Microsoft.MachineLearningServices workspaces/schedules
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@2024-07-01-preview' = {
name: 'string'
parent: resourceSymbolicName
properties: {
action: {
actionType: 'string'
// For remaining properties, see ScheduleActionBase objects
}
description: 'string'
displayName: 'string'
isEnabled: bool
properties: {
{customized property}: 'string'
}
tags: {}
trigger: {
endTime: 'string'
startTime: 'string'
timeZone: 'string'
triggerType: 'string'
// For remaining properties, see TriggerBase objects
}
}
}
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'
}
services: {
{customized property}: {
endpoint: 'string'
jobServiceType: 'string'
nodes: {
nodesValueType: 'string'
// For remaining properties, see Nodes objects
}
port: int
properties: {
{customized property}: 'string'
}
}
}
tags: {}
jobType: 'string'
// For remaining properties, see JobBaseProperties objects
}
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: 'AmlNotification'
properties: {
{customized property}: 'string'
}
signalType: 'string'
// For remaining properties, see MonitoringSignalBase objects
}
}
}
For InvokeBatchEndpoint, use:
actionType: 'InvokeBatchEndpoint'
endpointInvocationDefinition: any()
JobBaseProperties objects
Set the jobType property to specify the type of object.
For AutoML, use:
jobType: 'AutoML'
environmentId: 'string'
environmentVariables: {
{customized property}: 'string'
}
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
queueSettings: {
jobTier: 'string'
}
resources: {
dockerArgs: 'string'
instanceCount: int
instanceType: 'string'
properties: {
{customized property}: any()
}
shmSize: 'string'
}
taskDetails: {
logVerbosity: 'string'
targetColumnName: 'string'
trainingData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
taskType: 'string'
// For remaining properties, see AutoMLVertical objects
}
For Command, use:
jobType: 'Command'
codeId: 'string'
command: 'string'
distribution: {
distributionType: 'string'
// For remaining properties, see DistributionConfiguration objects
}
environmentId: 'string'
environmentVariables: {
{customized property}: 'string'
}
inputs: {
{customized property}: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
}
limits: {
jobLimitsType: 'string'
timeout: 'string'
}
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
queueSettings: {
jobTier: 'string'
}
resources: {
dockerArgs: 'string'
instanceCount: int
instanceType: 'string'
properties: {
{customized property}: any()
}
shmSize: 'string'
}
For FineTuning, use:
jobType: 'FineTuning'
fineTuningDetails: {
model: {
description: 'string'
jobInputType: 'string'
mode: '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
}
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
queueSettings: {
jobTier: 'string'
}
resources: {
instanceTypes: [
'string'
]
}
For Pipeline, use:
jobType: 'Pipeline'
inputs: {
{customized property}: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
}
jobs: {
{customized property}: any()
}
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
settings: any()
sourceJobId: 'string'
For Spark, use:
jobType: 'Spark'
archives: [
'string'
]
args: 'string'
codeId: 'string'
conf: {
{customized property}: 'string'
}
entry: {
sparkJobEntryType: 'string'
// For remaining properties, see SparkJobEntry objects
}
environmentId: 'string'
environmentVariables: {
{customized property}: 'string'
}
files: [
'string'
]
inputs: {
{customized property}: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
}
jars: [
'string'
]
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
pyFiles: [
'string'
]
queueSettings: {
jobTier: 'string'
}
resources: {
instanceType: 'string'
runtimeVersion: 'string'
}
For Sweep, use:
jobType: 'Sweep'
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
inputs: {
{customized property}: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
}
limits: {
jobLimitsType: 'string'
maxConcurrentTrials: int
maxTotalTrials: int
timeout: 'string'
trialTimeout: 'string'
}
objective: {
goal: 'string'
primaryMetric: 'string'
}
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
queueSettings: {
jobTier: 'string'
}
samplingAlgorithm: {
samplingAlgorithmType: 'string'
// For remaining properties, see SamplingAlgorithm objects
}
searchSpace: any()
trial: {
codeId: 'string'
command: 'string'
distribution: {
distributionType: 'string'
// For remaining properties, see DistributionConfiguration objects
}
environmentId: 'string'
environmentVariables: {
{customized property}: 'string'
}
resources: {
dockerArgs: 'string'
instanceCount: int
instanceType: 'string'
properties: {
{customized property}: any()
}
shmSize: 'string'
}
}
IdentityConfiguration objects
Set the identityType property to specify the type of object.
For AMLToken, use:
identityType: 'AMLToken'
For Managed, use:
identityType: 'Managed'
clientId: 'string'
objectId: 'string'
resourceId: 'string'
For UserIdentity, use:
identityType: 'UserIdentity'
Webhook objects
Set the webhookType property to specify the type of object.
For AzureDevOps, use:
webhookType: 'AzureDevOps'
Nodes objects
Set the nodesValueType property to specify the type of object.
For All, use:
nodesValueType: 'All'
JobOutput objects
Set the jobOutputType property to specify the type of object.
For custom_model, use:
jobOutputType: 'custom_model'
mode: 'string'
uri: 'string'
For mlflow_model, use:
jobOutputType: 'mlflow_model'
mode: 'string'
uri: 'string'
For mltable, use:
jobOutputType: 'mltable'
mode: 'string'
uri: 'string'
For triton_model, use:
jobOutputType: 'triton_model'
mode: 'string'
uri: 'string'
For uri_file, use:
jobOutputType: 'uri_file'
mode: 'string'
uri: 'string'
For uri_folder, use:
jobOutputType: 'uri_folder'
mode: 'string'
uri: 'string'
AutoMLVertical objects
Set the taskType property to specify the type of object.
For Classification, use:
taskType: 'Classification'
cvSplitColumnNames: [
'string'
]
featurizationSettings: {
blockedTransformers: [
'string'
]
columnNameAndTypes: {
{customized property}: 'string'
}
datasetLanguage: 'string'
enableDnnFeaturization: bool
mode: 'string'
transformerParams: {
{customized property}: [
{
fields: [
'string'
]
parameters: any()
}
]
}
}
limitSettings: {
enableEarlyTermination: bool
exitScore: int
maxConcurrentTrials: int
maxCoresPerTrial: int
maxTrials: int
timeout: 'string'
trialTimeout: 'string'
}
nCrossValidations: {
mode: 'string'
// For remaining properties, see NCrossValidations objects
}
positiveLabel: 'string'
primaryMetric: 'string'
testData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
testDataSize: int
trainingSettings: {
allowedTrainingAlgorithms: [
'string'
]
blockedTrainingAlgorithms: [
'string'
]
enableDnnTraining: bool
enableModelExplainability: bool
enableOnnxCompatibleModels: bool
enableStackEnsemble: bool
enableVoteEnsemble: bool
ensembleModelDownloadTimeout: 'string'
stackEnsembleSettings: {
stackMetaLearnerKWargs: any()
stackMetaLearnerTrainPercentage: int
stackMetaLearnerType: 'string'
}
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
weightColumnName: 'string'
For Forecasting, use:
taskType: 'Forecasting'
cvSplitColumnNames: [
'string'
]
featurizationSettings: {
blockedTransformers: [
'string'
]
columnNameAndTypes: {
{customized property}: 'string'
}
datasetLanguage: 'string'
enableDnnFeaturization: bool
mode: 'string'
transformerParams: {
{customized property}: [
{
fields: [
'string'
]
parameters: any()
}
]
}
}
forecastingSettings: {
countryOrRegionForHolidays: 'string'
cvStepSize: int
featureLags: 'string'
forecastHorizon: {
mode: 'string'
// For remaining properties, see ForecastHorizon objects
}
frequency: 'string'
seasonality: {
mode: 'string'
// For remaining properties, see Seasonality objects
}
shortSeriesHandlingConfig: 'string'
targetAggregateFunction: 'string'
targetLags: {
mode: 'string'
// For remaining properties, see TargetLags objects
}
targetRollingWindowSize: {
mode: 'string'
// For remaining properties, see TargetRollingWindowSize objects
}
timeColumnName: 'string'
timeSeriesIdColumnNames: [
'string'
]
useStl: 'string'
}
limitSettings: {
enableEarlyTermination: bool
exitScore: int
maxConcurrentTrials: int
maxCoresPerTrial: int
maxTrials: int
timeout: 'string'
trialTimeout: 'string'
}
nCrossValidations: {
mode: 'string'
// For remaining properties, see NCrossValidations objects
}
primaryMetric: 'string'
testData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
testDataSize: int
trainingSettings: {
allowedTrainingAlgorithms: [
'string'
]
blockedTrainingAlgorithms: [
'string'
]
enableDnnTraining: bool
enableModelExplainability: bool
enableOnnxCompatibleModels: bool
enableStackEnsemble: bool
enableVoteEnsemble: bool
ensembleModelDownloadTimeout: 'string'
stackEnsembleSettings: {
stackMetaLearnerKWargs: any()
stackMetaLearnerTrainPercentage: int
stackMetaLearnerType: 'string'
}
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
weightColumnName: 'string'
For ImageClassification, use:
taskType: 'ImageClassification'
limitSettings: {
maxConcurrentTrials: int
maxTrials: int
timeout: 'string'
}
modelSettings: {
advancedSettings: 'string'
amsGradient: bool
augmentations: 'string'
beta1: int
beta2: int
checkpointFrequency: int
checkpointModel: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
checkpointRunId: 'string'
distributed: bool
earlyStopping: bool
earlyStoppingDelay: int
earlyStoppingPatience: int
enableOnnxNormalization: bool
evaluationFrequency: int
gradientAccumulationStep: int
layersToFreeze: int
learningRate: int
learningRateScheduler: 'string'
modelName: 'string'
momentum: int
nesterov: bool
numberOfEpochs: int
numberOfWorkers: int
optimizer: 'string'
randomSeed: int
stepLRGamma: int
stepLRStepSize: int
trainingBatchSize: int
trainingCropSize: int
validationBatchSize: int
validationCropSize: int
validationResizeSize: int
warmupCosineLRCycles: int
warmupCosineLRWarmupEpochs: int
weightDecay: int
weightedLoss: int
}
primaryMetric: 'string'
