to 'Categorical' for type CategoricalDataDriftMetricThreshold. Set to 'Numerical' for type NumericalDataDriftMetricThreshold. | 'Categorical' 'Numerical' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
DataDriftMonitoringSignal
| Name | Description | Value |
|---|---|---|
| dataSegment | The data segment used for scoping on a subset of the data population. | MonitoringDataSegment |
| featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | DataDriftMonitoringSignalFeatureDataTypeOverride |
| featureImportanceSettings | The settings for computing feature importance. | FeatureImportanceSettings |
| features | The feature filter which identifies which feature to calculate drift over. | MonitoringFeatureFilterBase |
| metricThresholds | [Required] A list of metrics to calculate and their associated thresholds. | DataDriftMetricThresholdBase[] (required) |
| productionData | [Required] The data which drift will be calculated for. | MonitoringInputDataBase (required) |
| referenceData | [Required] The data to calculate drift against. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'DataDrift' (required) |
DataDriftMonitoringSignalFeatureDataTypeOverride
| Name | Description | Value |
|---|
DataImport
| Name | Description | Value |
|---|---|---|
| assetName | Name of the asset for data import job to create | string |
| autoDeleteSetting | Specifies the lifecycle setting of managed data asset. | AutoDeleteSetting |
| dataType | [Required] Specifies the type of data. | 'mltable' 'uri_file' 'uri_folder' (required) |
| dataUri | [Required] Uri of the data. Example: https://go.microsoft.com/fwlink/?linkid=2202330 | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| description | The asset description text. | string |
| intellectualProperty | Intellectual Property details. Used if data is an Intellectual Property. | IntellectualProperty |
| isAnonymous | If the name version are system generated (anonymous registration). For types where Stage is defined, when Stage is provided it will be used to populate IsAnonymous | bool |
| isArchived | Is the asset archived? For types where Stage is defined, when Stage is provided it will be used to populate IsArchived | bool |
| properties | The asset property dictionary. | ResourceBaseProperties |
| source | Source data of the asset to import from | DataImportSource |
| stage | Stage in the data lifecycle assigned to this data asset | string |
| tags | Tag dictionary. Tags can be added, removed, and updated. | ResourceBaseTags |
DataImportSource
| Name | Description | Value |
|---|---|---|
| connection | Workspace connection for data import source storage | string |
| sourceType | Set to 'database' for type DatabaseSource. Set to 'file_system' for type FileSystemSource. | 'database' 'file_system' (required) |
DataQualityMetricThresholdBase
| Name | Description | Value |
|---|---|---|
| dataType | Set to 'Categorical' for type CategoricalDataQualityMetricThreshold. Set to 'Numerical' for type NumericalDataQualityMetricThreshold. | 'Categorical' 'Numerical' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
DataQualityMonitoringSignal
| Name | Description | Value |
|---|---|---|
| featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | DataQualityMonitoringSignalFeatureDataTypeOverride |
| featureImportanceSettings | The settings for computing feature importance. | FeatureImportanceSettings |
| features | The features to calculate drift over. | MonitoringFeatureFilterBase |
| metricThresholds | [Required] A list of metrics to calculate and their associated thresholds. | DataQualityMetricThresholdBase[] (required) |
| productionData | [Required] The data produced by the production service which drift will be calculated for. | MonitoringInputDataBase (required) |
| referenceData | [Required] The data to calculate drift against. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'DataQuality' (required) |
DataQualityMonitoringSignalFeatureDataTypeOverride
| Name | Description | Value |
|---|
DistributionConfiguration
| Name | Description | Value |
|---|---|---|
| distributionType | Set to 'Mpi' for type Mpi. Set to 'PyTorch' for type PyTorch. Set to 'Ray' for type Ray. Set to 'TensorFlow' for type TensorFlow. | 'Mpi' 'PyTorch' 'Ray' 'TensorFlow' (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 to 'Bandit' for type BanditPolicy. Set to 'MedianStopping' for type MedianStoppingPolicy. Set to 'TruncationSelection' for type TruncationSelectionPolicy. | 'Bandit' 'MedianStopping' 'TruncationSelection' (required) |
EndpointScheduleAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'InvokeBatchEndpoint' (required) |
| endpointInvocationDefinition | [Required] Defines Schedule action definition details. <see href="TBD" /> |
any (required) |
FeatureAttributionDriftMonitoringSignal
| Name | Description | Value |
|---|---|---|
| featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | FeatureAttributionDriftMonitoringSignalFeatureDataTypeOverride |
| featureImportanceSettings | [Required] The settings for computing feature importance. | FeatureImportanceSettings (required) |
| metricThreshold | [Required] A list of metrics to calculate and their associated thresholds. | FeatureAttributionMetricThreshold (required) |
| productionData | [Required] The data which drift will be calculated for. | MonitoringInputDataBase[] (required) |
| referenceData | [Required] The data to calculate drift against. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'FeatureAttributionDrift' (required) |
FeatureAttributionDriftMonitoringSignalFeatureDataTypeOverride
| Name | Description | Value |
|---|
FeatureAttributionMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] The feature attribution metric to calculate. | 'NormalizedDiscountedCumulativeGain' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
FeatureImportanceSettings
| Name | Description | Value |
|---|---|---|
| mode | The mode of operation for computing feature importance. | 'Disabled' 'Enabled' |
| targetColumn | The name of the target column within the input data asset. | string |
FeatureSubset
| Name | Description | Value |
|---|---|---|
| features | [Required] The list of features to include. | string[] (required) |
| filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | 'FeatureSubset' (required) |
FileSystemSource
| Name | Description | Value |
|---|---|---|
| path | Path on data import FileSystem source | string |
| sourceType | [Required] Specifies the type of data. | 'file_system' (required) |
FineTuningJob
| Name | Description | Value |
|---|---|---|
| fineTuningDetails | [Required] | FineTuningVertical (required) |
| jobType | [Required] Specifies the type of job. | 'FineTuning' (required) |
| outputs | [Required] | FineTuningJobOutputs (required) |
FineTuningJobOutputs
| Name | Description | Value |
|---|
FineTuningVertical
| Name | Description | Value |
|---|---|---|
| model | [Required] Input model for fine tuning. | MLFlowModelJobInput (required) |
| modelProvider | Set to 'AzureOpenAI' for type AzureOpenAiFineTuning. Set to 'Custom' for type CustomModelFineTuning. | 'AzureOpenAI' 'Custom' (required) |
| taskType | [Required] Fine tuning task type. | 'ChatCompletion' 'ImageClassification' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'QuestionAnswering' 'TextClassification' 'TextCompletion' 'TextSummarization' 'TextTranslation' 'TokenClassification' 'VideoMultiObjectTracking' (required) |
| trainingData | [Required] Training data for fine tuning. | JobInput (required) |
| validationData | Validation data for fine tuning. | JobInput |
FixedInputData
| Name | Description | Value |
|---|---|---|
| inputDataType | [Required] Specifies the type of signal to monitor. | 'Fixed' (required) |
ForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoForecastHorizon. Set to 'Custom' for type CustomForecastHorizon. | 'Auto' 'Custom' (required) |
Forecasting
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'Forecasting' (required) |
| 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 bethree days apart. |
int |
| featureLags | Flag for generating lags for the numeric features with 'auto' or null. | 'Auto' 'None' |
| featuresUnknownAtForecastTime | The feature columns that are available for training but unknown at the time of forecast/inference. If features_unknown_at_forecast_time is not set, it is assumed that all the feature columns in the dataset are known at inference time. |
string[] |
| 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' |
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' 'SeasonalAverage' 'SeasonalNaive' 'SGD' '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' 'SeasonalAverage' 'SeasonalNaive' 'SGD' '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' |
GenerationSafetyQualityMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] Gets or sets the feature attribution metric to calculate. | 'AcceptableCoherenceScorePerInstance' 'AcceptableFluencyScorePerInstance' 'AcceptableGroundednessScorePerInstance' 'AcceptableRelevanceScorePerInstance' 'AcceptableSimilarityScorePerInstance' 'AggregatedCoherencePassRate' 'AggregatedFluencyPassRate' 'AggregatedGroundednessPassRate' 'AggregatedRelevancePassRate' 'AggregatedSimilarityPassRate' (required) |
| threshold | Gets or sets the threshold value. If null, a default value will be set depending on the selected metric. |
MonitoringThreshold |
GenerationSafetyQualityMonitoringSignal
| Name | Description | Value |
|---|---|---|
| metricThresholds | [Required] Gets or sets the metrics to calculate and the corresponding thresholds. | GenerationSafetyQualityMetricThreshold[] (required) |
| productionData | Gets or sets the production data for computing metrics. | MonitoringInputDataBase[] |
| samplingRate | [Required] The sample rate of the production data, should be greater than 0 and at most 1. | int (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'GenerationSafetyQuality' (required) |
| workspaceConnectionId | Gets or sets the workspace connection ID used to connect to the content generation endpoint. | string |
GenerationTokenUsageMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] Gets or sets the feature attribution metric to calculate. | 'TotalTokenCount' 'TotalTokenCountPerGroup' (required) |
| threshold | Gets or sets the threshold value. If null, a default value will be set depending on the selected metric. |
MonitoringThreshold |
GenerationTokenUsageSignal
| Name | Description | Value |
|---|---|---|
| metricThresholds | [Required] Gets or sets the metrics to calculate and the corresponding thresholds. | GenerationTokenUsageMetricThreshold[] (required) |
| productionData | Gets or sets the production data for computing metrics. | MonitoringInputDataBase[] |
| samplingRate | [Required] The sample rate of the production data, should be greater than 0 and at most 1. | int (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'GenerationTokenStatistics' (required) |
GridSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Grid' (required) |
IdentityConfiguration
| Name | Description | Value |
|---|---|---|
| identityType | Set to 'AMLToken' for type AmlToken. Set to 'Managed' for type ManagedIdentity. Set to 'UserIdentity' for type UserIdentity. | 'AMLToken' 'Managed' 'UserIdentity' (required) |
ImageClassification
| Name | Description | Value |
|---|---|---|
| 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. | 'Accuracy' 'AUCWeighted' '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 |
| taskType | [Required] Task type for AutoMLJob. | 'ImageClassification' (required) |
| 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 |
ImageClassificationMultilabel
| Name | Description | Value |
|---|---|---|
| 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. | 'Accuracy' 'AUCWeighted' '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 |
| taskType | [Required] Task type for AutoMLJob. | 'ImageClassificationMultilabel' (required) |
| 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 |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'ImageInstanceSegmentation' (required) |
| 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 |
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 |
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 |
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 |
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' |
| logTrainingMetrics | Enable computing and logging training metrics. | 'Disable' 'Enable' |
| logValidationLoss | Enable computing and logging validation loss. | 'Disable' 'Enable' |
| 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 |
ImageObjectDetection
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'ImageObjectDetection' (required) |
| 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 |
ImageSweepSettings
| Name | Description | Value |
|---|---|---|
| earlyTermination | Type of early termination policy. | EarlyTerminationPolicy |
| samplingAlgorithm | [Required] Type of the hyperparameter sampling algorithms. | 'Bayesian' 'Grid' 'Random' (required) |
ImportDataAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'ImportData' (required) |
| dataImportDefinition | [Required] Defines Schedule action definition details. | DataImport (required) |
IntellectualProperty
| Name | Description | Value |
|---|---|---|
| protectionLevel | Protection level of the Intellectual Property. | 'All' 'None' |
| publisher | [Required] Publisher of the Intellectual Property. Must be the same as Registry publisher name. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (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 |
| jobType | Set to 'AutoML' for type AutoMLJob. Set to 'Command' for type CommandJob. Set to 'FineTuning' for type FineTuningJob. Set to 'Labeling' for type LabelingJobProperties. Set to 'Pipeline' for type PipelineJob. Set to 'Spark' for type SparkJob. Set to 'Sweep' for type SweepJob. | 'AutoML' 'Command' 'FineTuning' 'Labeling' 'Pipeline' 'Spark' 'Sweep' (required) |
| notificationSetting | Notification setting for the job | NotificationSetting |
| properties | The asset property dictionary. | ResourceBaseProperties |
| secretsConfiguration | Configuration for secrets to be made available during runtime. | JobBaseSecretsConfiguration |
| 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. | ResourceBaseTags |
JobBaseSecretsConfiguration
| Name | Description | Value |
|---|
JobBaseServices
| Name | Description | Value |
|---|
JobInput
| Name | Description | Value |
|---|---|---|
| description | Description for the input. | string |
| jobInputType | Set to 'custom_model' for type CustomModelJobInput. Set to 'literal' for type LiteralJobInput. Set to 'mlflow_model' for type MLFlowModelJobInput. Set to 'mltable' for type MLTableJobInput. Set to 'triton_model' for type TritonModelJobInput. Set to 'uri_file' for type UriFileJobInput. Set to 'uri_folder' for type UriFolderJobInput. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
JobOutput
| Name | Description | Value |
|---|---|---|
| description | Description for the output. | string |
| jobOutputType | Set to 'custom_model' for type CustomModelJobOutput. Set to 'mlflow_model' for type MLFlowModelJobOutput. Set to 'mltable' for type MLTableJobOutput. Set to 'triton_model' for type TritonModelJobOutput. Set to 'uri_file' for type UriFileJobOutput. Set to 'uri_folder' for type UriFolderJobOutput. | 'custom_model' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
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 |
| locations | Locations where the job can run. | string[] |
| maxInstanceCount | Optional max allowed number of instances or nodes to be used by the compute target. For use with elastic training, currently supported by PyTorch distribution type only. |
int |
| 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] |
JobScheduleAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'CreateJob' (required) |
| jobDefinition | [Required] Defines Schedule action definition details. | JobBaseProperties (required) |
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 |
JobServiceProperties
| Name | Description | Value |
|---|
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 |
|---|
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 |
|---|
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' |
LabelingJobImageProperties
| Name | Description | Value |
|---|---|---|
| annotationType | Annotation type of image labeling job. | 'BoundingBox' 'Classification' 'InstanceSegmentation' |
| mediaType | [Required] Media type of the job. | 'Image' (required) |
LabelingJobInstructions
| Name | Description | Value |
|---|---|---|
| uri | The link to a page with detailed labeling instructions for labelers. | string |
LabelingJobLabelCategories
| Name | Description | Value |
|---|
LabelingJobMediaProperties
| Name | Description | Value |
|---|---|---|
| mediaType | Set to 'Image' for type LabelingJobImageProperties. Set to 'Text' for type LabelingJobTextProperties. | 'Image' 'Text' (required) |
LabelingJobProperties
| Name | Description | Value |
|---|---|---|
| dataConfiguration | Configuration of data used in the job. | LabelingDataConfiguration |
| jobInstructions | Labeling instructions of the job. | LabelingJobInstructions |
| jobType | [Required] Specifies the type of job. | 'Labeling' (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 |
LabelingJobTextProperties
| Name | Description | Value |
|---|---|---|
| annotationType | Annotation type of text labeling job. | 'Classification' 'NamedEntityRecognition' |
| mediaType | [Required] Media type of the job. | 'Text' (required) |
LiteralJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'literal' (required) |
| value | [Required] Literal value for the input. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
ManagedComputeIdentity
| Name | Description | Value |
|---|---|---|
| computeIdentityType | [Required] Monitor compute identity type enum. | 'ManagedIdentity' (required) |
| identity | Managed service identity (system assigned and/or user assigned identities) | ManagedServiceIdentity |
ManagedIdentity
| Name | Description | Value |
|---|---|---|
| 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}$ |
| identityType | [Required] Specifies the type of identity framework. | 'Managed' (required) |
| 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 |
ManagedServiceIdentity
| Name | Description | Value |
|---|---|---|
| type | Type of managed service identity (where both SystemAssigned and UserAssigned types are allowed). | 'None' 'SystemAssigned' 'SystemAssigned,UserAssigned' 'UserAssigned' (required) |
| userAssignedIdentities | The set of user assigned identities associated with the resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form: '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}. The dictionary values can be empty objects ({}) in requests. | UserAssignedIdentities |
MedianStoppingPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'MedianStopping' (required) |
Microsoft.MachineLearningServices/workspaces/schedules
| Name | Description | Value |
|---|---|---|
| apiVersion | The api version | '2024-04-01-preview' |
| name | The resource name | string Constraints: Pattern = ^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$ (required) |
| properties | [Required] Additional attributes of the entity. | ScheduleProperties (required) |
| type | The resource type | 'Microsoft.MachineLearningServices/workspaces/schedules' |
MLAssistConfiguration
| Name | Description | Value |
|---|---|---|
| mlAssist | Set to 'Disabled' for type MLAssistConfigurationDisabled. Set to 'Enabled' for type MLAssistConfigurationEnabled. | 'Disabled' 'Enabled' (required) |
MLAssistConfigurationDisabled
| Name | Description | Value |
|---|---|---|
| mlAssist | [Required] Indicates whether MLAssist feature is enabled. | 'Disabled' (required) |
MLAssistConfigurationEnabled
| Name | Description | Value |
|---|---|---|
| inferencingComputeBinding | [Required] AML compute binding used in inferencing. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| mlAssist | [Required] Indicates whether MLAssist feature is enabled. | 'Enabled' (required) |
| trainingComputeBinding | [Required] AML compute binding used in training. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'mlflow_model' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'mlflow_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLTableJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'mltable' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLTableJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'mltable' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
ModelPerformanceMetricThresholdBase
| Name | Description | Value |
|---|---|---|
| modelType | Set to 'Classification' for type ClassificationModelPerformanceMetricThreshold. Set to 'Regression' for type RegressionModelPerformanceMetricThreshold. | 'Classification' 'Regression' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
ModelPerformanceSignal
| Name | Description | Value |
|---|---|---|
| dataSegment | The data segment. | MonitoringDataSegment |
| metricThreshold | [Required] A list of metrics to calculate and their associated thresholds. | ModelPerformanceMetricThresholdBase (required) |
| productionData | [Required] The data produced by the production service which performance will be calculated for. | MonitoringInputDataBase[] (required) |
| referenceData | [Required] The reference data used as the basis to calculate model performance. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'ModelPerformance' (required) |
MonitorComputeConfigurationBase
| Name | Description | Value |
|---|---|---|
| computeType | Set to 'ServerlessSpark' for type MonitorServerlessSparkCompute. | 'ServerlessSpark' (required) |
MonitorComputeIdentityBase
| Name | Description | Value |
|---|---|---|
| computeIdentityType | Set to 'AmlToken' for type AmlTokenComputeIdentity. Set to 'ManagedIdentity' for type ManagedComputeIdentity. | 'AmlToken' 'ManagedIdentity' (required) |
MonitorDefinition
| Name | Description | Value |
|---|---|---|
| alertNotificationSettings | The monitor's notification settings. | MonitorNotificationSettings |
| computeConfiguration | [Required] The ARM resource ID of the compute resource to run the monitoring job on. | MonitorComputeConfigurationBase (required) |
| monitoringTarget | The ARM resource ID of either the model or deployment targeted by this monitor. | MonitoringTarget |
| signals | [Required] The signals to monitor. | MonitorDefinitionSignals (required) |
MonitorDefinitionSignals
| Name | Description | Value |
|---|
MonitorEmailNotificationSettings
| Name | Description | Value |
|---|---|---|
| emails | This is the email recipient list which has a limitation of 499 characters in total. | string[] |
MonitoringDataSegment
| Name | Description | Value |
|---|---|---|
| feature | The feature to segment the data on. | string |
| values | Filters for only the specified values of the given segmented feature. | string[] |
MonitoringFeatureFilterBase
| Name | Description | Value |
|---|---|---|
| filterType | Set to 'AllFeatures' for type AllFeatures. Set to 'FeatureSubset' for type FeatureSubset. Set to 'TopNByAttribution' for type TopNFeaturesByAttribution. | 'AllFeatures' 'FeatureSubset' 'TopNByAttribution' (required) |
MonitoringInputDataBase
| Name | Description | Value |
|---|---|---|
| columns | Mapping of column names to special uses. | MonitoringInputDataBaseColumns |
| dataContext | The context metadata of the data source. | string |
| inputDataType | Set to 'Fixed' for type FixedInputData. Set to 'Rolling' for type RollingInputData. Set to 'Static' for type StaticInputData. | 'Fixed' 'Rolling' 'Static' (required) |
| jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MonitoringInputDataBaseColumns
| Name | Description | Value |
|---|
MonitoringSignalBase
| Name | Description | Value |
|---|---|---|
| notificationTypes | The current notification mode for this signal. | String array containing any of: 'AmlNotification' 'AzureMonitor' |
| properties | Property dictionary. Properties can be added, but not removed or altered. | MonitoringSignalBaseProperties |
| signalType | Set to 'Custom' for type CustomMonitoringSignal. Set to 'DataDrift' for type DataDriftMonitoringSignal. Set to 'DataQuality' for type DataQualityMonitoringSignal. Set to 'FeatureAttributionDrift' for type FeatureAttributionDriftMonitoringSignal. Set to 'GenerationSafetyQuality' for type GenerationSafetyQualityMonitoringSignal. Set to 'GenerationTokenStatistics' for type GenerationTokenUsageSignal. Set to 'ModelPerformance' for type ModelPerformanceSignal. Set to 'PredictionDrift' for type PredictionDriftMonitoringSignal. | 'Custom' 'DataDrift' 'DataQuality' 'FeatureAttributionDrift' 'GenerationSafetyQuality' 'GenerationTokenStatistics' 'ModelPerformance' 'PredictionDrift' (required) |
MonitoringSignalBaseProperties
| Name | Description | Value |
|---|
MonitoringTarget
| Name | Description | Value |
|---|---|---|
| deploymentId | The ARM resource ID of either the deployment targeted by this monitor. | string |
| modelId | The ARM resource ID of either the model targeted by this monitor. | string |
| taskType | [Required] The machine learning task type of the model. | 'Classification' 'QuestionAnswering' 'Regression' (required) |
MonitoringThreshold
| Name | Description | Value |
|---|---|---|
| value | The threshold value. If null, the set default is dependent on the metric type. | int |
MonitoringWorkspaceConnection
| Name | Description | Value |
|---|---|---|
| environmentVariables | The properties of a workspace service connection to store as environment variables in the submitted jobs. Key is workspace connection property path, name is environment variable key. |
MonitoringWorkspaceConnectionEnvironmentVariables |
| secrets | The properties of a workspace service connection to store as secrets in the submitted jobs. Key is workspace connection property path, name is secret key. |
MonitoringWorkspaceConnectionSecrets |
MonitoringWorkspaceConnectionEnvironmentVariables
| Name | Description | Value |
|---|
MonitoringWorkspaceConnectionSecrets
| Name | Description | Value |
|---|
MonitorNotificationSettings
| Name | Description | Value |
|---|---|---|
| emailNotificationSettings | The AML notification email settings. | MonitorEmailNotificationSettings |
MonitorServerlessSparkCompute
| Name | Description | Value |
|---|---|---|
| computeIdentity | [Required] The identity scheme leveraged to by the spark jobs running on serverless Spark. | MonitorComputeIdentityBase (required) |
| computeType | [Required] Specifies the type of signal to monitor. | 'ServerlessSpark' (required) |
| instanceType | [Required] The instance type running the Spark job. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| runtimeVersion | [Required] The Spark runtime version. | string Constraints: Min length = 1 Pattern = ^[0-9]+\.[0-9]+$ (required) |
Mpi
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'Mpi' (required) |
| processCountPerInstance | Number of processes per MPI node. | int |
NCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoNCrossValidations. Set to 'Custom' for type CustomNCrossValidations. | 'Auto' 'Custom' (required) |
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 |
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) |
NlpVerticalFeaturizationSettings
| Name | Description | Value |
|---|---|---|
| datasetLanguage | Dataset language, useful for the text data. | string |
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 |
Nodes
| Name | Description | Value |
|---|---|---|
| nodesValueType | Set to 'All' for type AllNodes. | 'All' (required) |
NotificationSetting
| Name | Description | Value |
|---|---|---|
| emailOn | Send email notification to user on specified notification type | String array containing any of: 'JobCancelled' 'JobCompleted' 'JobFailed' |
| emails | This is the email recipient list which has a limitation of 499 characters in total concat with comma separator | string[] |
| webhooks | Send webhook callback to a service. Key is a user-provided name for the webhook. | NotificationSettingWebhooks |
NotificationSettingWebhooks
| Name | Description | Value |
|---|
NumericalDataDriftMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Numerical' (required) |
| metric | [Required] The numerical data drift metric to calculate. | 'JensenShannonDistance' 'NormalizedWassersteinDistance' 'PopulationStabilityIndex' 'TwoSampleKolmogorovSmirnovTest' (required) |
NumericalDataQualityMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Numerical' (required) |
| metric | [Required] The numerical data quality metric to calculate. | 'DataTypeErrorRate' 'NullValueRate' 'OutOfBoundsRate' (required) |
NumericalPredictionDriftMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Numerical' (required) |
| metric | [Required] The numerical prediction drift metric to calculate. | 'JensenShannonDistance' 'NormalizedWassersteinDistance' 'PopulationStabilityIndex' 'TwoSampleKolmogorovSmirnovTest' (required) |
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 Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
PipelineJob
| Name | Description | Value |
|---|---|---|
| inputs | Inputs for the pipeline job. | PipelineJobInputs |
| jobs | Jobs construct the Pipeline Job. | PipelineJobJobs |
| jobType | [Required] Specifies the type of job. | 'Pipeline' (required) |
| outputs | Outputs for the pipeline job | PipelineJobOutputs |
| settings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | any |
| sourceJobId | ARM resource ID of source job. | string |
PipelineJobInputs
| Name | Description | Value |
|---|
PipelineJobJobs
| Name | Description | Value |
|---|
PipelineJobOutputs
| Name | Description | Value |
|---|
PredictionDriftMetricThresholdBase
| Name | Description | Value |
|---|---|---|
| dataType | Set to 'Categorical' for type CategoricalPredictionDriftMetricThreshold. Set to 'Numerical' for type NumericalPredictionDriftMetricThreshold. | 'Categorical' 'Numerical' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
PredictionDriftMonitoringSignal
| Name | Description | Value |
|---|---|---|
| featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | PredictionDriftMonitoringSignalFeatureDataTypeOverride |