searchSpace: [
{
amsGradient: 'string'
augmentations: 'string'
beta1: 'string'
beta2: 'string'
distributed: 'string'
earlyStopping: 'string'
earlyStoppingDelay: 'string'
earlyStoppingPatience: 'string'
enableOnnxNormalization: 'string'
evaluationFrequency: 'string'
gradientAccumulationStep: 'string'
layersToFreeze: 'string'
learningRate: 'string'
learningRateScheduler: 'string'
modelName: 'string'
momentum: 'string'
nesterov: 'string'
numberOfEpochs: 'string'
numberOfWorkers: 'string'
optimizer: 'string'
randomSeed: 'string'
stepLRGamma: 'string'
stepLRStepSize: 'string'
trainingBatchSize: 'string'
trainingCropSize: 'string'
validationBatchSize: 'string'
validationCropSize: 'string'
validationResizeSize: 'string'
warmupCosineLRCycles: 'string'
warmupCosineLRWarmupEpochs: 'string'
weightDecay: 'string'
weightedLoss: 'string'
}
]
sweepSettings: {
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm: 'string'
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
For ImageClassificationMultilabel, use:
taskType: 'ImageClassificationMultilabel'
limitSettings: {
maxConcurrentTrials: int
maxTrials: int
timeout: 'string'
}
modelSettings: {
advancedSettings: 'string'
amsGradient: bool
augmentations: 'string'
beta1: int
beta2: int
checkpointFrequency: int
checkpointModel: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
checkpointRunId: 'string'
distributed: bool
earlyStopping: bool
earlyStoppingDelay: int
earlyStoppingPatience: int
enableOnnxNormalization: bool
evaluationFrequency: int
gradientAccumulationStep: int
layersToFreeze: int
learningRate: int
learningRateScheduler: 'string'
modelName: 'string'
momentum: int
nesterov: bool
numberOfEpochs: int
numberOfWorkers: int
optimizer: 'string'
randomSeed: int
stepLRGamma: int
stepLRStepSize: int
trainingBatchSize: int
trainingCropSize: int
validationBatchSize: int
validationCropSize: int
validationResizeSize: int
warmupCosineLRCycles: int
warmupCosineLRWarmupEpochs: int
weightDecay: int
weightedLoss: int
}
primaryMetric: 'string'
searchSpace: [
{
amsGradient: 'string'
augmentations: 'string'
beta1: 'string'
beta2: 'string'
distributed: 'string'
earlyStopping: 'string'
earlyStoppingDelay: 'string'
earlyStoppingPatience: 'string'
enableOnnxNormalization: 'string'
evaluationFrequency: 'string'
gradientAccumulationStep: 'string'
layersToFreeze: 'string'
learningRate: 'string'
learningRateScheduler: 'string'
modelName: 'string'
momentum: 'string'
nesterov: 'string'
numberOfEpochs: 'string'
numberOfWorkers: 'string'
optimizer: 'string'
randomSeed: 'string'
stepLRGamma: 'string'
stepLRStepSize: 'string'
trainingBatchSize: 'string'
trainingCropSize: 'string'
validationBatchSize: 'string'
validationCropSize: 'string'
validationResizeSize: 'string'
warmupCosineLRCycles: 'string'
warmupCosineLRWarmupEpochs: 'string'
weightDecay: 'string'
weightedLoss: 'string'
}
]
sweepSettings: {
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm: 'string'
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
For ImageInstanceSegmentation, use:
taskType: 'ImageInstanceSegmentation'
limitSettings: {
maxConcurrentTrials: int
maxTrials: int
timeout: 'string'
}
modelSettings: {
advancedSettings: 'string'
amsGradient: bool
augmentations: 'string'
beta1: int
beta2: int
boxDetectionsPerImage: int
boxScoreThreshold: int
checkpointFrequency: int
checkpointModel: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
checkpointRunId: 'string'
distributed: bool
earlyStopping: bool
earlyStoppingDelay: int
earlyStoppingPatience: int
enableOnnxNormalization: bool
evaluationFrequency: int
gradientAccumulationStep: int
imageSize: int
layersToFreeze: int
learningRate: int
learningRateScheduler: 'string'
maxSize: int
minSize: int
modelName: 'string'
modelSize: 'string'
momentum: int
multiScale: bool
nesterov: bool
nmsIouThreshold: int
numberOfEpochs: int
numberOfWorkers: int
optimizer: 'string'
randomSeed: int
stepLRGamma: int
stepLRStepSize: int
tileGridSize: 'string'
tileOverlapRatio: int
tilePredictionsNmsThreshold: int
trainingBatchSize: int
validationBatchSize: int
validationIouThreshold: int
validationMetricType: 'string'
warmupCosineLRCycles: int
warmupCosineLRWarmupEpochs: int
weightDecay: int
}
primaryMetric: 'MeanAveragePrecision'
searchSpace: [
{
amsGradient: 'string'
augmentations: 'string'
beta1: 'string'
beta2: 'string'
boxDetectionsPerImage: 'string'
boxScoreThreshold: 'string'
distributed: 'string'
earlyStopping: 'string'
earlyStoppingDelay: 'string'
earlyStoppingPatience: 'string'
enableOnnxNormalization: 'string'
evaluationFrequency: 'string'
gradientAccumulationStep: 'string'
imageSize: 'string'
layersToFreeze: 'string'
learningRate: 'string'
learningRateScheduler: 'string'
maxSize: 'string'
minSize: 'string'
modelName: 'string'
modelSize: 'string'
momentum: 'string'
multiScale: 'string'
nesterov: 'string'
nmsIouThreshold: 'string'
numberOfEpochs: 'string'
numberOfWorkers: 'string'
optimizer: 'string'
randomSeed: 'string'
stepLRGamma: 'string'
stepLRStepSize: 'string'
tileGridSize: 'string'
tileOverlapRatio: 'string'
tilePredictionsNmsThreshold: 'string'
trainingBatchSize: 'string'
validationBatchSize: 'string'
validationIouThreshold: 'string'
validationMetricType: 'string'
warmupCosineLRCycles: 'string'
warmupCosineLRWarmupEpochs: 'string'
weightDecay: 'string'
}
]
sweepSettings: {
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm: 'string'
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
For ImageObjectDetection, use:
taskType: 'ImageObjectDetection'
limitSettings: {
maxConcurrentTrials: int
maxTrials: int
timeout: 'string'
}
modelSettings: {
advancedSettings: 'string'
amsGradient: bool
augmentations: 'string'
beta1: int
beta2: int
boxDetectionsPerImage: int
boxScoreThreshold: int
checkpointFrequency: int
checkpointModel: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
checkpointRunId: 'string'
distributed: bool
earlyStopping: bool
earlyStoppingDelay: int
earlyStoppingPatience: int
enableOnnxNormalization: bool
evaluationFrequency: int
gradientAccumulationStep: int
imageSize: int
layersToFreeze: int
learningRate: int
learningRateScheduler: 'string'
maxSize: int
minSize: int
modelName: 'string'
modelSize: 'string'
momentum: int
multiScale: bool
nesterov: bool
nmsIouThreshold: int
numberOfEpochs: int
numberOfWorkers: int
optimizer: 'string'
randomSeed: int
stepLRGamma: int
stepLRStepSize: int
tileGridSize: 'string'
tileOverlapRatio: int
tilePredictionsNmsThreshold: int
trainingBatchSize: int
validationBatchSize: int
validationIouThreshold: int
validationMetricType: 'string'
warmupCosineLRCycles: int
warmupCosineLRWarmupEpochs: int
weightDecay: int
}
primaryMetric: 'MeanAveragePrecision'
searchSpace: [
{
amsGradient: 'string'
augmentations: 'string'
beta1: 'string'
beta2: 'string'
boxDetectionsPerImage: 'string'
boxScoreThreshold: 'string'
distributed: 'string'
earlyStopping: 'string'
earlyStoppingDelay: 'string'
earlyStoppingPatience: 'string'
enableOnnxNormalization: 'string'
evaluationFrequency: 'string'
gradientAccumulationStep: 'string'
imageSize: 'string'
layersToFreeze: 'string'
learningRate: 'string'
learningRateScheduler: 'string'
maxSize: 'string'
minSize: 'string'
modelName: 'string'
modelSize: 'string'
momentum: 'string'
multiScale: 'string'
nesterov: 'string'
nmsIouThreshold: 'string'
numberOfEpochs: 'string'
numberOfWorkers: 'string'
optimizer: 'string'
randomSeed: 'string'
stepLRGamma: 'string'
stepLRStepSize: 'string'
tileGridSize: 'string'
tileOverlapRatio: 'string'
tilePredictionsNmsThreshold: 'string'
trainingBatchSize: 'string'
validationBatchSize: 'string'
validationIouThreshold: 'string'
validationMetricType: 'string'
warmupCosineLRCycles: 'string'
warmupCosineLRWarmupEpochs: 'string'
weightDecay: 'string'
}
]
sweepSettings: {
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm: 'string'
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
For Regression, use:
taskType: 'Regression'
cvSplitColumnNames: [
'string'
]
featurizationSettings: {
blockedTransformers: [
'string'
]
columnNameAndTypes: {
{customized property}: 'string'
}
datasetLanguage: 'string'
enableDnnFeaturization: bool
mode: 'string'
transformerParams: {
{customized property}: [
{
fields: [
'string'
]
parameters: any()
}
]
}
}
limitSettings: {
enableEarlyTermination: bool
exitScore: int
maxConcurrentTrials: int
maxCoresPerTrial: int
maxTrials: int
timeout: 'string'
trialTimeout: 'string'
}
nCrossValidations: {
mode: 'string'
// For remaining properties, see NCrossValidations objects
}
primaryMetric: 'string'
testData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
testDataSize: int
trainingSettings: {
allowedTrainingAlgorithms: [
'string'
]
blockedTrainingAlgorithms: [
'string'
]
enableDnnTraining: bool
enableModelExplainability: bool
enableOnnxCompatibleModels: bool
enableStackEnsemble: bool
enableVoteEnsemble: bool
ensembleModelDownloadTimeout: 'string'
stackEnsembleSettings: {
stackMetaLearnerKWargs: any()
stackMetaLearnerTrainPercentage: int
stackMetaLearnerType: 'string'
}
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
weightColumnName: 'string'
For TextClassification, use:
taskType: 'TextClassification'
featurizationSettings: {
datasetLanguage: 'string'
}
limitSettings: {
maxConcurrentTrials: int
maxTrials: int
timeout: 'string'
}
primaryMetric: 'string'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
For TextClassificationMultilabel, use:
taskType: 'TextClassificationMultilabel'
featurizationSettings: {
datasetLanguage: 'string'
}
limitSettings: {
maxConcurrentTrials: int
maxTrials: int
timeout: 'string'
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
For TextNER, use:
taskType: 'TextNER'
featurizationSettings: {
datasetLanguage: 'string'
}
limitSettings: {
maxConcurrentTrials: int
maxTrials: int
timeout: 'string'
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
NCrossValidations objects
Set the mode property to specify the type of object.