| metricThresholds | [Required] A list of metrics to calculate and their associated thresholds. | PredictionDriftMetricThresholdBase[] (required) |
| productionData | [Required] The data which drift will be calculated for. | MonitoringInputDataBase (required) |
| referenceData | [Required] The data to calculate drift against. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'PredictionDrift' (required) |
PredictionDriftMonitoringSignalFeatureDataTypeOverride
| Name | Description | Value |
|---|
PyTorch
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'PyTorch' (required) |
| processCountPerInstance | Number of processes per node. | int |
QueueSettings
| Name | Description | Value |
|---|---|---|
| jobTier | Controls the compute job tier | 'Basic' 'Null' 'Premium' 'Spot' 'Standard' |
| priority | Controls the priority of the job on a compute. | int |
RandomSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| 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' |
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Random' (required) |
| seed | An optional integer to use as the seed for random number generation | int |
Ray
| Name | Description | Value |
|---|---|---|
| address | The address of Ray head node. | string |
| dashboardPort | The port to bind the dashboard server to. | int |
| distributionType | [Required] Specifies the type of distribution framework. | 'Ray' (required) |
| headNodeAdditionalArgs | Additional arguments passed to ray start in head node. | string |
| includeDashboard | Provide this argument to start the Ray dashboard GUI. | bool |
| port | The port of the head ray process. | int |
| workerNodeAdditionalArgs | Additional arguments passed to ray start in worker node. | string |
RecurrenceSchedule
| Name | Description | Value |
|---|---|---|
| hours | [Required] List of hours for the schedule. | int[] (required) |
| minutes | [Required] List of minutes for the schedule. | int[] (required) |
| monthDays | List of month days for the schedule | int[] |
| weekDays | List of days for the schedule. | String array containing any of: 'Friday' 'Monday' 'Saturday' 'Sunday' 'Thursday' 'Tuesday' 'Wednesday' |
RecurrenceTrigger
| Name | Description | Value |
|---|---|---|
| frequency | [Required] The frequency to trigger schedule. | 'Day' 'Hour' 'Minute' 'Month' 'Week' (required) |
| interval | [Required] Specifies schedule interval in conjunction with frequency | int (required) |
| schedule | The recurrence schedule. | RecurrenceSchedule |
| triggerType | [Required] | 'Recurrence' (required) |
Regression
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'Regression' (required) |
| 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 |
RegressionModelPerformanceMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] The regression model performance metric to calculate. | 'MeanAbsoluteError' 'MeanSquaredError' 'RootMeanSquaredError' (required) |
| modelType | [Required] Specifies the data type of the metric threshold. | 'Regression' (required) |
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' |
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceConfigurationProperties
| Name | Description | Value |
|---|
RollingInputData
| Name | Description | Value |
|---|---|---|
| inputDataType | [Required] Specifies the type of signal to monitor. | 'Rolling' (required) |
| preprocessingComponentId | The ARM resource ID of the component resource used to preprocess the data. | string |
| windowOffset | [Required] The time offset between the end of the data window and the monitor's current run time. | string (required) |
| windowSize | [Required] The size of the trailing data window. | string (required) |
SamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | Set to 'Bayesian' for type BayesianSamplingAlgorithm. Set to 'Grid' for type GridSamplingAlgorithm. Set to 'Random' for type RandomSamplingAlgorithm. | 'Bayesian' 'Grid' 'Random' (required) |
ScheduleActionBase
| Name | Description | Value |
|---|---|---|
| actionType | Set to 'CreateJob' for type JobScheduleAction. Set to 'CreateMonitor' for type CreateMonitorAction. Set to 'ImportData' for type ImportDataAction. Set to 'InvokeBatchEndpoint' for type EndpointScheduleAction. | 'CreateJob' 'CreateMonitor' 'ImportData' 'InvokeBatchEndpoint' (required) |
ScheduleProperties
| Name | Description | Value |
|---|---|---|
| action | [Required] Specifies the action of the schedule | ScheduleActionBase (required) |
| description | The asset description text. | string |
| displayName | Display name of schedule. | string |
| isEnabled | Is the schedule enabled? | bool |
| properties | The asset property dictionary. | ResourceBaseProperties |
| tags | Tag dictionary. Tags can be added, removed, and updated. | ResourceBaseTags |
| trigger | [Required] Specifies the trigger details | TriggerBase (required) |
Seasonality
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoSeasonality. Set to 'Custom' for type CustomSeasonality. | 'Auto' 'Custom' (required) |
SecretConfiguration
| Name | Description | Value |
|---|---|---|
| uri | Secret Uri. Sample Uri : https://myvault.vault.azure.net/secrets/mysecretname/secretversion |
string |
| workspaceSecretName | Name of secret in workspace key vault. | string |
SparkJob
| Name | Description | Value |
|---|---|---|
| archives | Archive files used in the job. | string[] |
| args | Arguments for the job. | string |
| codeId | [Required] ARM resource ID of the code asset. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| 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 |
| environmentVariables | Environment variables included in the job. | SparkJobEnvironmentVariables |
| 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[] |
| jobType | [Required] Specifies the type of job. | 'Spark' (required) |
| outputs | Mapping of output data bindings used in the job. | SparkJobOutputs |
| pyFiles | Python files used in the job. | string[] |
| queueSettings | Queue settings for the job | QueueSettings |
| resources | Compute Resource configuration for the job. | SparkResourceConfiguration |
SparkJobConf
| Name | Description | Value |
|---|
SparkJobEntry
| Name | Description | Value |
|---|---|---|
| sparkJobEntryType | Set to 'SparkJobPythonEntry' for type SparkJobPythonEntry. Set to 'SparkJobScalaEntry' for type SparkJobScalaEntry. | 'SparkJobPythonEntry' 'SparkJobScalaEntry' (required) |
SparkJobEnvironmentVariables
| Name | Description | Value |
|---|
SparkJobInputs
| Name | Description | Value |
|---|
SparkJobOutputs
| Name | Description | Value |
|---|
SparkJobPythonEntry
| Name | Description | Value |
|---|---|---|
| file | [Required] Relative python file path for job entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobPythonEntry' (required) |
SparkJobScalaEntry
| Name | Description | Value |
|---|---|---|
| className | [Required] Scala class name used as entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobScalaEntry' (required) |
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 |
StackEnsembleSettings
| Name | Description | Value |
|---|---|---|
| stackMetaLearnerKWargs | Optional parameters to pass to the initializer of the meta-learner. | any |
| 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' |
StaticInputData
| Name | Description | Value |
|---|---|---|
| inputDataType | [Required] Specifies the type of signal to monitor. | 'Static' (required) |
| preprocessingComponentId | The ARM resource ID of the component resource used to preprocess the data. | string |
| windowEnd | [Required] The end date of the data window. | string (required) |
| windowStart | [Required] The start date of the data window. | string (required) |
SweepJob
| Name | Description | Value |
|---|---|---|
| componentConfiguration | Component Configuration for sweep over component | ComponentConfiguration |
| earlyTermination | Early termination policies enable canceling poor-performing runs before they complete | EarlyTerminationPolicy |
| inputs | Mapping of input data bindings used in the job. | SweepJobInputs |
| jobType | [Required] Specifies the type of job. | 'Sweep' (required) |
| limits | Sweep Job limit. | SweepJobLimits |
| objective | [Required] Optimization objective. | Objective (required) |
| outputs | Mapping of output data bindings used in the job. | SweepJobOutputs |
| queueSettings | Queue settings for the job | QueueSettings |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
| 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 | any (required) |
| trial | [Required] Trial component definition. | TrialComponent (required) |
SweepJobInputs
| Name | Description | Value |
|---|
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 |
SweepJobOutputs
| Name | Description | Value |
|---|
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 |
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) |
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 |
|---|
TableVerticalFeaturizationSettingsTransformerParams
| Name | Description | Value |
|---|
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 |
TargetLags
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoTargetLags. Set to 'Custom' for type CustomTargetLags. | 'Auto' 'Custom' (required) |
TargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoTargetRollingWindowSize. Set to 'Custom' for type CustomTargetRollingWindowSize. | 'Auto' 'Custom' (required) |
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 |
TextClassification
| Name | Description | Value |
|---|---|---|
| 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. | 'Accuracy' 'AUCWeighted' '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 |
| taskType | [Required] Task type for AutoMLJob. | 'TextClassification' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TextClassificationMultilabel
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'TextClassificationMultilabel' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TextNer
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'TextNER' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TopNFeaturesByAttribution
| Name | Description | Value |
|---|---|---|
| filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | 'TopNByAttribution' (required) |
| top | The number of top features to include. | 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 Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| 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 Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| environmentVariables | Environment variables included in the job. | TrialComponentEnvironmentVariables |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
TrialComponentEnvironmentVariables
| Name | Description | Value |
|---|
TriggerBase
| Name | Description | Value |
|---|---|---|
| endTime | Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely |
string |
| startTime | Specifies start time of schedule in ISO 8601 format, but without a UTC offset. | string |
| timeZone | Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones?view=windows-11 |
string |
| triggerType | Set to 'Cron' for type CronTrigger. Set to 'Recurrence' for type RecurrenceTrigger. | 'Cron' 'Recurrence' (required) |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
TritonModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'triton_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
TruncationSelectionPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'TruncationSelection' (required) |
| truncationPercentage | The percentage of runs to cancel at each evaluation interval. | int |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
UriFileJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'uri_file' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
UriFolderJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'uri_folder' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
UserAssignedIdentities
| Name | Description | Value |
|---|
UserAssignedIdentity
| Name | Description | Value |
|---|
UserIdentity
| Name | Description | Value |
|---|---|---|
| identityType | [Required] Specifies the type of identity framework. | 'UserIdentity' (required) |
Webhook
| Name | Description | Value |
|---|---|---|
| eventType | Send callback on a specified notification event | string |
| webhookType | Set to 'AzureDevOps' for type AzureDevOpsWebhook. | 'AzureDevOps' (required) |
Terraform (AzAPI provider) resource definition
The workspaces/schedules 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/schedules resource, add the following Terraform to your template.
resource "azapi_resource" "symbolicname" {
type = "Microsoft.MachineLearningServices/workspaces/schedules@2024-04-01-preview"
name = "string"
body = jsonencode({
properties = {
action = {
actionType = "string"
// For remaining properties, see ScheduleActionBase objects
}
description = "string"
displayName = "string"
isEnabled = bool
properties = {
{customized property} = "string"
}
tags = {
{customized property} = "string"
}
trigger = {
endTime = "string"
startTime = "string"
timeZone = "string"
triggerType = "string"
// For remaining properties, see TriggerBase objects
}
}
})
}
JobBaseProperties objects
Set the jobType property to specify the type of object.
For AutoML, use:
{
environmentId = "string"
environmentVariables = {
{customized property} = "string"
}
jobType = "AutoML"
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
queueSettings = {
jobTier = "string"
priority = int
}
resources = {
dockerArgs = "string"
instanceCount = int
instanceType = "string"
locations = [
"string"
]
maxInstanceCount = int
properties = {
{customized property} = ?
}
shmSize = "string"
}
taskDetails = {
logVerbosity = "string"
targetColumnName = "string"
trainingData = {
description = "string"
jobInputType = "string"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
taskType = "string"
// For remaining properties, see AutoMLVertical objects
}
}
For Command, use:
{
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
}
}
jobType = "Command"
limits = {
jobLimitsType = "string"
timeout = "string"
}
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
queueSettings = {
jobTier = "string"
priority = int
}
resources = {
dockerArgs = "string"
instanceCount = int
instanceType = "string"
locations = [
"string"
]
maxInstanceCount = int
properties = {
{customized property} = ?
}
shmSize = "string"
}
}
For FineTuning, use:
{
fineTuningDetails = {
model = {
description = "string"
jobInputType = "string"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
taskType = "string"
trainingData = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
validationData = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
modelProvider = "string"
// For remaining properties, see FineTuningVertical objects
}
jobType = "FineTuning"
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
}
For Labeling, use:
{
dataConfiguration = {
dataId = "string"
incrementalDataRefresh = "string"
}
jobInstructions = {
uri = "string"
}
jobType = "Labeling"
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:
{
inputs = {
{customized property} = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
}
jobs = {
{customized property} = ?