For Auto, use:
mode: 'Auto'
For Custom, use:
mode: 'Custom'
value: int
ForecastHorizon objects
Set the mode property to specify the type of object.
For Auto, use:
mode: 'Auto'
For Custom, use:
mode: 'Custom'
value: int
Seasonality objects
Set the mode property to specify the type of object.
For Auto, use:
mode: 'Auto'
For Custom, use:
mode: 'Custom'
value: int
TargetLags objects
Set the mode property to specify the type of object.
For Auto, use:
mode: 'Auto'
For Custom, use:
mode: 'Custom'
values: [
int
]
TargetRollingWindowSize objects
Set the mode property to specify the type of object.
For Auto, use:
mode: 'Auto'
For Custom, use:
mode: 'Custom'
value: int
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
DistributionConfiguration objects
Set the distributionType property to specify the type of object.
For Mpi, use:
distributionType: 'Mpi'
processCountPerInstance: int
For PyTorch, use:
distributionType: 'PyTorch'
processCountPerInstance: int
For TensorFlow, use:
distributionType: 'TensorFlow'
parameterServerCount: int
workerCount: int
JobInput objects
Set the jobInputType property to specify the type of object.
For custom_model, use:
jobInputType: 'custom_model'
mode: 'string'
uri: 'string'
For literal, use:
jobInputType: 'literal'
value: 'string'
For mlflow_model, use:
jobInputType: 'mlflow_model'
mode: 'string'
uri: 'string'
For mltable, use:
jobInputType: 'mltable'
mode: 'string'
uri: 'string'
For triton_model, use:
jobInputType: 'triton_model'
mode: 'string'
uri: 'string'
For uri_file, use:
jobInputType: 'uri_file'
mode: 'string'
uri: 'string'
For uri_folder, use:
jobInputType: 'uri_folder'
mode: 'string'
uri: 'string'
FineTuningVertical objects
Set the modelProvider property to specify the type of object.
For AzureOpenAI, use:
modelProvider: 'AzureOpenAI'
hyperParameters: {
batchSize: int
learningRateMultiplier: int
nEpochs: int
}
For Custom, use:
modelProvider: 'Custom'
hyperParameters: {
{customized property}: 'string'
}
SparkJobEntry objects
Set the sparkJobEntryType property to specify the type of object.
For SparkJobPythonEntry, use:
sparkJobEntryType: 'SparkJobPythonEntry'
file: 'string'
For SparkJobScalaEntry, use:
sparkJobEntryType: 'SparkJobScalaEntry'
className: 'string'
SamplingAlgorithm objects
Set the samplingAlgorithmType property to specify the type of object.
For Bayesian, use:
samplingAlgorithmType: 'Bayesian'
For Grid, use:
samplingAlgorithmType: 'Grid'
For Random, use:
samplingAlgorithmType: 'Random'
rule: 'string'
seed: int
MonitorComputeConfigurationBase objects
Set the computeType property to specify the type of object.
For ServerlessSpark, use:
computeType: 'ServerlessSpark'
computeIdentity: {
computeIdentityType: 'string'
// For remaining properties, see MonitorComputeIdentityBase objects
}
instanceType: 'string'
runtimeVersion: 'string'
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}: {}
}
}
MonitoringSignalBase objects
Set the signalType property to specify the type of object.
For Custom, use:
signalType: 'Custom'
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
}
}
]
For DataDrift, use:
signalType: 'DataDrift'
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
}
For DataQuality, use:
signalType: 'DataQuality'
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
}
For FeatureAttributionDrift, use:
signalType: 'FeatureAttributionDrift'
featureDataTypeOverride: {
{customized property}: 'string'
}
featureImportanceSettings: {
mode: 'string'
targetColumn: 'string'
}
metricThreshold: {
metric: 'NormalizedDiscountedCumulativeGain'
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
}
For PredictionDrift, use:
signalType: 'PredictionDrift'
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
}
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'
MonitoringFeatureFilterBase objects
Set the filterType property to specify the type of object.
For AllFeatures, use:
filterType: 'AllFeatures'
For FeatureSubset, use:
filterType: 'FeatureSubset'
features: [
'string'
]
For TopNByAttribution, use:
filterType: 'TopNByAttribution'
top: int
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'
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'
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:
triggerType: 'Cron'
expression: 'string'
For Recurrence, use:
triggerType: 'Recurrence'
frequency: 'string'
interval: int
schedule: {
hours: [
int
]
minutes: [
int
]
monthDays: [
int
]
weekDays: [
'string'
]
}
Property values
workspaces/schedules
Name | Description | Value |
---|---|---|
name | The resource name See how to set names and types for child resources in Bicep. |
string (required) |
parent | In Bicep, you can specify the parent resource for a child resource. You only need to add this property when the child resource is declared outside of the parent resource. For more information, see Child resource outside parent resource. |
Symbolic name for resource of type: workspaces |
properties | [Required] Additional attributes of the entity. | ScheduleProperties (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. | object |
trigger | [Required] Specifies the trigger details | TriggerBase (required) |
ScheduleActionBase
Name | Description | Value |
---|---|---|
actionType | Set the object type | CreateJob CreateMonitor InvokeBatchEndpoint (required) |
JobScheduleAction
Name | Description | Value |
---|---|---|
actionType | [Required] Specifies the action type of the schedule | 'CreateJob' (required) |
jobDefinition | [Required] Defines Schedule action definition details. | JobBaseProperties (required) |
JobBaseProperties
Name | Description | Value |
---|---|---|
componentId | ARM resource ID of the component resource. | string |
computeId | ARM resource ID of the compute resource. | string |
description | The asset description text. | string |
displayName | Display name of job. | string |
experimentName | The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment. | string |
identity | Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null. |
IdentityConfiguration |
isArchived | Is the asset archived? | bool |
notificationSetting | Notification setting for the job | NotificationSetting |
properties | The asset property dictionary. | ResourceBaseProperties |
services | List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject. |
JobBaseServices |
tags | Tag dictionary. Tags can be added, removed, and updated. | object |
jobType | Set the object type | AutoML Command FineTuning Pipeline Spark Sweep (required) |
IdentityConfiguration
Name | Description | Value |
---|---|---|
identityType | Set the object type | AMLToken Managed UserIdentity (required) |
AmlToken
Name | Description | Value |
---|---|---|
identityType | [Required] Specifies the type of identity framework. | 'AMLToken' (required) |
ManagedIdentity
Name | Description | Value |
---|---|---|
identityType | [Required] Specifies the type of identity framework. | 'Managed' (required) |
clientId | Specifies a user-assigned identity by client ID. For system-assigned, do not set this field. | string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$ |
objectId | Specifies a user-assigned identity by object ID. For system-assigned, do not set this field. | string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$ |
resourceId | Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field. | string |
UserIdentity
Name | Description | Value |
---|---|---|
identityType | [Required] Specifies the type of identity framework. | 'UserIdentity' (required) |
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 |
---|---|---|
{customized property} | Webhook |
Webhook
Name | Description | Value |
---|---|---|
eventType | Send callback on a specified notification event | string |
webhookType | Set the object type | AzureDevOps (required) |
AzureDevOpsWebhook
Name | Description | Value |
---|---|---|
webhookType | [Required] Specifies the type of service to send a callback | 'AzureDevOps' (required) |
ResourceBaseProperties
Name | Description | Value |
---|---|---|
{customized property} | string |
JobBaseServices
Name | Description | Value |
---|---|---|
{customized property} | JobService |
JobService
Name | Description | Value |
---|---|---|
endpoint | Url for endpoint. | string |
jobServiceType | Endpoint type. | string |
nodes | Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node. |
Nodes |
port | Port for endpoint. | int |
properties | Additional properties to set on the endpoint. | JobServiceProperties |
Nodes
Name | Description | Value |
---|---|---|
nodesValueType | Set the object type | All (required) |
AllNodes
Name | Description | Value |
---|---|---|
nodesValueType | [Required] Type of the Nodes value | 'All' (required) |
JobServiceProperties
Name | Description | Value |
---|---|---|
{customized property} | string |
AutoMLJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | 'AutoML' (required) |
environmentId | The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job. |
string |
environmentVariables | Environment variables included in the job. | AutoMLJobEnvironmentVariables |
outputs | Mapping of output data bindings used in the job. | AutoMLJobOutputs |
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 |
---|---|---|
{customized property} | string |
AutoMLJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
JobOutput
Name | Description | Value |
---|---|---|
description | Description for the output. | string |
jobOutputType | Set the object type | custom_model mlflow_model mltable triton_model uri_file uri_folder (required) |
CustomModelJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | 'custom_model' (required) |
mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
uri | Output Asset URI. | string |
MLFlowModelJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | 'mlflow_model' (required) |
mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
uri | Output Asset URI. | string |
MLTableJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | 'mltable' (required) |
mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
uri | Output Asset URI. | string |
TritonModelJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | 'triton_model' (required) |
mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
uri | Output Asset URI. | string |
UriFileJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | 'uri_file' (required) |
mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
uri | Output Asset URI. | string |
UriFolderJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | 'uri_folder' (required) |
mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
uri | Output Asset URI. | string |
QueueSettings
Name | Description | Value |
---|---|---|
jobTier | Controls the compute job tier | 'Basic' 'Null' 'Premium' 'Spot' 'Standard' |
JobResourceConfiguration
Name | Description | Value |
---|---|---|
dockerArgs | Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types. | string |
instanceCount | Optional number of instances or nodes used by the compute target. | int |
instanceType | Optional type of VM used as supported by the compute target. | string |
properties | Additional properties bag. | ResourceConfigurationProperties |
shmSize | Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes). | string Constraints: Pattern = \d+[bBkKmMgG] |
ResourceConfigurationProperties
Name | Description | Value |
---|---|---|
{customized property} | For Bicep, you can use the any() function. |
AutoMLVertical
Name | Description | Value |
---|---|---|
logVerbosity | Log verbosity for the job. | 'Critical' 'Debug' 'Error' 'Info' 'NotSet' 'Warning' |
targetColumnName | Target column name: This is prediction values column. Also known as label column name in context of classification tasks. |
string |
trainingData | [Required] Training data input. | MLTableJobInput (required) |
taskType | Set the object type | Classification Forecasting ImageClassification ImageClassificationMultilabel ImageInstanceSegmentation ImageObjectDetection Regression TextClassification TextClassificationMultilabel TextNER (required) |
MLTableJobInput
Name | Description | Value |
---|---|---|
description | Description for the input. | string |
jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
Classification
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'Classification' (required) |
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
positiveLabel | Positive label for binary metrics calculation. | string |
primaryMetric | Primary metric for the task. | 'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
testData | Test data input. | MLTableJobInput |
testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
trainingSettings | Inputs for training phase for an AutoML Job. | ClassificationTrainingSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
TableVerticalFeaturizationSettings
Name | Description | Value |
---|---|---|
blockedTransformers | These transformers shall not be used in featurization. | String array containing any of: 'CatTargetEncoder' 'CountVectorizer' 'HashOneHotEncoder' 'LabelEncoder' 'NaiveBayes' 'OneHotEncoder' 'TextTargetEncoder' 'TfIdf' 'WoETargetEncoder' 'WordEmbedding' |
columnNameAndTypes | Dictionary of column name and its type (int, float, string, datetime etc). | TableVerticalFeaturizationSettingsColumnNameAndTypes |
datasetLanguage | Dataset language, useful for the text data. | string |
enableDnnFeaturization | Determines whether to use Dnn based featurizers for data featurization. | bool |
mode | Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done. |
'Auto' 'Custom' 'Off' |
transformerParams | User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor. | TableVerticalFeaturizationSettingsTransformerParams |
TableVerticalFeaturizationSettingsColumnNameAndTypes
Name | Description | Value |
---|---|---|
{customized property} | string |
TableVerticalFeaturizationSettingsTransformerParams
Name | Description | Value |
---|---|---|
{customized property} | ColumnTransformer[] |
ColumnTransformer
Name | Description | Value |
---|---|---|
fields | Fields to apply transformer logic on. | string[] |
parameters | Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format. |
For Bicep, you can use the any() function. |
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 |
maxTrials | Number of iterations. | int |
timeout | AutoML job timeout. | string |
trialTimeout | Iteration timeout. | string |
NCrossValidations
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoNCrossValidations
Name | Description | Value |
---|---|---|
mode | [Required] Mode for determining N-Cross validations. | 'Auto' (required) |
CustomNCrossValidations
Name | Description | Value |
---|---|---|
mode | [Required] Mode for determining N-Cross validations. | 'Custom' (required) |
value | [Required] N-Cross validations value. | int (required) |
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 |
StackEnsembleSettings
Name | Description | Value |
---|---|---|
stackMetaLearnerKWargs | Optional parameters to pass to the initializer of the meta-learner. | For Bicep, you can use the any() function. |
stackMetaLearnerTrainPercentage | Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2. | int |
stackMetaLearnerType | The meta-learner is a model trained on the output of the individual heterogeneous models. | 'ElasticNet' 'ElasticNetCV' 'LightGBMClassifier' 'LightGBMRegressor' 'LinearRegression' 'LogisticRegression' 'LogisticRegressionCV' 'None' |
Forecasting
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'Forecasting' (required) |
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
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' |
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' |
forecastHorizon | The desired maximum forecast horizon in units of time-series frequency. | ForecastHorizon |
frequency | When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default. | string |
seasonality | Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred. |
Seasonality |
shortSeriesHandlingConfig | The parameter defining how if AutoML should handle short time series. | 'Auto' 'Drop' 'None' 'Pad' |
targetAggregateFunction | The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean". |
'Max' 'Mean' 'Min' 'None' 'Sum' |
targetLags | The number of past periods to lag from the target column. | TargetLags |
targetRollingWindowSize | The number of past periods used to create a rolling window average of the target column. | TargetRollingWindowSize |
timeColumnName | The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. | string |
timeSeriesIdColumnNames | The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting. |
string[] |
useStl | Configure STL Decomposition of the time-series target column. | 'None' 'Season' 'SeasonTrend' |
ForecastHorizon
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoForecastHorizon
Name | Description | Value |
---|---|---|
mode | [Required] Set forecast horizon value selection mode. | 'Auto' (required) |
CustomForecastHorizon
Name | Description | Value |
---|---|---|
mode | [Required] Set forecast horizon value selection mode. | 'Custom' (required) |
value | [Required] Forecast horizon value. | int (required) |
Seasonality
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoSeasonality
Name | Description | Value |
---|---|---|
mode | [Required] Seasonality mode. | 'Auto' (required) |
CustomSeasonality
Name | Description | Value |
---|---|---|
mode | [Required] Seasonality mode. | 'Custom' (required) |
value | [Required] Seasonality value. | int (required) |
TargetLags
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoTargetLags
Name | Description | Value |
---|---|---|
mode | [Required] Set target lags mode - Auto/Custom | 'Auto' (required) |
CustomTargetLags
Name | Description | Value |
---|---|---|
mode | [Required] Set target lags mode - Auto/Custom | 'Custom' (required) |
values | [Required] Set target lags values. | int[] (required) |
TargetRollingWindowSize
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoTargetRollingWindowSize
Name | Description | Value |
---|---|---|
mode | [Required] TargetRollingWindowSiz detection mode. | 'Auto' (required) |
CustomTargetRollingWindowSize
Name | Description | Value |
---|---|---|
mode | [Required] TargetRollingWindowSiz detection mode. | 'Custom' (required) |
value | [Required] TargetRollingWindowSize value. | int (required) |
ForecastingTrainingSettings
Name | Description | Value |
---|---|---|
allowedTrainingAlgorithms | Allowed models for forecasting task. | String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SGD' 'SeasonalAverage' 'SeasonalNaive' 'TCNForecaster' 'XGBoostRegressor' |
blockedTrainingAlgorithms | Blocked models for forecasting task. | String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SGD' 'SeasonalAverage' 'SeasonalNaive' 'TCNForecaster' 'XGBoostRegressor' |
enableDnnTraining | Enable recommendation of DNN models. | bool |
enableModelExplainability | Flag to turn on explainability on best model. | bool |
enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
enableStackEnsemble | Enable stack ensemble run. | bool |
enableVoteEnsemble | Enable voting ensemble run. | bool |
ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
ImageClassification
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'ImageClassification' (required) |
limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
modelSettings | Settings used for training the model. | ImageModelSettingsClassification |
primaryMetric | Primary metric to optimize for this task. | 'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsClassification[] |
sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageLimitSettings
Name | Description | Value |
---|---|---|
maxConcurrentTrials | Maximum number of concurrent AutoML iterations. | int |
maxTrials | Maximum number of AutoML iterations. | int |
timeout | AutoML job timeout. | string |
ImageModelSettingsClassification
Name | Description | Value |
---|---|---|
advancedSettings | Settings for advanced scenarios. | string |
amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | bool |
augmentations | Settings for using Augmentations. | string |
beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
checkpointFrequency | Frequency to store model checkpoints. Must be a positive integer. | int |
checkpointModel | The pretrained checkpoint model for incremental training. | MLFlowModelJobInput |
checkpointRunId | The id of a previous run that has a pretrained checkpoint for incremental training. | string |
distributed | Whether to use distributed training. | bool |
earlyStopping | Enable early stopping logic during training. | bool |
earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
int |
earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
int |
enableOnnxNormalization | Enable normalization when exporting ONNX model. | bool |
evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | int |
gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
int |
layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
int |
learningRate | Initial learning rate. Must be a float in the range [0, 1]. | int |
learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | 'None' 'Step' 'WarmupCosine' |
modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | int |
nesterov | Enable nesterov when optimizer is 'sgd'. | bool |
numberOfEpochs | Number of training epochs. Must be a positive integer. | int |
numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | int |
optimizer | Type of optimizer. | 'Adam' 'Adamw' 'None' 'Sgd' |
randomSeed | Random seed to be used when using deterministic training. | int |
stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | int |
stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | int |
trainingBatchSize | Training batch size. Must be a positive integer. | int |
trainingCropSize | Image crop size that is input to the neural network for the training dataset. Must be a positive integer. | int |
validationBatchSize | Validation batch size. Must be a positive integer. | int |
validationCropSize | Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. | int |
validationResizeSize | Image size to which to resize before cropping for validation dataset. Must be a positive integer. | int |
warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | int |
warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | int |
weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | int |
weightedLoss | Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2. |
int |
MLFlowModelJobInput
Name | Description | Value |
---|---|---|
description | Description for the input. | string |
jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
ImageModelDistributionSettingsClassification
Name | Description | Value |
---|---|---|
amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | string |
augmentations | Settings for using Augmentations. | string |
beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
distributed | Whether to use distributer training. | string |
earlyStopping | Enable early stopping logic during training. | string |
earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
string |
earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
string |
enableOnnxNormalization | Enable normalization when exporting ONNX model. | string |
evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | string |
gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
string |
layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
string |
learningRate | Initial learning rate. Must be a float in the range [0, 1]. | string |
learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | string |
modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | string |
nesterov | Enable nesterov when optimizer is 'sgd'. | string |
numberOfEpochs | Number of training epochs. Must be a positive integer. | string |
numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | string |
optimizer | Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. | string |