}
jobType = "Pipeline"
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
settings = ?
sourceJobId = "string"
}
For Spark, use:
{
archives = [
"string"
]
args = "string"
codeId = "string"
conf = {
{customized property} = "string"
}
entry = {
sparkJobEntryType = "string"
// For remaining properties, see SparkJobEntry objects
}
environmentId = "string"
environmentVariables = {
{customized property} = "string"
}
files = [
"string"
]
inputs = {
{customized property} = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
}
jars = [
"string"
]
jobType = "Spark"
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
pyFiles = [
"string"
]
queueSettings = {
jobTier = "string"
priority = int
}
resources = {
instanceType = "string"
runtimeVersion = "string"
}
}
For Sweep, use:
{
componentConfiguration = {
pipelineSettings = ?
}
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
}
}
jobType = "Sweep"
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
}
}
queueSettings = {
jobTier = "string"
priority = int
}
resources = {
dockerArgs = "string"
instanceCount = int
instanceType = "string"
locations = [
"string"
]
maxInstanceCount = int
properties = {
{customized property} = ?
}
shmSize = "string"
}
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"
locations = [
"string"
]
maxInstanceCount = int
properties = {
{customized property} = ?
}
shmSize = "string"
}
}
}
MLAssistConfiguration objects
Set the mlAssist property to specify the type of object.
For Disabled, use:
{
mlAssist = "Disabled"
}
For Enabled, use:
{
inferencingComputeBinding = "string"
mlAssist = "Enabled"
trainingComputeBinding = "string"
}
AutoMLVertical objects
Set the taskType property to specify the type of object.
For Classification, use:
{
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"
}
taskType = "Classification"
testData = {
description = "string"
jobInputType = "string"
mode = "string"
pathOnCompute = "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"
pathOnCompute = "string"
uri = "string"
}
validationDataSize = int
weightColumnName = "string"
}
For Forecasting, use:
{
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"
featuresUnknownAtForecastTime = [
"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"
}
taskType = "Forecasting"
testData = {
description = "string"
jobInputType = "string"
mode = "string"
pathOnCompute = "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"
pathOnCompute = "string"
uri = "string"
}
validationDataSize = int
weightColumnName = "string"
}
For ImageClassification, use:
{
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"
pathOnCompute = "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"
}
taskType = "ImageClassification"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
validationDataSize = int
}
For ImageClassificationMultilabel, use:
{
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"
pathOnCompute = "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"
}
taskType = "ImageClassificationMultilabel"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
validationDataSize = int
}
For ImageInstanceSegmentation, use:
{
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"
pathOnCompute = "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"
logTrainingMetrics = "string"
logValidationLoss = "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 = "string"
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"
}
taskType = "ImageInstanceSegmentation"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
validationDataSize = int
}
For ImageObjectDetection, use:
{
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"
pathOnCompute = "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"
logTrainingMetrics = "string"
logValidationLoss = "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 = "string"
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"
}
taskType = "ImageObjectDetection"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
validationDataSize = int
}
For Regression, use:
{
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"
}
taskType = "Regression"
testData = {
description = "string"
jobInputType = "string"
mode = "string"
pathOnCompute = "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"
pathOnCompute = "string"
uri = "string"
}
validationDataSize = int
weightColumnName = "string"
}
For TextClassification, use:
{
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"
}
taskType = "TextClassification"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
}
For TextClassificationMultilabel, use:
{
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"
}
taskType = "TextClassificationMultilabel"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
}
For TextNER, use:
{
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"
}
taskType = "TextNER"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
}
LabelingJobMediaProperties objects
Set the mediaType property to specify the type of object.
For Image, use:
{
annotationType = "string"
mediaType = "Image"
}
For Text, use:
{
annotationType = "string"
mediaType = "Text"
}
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 Ray, use:
{
address = "string"
dashboardPort = int
distributionType = "Ray"
headNodeAdditionalArgs = "string"
includeDashboard = bool
port = int
workerNodeAdditionalArgs = "string"
}
For TensorFlow, use:
{
distributionType = "TensorFlow"
parameterServerCount = int
workerCount = int
}
NCrossValidations 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
]
}
MonitorComputeIdentityBase objects
Set the computeIdentityType property to specify the type of object.
For AmlToken, use:
{
computeIdentityType = "AmlToken"
}
For ManagedIdentity, use:
{
computeIdentityType = "ManagedIdentity"
identity = {
type = "string"
userAssignedIdentities = {
{customized property} = {
}
}
}
}
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
}
JobOutput objects
Set the jobOutputType property to specify the type of object.
For custom_model, use:
{
assetName = "string"
assetVersion = "string"
autoDeleteSetting = {
condition = "string"
value = "string"
}
jobOutputType = "custom_model"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
For mlflow_model, use:
{
assetName = "string"
assetVersion = "string"
autoDeleteSetting = {
condition = "string"
value = "string"
}
jobOutputType = "mlflow_model"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
For mltable, use:
{
assetName = "string"
assetVersion = "string"
autoDeleteSetting = {
condition = "string"
value = "string"
}
jobOutputType = "mltable"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
For triton_model, use:
{
assetName = "string"
assetVersion = "string"
autoDeleteSetting = {
condition = "string"
value = "string"
}
jobOutputType = "triton_model"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
For uri_file, use:
{
assetName = "string"
assetVersion = "string"
autoDeleteSetting = {
condition = "string"
value = "string"
}
jobOutputType = "uri_file"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
For uri_folder, use:
{
assetName = "string"
assetVersion = "string"
autoDeleteSetting = {
condition = "string"
value = "string"
}
jobOutputType = "uri_folder"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
MonitoringInputDataBase objects
Set the inputDataType property to specify the type of object.
For Fixed, use:
{
inputDataType = "Fixed"
}
For Rolling, use:
{
inputDataType = "Rolling"
preprocessingComponentId = "string"
windowOffset = "string"
windowSize = "string"
}
For Static, use:
{
inputDataType = "Static"
preprocessingComponentId = "string"
windowEnd = "string"
windowStart = "string"
}
SparkJobEntry objects
Set the sparkJobEntryType property to specify the type of object.
For SparkJobPythonEntry, use:
{
file = "string"
sparkJobEntryType = "SparkJobPythonEntry"
}
For SparkJobScalaEntry, use:
{
className = "string"
sparkJobEntryType = "SparkJobScalaEntry"
}
Webhook objects
Set the webhookType property to specify the type of object.
For AzureDevOps, use:
{
webhookType = "AzureDevOps"
}
IdentityConfiguration objects
Set the identityType property to specify the type of object.
For AMLToken, use:
{
identityType = "AMLToken"
}
For Managed, use:
{
clientId = "string"
identityType = "Managed"
objectId = "string"
resourceId = "string"
}
For UserIdentity, use:
{
identityType = "UserIdentity"
}
ScheduleActionBase objects
Set the actionType property to specify the type of object.
For CreateJob, use:
{
actionType = "CreateJob"
jobDefinition = {
componentId = "string"
computeId = "string"
description = "string"
displayName = "string"
experimentName = "string"
identity = {
identityType = "string"
// For remaining properties, see IdentityConfiguration objects
}
isArchived = bool
notificationSetting = {
emailOn = [
"string"
]
emails = [
"string"
]
webhooks = {
{customized property} = {
eventType = "string"
webhookType = "string"
// For remaining properties, see Webhook objects
}
}
}
properties = {
{customized property} = "string"
}
secretsConfiguration = {
{customized property} = {
uri = "string"
workspaceSecretName = "string"
}
}
services = {
{customized property} = {
endpoint = "string"
jobServiceType = "string"
nodes = {
nodesValueType = "string"
// For remaining properties, see Nodes objects
}
port = int
properties = {
{customized property} = "string"
}
}
}
tags = {
{customized property} = "string"
}
jobType = "string"
// For remaining properties, see JobBaseProperties objects
}
}
For CreateMonitor, use:
{
actionType = "CreateMonitor"
monitorDefinition = {
alertNotificationSettings = {
emailNotificationSettings = {
emails = [
"string"
]
}
}
computeConfiguration = {
computeType = "string"
// For remaining properties, see MonitorComputeConfigurationBase objects
}
monitoringTarget = {
deploymentId = "string"
modelId = "string"
taskType = "string"
}
signals = {
{customized property} = {
notificationTypes = [
"string"
]
properties = {
{customized property} = "string"
}
signalType = "string"
// For remaining properties, see MonitoringSignalBase objects
}
}
}
}
For ImportData, use:
{
actionType = "ImportData"
dataImportDefinition = {
assetName = "string"
autoDeleteSetting = {
condition = "string"
value = "string"
}
dataType = "string"
dataUri = "string"
description = "string"
intellectualProperty = {
protectionLevel = "string"
publisher = "string"
}
isAnonymous = bool
isArchived = bool
properties = {
{customized property} = "string"
}
source = {
connection = "string"
sourceType = "string"
// For remaining properties, see DataImportSource objects
}
stage = "string"
tags = {
{customized property} = "string"
}
}
}
For InvokeBatchEndpoint, use:
{
actionType = "InvokeBatchEndpoint"
endpointInvocationDefinition = ?
}
ModelPerformanceMetricThresholdBase objects
Set the modelType property to specify the type of object.
For Classification, use:
{
metric = "string"
modelType = "Classification"
}
For Regression, use:
{
metric = "string"
modelType = "Regression"
}
DataQualityMetricThresholdBase objects
Set the dataType property to specify the type of object.
For Categorical, use:
{
dataType = "Categorical"
metric = "string"
}
For Numerical, use:
{
dataType = "Numerical"
metric = "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:
{
logbase = "string"
rule = "string"
samplingAlgorithmType = "Random"
seed = int
}
FineTuningVertical objects
Set the modelProvider property to specify the type of object.
For AzureOpenAI, use:
{
hyperParameters = {
batchSize = int
learningRateMultiplier = int
nEpochs = int
}
modelProvider = "AzureOpenAI"
}
For Custom, use:
{
hyperParameters = {
{customized property} = "string"
}
modelProvider = "Custom"
}
MonitoringSignalBase objects
Set the signalType property to specify the type of object.