randomSeed | Random seed to be used when using deterministic training. | string |
stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | string |
stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | string |
trainingBatchSize | Training batch size. Must be a positive integer. | string |
trainingCropSize | Image crop size that is input to the neural network for the training dataset. Must be a positive integer. | string |
validationBatchSize | Validation batch size. Must be a positive integer. | string |
validationCropSize | Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. | string |
validationResizeSize | Image size to which to resize before cropping for validation dataset. Must be a positive integer. | string |
warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | string |
warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | string |
weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | string |
weightedLoss | Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2. |
string |
ImageSweepSettings
Name | Description | Value |
---|---|---|
earlyTermination | Type of early termination policy. | EarlyTerminationPolicy |
samplingAlgorithm | [Required] Type of the hyperparameter sampling algorithms. | 'Bayesian' 'Grid' 'Random' (required) |
EarlyTerminationPolicy
Name | Description | Value |
---|---|---|
delayEvaluation | Number of intervals by which to delay the first evaluation. | int |
evaluationInterval | Interval (number of runs) between policy evaluations. | int |
policyType | Set the object type | Bandit MedianStopping TruncationSelection (required) |
BanditPolicy
Name | Description | Value |
---|---|---|
policyType | [Required] Name of policy configuration | 'Bandit' (required) |
slackAmount | Absolute distance allowed from the best performing run. | int |
slackFactor | Ratio of the allowed distance from the best performing run. | int |
MedianStoppingPolicy
Name | Description | Value |
---|---|---|
policyType | [Required] Name of policy configuration | 'MedianStopping' (required) |
TruncationSelectionPolicy
Name | Description | Value |
---|---|---|
policyType | [Required] Name of policy configuration | 'TruncationSelection' (required) |
truncationPercentage | The percentage of runs to cancel at each evaluation interval. | int |
ImageClassificationMultilabel
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'ImageClassificationMultilabel' (required) |
limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
modelSettings | Settings used for training the model. | ImageModelSettingsClassification |
primaryMetric | Primary metric to optimize for this task. | 'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'IOU' 'NormMacroRecall' 'PrecisionScoreWeighted' |
searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsClassification[] |
sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageInstanceSegmentation
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'ImageInstanceSegmentation' (required) |
limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
modelSettings | Settings used for training the model. | ImageModelSettingsObjectDetection |
primaryMetric | Primary metric to optimize for this task. | 'MeanAveragePrecision' |
searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsObjectDetection[] |
sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageModelSettingsObjectDetection
Name | Description | Value |
---|---|---|
advancedSettings | Settings for advanced scenarios. | string |
amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | bool |
augmentations | Settings for using Augmentations. | string |
beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
boxDetectionsPerImage | Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
boxScoreThreshold | During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
int |
checkpointFrequency | Frequency to store model checkpoints. Must be a positive integer. | int |
checkpointModel | The pretrained checkpoint model for incremental training. | MLFlowModelJobInput |
checkpointRunId | The id of a previous run that has a pretrained checkpoint for incremental training. | string |
distributed | Whether to use distributed training. | bool |
earlyStopping | Enable early stopping logic during training. | bool |
earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
int |
earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
int |
enableOnnxNormalization | Enable normalization when exporting ONNX model. | bool |
evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | int |
gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
int |
imageSize | Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
int |
layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
int |
learningRate | Initial learning rate. Must be a float in the range [0, 1]. | int |
learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | 'None' 'Step' 'WarmupCosine' |
maxSize | Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
minSize | Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
modelSize | Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
'ExtraLarge' 'Large' 'Medium' 'None' 'Small' |
momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | int |
multiScale | Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm. |
bool |
nesterov | Enable nesterov when optimizer is 'sgd'. | bool |
nmsIouThreshold | IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1]. | int |
numberOfEpochs | Number of training epochs. Must be a positive integer. | int |
numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | int |
optimizer | Type of optimizer. | 'Adam' 'Adamw' 'None' 'Sgd' |
randomSeed | Random seed to be used when using deterministic training. | int |
stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | int |
stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | int |
tileGridSize | The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
tileOverlapRatio | Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm. |
int |
tilePredictionsNmsThreshold | The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
trainingBatchSize | Training batch size. Must be a positive integer. | int |
validationBatchSize | Validation batch size. Must be a positive integer. | int |
validationIouThreshold | IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. | int |
validationMetricType | Metric computation method to use for validation metrics. | 'Coco' 'CocoVoc' 'None' 'Voc' |
warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | int |
warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | int |
weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | int |
ImageModelDistributionSettingsObjectDetection
Name | Description | Value |
---|---|---|
amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | string |
augmentations | Settings for using Augmentations. | string |
beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
boxDetectionsPerImage | Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
boxScoreThreshold | During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
string |
distributed | Whether to use distributer training. | string |
earlyStopping | Enable early stopping logic during training. | string |
earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
string |
earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
string |
enableOnnxNormalization | Enable normalization when exporting ONNX model. | string |
evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | string |
gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
string |
imageSize | Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
string |
learningRate | Initial learning rate. Must be a float in the range [0, 1]. | string |
learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | string |
maxSize | Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
minSize | Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
modelSize | Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | string |
multiScale | Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
nesterov | Enable nesterov when optimizer is 'sgd'. | string |
nmsIouThreshold | IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1]. | string |
numberOfEpochs | Number of training epochs. Must be a positive integer. | string |
numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | string |
optimizer | Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. | string |
randomSeed | Random seed to be used when using deterministic training. | string |
stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | string |
stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | string |
tileGridSize | The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
tileOverlapRatio | Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm. |
string |
tilePredictionsNmsThreshold | The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression |
string |
trainingBatchSize | Training batch size. Must be a positive integer. | string |
validationBatchSize | Validation batch size. Must be a positive integer. | string |
validationIouThreshold | IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. | string |
validationMetricType | Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'. | string |
warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | string |
warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | string |
weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | string |
ImageObjectDetection
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'ImageObjectDetection' (required) |
limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
modelSettings | Settings used for training the model. | ImageModelSettingsObjectDetection |
primaryMetric | Primary metric to optimize for this task. | 'MeanAveragePrecision' |
searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsObjectDetection[] |
sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
Regression
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'Regression' (required) |
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
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' |
testData | Test data input. | MLTableJobInput |
testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
trainingSettings | Inputs for training phase for an AutoML Job. | RegressionTrainingSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
RegressionTrainingSettings
Name | Description | Value |
---|---|---|
allowedTrainingAlgorithms | Allowed models for regression task. | String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor' |
blockedTrainingAlgorithms | Blocked models for regression task. | String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor' |
enableDnnTraining | Enable recommendation of DNN models. | bool |
enableModelExplainability | Flag to turn on explainability on best model. | bool |
enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
enableStackEnsemble | Enable stack ensemble run. | bool |
enableVoteEnsemble | Enable voting ensemble run. | bool |
ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
TextClassification
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'TextClassification' (required) |
featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
primaryMetric | Primary metric for Text-Classification task. | 'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
validationData | Validation data inputs. | MLTableJobInput |
NlpVerticalFeaturizationSettings
Name | Description | Value |
---|---|---|
datasetLanguage | Dataset language, useful for the text data. | string |
NlpVerticalLimitSettings
Name | Description | Value |
---|---|---|
maxConcurrentTrials | Maximum Concurrent AutoML iterations. | int |
maxTrials | Number of AutoML iterations. | int |
timeout | AutoML job timeout. | string |
TextClassificationMultilabel
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'TextClassificationMultilabel' (required) |
featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