For Custom, use:
{
componentId = "string"
inputAssets = {
{customized property} = {
columns = {
{customized property} = "string"
}
dataContext = "string"
jobInputType = "string"
uri = "string"
inputDataType = "string"
// For remaining properties, see MonitoringInputDataBase objects
}
}
inputs = {
{customized property} = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
}
metricThresholds = [
{
metric = "string"
threshold = {
value = int
}
}
]
signalType = "Custom"
workspaceConnection = {
environmentVariables = {
{customized property} = "string"
}
secrets = {
{customized property} = "string"
}
}
}
For DataDrift, use:
{
dataSegment = {
feature = "string"
values = [
"string"
]
}
featureDataTypeOverride = {
{customized property} = "string"
}
featureImportanceSettings = {
mode = "string"
targetColumn = "string"
}
features = {
filterType = "string"
// For remaining properties, see MonitoringFeatureFilterBase objects
}
metricThresholds = [
{
threshold = {
value = int
}
dataType = "string"
// For remaining properties, see DataDriftMetricThresholdBase objects
}
]
productionData = {
columns = {
{customized property} = "string"
}
dataContext = "string"
jobInputType = "string"
uri = "string"
inputDataType = "string"
// For remaining properties, see MonitoringInputDataBase objects
}
referenceData = {
columns = {
{customized property} = "string"
}
dataContext = "string"
jobInputType = "string"
uri = "string"
inputDataType = "string"
// For remaining properties, see MonitoringInputDataBase objects
}
signalType = "DataDrift"
}
For DataQuality, use:
{
featureDataTypeOverride = {
{customized property} = "string"
}
featureImportanceSettings = {
mode = "string"
targetColumn = "string"
}
features = {
filterType = "string"
// For remaining properties, see MonitoringFeatureFilterBase objects
}
metricThresholds = [
{
threshold = {
value = int
}
dataType = "string"
// For remaining properties, see DataQualityMetricThresholdBase objects
}
]
productionData = {
columns = {
{customized property} = "string"
}
dataContext = "string"
jobInputType = "string"
uri = "string"
inputDataType = "string"
// For remaining properties, see MonitoringInputDataBase objects
}
referenceData = {
columns = {
{customized property} = "string"
}
dataContext = "string"
jobInputType = "string"
uri = "string"
inputDataType = "string"
// For remaining properties, see MonitoringInputDataBase objects
}
signalType = "DataQuality"
}
For FeatureAttributionDrift, use:
{
featureDataTypeOverride = {
{customized property} = "string"
}
featureImportanceSettings = {
mode = "string"
targetColumn = "string"
}
metricThreshold = {
metric = "string"
threshold = {
value = int
}
}
productionData = [
{
columns = {
{customized property} = "string"
}
dataContext = "string"
jobInputType = "string"
uri = "string"
inputDataType = "string"
// For remaining properties, see MonitoringInputDataBase objects
}
]
referenceData = {
columns = {
{customized property} = "string"
}
dataContext = "string"
jobInputType = "string"
uri = "string"
inputDataType = "string"
// For remaining properties, see MonitoringInputDataBase objects
}
signalType = "FeatureAttributionDrift"
}
For GenerationSafetyQuality, use:
{
metricThresholds = [
{
metric = "string"
threshold = {
value = int
}
}
]
productionData = [
{
columns = {
{customized property} = "string"
}
dataContext = "string"
jobInputType = "string"
uri = "string"
inputDataType = "string"
// For remaining properties, see MonitoringInputDataBase objects
}
]
samplingRate = int
signalType = "GenerationSafetyQuality"
workspaceConnectionId = "string"
}
For GenerationTokenStatistics, use:
{
metricThresholds = [
{
metric = "string"
threshold = {
value = int
}
}
]
productionData = [
{
columns = {
{customized property} = "string"
}
dataContext = "string"
jobInputType = "string"
uri = "string"
inputDataType = "string"
// For remaining properties, see MonitoringInputDataBase objects
}
]
samplingRate = int
signalType = "GenerationTokenStatistics"
}
For ModelPerformance, use:
{
dataSegment = {
feature = "string"
values = [
"string"
]
}
metricThreshold = {
threshold = {
value = int
}
modelType = "string"
// For remaining properties, see ModelPerformanceMetricThresholdBase objects
}
productionData = [
{
columns = {
{customized property} = "string"
}
dataContext = "string"
jobInputType = "string"
uri = "string"
inputDataType = "string"
// For remaining properties, see MonitoringInputDataBase objects
}
]
referenceData = {
columns = {
{customized property} = "string"
}
dataContext = "string"
jobInputType = "string"
uri = "string"
inputDataType = "string"
// For remaining properties, see MonitoringInputDataBase objects
}
signalType = "ModelPerformance"
}
For PredictionDrift, use:
{
featureDataTypeOverride = {
{customized property} = "string"
}
metricThresholds = [
{
threshold = {
value = int
}
dataType = "string"
// For remaining properties, see PredictionDriftMetricThresholdBase objects
}
]
productionData = {
columns = {
{customized property} = "string"
}
dataContext = "string"
jobInputType = "string"
uri = "string"
inputDataType = "string"
// For remaining properties, see MonitoringInputDataBase objects
}
referenceData = {
columns = {
{customized property} = "string"
}
dataContext = "string"
jobInputType = "string"
uri = "string"
inputDataType = "string"
// For remaining properties, see MonitoringInputDataBase objects
}
signalType = "PredictionDrift"
}
DataDriftMetricThresholdBase objects
Set the dataType property to specify the type of object.
For Categorical, use:
{
dataType = "Categorical"
metric = "string"
}
For Numerical, use:
{
dataType = "Numerical"
metric = "string"
}
JobInput objects
Set the jobInputType property to specify the type of object.
For custom_model, use:
{
jobInputType = "custom_model"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
For literal, use:
{
jobInputType = "literal"
value = "string"
}
For mlflow_model, use:
{
jobInputType = "mlflow_model"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
For mltable, use:
{
jobInputType = "mltable"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
For triton_model, use:
{
jobInputType = "triton_model"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
For uri_file, use:
{
jobInputType = "uri_file"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
For uri_folder, use:
{
jobInputType = "uri_folder"
mode = "string"
pathOnCompute = "string"
uri = "string"
}
Seasonality objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode = "Auto"
}
For Custom, use:
{
mode = "Custom"
value = 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
}
MonitorComputeConfigurationBase objects
Set the computeType property to specify the type of object.
For ServerlessSpark, use:
{
computeIdentity = {
computeIdentityType = "string"
// For remaining properties, see MonitorComputeIdentityBase objects
}
computeType = "ServerlessSpark"
instanceType = "string"
runtimeVersion = "string"
}
DataImportSource objects
Set the sourceType property to specify the type of object.
For database, use:
{
query = "string"
sourceType = "database"
storedProcedure = "string"
storedProcedureParams = [
{
{customized property} = "string"
}
]
tableName = "string"
}
For file_system, use:
{
path = "string"
sourceType = "file_system"
}
MonitoringFeatureFilterBase objects
Set the filterType property to specify the type of object.
For AllFeatures, use:
{
filterType = "AllFeatures"
}
For FeatureSubset, use:
{
features = [
"string"
]
filterType = "FeatureSubset"
}
For TopNByAttribution, use:
{
filterType = "TopNByAttribution"
top = int
}
PredictionDriftMetricThresholdBase objects
Set the dataType property to specify the type of object.
For Categorical, use:
{
dataType = "Categorical"
metric = "string"
}
For Numerical, use:
{
dataType = "Numerical"
metric = "string"
}
TriggerBase objects
Set the triggerType property to specify the type of object.
For Cron, use:
{
expression = "string"
triggerType = "Cron"
}
For Recurrence, use:
{
frequency = "string"
interval = int
schedule = {
hours = [
int
]
minutes = [
int
]
monthDays = [
int
]
weekDays = [
"string"
]
}
triggerType = "Recurrence"
}
ForecastHorizon objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode = "Auto"
}
For Custom, use:
{
mode = "Custom"
value = int
}
Nodes objects
Set the nodesValueType property to specify the type of object.
For All, use:
{
nodesValueType = "All"
}
Property values
AllFeatures
| Name | Description | Value |
|---|---|---|
| filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | 'AllFeatures' (required) |
AllNodes
| Name | Description | Value |
|---|---|---|
| nodesValueType | [Required] Type of the Nodes value | 'All' (required) |
AmlToken
| Name | Description | Value |
|---|---|---|
| identityType | [Required] Specifies the type of identity framework. | 'AMLToken' (required) |
AmlTokenComputeIdentity
| Name | Description | Value |
|---|---|---|
| computeIdentityType | [Required] Monitor compute identity type enum. | 'AmlToken' (required) |
AutoDeleteSetting
| Name | Description | Value |
|---|---|---|
| condition | When to check if an asset is expired | 'CreatedGreaterThan' 'LastAccessedGreaterThan' |
| value | Expiration condition value. | string |
AutoForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set forecast horizon value selection mode. | 'Auto' (required) |
AutologgerSettings
| Name | Description | Value |
|---|---|---|
| mlflowAutologger | [Required] Indicates whether mlflow autologger is enabled. | 'Disabled' 'Enabled' (required) |
AutoMLJob
| Name | Description | Value |
|---|---|---|
| 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 |
| jobType | [Required] Specifies the type of job. | 'AutoML' (required) |
| outputs | Mapping of output data bindings used in the job. | AutoMLJobOutputs |
| queueSettings | Queue settings for the job | QueueSettings |
| 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 |
|---|
AutoMLJobOutputs
| Name | Description | Value |
|---|
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 |
| taskType | Set to 'Classification' for type Classification. Set to 'Forecasting' for type Forecasting. Set to 'ImageClassification' for type ImageClassification. Set to 'ImageClassificationMultilabel' for type ImageClassificationMultilabel. Set to 'ImageInstanceSegmentation' for type ImageInstanceSegmentation. Set to 'ImageObjectDetection' for type ImageObjectDetection. Set to 'Regression' for type Regression. Set to 'TextClassification' for type TextClassification. Set to 'TextClassificationMultilabel' for type TextClassificationMultilabel. Set to 'TextNER' for type TextNer. | 'Classification' 'Forecasting' 'ImageClassification' 'ImageClassificationMultilabel' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'Regression' 'TextClassification' 'TextClassificationMultilabel' 'TextNER' (required) |
| trainingData | [Required] Training data input. | MLTableJobInput (required) |
AutoNCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | [Required] Mode for determining N-Cross validations. | 'Auto' (required) |
AutoSeasonality
| Name | Description | Value |
|---|---|---|
| mode | [Required] Seasonality mode. | 'Auto' (required) |
AutoTargetLags
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set target lags mode - Auto/Custom | 'Auto' (required) |
AutoTargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | [Required] TargetRollingWindowSiz detection mode. | 'Auto' (required) |
AzureDevOpsWebhook
| Name | Description | Value |
|---|---|---|
| webhookType | [Required] Specifies the type of service to send a callback | 'AzureDevOps' (required) |
AzureOpenAiFineTuning
| Name | Description | Value |
|---|---|---|
| hyperParameters | HyperParameters for fine tuning Azure Open AI model. | AzureOpenAiHyperParameters |
| modelProvider | [Required] Enum to determine the type of fine tuning. | 'AzureOpenAI' (required) |
AzureOpenAiHyperParameters
| Name | Description | Value |
|---|---|---|
| batchSize | Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. | int |
| learningRateMultiplier | Scaling factor for the learning rate. A smaller learning rate may be useful to avoid over fitting. | int |
| nEpochs | The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. | int |
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 |
BayesianSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Bayesian' (required) |
CategoricalDataDriftMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Categorical' (required) |
| metric | [Required] The categorical data drift metric to calculate. | 'JensenShannonDistance' 'PearsonsChiSquaredTest' 'PopulationStabilityIndex' (required) |
CategoricalDataQualityMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Categorical' (required) |
| metric | [Required] The categorical data quality metric to calculate. | 'DataTypeErrorRate' 'NullValueRate' 'OutOfBoundsRate' (required) |
CategoricalPredictionDriftMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Categorical' (required) |
| metric | [Required] The categorical prediction drift metric to calculate. | 'JensenShannonDistance' 'PearsonsChiSquaredTest' 'PopulationStabilityIndex' (required) |
Classification
| Name | Description | Value |
|---|---|---|
| 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. | 'Accuracy' 'AUCWeighted' '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 |
| taskType | [Required] Task type for AutoMLJob. | 'Classification' (required) |
| 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 |
ClassificationModelPerformanceMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] The classification model performance to calculate. | 'Accuracy' 'Precision' 'Recall' (required) |
| modelType | [Required] Specifies the data type of the metric threshold. | 'Classification' (required) |
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' |
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. |
any |
CommandJob
| Name | Description | Value |
|---|---|---|
| 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 Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, Ray, or null. | DistributionConfiguration |
| environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| environmentVariables | Environment variables included in the job. | CommandJobEnvironmentVariables |
| inputs | Mapping of input data bindings used in the job. | CommandJobInputs |
| jobType | [Required] Specifies the type of job. | 'Command' (required) |
| limits | Command Job limit. | CommandJobLimits |
| outputs | Mapping of output data bindings used in the job. | CommandJobOutputs |
| queueSettings | Queue settings for the job | QueueSettings |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
CommandJobEnvironmentVariables
| Name | Description | Value |
|---|
CommandJobInputs
| Name | Description | Value |
|---|
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 |
|---|
ComponentConfiguration
| Name | Description | Value |
|---|---|---|
| pipelineSettings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | any |
CreateMonitorAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'CreateMonitor' (required) |
| monitorDefinition | [Required] Defines the monitor. | MonitorDefinition (required) |
CronTrigger
| Name | Description | Value |
|---|---|---|
| expression | [Required] Specifies cron expression of schedule. The expression should follow NCronTab format. |
string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| triggerType | [Required] | 'Cron' (required) |
CustomForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set forecast horizon value selection mode. | 'Custom' (required) |