validationData | Validation data inputs. | MLTableJobInput |
TextNer
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'TextNER' (required) |
featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
validationData | Validation data inputs. | MLTableJobInput |
CommandJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | 'Command' (required) |
codeId | ARM resource ID of the code asset. | string |
command | [Required] The command to execute on startup of the job. eg. "python train.py" | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
environmentVariables | Environment variables included in the job. | CommandJobEnvironmentVariables |
inputs | Mapping of input data bindings used in the job. | CommandJobInputs |
limits | Command Job limit. | CommandJobLimits |
outputs | Mapping of output data bindings used in the job. | CommandJobOutputs |
queueSettings | Queue settings for the job | QueueSettings |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
DistributionConfiguration
Name | Description | Value |
---|---|---|
distributionType | Set the object type | Mpi PyTorch TensorFlow (required) |
Mpi
Name | Description | Value |
---|---|---|
distributionType | [Required] Specifies the type of distribution framework. | 'Mpi' (required) |
processCountPerInstance | Number of processes per MPI node. | int |
PyTorch
Name | Description | Value |
---|---|---|
distributionType | [Required] Specifies the type of distribution framework. | 'PyTorch' (required) |
processCountPerInstance | Number of processes per node. | int |
TensorFlow
Name | Description | Value |
---|---|---|
distributionType | [Required] Specifies the type of distribution framework. | 'TensorFlow' (required) |
parameterServerCount | Number of parameter server tasks. | int |
workerCount | Number of workers. If not specified, will default to the instance count. | int |
CommandJobEnvironmentVariables
Name | Description | Value |
---|---|---|
{customized property} | string |
CommandJobInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
JobInput
Name | Description | Value |
---|---|---|
description | Description for the input. | string |
jobInputType | Set the object type | custom_model literal mlflow_model mltable triton_model uri_file uri_folder (required) |
CustomModelJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'custom_model' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
LiteralJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'literal' (required) |
value | [Required] Literal value for the input. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
TritonModelJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'triton_model' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
UriFileJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'uri_file' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
UriFolderJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'uri_folder' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
CommandJobLimits
Name | Description | Value |
---|---|---|
jobLimitsType | [Required] JobLimit type. | 'Command' 'Sweep' (required) |
timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
CommandJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
FineTuningJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | 'FineTuning' (required) |
fineTuningDetails | [Required] | FineTuningVertical (required) |
outputs | [Required] | FineTuningJobOutputs (required) |
queueSettings | Queue settings for the job | QueueSettings |
resources | Instance types and other resources for the job | JobResources |
FineTuningVertical
Name | Description | Value |
---|---|---|
model | [Required] Input model for fine tuning. | MLFlowModelJobInput (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 |
modelProvider | Set the object type | AzureOpenAI Custom (required) |
AzureOpenAiFineTuning
Name | Description | Value |
---|---|---|
modelProvider | [Required] Enum to determine the type of fine tuning. | 'AzureOpenAI' (required) |
hyperParameters | HyperParameters for fine tuning Azure Open AI model. | AzureOpenAiHyperParameters |
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 |
CustomModelFineTuning
Name | Description | Value |
---|---|---|
modelProvider | [Required] Enum to determine the type of fine tuning. | 'Custom' (required) |
hyperParameters | HyperParameters for fine tuning custom model. | CustomModelFineTuningHyperParameters |
CustomModelFineTuningHyperParameters
Name | Description | Value |
---|---|---|
{customized property} | string |
FineTuningJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
JobResources
Name | Description | Value |
---|---|---|
instanceTypes | List of instance types to choose from. | string[] |
PipelineJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | 'Pipeline' (required) |
inputs | Inputs for the pipeline job. | PipelineJobInputs |
jobs | Jobs construct the Pipeline Job. | PipelineJobJobs |
outputs | Outputs for the pipeline job | PipelineJobOutputs |
settings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | For Bicep, you can use the any() function. |
sourceJobId | ARM resource ID of source job. | string |
PipelineJobInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
PipelineJobJobs
Name | Description | Value |
---|---|---|
{customized property} | For Bicep, you can use the any() function. |
PipelineJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
SparkJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | 'Spark' (required) |
archives | Archive files used in the job. | string[] |
args | Arguments for the job. | string |
codeId | [Required] arm-id of the code asset. | string (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[] |
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 |
---|---|---|
{customized property} | string |
SparkJobEntry
Name | Description | Value |
---|---|---|
sparkJobEntryType | Set the object type | SparkJobPythonEntry SparkJobScalaEntry (required) |
SparkJobPythonEntry
Name | Description | Value |
---|---|---|
sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobPythonEntry' (required) |
file | [Required] Relative python file path for job entry point. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
SparkJobScalaEntry
Name | Description | Value |
---|---|---|
sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobScalaEntry' (required) |
className | [Required] Scala class name used as entry point. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
SparkJobEnvironmentVariables
Name | Description | Value |
---|---|---|
{customized property} | string |
SparkJobInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
SparkJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
SparkResourceConfiguration
Name | Description | Value |
---|---|---|
instanceType | Optional type of VM used as supported by the compute target. | string |
runtimeVersion | Version of spark runtime used for the job. | string |
SweepJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | 'Sweep' (required) |
earlyTermination | Early termination policies enable canceling poor-performing runs before they complete | EarlyTerminationPolicy |
inputs | Mapping of input data bindings used in the job. | SweepJobInputs |
limits | Sweep Job limit. | SweepJobLimits |
objective | [Required] Optimization objective. | Objective (required) |
outputs | Mapping of output data bindings used in the job. | SweepJobOutputs |
queueSettings | Queue settings for the job | QueueSettings |
samplingAlgorithm | [Required] The hyperparameter sampling algorithm | SamplingAlgorithm (required) |
searchSpace | [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter | For Bicep, you can use the any() function.(required) |
trial | [Required] Trial component definition. | TrialComponent (required) |
SweepJobInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
SweepJobLimits
Name | Description | Value |
---|---|---|
jobLimitsType | [Required] JobLimit type. | 'Command' 'Sweep' (required) |
maxConcurrentTrials | Sweep Job max concurrent trials. | int |
maxTotalTrials | Sweep Job max total trials. | int |
timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
trialTimeout | Sweep Job Trial timeout value. | string |
Objective
Name | Description | Value |
---|---|---|
goal | [Required] Defines supported metric goals for hyperparameter tuning | 'Maximize' 'Minimize' (required) |
primaryMetric | [Required] Name of the metric to optimize. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
SweepJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
SamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | Set the object type | Bayesian Grid Random (required) |
BayesianSamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Bayesian' (required) |
GridSamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Grid' (required) |
RandomSamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Random' (required) |
rule | The specific type of random algorithm | 'Random' 'Sobol' |
seed | An optional integer to use as the seed for random number generation | int |
TrialComponent
Name | Description | Value |
---|---|---|
codeId | ARM resource ID of the code asset. | string |
command | [Required] The command to execute on startup of the job. eg. "python train.py" | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
environmentVariables | Environment variables included in the job. | TrialComponentEnvironmentVariables |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
TrialComponentEnvironmentVariables
Name | Description | Value |
---|---|---|
{customized property} | string |
CreateMonitorAction
Name | Description | Value |
---|---|---|
actionType | [Required] Specifies the action type of the schedule | 'CreateMonitor' (required) |
monitorDefinition | [Required] Defines the monitor. | MonitorDefinition (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 entities targeted by the monitor. | MonitoringTarget |
signals | [Required] The signals to monitor. | MonitorDefinitionSignals (required) |
MonitorNotificationSettings
Name | Description | Value |
---|---|---|
emailNotificationSettings | The AML notification email settings. | MonitorEmailNotificationSettings |
MonitorEmailNotificationSettings
Name | Description | Value |
---|---|---|
emails | The email recipient list which has a limitation of 499 characters in total. | string[] |
MonitorComputeConfigurationBase
Name | Description | Value |
---|---|---|
computeType | Set the object type | ServerlessSpark (required) |
MonitorServerlessSparkCompute
Name | Description | Value |
---|---|---|
computeType | [Required] Specifies the type of signal to monitor. | 'ServerlessSpark' (required) |
computeIdentity | [Required] The identity scheme leveraged to by the spark jobs running on serverless Spark. | MonitorComputeIdentityBase (required) |
instanceType | [Required] The instance type running the Spark job. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
runtimeVersion | [Required] The Spark runtime version. | string (required) Constraints: Min length = 1 Pattern = ^[0-9]+\.[0-9]+$ |
MonitorComputeIdentityBase
Name | Description | Value |
---|---|---|
computeIdentityType | Set the object type | AmlToken ManagedIdentity (required) |
AmlTokenComputeIdentity
Name | Description | Value |
---|---|---|
computeIdentityType | [Required] Specifies the type of identity to use within the monitoring jobs. | 'AmlToken' (required) |
ManagedComputeIdentity
Name | Description | Value |
---|---|---|
computeIdentityType | [Required] Specifies the type of identity to use within the monitoring jobs. | 'ManagedIdentity' (required) |
identity | The identity which will be leveraged by the monitoring jobs. | ManagedServiceIdentity |
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 |
UserAssignedIdentities
Name | Description | Value |
---|---|---|
{customized property} | UserAssignedIdentity |
UserAssignedIdentity
This object doesn't contain any properties to set during deployment. All properties are ReadOnly.
MonitoringTarget
Name | Description | Value |
---|---|---|
deploymentId | Reference to the deployment asset targeted by this monitor. | string |
modelId | Reference to the model asset targeted by this monitor. | string |
taskType | [Required] The machine learning task type of the monitored model. | 'Classification' 'Regression' (required) |
MonitorDefinitionSignals
Name | Description | Value |
---|---|---|
{customized property} | MonitoringSignalBase |
MonitoringSignalBase
Name | Description | Value |
---|---|---|
notificationTypes | The current notification mode for this signal. | String array containing any of: 'AmlNotification' |
properties | Property dictionary. Properties can be added, but not removed or altered. | MonitoringSignalBaseProperties |
signalType | Set the object type | Custom DataDrift DataQuality FeatureAttributionDrift PredictionDrift (required) |
MonitoringSignalBaseProperties
Name | Description | Value |
---|---|---|
{customized property} | string |
CustomMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | 'Custom' (required) |
componentId | [Required] Reference to the component asset used to calculate the custom metrics. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
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) |
CustomMonitoringSignalInputAssets
Name | Description | Value |
---|---|---|
{customized property} | MonitoringInputDataBase |
MonitoringInputDataBase
Name | Description | Value |
---|---|---|
columns | Mapping of column names to special uses. | MonitoringInputDataBaseColumns |
dataContext | The context metadata of the data source. | string |
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 (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
inputDataType | Set the object type | Fixed Rolling Static (required) |
MonitoringInputDataBaseColumns
Name | Description | Value |
---|---|---|
{customized property} | string |
FixedInputData
Name | Description | Value |
---|---|---|
inputDataType | [Required] Specifies the type of signal to monitor. | 'Fixed' (required) |
RollingInputData
Name | Description | Value |
---|---|---|
inputDataType | [Required] Specifies the type of signal to monitor. | 'Rolling' (required) |
preprocessingComponentId | Reference to the component asset 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 rolling data window. | string (required) |
StaticInputData
Name | Description | Value |
---|---|---|
inputDataType | [Required] Specifies the type of signal to monitor. | 'Static' (required) |
preprocessingComponentId | Reference to the component asset 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) |
CustomMonitoringSignalInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
CustomMetricThreshold
Name | Description | Value |
---|---|---|
metric | [Required] The user-defined metric to calculate. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
MonitoringThreshold
Name | Description | Value |
---|---|---|
value | The threshold value. If null, the set default is dependent on the metric type. | int |
DataDriftMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | 'DataDrift' (required) |
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) |
DataDriftMonitoringSignalFeatureDataTypeOverride
Name | Description | Value |
---|---|---|
{customized property} | 'Categorical' 'Numerical' |
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 |
MonitoringFeatureFilterBase
Name | Description | Value |
---|---|---|
filterType | Set the object type | AllFeatures FeatureSubset TopNByAttribution (required) |
AllFeatures
Name | Description | Value |
---|---|---|
filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | 'AllFeatures' (required) |
FeatureSubset
Name | Description | Value |
---|---|---|
filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | 'FeatureSubset' (required) |
features | [Required] The list of features to include. | string[] (required) |
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 |
DataDriftMetricThresholdBase
Name | Description | Value |
---|---|---|
threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
dataType | Set the object type | Categorical Numerical (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) |
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) |
DataQualityMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | 'DataQuality' (required) |
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) |
DataQualityMonitoringSignalFeatureDataTypeOverride
Name | Description | Value |
---|---|---|
{customized property} | 'Categorical' 'Numerical' |
DataQualityMetricThresholdBase
Name | Description | Value |
---|---|---|
threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
dataType | Set the object type | Categorical Numerical (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) |
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) |
FeatureAttributionDriftMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | 'FeatureAttributionDrift' (required) |
featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | FeatureAttributionDriftMonitoringSignalFeatureDataTy... |
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) |
FeatureAttributionDriftMonitoringSignalFeatureDataTy...
Name | Description | Value |
---|---|---|
{customized property} | 'Categorical' 'Numerical' |
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 |
PredictionDriftMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | 'PredictionDrift' (required) |
featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | PredictionDriftMonitoringSignalFeatureDataTypeOverri... |
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) |
PredictionDriftMonitoringSignalFeatureDataTypeOverri...
Name | Description | Value |
---|---|---|
{customized property} | 'Categorical' 'Numerical' |
PredictionDriftMetricThresholdBase
Name | Description | Value |
---|---|---|
threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
dataType | Set the object type | Categorical Numerical (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) |
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) |
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" /} |
For Bicep, you can use the any() function.(required) |
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 /> |
string |
triggerType | Set the object type | Cron Recurrence (required) |
CronTrigger
Name | Description | Value |
---|---|---|
triggerType | [Required] | 'Cron' (required) |
expression | [Required] Specifies cron expression of schedule. The expression should follow NCronTab format. |
string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
RecurrenceTrigger
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 |
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 |
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 |
string |
triggerType | [Required] | 'Cron' 'Recurrence' (required) |
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' |
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": "2024-07-01-preview",
"name": "string",
"properties": {
"action": {
"actionType": "string"
// For remaining properties, see ScheduleActionBase objects
},
"description": "string",
"displayName": "string",
"isEnabled": "bool",
"properties": {
"{customized property}": "string"
},
"tags": {},
"trigger": {
"endTime": "string",
"startTime": "string",
"timeZone": "string",
"triggerType": "string"
// For remaining properties, see TriggerBase objects
}
}
}
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"
},
"services": {
"{customized property}": {
"endpoint": "string",
"jobServiceType": "string",
"nodes": {
"nodesValueType": "string"
// For remaining properties, see Nodes objects
},
"port": "int",
"properties": {
"{customized property}": "string"
}
}
},
"tags": {},
"jobType": "string"
// For remaining properties, see JobBaseProperties objects
}
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": "AmlNotification",
"properties": {
"{customized property}": "string"
},
"signalType": "string"
// For remaining properties, see MonitoringSignalBase objects
}
}
}
For InvokeBatchEndpoint, use:
"actionType": "InvokeBatchEndpoint",
"endpointInvocationDefinition": {}
JobBaseProperties objects
Set the jobType property to specify the type of object.
For AutoML, use:
"jobType": "AutoML",
"environmentId": "string",
"environmentVariables": {
"{customized property}": "string"
},
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"queueSettings": {
"jobTier": "string"
},
"resources": {
"dockerArgs": "string",
"instanceCount": "int",
"instanceType": "string",
"properties": {
"{customized property}": {}
},
"shmSize": "string"
},
"taskDetails": {
"logVerbosity": "string",
"targetColumnName": "string",
"trainingData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"taskType": "string"
// For remaining properties, see AutoMLVertical objects
}
For Command, use:
"jobType": "Command",
"codeId": "string",
"command": "string",
"distribution": {
"distributionType": "string"
// For remaining properties, see DistributionConfiguration objects
},
"environmentId": "string",
"environmentVariables": {
"{customized property}": "string"
},
"inputs": {
"{customized property}": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
}
},
"limits": {
"jobLimitsType": "string",
"timeout": "string"
},
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"queueSettings": {
"jobTier": "string"
},
"resources": {
"dockerArgs": "string",
"instanceCount": "int",
"instanceType": "string",
"properties": {
"{customized property}": {}
},
"shmSize": "string"
}
For FineTuning, use:
"jobType": "FineTuning",
"fineTuningDetails": {
"model": {
"description": "string",
"jobInputType": "string",
"mode": "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
},
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"queueSettings": {
"jobTier": "string"
},
"resources": {
"instanceTypes": [ "string" ]
}
For Pipeline, use:
"jobType": "Pipeline",
"inputs": {
"{customized property}": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
}
},
"jobs": {
"{customized property}": {}
},
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"settings": {},
"sourceJobId": "string"
For Spark, use:
"jobType": "Spark",
"archives": [ "string" ],
"args": "string",
"codeId": "string",
"conf": {
"{customized property}": "string"
},
"entry": {
"sparkJobEntryType": "string"
// For remaining properties, see SparkJobEntry objects
},
"environmentId": "string",
"environmentVariables": {
"{customized property}": "string"
},
"files": [ "string" ],
"inputs": {
"{customized property}": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
}
},
"jars": [ "string" ],
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"pyFiles": [ "string" ],
"queueSettings": {
"jobTier": "string"
},
"resources": {
"instanceType": "string",
"runtimeVersion": "string"
}
For Sweep, use:
"jobType": "Sweep",
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"inputs": {
"{customized property}": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
}
},
"limits": {
"jobLimitsType": "string",
"maxConcurrentTrials": "int",
"maxTotalTrials": "int",
"timeout": "string",
"trialTimeout": "string"
},
"objective": {
"goal": "string",
"primaryMetric": "string"
},
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"queueSettings": {
"jobTier": "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",
"properties": {
"{customized property}": {}
},
"shmSize": "string"
}
}
IdentityConfiguration objects
Set the identityType property to specify the type of object.