| value | [Required] Forecast horizon value. | int (required) |
CustomMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] The user-defined metric to calculate. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
CustomModelFineTuning
| Name | Description | Value |
|---|---|---|
| hyperParameters | HyperParameters for fine tuning custom model. | CustomModelFineTuningHyperParameters |
| modelProvider | [Required] Enum to determine the type of fine tuning. | 'Custom' (required) |
CustomModelFineTuningHyperParameters
| Name | Description | Value |
|---|
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
CustomModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'custom_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
CustomMonitoringSignal
| Name | Description | Value |
|---|---|---|
| componentId | [Required] ARM resource ID of the component resource used to calculate the custom metrics. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| inputAssets | Monitoring assets to take as input. Key is the component input port name, value is the data asset. | CustomMonitoringSignalInputAssets |
| inputs | Extra component parameters to take as input. Key is the component literal input port name, value is the parameter value. | CustomMonitoringSignalInputs |
| metricThresholds | [Required] A list of metrics to calculate and their associated thresholds. | CustomMetricThreshold[] (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'Custom' (required) |
| workspaceConnection | A list of metrics to calculate and their associated thresholds. | MonitoringWorkspaceConnection |
CustomMonitoringSignalInputAssets
| Name | Description | Value |
|---|
CustomMonitoringSignalInputs
| Name | Description | Value |
|---|
CustomNCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | [Required] Mode for determining N-Cross validations. | 'Custom' (required) |
| value | [Required] N-Cross validations value. | int (required) |
CustomSeasonality
| Name | Description | Value |
|---|---|---|
| mode | [Required] Seasonality mode. | 'Custom' (required) |
| value | [Required] Seasonality value. | int (required) |
CustomTargetLags
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set target lags mode - Auto/Custom | 'Custom' (required) |
| values | [Required] Set target lags values. | int[] (required) |
CustomTargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | [Required] TargetRollingWindowSiz detection mode. | 'Custom' (required) |
| value | [Required] TargetRollingWindowSize value. | int (required) |
DatabaseSource
| Name | Description | Value |
|---|---|---|
| query | SQL Query statement for data import Database source | string |
| sourceType | [Required] Specifies the type of data. | 'database' (required) |
| storedProcedure | SQL StoredProcedure on data import Database source | string |
| storedProcedureParams | SQL StoredProcedure parameters | DatabaseSourceStoredProcedureParamsItem[] |
| tableName | Name of the table on data import Database source | string |
DatabaseSourceStoredProcedureParamsItem
| Name | Description | Value |
|---|
DataDriftMetricThresholdBase
| Name | Description | Value |
|---|---|---|
| dataType | Set to 'Categorical' for type CategoricalDataDriftMetricThreshold. Set to 'Numerical' for type NumericalDataDriftMetricThreshold. | 'Categorical' 'Numerical' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
DataDriftMonitoringSignal
| Name | Description | Value |
|---|---|---|
| dataSegment | The data segment used for scoping on a subset of the data population. | MonitoringDataSegment |
| featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | DataDriftMonitoringSignalFeatureDataTypeOverride |
| featureImportanceSettings | The settings for computing feature importance. | FeatureImportanceSettings |
| features | The feature filter which identifies which feature to calculate drift over. | MonitoringFeatureFilterBase |
| metricThresholds | [Required] A list of metrics to calculate and their associated thresholds. | DataDriftMetricThresholdBase[] (required) |
| productionData | [Required] The data which drift will be calculated for. | MonitoringInputDataBase (required) |
| referenceData | [Required] The data to calculate drift against. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'DataDrift' (required) |
DataDriftMonitoringSignalFeatureDataTypeOverride
| Name | Description | Value |
|---|
DataImport
| Name | Description | Value |
|---|---|---|
| assetName | Name of the asset for data import job to create | string |
| autoDeleteSetting | Specifies the lifecycle setting of managed data asset. | AutoDeleteSetting |
| dataType | [Required] Specifies the type of data. | 'mltable' 'uri_file' 'uri_folder' (required) |
| dataUri | [Required] Uri of the data. Example: https://go.microsoft.com/fwlink/?linkid=2202330 | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| description | The asset description text. | string |
| intellectualProperty | Intellectual Property details. Used if data is an Intellectual Property. | IntellectualProperty |
| isAnonymous | If the name version are system generated (anonymous registration). For types where Stage is defined, when Stage is provided it will be used to populate IsAnonymous | bool |
| isArchived | Is the asset archived? For types where Stage is defined, when Stage is provided it will be used to populate IsArchived | bool |
| properties | The asset property dictionary. | ResourceBaseProperties |
| source | Source data of the asset to import from | DataImportSource |
| stage | Stage in the data lifecycle assigned to this data asset | string |
| tags | Tag dictionary. Tags can be added, removed, and updated. | ResourceBaseTags |
DataImportSource
| Name | Description | Value |
|---|---|---|
| connection | Workspace connection for data import source storage | string |
| sourceType | Set to 'database' for type DatabaseSource. Set to 'file_system' for type FileSystemSource. | 'database' 'file_system' (required) |
DataQualityMetricThresholdBase
| Name | Description | Value |
|---|---|---|
| dataType | Set to 'Categorical' for type CategoricalDataQualityMetricThreshold. Set to 'Numerical' for type NumericalDataQualityMetricThreshold. | 'Categorical' 'Numerical' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
DataQualityMonitoringSignal
| Name | Description | Value |
|---|---|---|
| featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | DataQualityMonitoringSignalFeatureDataTypeOverride |
| featureImportanceSettings | The settings for computing feature importance. | FeatureImportanceSettings |
| features | The features to calculate drift over. | MonitoringFeatureFilterBase |
| metricThresholds | [Required] A list of metrics to calculate and their associated thresholds. | DataQualityMetricThresholdBase[] (required) |
| productionData | [Required] The data produced by the production service which drift will be calculated for. | MonitoringInputDataBase (required) |
| referenceData | [Required] The data to calculate drift against. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'DataQuality' (required) |
DataQualityMonitoringSignalFeatureDataTypeOverride
| Name | Description | Value |
|---|
DistributionConfiguration
| Name | Description | Value |
|---|---|---|
| distributionType | Set to 'Mpi' for type Mpi. Set to 'PyTorch' for type PyTorch. Set to 'Ray' for type Ray. Set to 'TensorFlow' for type TensorFlow. | 'Mpi' 'PyTorch' 'Ray' 'TensorFlow' (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 to 'Bandit' for type BanditPolicy. Set to 'MedianStopping' for type MedianStoppingPolicy. Set to 'TruncationSelection' for type TruncationSelectionPolicy. | 'Bandit' 'MedianStopping' 'TruncationSelection' (required) |
EndpointScheduleAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'InvokeBatchEndpoint' (required) |
| endpointInvocationDefinition | [Required] Defines Schedule action definition details. <see href="TBD" /> |
any (required) |
FeatureAttributionDriftMonitoringSignal
| Name | Description | Value |
|---|---|---|
| featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | FeatureAttributionDriftMonitoringSignalFeatureDataTypeOverride |
| featureImportanceSettings | [Required] The settings for computing feature importance. | FeatureImportanceSettings (required) |
| metricThreshold | [Required] A list of metrics to calculate and their associated thresholds. | FeatureAttributionMetricThreshold (required) |
| productionData | [Required] The data which drift will be calculated for. | MonitoringInputDataBase[] (required) |
| referenceData | [Required] The data to calculate drift against. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'FeatureAttributionDrift' (required) |
FeatureAttributionDriftMonitoringSignalFeatureDataTypeOverride
| Name | Description | Value |
|---|
FeatureAttributionMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] The feature attribution metric to calculate. | 'NormalizedDiscountedCumulativeGain' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
FeatureImportanceSettings
| Name | Description | Value |
|---|---|---|
| mode | The mode of operation for computing feature importance. | 'Disabled' 'Enabled' |
| targetColumn | The name of the target column within the input data asset. | string |
FeatureSubset
| Name | Description | Value |
|---|---|---|
| features | [Required] The list of features to include. | string[] (required) |
| filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | 'FeatureSubset' (required) |
FileSystemSource
| Name | Description | Value |
|---|---|---|
| path | Path on data import FileSystem source | string |
| sourceType | [Required] Specifies the type of data. | 'file_system' (required) |
FineTuningJob
| Name | Description | Value |
|---|---|---|
| fineTuningDetails | [Required] | FineTuningVertical (required) |
| jobType | [Required] Specifies the type of job. | 'FineTuning' (required) |
| outputs | [Required] | FineTuningJobOutputs (required) |
FineTuningJobOutputs
| Name | Description | Value |
|---|
FineTuningVertical
| Name | Description | Value |
|---|---|---|
| model | [Required] Input model for fine tuning. | MLFlowModelJobInput (required) |
| modelProvider | Set to 'AzureOpenAI' for type AzureOpenAiFineTuning. Set to 'Custom' for type CustomModelFineTuning. | 'AzureOpenAI' 'Custom' (required) |
| taskType | [Required] Fine tuning task type. | 'ChatCompletion' 'ImageClassification' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'QuestionAnswering' 'TextClassification' 'TextCompletion' 'TextSummarization' 'TextTranslation' 'TokenClassification' 'VideoMultiObjectTracking' (required) |
| trainingData | [Required] Training data for fine tuning. | JobInput (required) |
| validationData | Validation data for fine tuning. | JobInput |
FixedInputData
| Name | Description | Value |
|---|---|---|
| inputDataType | [Required] Specifies the type of signal to monitor. | 'Fixed' (required) |
ForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoForecastHorizon. Set to 'Custom' for type CustomForecastHorizon. | 'Auto' 'Custom' (required) |
Forecasting
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'Forecasting' (required) |
| 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 bethree days apart. |
int |
| featureLags | Flag for generating lags for the numeric features with 'auto' or null. | 'Auto' 'None' |
| featuresUnknownAtForecastTime | The feature columns that are available for training but unknown at the time of forecast/inference. If features_unknown_at_forecast_time is not set, it is assumed that all the feature columns in the dataset are known at inference time. |
string[] |
| 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' |
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' 'SeasonalAverage' 'SeasonalNaive' 'SGD' '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' 'SeasonalAverage' 'SeasonalNaive' 'SGD' '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' |
GenerationSafetyQualityMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] Gets or sets the feature attribution metric to calculate. | 'AcceptableCoherenceScorePerInstance' 'AcceptableFluencyScorePerInstance' 'AcceptableGroundednessScorePerInstance' 'AcceptableRelevanceScorePerInstance' 'AcceptableSimilarityScorePerInstance' 'AggregatedCoherencePassRate' 'AggregatedFluencyPassRate' 'AggregatedGroundednessPassRate' 'AggregatedRelevancePassRate' 'AggregatedSimilarityPassRate' (required) |
| threshold | Gets or sets the threshold value. If null, a default value will be set depending on the selected metric. |
MonitoringThreshold |
GenerationSafetyQualityMonitoringSignal
| Name | Description | Value |
|---|---|---|
| metricThresholds | [Required] Gets or sets the metrics to calculate and the corresponding thresholds. | GenerationSafetyQualityMetricThreshold[] (required) |
| productionData | Gets or sets the production data for computing metrics. | MonitoringInputDataBase[] |
| samplingRate | [Required] The sample rate of the production data, should be greater than 0 and at most 1. | int (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'GenerationSafetyQuality' (required) |
| workspaceConnectionId | Gets or sets the workspace connection ID used to connect to the content generation endpoint. | string |
GenerationTokenUsageMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] Gets or sets the feature attribution metric to calculate. | 'TotalTokenCount' 'TotalTokenCountPerGroup' (required) |
| threshold | Gets or sets the threshold value. If null, a default value will be set depending on the selected metric. |
MonitoringThreshold |
GenerationTokenUsageSignal
| Name | Description | Value |
|---|---|---|
| metricThresholds | [Required] Gets or sets the metrics to calculate and the corresponding thresholds. | GenerationTokenUsageMetricThreshold[] (required) |
| productionData | Gets or sets the production data for computing metrics. | MonitoringInputDataBase[] |
| samplingRate | [Required] The sample rate of the production data, should be greater than 0 and at most 1. | int (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'GenerationTokenStatistics' (required) |
GridSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Grid' (required) |
IdentityConfiguration
| Name | Description | Value |
|---|---|---|
| identityType | Set to 'AMLToken' for type AmlToken. Set to 'Managed' for type ManagedIdentity. Set to 'UserIdentity' for type UserIdentity. | 'AMLToken' 'Managed' 'UserIdentity' (required) |
ImageClassification
| Name | Description | Value |
|---|---|---|
| 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. | 'Accuracy' 'AUCWeighted' '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 |
| taskType | [Required] Task type for AutoMLJob. | 'ImageClassification' (required) |
| 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 |
ImageClassificationMultilabel
| Name | Description | Value |
|---|---|---|
| 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. | 'Accuracy' 'AUCWeighted' '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 |
| taskType | [Required] Task type for AutoMLJob. | 'ImageClassificationMultilabel' (required) |
| 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 |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'ImageInstanceSegmentation' (required) |
| 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 |
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 |
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 |
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 |
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' |
| logTrainingMetrics | Enable computing and logging training metrics. | 'Disable' 'Enable' |
| logValidationLoss | Enable computing and logging validation loss. | 'Disable' 'Enable' |
| 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 |
ImageObjectDetection
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'ImageObjectDetection' (required) |
| 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 |
ImageSweepSettings
| Name | Description | Value |
|---|---|---|
| earlyTermination | Type of early termination policy. | EarlyTerminationPolicy |
| samplingAlgorithm | [Required] Type of the hyperparameter sampling algorithms. | 'Bayesian' 'Grid' 'Random' (required) |
ImportDataAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'ImportData' (required) |
| dataImportDefinition | [Required] Defines Schedule action definition details. | DataImport (required) |
IntellectualProperty
| Name | Description | Value |
|---|---|---|
| protectionLevel | Protection level of the Intellectual Property. | 'All' 'None' |
| publisher | [Required] Publisher of the Intellectual Property. Must be the same as Registry publisher name. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (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 |
| jobType | Set to 'AutoML' for type AutoMLJob. Set to 'Command' for type CommandJob. Set to 'FineTuning' for type FineTuningJob. Set to 'Labeling' for type LabelingJobProperties. Set to 'Pipeline' for type PipelineJob. Set to 'Spark' for type SparkJob. Set to 'Sweep' for type SweepJob. | 'AutoML' 'Command' 'FineTuning' 'Labeling' 'Pipeline' 'Spark' 'Sweep' (required) |
| notificationSetting | Notification setting for the job | NotificationSetting |
| properties | The asset property dictionary. | ResourceBaseProperties |
| secretsConfiguration | Configuration for secrets to be made available during runtime. | JobBaseSecretsConfiguration |
| 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. | ResourceBaseTags |
JobBaseSecretsConfiguration
| Name | Description | Value |
|---|
JobBaseServices
| Name | Description | Value |
|---|
JobInput
| Name | Description | Value |
|---|---|---|
| description | Description for the input. | string |
| jobInputType | Set to 'custom_model' for type CustomModelJobInput. Set to 'literal' for type LiteralJobInput. Set to 'mlflow_model' for type MLFlowModelJobInput. Set to 'mltable' for type MLTableJobInput. Set to 'triton_model' for type TritonModelJobInput. Set to 'uri_file' for type UriFileJobInput. Set to 'uri_folder' for type UriFolderJobInput. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
JobOutput
| Name | Description | Value |
|---|---|---|
| description | Description for the output. | string |
| jobOutputType | Set to 'custom_model' for type CustomModelJobOutput. Set to 'mlflow_model' for type MLFlowModelJobOutput. Set to 'mltable' for type MLTableJobOutput. Set to 'triton_model' for type TritonModelJobOutput. Set to 'uri_file' for type UriFileJobOutput. Set to 'uri_folder' for type UriFolderJobOutput. | 'custom_model' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
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 |
| locations | Locations where the job can run. | string[] |
| maxInstanceCount | Optional max allowed number of instances or nodes to be used by the compute target. For use with elastic training, currently supported by PyTorch distribution type only. |
int |
| 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] |
JobScheduleAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'CreateJob' (required) |
| jobDefinition | [Required] Defines Schedule action definition details. | JobBaseProperties (required) |
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 |
JobServiceProperties
| Name | Description | Value |
|---|
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 |
|---|
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 |
|---|
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' |
LabelingJobImageProperties
| Name | Description | Value |
|---|---|---|
| annotationType | Annotation type of image labeling job. | 'BoundingBox' 'Classification' 'InstanceSegmentation' |
| mediaType | [Required] Media type of the job. | 'Image' (required) |
LabelingJobInstructions
| Name | Description | Value |
|---|---|---|
| uri | The link to a page with detailed labeling instructions for labelers. | string |
LabelingJobLabelCategories
| Name | Description | Value |
|---|
LabelingJobMediaProperties
| Name | Description | Value |
|---|---|---|
| mediaType | Set to 'Image' for type LabelingJobImageProperties. Set to 'Text' for type LabelingJobTextProperties. | 'Image' 'Text' (required) |
LabelingJobProperties
| Name | Description | Value |
|---|---|---|
| dataConfiguration | Configuration of data used in the job. | LabelingDataConfiguration |
| jobInstructions | Labeling instructions of the job. | LabelingJobInstructions |
| jobType | [Required] Specifies the type of job. | 'Labeling' (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 |
LabelingJobTextProperties
| Name | Description | Value |
|---|---|---|
| annotationType | Annotation type of text labeling job. | 'Classification' 'NamedEntityRecognition' |
| mediaType | [Required] Media type of the job. | 'Text' (required) |
LiteralJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'literal' (required) |
| value | [Required] Literal value for the input. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
ManagedComputeIdentity
| Name | Description | Value |
|---|---|---|
| computeIdentityType | [Required] Monitor compute identity type enum. | 'ManagedIdentity' (required) |
| identity | Managed service identity (system assigned and/or user assigned identities) | ManagedServiceIdentity |
ManagedIdentity
| Name | Description | Value |
|---|---|---|
| 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}$ |
| identityType | [Required] Specifies the type of identity framework. | 'Managed' (required) |
| 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 |
ManagedServiceIdentity
| Name | Description | Value |
|---|---|---|
| type | Type of managed service identity (where both SystemAssigned and UserAssigned types are allowed). | 'None' 'SystemAssigned' 'SystemAssigned,UserAssigned' 'UserAssigned' (required) |
| userAssignedIdentities | The set of user assigned identities associated with the resource. The userAssignedIdentities dictionary keys will be ARM resource ids in the form: '/subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.ManagedIdentity/userAssignedIdentities/{identityName}. The dictionary values can be empty objects ({}) in requests. | UserAssignedIdentities |
MedianStoppingPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'MedianStopping' (required) |
Microsoft.MachineLearningServices/workspaces/schedules
| Name | Description | Value |
|---|---|---|
| name | The resource name | string Constraints: Pattern = ^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$ (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. | ScheduleProperties (required) |
| type | The resource type | "Microsoft.MachineLearningServices/workspaces/schedules@2024-04-01-preview" |
MLAssistConfiguration
| Name | Description | Value |
|---|---|---|
| mlAssist | Set to 'Disabled' for type MLAssistConfigurationDisabled. Set to 'Enabled' for type MLAssistConfigurationEnabled. | 'Disabled' 'Enabled' (required) |
MLAssistConfigurationDisabled
| Name | Description | Value |
|---|---|---|
| mlAssist | [Required] Indicates whether MLAssist feature is enabled. | 'Disabled' (required) |
MLAssistConfigurationEnabled
| Name | Description | Value |
|---|---|---|
| inferencingComputeBinding | [Required] AML compute binding used in inferencing. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| mlAssist | [Required] Indicates whether MLAssist feature is enabled. | 'Enabled' (required) |
| trainingComputeBinding | [Required] AML compute binding used in training. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'mlflow_model' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'mlflow_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLTableJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'mltable' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLTableJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'mltable' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
ModelPerformanceMetricThresholdBase
| Name | Description | Value |
|---|---|---|
| modelType | Set to 'Classification' for type ClassificationModelPerformanceMetricThreshold. Set to 'Regression' for type RegressionModelPerformanceMetricThreshold. | 'Classification' 'Regression' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
ModelPerformanceSignal
| Name | Description | Value |
|---|---|---|
| dataSegment | The data segment. | MonitoringDataSegment |
| metricThreshold | [Required] A list of metrics to calculate and their associated thresholds. | ModelPerformanceMetricThresholdBase (required) |
| productionData | [Required] The data produced by the production service which performance will be calculated for. | MonitoringInputDataBase[] (required) |
| referenceData | [Required] The reference data used as the basis to calculate model performance. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'ModelPerformance' (required) |
MonitorComputeConfigurationBase
| Name | Description | Value |
|---|---|---|
| computeType | Set to 'ServerlessSpark' for type MonitorServerlessSparkCompute. | 'ServerlessSpark' (required) |
MonitorComputeIdentityBase
| Name | Description | Value |
|---|---|---|
| computeIdentityType | Set to 'AmlToken' for type AmlTokenComputeIdentity. Set to 'ManagedIdentity' for type ManagedComputeIdentity. | 'AmlToken' 'ManagedIdentity' (required) |
MonitorDefinition
| Name | Description | Value |
|---|---|---|
| alertNotificationSettings | The monitor's notification settings. | MonitorNotificationSettings |
| computeConfiguration | [Required] The ARM resource ID of the compute resource to run the monitoring job on. | MonitorComputeConfigurationBase (required) |
| monitoringTarget | The ARM resource ID of either the model or deployment targeted by this monitor. | MonitoringTarget |
| signals | [Required] The signals to monitor. | MonitorDefinitionSignals (required) |
MonitorDefinitionSignals
| Name | Description | Value |
|---|
MonitorEmailNotificationSettings
| Name | Description | Value |
|---|---|---|
| emails | This is the email recipient list which has a limitation of 499 characters in total. | string[] |
MonitoringDataSegment
| Name | Description | Value |
|---|---|---|
| feature | The feature to segment the data on. | string |
| values | Filters for only the specified values of the given segmented feature. | string[] |
MonitoringFeatureFilterBase
| Name | Description | Value |
|---|---|---|
| filterType | Set to 'AllFeatures' for type AllFeatures. Set to 'FeatureSubset' for type FeatureSubset. Set to 'TopNByAttribution' for type TopNFeaturesByAttribution. | 'AllFeatures' 'FeatureSubset' 'TopNByAttribution' (required) |
MonitoringInputDataBase
| Name | Description | Value |
|---|---|---|
| columns | Mapping of column names to special uses. | MonitoringInputDataBaseColumns |
| dataContext | The context metadata of the data source. | string |
| inputDataType | Set to 'Fixed' for type FixedInputData. Set to 'Rolling' for type RollingInputData. Set to 'Static' for type StaticInputData. | 'Fixed' 'Rolling' 'Static' (required) |
| jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MonitoringInputDataBaseColumns
| Name | Description | Value |
|---|
MonitoringSignalBase
| Name | Description | Value |
|---|---|---|
| notificationTypes | The current notification mode for this signal. | String array containing any of: 'AmlNotification' 'AzureMonitor' |
| properties | Property dictionary. Properties can be added, but not removed or altered. | MonitoringSignalBaseProperties |
| signalType | Set to 'Custom' for type CustomMonitoringSignal. Set to 'DataDrift' for type DataDriftMonitoringSignal. Set to 'DataQuality' for type DataQualityMonitoringSignal. Set to 'FeatureAttributionDrift' for type FeatureAttributionDriftMonitoringSignal. Set to 'GenerationSafetyQuality' for type GenerationSafetyQualityMonitoringSignal. Set to 'GenerationTokenStatistics' for type GenerationTokenUsageSignal. Set to 'ModelPerformance' for type ModelPerformanceSignal. Set to 'PredictionDrift' for type PredictionDriftMonitoringSignal. | 'Custom' 'DataDrift' 'DataQuality' 'FeatureAttributionDrift' 'GenerationSafetyQuality' 'GenerationTokenStatistics' 'ModelPerformance' 'PredictionDrift' (required) |
MonitoringSignalBaseProperties
| Name | Description | Value |
|---|
MonitoringTarget
| Name | Description | Value |
|---|---|---|
| deploymentId | The ARM resource ID of either the deployment targeted by this monitor. | string |
| modelId | The ARM resource ID of either the model targeted by this monitor. | string |
| taskType | [Required] The machine learning task type of the model. | 'Classification' 'QuestionAnswering' 'Regression' (required) |
MonitoringThreshold
| Name | Description | Value |
|---|---|---|
| value | The threshold value. If null, the set default is dependent on the metric type. | int |
MonitoringWorkspaceConnection
| Name | Description | Value |
|---|---|---|
| environmentVariables | The properties of a workspace service connection to store as environment variables in the submitted jobs. Key is workspace connection property path, name is environment variable key. |
MonitoringWorkspaceConnectionEnvironmentVariables |
| secrets | The properties of a workspace service connection to store as secrets in the submitted jobs. Key is workspace connection property path, name is secret key. |
MonitoringWorkspaceConnectionSecrets |
MonitoringWorkspaceConnectionEnvironmentVariables
| Name | Description | Value |
|---|
MonitoringWorkspaceConnectionSecrets
| Name | Description | Value |
|---|
MonitorNotificationSettings
| Name | Description | Value |
|---|---|---|
| emailNotificationSettings | The AML notification email settings. | MonitorEmailNotificationSettings |
MonitorServerlessSparkCompute
| Name | Description | Value |
|---|---|---|
| computeIdentity | [Required] The identity scheme leveraged to by the spark jobs running on serverless Spark. | MonitorComputeIdentityBase (required) |
| computeType | [Required] Specifies the type of signal to monitor. | 'ServerlessSpark' (required) |
| instanceType | [Required] The instance type running the Spark job. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| runtimeVersion | [Required] The Spark runtime version. | string Constraints: Min length = 1 Pattern = ^[0-9]+\.[0-9]+$ (required) |
Mpi
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'Mpi' (required) |
| processCountPerInstance | Number of processes per MPI node. | int |
NCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoNCrossValidations. Set to 'Custom' for type CustomNCrossValidations. | 'Auto' 'Custom' (required) |
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 |
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) |
NlpVerticalFeaturizationSettings
| Name | Description | Value |
|---|---|---|
| datasetLanguage | Dataset language, useful for the text data. | string |
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 |
Nodes
| Name | Description | Value |
|---|---|---|
| nodesValueType | Set to 'All' for type AllNodes. | 'All' (required) |
NotificationSetting
| Name | Description | Value |
|---|---|---|
| emailOn | Send email notification to user on specified notification type | String array containing any of: 'JobCancelled' 'JobCompleted' 'JobFailed' |
| emails | This is the email recipient list which has a limitation of 499 characters in total concat with comma separator | string[] |
| webhooks | Send webhook callback to a service. Key is a user-provided name for the webhook. | NotificationSettingWebhooks |
NotificationSettingWebhooks
| Name | Description | Value |
|---|
NumericalDataDriftMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Numerical' (required) |
| metric | [Required] The numerical data drift metric to calculate. | 'JensenShannonDistance' 'NormalizedWassersteinDistance' 'PopulationStabilityIndex' 'TwoSampleKolmogorovSmirnovTest' (required) |
NumericalDataQualityMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Numerical' (required) |
| metric | [Required] The numerical data quality metric to calculate. | 'DataTypeErrorRate' 'NullValueRate' 'OutOfBoundsRate' (required) |
NumericalPredictionDriftMetricThreshold
| Name | Description | Value |
|---|---|---|
| dataType | [Required] Specifies the data type of the metric threshold. | 'Numerical' (required) |
| metric | [Required] The numerical prediction drift metric to calculate. | 'JensenShannonDistance' 'NormalizedWassersteinDistance' 'PopulationStabilityIndex' 'TwoSampleKolmogorovSmirnovTest' (required) |
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 Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
PipelineJob
| Name | Description | Value |
|---|---|---|
| inputs | Inputs for the pipeline job. | PipelineJobInputs |
| jobs | Jobs construct the Pipeline Job. | PipelineJobJobs |
| jobType | [Required] Specifies the type of job. | 'Pipeline' (required) |
| outputs | Outputs for the pipeline job | PipelineJobOutputs |
| settings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | any |
| sourceJobId | ARM resource ID of source job. | string |
PipelineJobInputs
| Name | Description | Value |
|---|
PipelineJobJobs
| Name | Description | Value |
|---|
PipelineJobOutputs
| Name | Description | Value |
|---|
PredictionDriftMetricThresholdBase
| Name | Description | Value |
|---|---|---|
| dataType | Set to 'Categorical' for type CategoricalPredictionDriftMetricThreshold. Set to 'Numerical' for type NumericalPredictionDriftMetricThreshold. | 'Categorical' 'Numerical' (required) |
| threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
PredictionDriftMonitoringSignal
| Name | Description | Value |
|---|---|---|
| featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | PredictionDriftMonitoringSignalFeatureDataTypeOverride |
| metricThresholds | [Required] A list of metrics to calculate and their associated thresholds. | PredictionDriftMetricThresholdBase[] (required) |
| productionData | [Required] The data which drift will be calculated for. | MonitoringInputDataBase (required) |
| referenceData | [Required] The data to calculate drift against. | MonitoringInputDataBase (required) |
| signalType | [Required] Specifies the type of signal to monitor. | 'PredictionDrift' (required) |
PredictionDriftMonitoringSignalFeatureDataTypeOverride
| Name | Description | Value |
|---|
PyTorch
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'PyTorch' (required) |
| processCountPerInstance | Number of processes per node. | int |
QueueSettings
| Name | Description | Value |
|---|---|---|
| jobTier | Controls the compute job tier | 'Basic' 'Null' 'Premium' 'Spot' 'Standard' |
| priority | Controls the priority of the job on a compute. | int |
RandomSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| 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' |
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Random' (required) |
| seed | An optional integer to use as the seed for random number generation | int |
Ray
| Name | Description | Value |
|---|---|---|
| address | The address of Ray head node. | string |
| dashboardPort | The port to bind the dashboard server to. | int |
| distributionType | [Required] Specifies the type of distribution framework. | 'Ray' (required) |
| headNodeAdditionalArgs | Additional arguments passed to ray start in head node. | string |
| includeDashboard | Provide this argument to start the Ray dashboard GUI. | bool |
| port | The port of the head ray process. | int |
| workerNodeAdditionalArgs | Additional arguments passed to ray start in worker node. | string |
RecurrenceSchedule
| Name | Description | Value |
|---|---|---|
| hours | [Required] List of hours for the schedule. | int[] (required) |
| minutes | [Required] List of minutes for the schedule. | int[] (required) |
| monthDays | List of month days for the schedule | int[] |
| weekDays | List of days for the schedule. | String array containing any of: 'Friday' 'Monday' 'Saturday' 'Sunday' 'Thursday' 'Tuesday' 'Wednesday' |
RecurrenceTrigger
| Name | Description | Value |
|---|---|---|
| frequency | [Required] The frequency to trigger schedule. | 'Day' 'Hour' 'Minute' 'Month' 'Week' (required) |
| interval | [Required] Specifies schedule interval in conjunction with frequency | int (required) |
| schedule | The recurrence schedule. | RecurrenceSchedule |
| triggerType | [Required] | 'Recurrence' (required) |
Regression
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'Regression' (required) |
| 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 |
RegressionModelPerformanceMetricThreshold
| Name | Description | Value |
|---|---|---|
| metric | [Required] The regression model performance metric to calculate. | 'MeanAbsoluteError' 'MeanSquaredError' 'RootMeanSquaredError' (required) |
| modelType | [Required] Specifies the data type of the metric threshold. | 'Regression' (required) |
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' |
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceConfigurationProperties
| Name | Description | Value |
|---|
RollingInputData
| Name | Description | Value |
|---|---|---|
| inputDataType | [Required] Specifies the type of signal to monitor. | 'Rolling' (required) |
| preprocessingComponentId | The ARM resource ID of the component resource used to preprocess the data. | string |
| windowOffset | [Required] The time offset between the end of the data window and the monitor's current run time. | string (required) |
| windowSize | [Required] The size of the trailing data window. | string (required) |
SamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | Set to 'Bayesian' for type BayesianSamplingAlgorithm. Set to 'Grid' for type GridSamplingAlgorithm. Set to 'Random' for type RandomSamplingAlgorithm. | 'Bayesian' 'Grid' 'Random' (required) |
ScheduleActionBase
| Name | Description | Value |
|---|---|---|
| actionType | Set to 'CreateJob' for type JobScheduleAction. Set to 'CreateMonitor' for type CreateMonitorAction. Set to 'ImportData' for type ImportDataAction. Set to 'InvokeBatchEndpoint' for type EndpointScheduleAction. | 'CreateJob' 'CreateMonitor' 'ImportData' 'InvokeBatchEndpoint' (required) |
ScheduleProperties
| Name | Description | Value |
|---|---|---|
| action | [Required] Specifies the action of the schedule | ScheduleActionBase (required) |
| description | The asset description text. | string |
| displayName | Display name of schedule. | string |
| isEnabled | Is the schedule enabled? | bool |
| properties | The asset property dictionary. | ResourceBaseProperties |
| tags | Tag dictionary. Tags can be added, removed, and updated. | ResourceBaseTags |
| trigger | [Required] Specifies the trigger details | TriggerBase (required) |
Seasonality
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoSeasonality. Set to 'Custom' for type CustomSeasonality. | 'Auto' 'Custom' (required) |
SecretConfiguration
| Name | Description | Value |
|---|---|---|
| uri | Secret Uri. Sample Uri : https://myvault.vault.azure.net/secrets/mysecretname/secretversion |
string |
| workspaceSecretName | Name of secret in workspace key vault. | string |
SparkJob
| Name | Description | Value |
|---|---|---|
| archives | Archive files used in the job. | string[] |
| args | Arguments for the job. | string |
| codeId | [Required] ARM resource ID of the code asset. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| 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 |
| environmentVariables | Environment variables included in the job. | SparkJobEnvironmentVariables |
| 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[] |
| jobType | [Required] Specifies the type of job. | 'Spark' (required) |
| outputs | Mapping of output data bindings used in the job. | SparkJobOutputs |
| pyFiles | Python files used in the job. | string[] |
| queueSettings | Queue settings for the job | QueueSettings |
| resources | Compute Resource configuration for the job. | SparkResourceConfiguration |
SparkJobConf
| Name | Description | Value |
|---|
SparkJobEntry
| Name | Description | Value |
|---|---|---|
| sparkJobEntryType | Set to 'SparkJobPythonEntry' for type SparkJobPythonEntry. Set to 'SparkJobScalaEntry' for type SparkJobScalaEntry. | 'SparkJobPythonEntry' 'SparkJobScalaEntry' (required) |
SparkJobEnvironmentVariables
| Name | Description | Value |
|---|
SparkJobInputs
| Name | Description | Value |
|---|
SparkJobOutputs
| Name | Description | Value |
|---|
SparkJobPythonEntry
| Name | Description | Value |
|---|---|---|
| file | [Required] Relative python file path for job entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobPythonEntry' (required) |
SparkJobScalaEntry
| Name | Description | Value |
|---|---|---|
| className | [Required] Scala class name used as entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobScalaEntry' (required) |
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 |
StackEnsembleSettings
| Name | Description | Value |
|---|---|---|
| stackMetaLearnerKWargs | Optional parameters to pass to the initializer of the meta-learner. | any |
| 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' |
StaticInputData
| Name | Description | Value |
|---|---|---|
| inputDataType | [Required] Specifies the type of signal to monitor. | 'Static' (required) |
| preprocessingComponentId | The ARM resource ID of the component resource used to preprocess the data. | string |
| windowEnd | [Required] The end date of the data window. | string (required) |
| windowStart | [Required] The start date of the data window. | string (required) |
SweepJob
| Name | Description | Value |
|---|---|---|
| componentConfiguration | Component Configuration for sweep over component | ComponentConfiguration |
| earlyTermination | Early termination policies enable canceling poor-performing runs before they complete | EarlyTerminationPolicy |
| inputs | Mapping of input data bindings used in the job. | SweepJobInputs |
| jobType | [Required] Specifies the type of job. | 'Sweep' (required) |
| limits | Sweep Job limit. | SweepJobLimits |
| objective | [Required] Optimization objective. | Objective (required) |
| outputs | Mapping of output data bindings used in the job. | SweepJobOutputs |
| queueSettings | Queue settings for the job | QueueSettings |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
| 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 | any (required) |
| trial | [Required] Trial component definition. | TrialComponent (required) |
SweepJobInputs
| Name | Description | Value |
|---|
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 |
SweepJobOutputs
| Name | Description | Value |
|---|
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 |
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) |
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 |
|---|
TableVerticalFeaturizationSettingsTransformerParams
| Name | Description | Value |
|---|
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 |
TargetLags
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoTargetLags. Set to 'Custom' for type CustomTargetLags. | 'Auto' 'Custom' (required) |
TargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoTargetRollingWindowSize. Set to 'Custom' for type CustomTargetRollingWindowSize. | 'Auto' 'Custom' (required) |
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 |
TextClassification
| Name | Description | Value |
|---|---|---|
| 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. | 'Accuracy' 'AUCWeighted' '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 |
| taskType | [Required] Task type for AutoMLJob. | 'TextClassification' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TextClassificationMultilabel
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'TextClassificationMultilabel' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TextNer
| Name | Description | Value |
|---|---|---|
| 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 |
| taskType | [Required] Task type for AutoMLJob. | 'TextNER' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TopNFeaturesByAttribution
| Name | Description | Value |
|---|---|---|
| filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | 'TopNByAttribution' (required) |
| top | The number of top features to include. | 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 Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| 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 Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| environmentVariables | Environment variables included in the job. | TrialComponentEnvironmentVariables |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
TrialComponentEnvironmentVariables
| Name | Description | Value |
|---|
TriggerBase
| Name | Description | Value |
|---|---|---|
| endTime | Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely |
string |
| startTime | Specifies start time of schedule in ISO 8601 format, but without a UTC offset. | string |
| timeZone | Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones?view=windows-11 |
string |
| triggerType | Set to 'Cron' for type CronTrigger. Set to 'Recurrence' for type RecurrenceTrigger. | 'Cron' 'Recurrence' (required) |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
TritonModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'triton_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
TruncationSelectionPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'TruncationSelection' (required) |
| truncationPercentage | The percentage of runs to cancel at each evaluation interval. | int |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
UriFileJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'uri_file' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
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' |
| pathOnCompute | Input Asset Delivery Path. | string |
| uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
UriFolderJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| autoDeleteSetting | Auto delete setting of output data asset. | AutoDeleteSetting |
| jobOutputType | [Required] Specifies the type of job. | 'uri_folder' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| pathOnCompute | Output Asset Delivery Path. | string |
| uri | Output Asset URI. | string |
UserAssignedIdentities
| Name | Description | Value |
|---|
UserAssignedIdentity
| Name | Description | Value |
|---|
UserIdentity
| Name | Description | Value |
|---|---|---|
| identityType | [Required] Specifies the type of identity framework. | 'UserIdentity' (required) |
Webhook
| Name | Description | Value |
|---|---|---|
| eventType | Send callback on a specified notification event | string |
| webhookType | Set to 'AzureDevOps' for type AzureDevOpsWebhook. | 'AzureDevOps' (required) |