For AMLToken, use:
"identityType": "AMLToken"
For Managed, use:
"identityType": "Managed",
"clientId": "string",
"objectId": "string",
"resourceId": "string"
For UserIdentity, use:
"identityType": "UserIdentity"
Webhook objects
Set the webhookType property to specify the type of object.
For AzureDevOps, use:
"webhookType": "AzureDevOps"
Nodes objects
Set the nodesValueType property to specify the type of object.
For All, use:
"nodesValueType": "All"
JobOutput objects
Set the jobOutputType property to specify the type of object.
For custom_model, use:
"jobOutputType": "custom_model",
"mode": "string",
"uri": "string"
For mlflow_model, use:
"jobOutputType": "mlflow_model",
"mode": "string",
"uri": "string"
For mltable, use:
"jobOutputType": "mltable",
"mode": "string",
"uri": "string"
For triton_model, use:
"jobOutputType": "triton_model",
"mode": "string",
"uri": "string"
For uri_file, use:
"jobOutputType": "uri_file",
"mode": "string",
"uri": "string"
For uri_folder, use:
"jobOutputType": "uri_folder",
"mode": "string",
"uri": "string"
AutoMLVertical objects
Set the taskType property to specify the type of object.
For Classification, use:
"taskType": "Classification",
"cvSplitColumnNames": [ "string" ],
"featurizationSettings": {
"blockedTransformers": [ "string" ],
"columnNameAndTypes": {
"{customized property}": "string"
},
"datasetLanguage": "string",
"enableDnnFeaturization": "bool",
"mode": "string",
"transformerParams": {
"{customized property}": [
{
"fields": [ "string" ],
"parameters": {}
}
]
}
},
"limitSettings": {
"enableEarlyTermination": "bool",
"exitScore": "int",
"maxConcurrentTrials": "int",
"maxCoresPerTrial": "int",
"maxTrials": "int",
"timeout": "string",
"trialTimeout": "string"
},
"nCrossValidations": {
"mode": "string"
// For remaining properties, see NCrossValidations objects
},
"positiveLabel": "string",
"primaryMetric": "string",
"testData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"testDataSize": "int",
"trainingSettings": {
"allowedTrainingAlgorithms": [ "string" ],
"blockedTrainingAlgorithms": [ "string" ],
"enableDnnTraining": "bool",
"enableModelExplainability": "bool",
"enableOnnxCompatibleModels": "bool",
"enableStackEnsemble": "bool",
"enableVoteEnsemble": "bool",
"ensembleModelDownloadTimeout": "string",
"stackEnsembleSettings": {
"stackMetaLearnerKWargs": {},
"stackMetaLearnerTrainPercentage": "int",
"stackMetaLearnerType": "string"
}
},
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int",
"weightColumnName": "string"
For Forecasting, use:
"taskType": "Forecasting",
"cvSplitColumnNames": [ "string" ],
"featurizationSettings": {
"blockedTransformers": [ "string" ],
"columnNameAndTypes": {
"{customized property}": "string"
},
"datasetLanguage": "string",
"enableDnnFeaturization": "bool",
"mode": "string",
"transformerParams": {
"{customized property}": [
{
"fields": [ "string" ],
"parameters": {}
}
]
}
},
"forecastingSettings": {
"countryOrRegionForHolidays": "string",
"cvStepSize": "int",
"featureLags": "string",
"forecastHorizon": {
"mode": "string"
// For remaining properties, see ForecastHorizon objects
},
"frequency": "string",
"seasonality": {
"mode": "string"
// For remaining properties, see Seasonality objects
},
"shortSeriesHandlingConfig": "string",
"targetAggregateFunction": "string",
"targetLags": {
"mode": "string"
// For remaining properties, see TargetLags objects
},
"targetRollingWindowSize": {
"mode": "string"
// For remaining properties, see TargetRollingWindowSize objects
},
"timeColumnName": "string",
"timeSeriesIdColumnNames": [ "string" ],
"useStl": "string"
},
"limitSettings": {
"enableEarlyTermination": "bool",
"exitScore": "int",
"maxConcurrentTrials": "int",
"maxCoresPerTrial": "int",
"maxTrials": "int",
"timeout": "string",
"trialTimeout": "string"
},
"nCrossValidations": {
"mode": "string"
// For remaining properties, see NCrossValidations objects
},
"primaryMetric": "string",
"testData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"testDataSize": "int",
"trainingSettings": {
"allowedTrainingAlgorithms": [ "string" ],
"blockedTrainingAlgorithms": [ "string" ],
"enableDnnTraining": "bool",
"enableModelExplainability": "bool",
"enableOnnxCompatibleModels": "bool",
"enableStackEnsemble": "bool",
"enableVoteEnsemble": "bool",
"ensembleModelDownloadTimeout": "string",
"stackEnsembleSettings": {
"stackMetaLearnerKWargs": {},
"stackMetaLearnerTrainPercentage": "int",
"stackMetaLearnerType": "string"
}
},
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int",
"weightColumnName": "string"
For ImageClassification, use:
"taskType": "ImageClassification",
"limitSettings": {
"maxConcurrentTrials": "int",
"maxTrials": "int",
"timeout": "string"
},
"modelSettings": {
"advancedSettings": "string",
"amsGradient": "bool",
"augmentations": "string",
"beta1": "int",
"beta2": "int",
"checkpointFrequency": "int",
"checkpointModel": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"checkpointRunId": "string",
"distributed": "bool",
"earlyStopping": "bool",
"earlyStoppingDelay": "int",
"earlyStoppingPatience": "int",
"enableOnnxNormalization": "bool",
"evaluationFrequency": "int",
"gradientAccumulationStep": "int",
"layersToFreeze": "int",
"learningRate": "int",
"learningRateScheduler": "string",
"modelName": "string",
"momentum": "int",
"nesterov": "bool",
"numberOfEpochs": "int",
"numberOfWorkers": "int",
"optimizer": "string",
"randomSeed": "int",
"stepLRGamma": "int",
"stepLRStepSize": "int",
"trainingBatchSize": "int",
"trainingCropSize": "int",
"validationBatchSize": "int",
"validationCropSize": "int",
"validationResizeSize": "int",
"warmupCosineLRCycles": "int",
"warmupCosineLRWarmupEpochs": "int",
"weightDecay": "int",
"weightedLoss": "int"
},
"primaryMetric": "string",
"searchSpace": [
{
"amsGradient": "string",
"augmentations": "string",
"beta1": "string",
"beta2": "string",
"distributed": "string",
"earlyStopping": "string",
"earlyStoppingDelay": "string",
"earlyStoppingPatience": "string",
"enableOnnxNormalization": "string",
"evaluationFrequency": "string",
"gradientAccumulationStep": "string",
"layersToFreeze": "string",
"learningRate": "string",
"learningRateScheduler": "string",
"modelName": "string",
"momentum": "string",
"nesterov": "string",
"numberOfEpochs": "string",
"numberOfWorkers": "string",
"optimizer": "string",
"randomSeed": "string",
"stepLRGamma": "string",
"stepLRStepSize": "string",
"trainingBatchSize": "string",
"trainingCropSize": "string",
"validationBatchSize": "string",
"validationCropSize": "string",
"validationResizeSize": "string",
"warmupCosineLRCycles": "string",
"warmupCosineLRWarmupEpochs": "string",
"weightDecay": "string",
"weightedLoss": "string"
}
],
"sweepSettings": {
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"samplingAlgorithm": "string"
},
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int"
For ImageClassificationMultilabel, use:
"taskType": "ImageClassificationMultilabel",
"limitSettings": {
"maxConcurrentTrials": "int",
"maxTrials": "int",
"timeout": "string"
},
"modelSettings": {
"advancedSettings": "string",
"amsGradient": "bool",
"augmentations": "string",
"beta1": "int",
"beta2": "int",
"checkpointFrequency": "int",
"checkpointModel": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"checkpointRunId": "string",
"distributed": "bool",
"earlyStopping": "bool",
"earlyStoppingDelay": "int",
"earlyStoppingPatience": "int",
"enableOnnxNormalization": "bool",
"evaluationFrequency": "int",
"gradientAccumulationStep": "int",
"layersToFreeze": "int",
"learningRate": "int",
"learningRateScheduler": "string",
"modelName": "string",
"momentum": "int",
"nesterov": "bool",
"numberOfEpochs": "int",
"numberOfWorkers": "int",
"optimizer": "string",
"randomSeed": "int",
"stepLRGamma": "int",
"stepLRStepSize": "int",
"trainingBatchSize": "int",
"trainingCropSize": "int",
"validationBatchSize": "int",
"validationCropSize": "int",
"validationResizeSize": "int",
"warmupCosineLRCycles": "int",
"warmupCosineLRWarmupEpochs": "int",
"weightDecay": "int",
"weightedLoss": "int"
},
"primaryMetric": "string",
"searchSpace": [
{
"amsGradient": "string",
"augmentations": "string",
"beta1": "string",
"beta2": "string",
"distributed": "string",
"earlyStopping": "string",
"earlyStoppingDelay": "string",
"earlyStoppingPatience": "string",
"enableOnnxNormalization": "string",
"evaluationFrequency": "string",
"gradientAccumulationStep": "string",
"layersToFreeze": "string",
"learningRate": "string",
"learningRateScheduler": "string",
"modelName": "string",
"momentum": "string",
"nesterov": "string",
"numberOfEpochs": "string",
"numberOfWorkers": "string",
"optimizer": "string",
"randomSeed": "string",
"stepLRGamma": "string",
"stepLRStepSize": "string",
"trainingBatchSize": "string",
"trainingCropSize": "string",
"validationBatchSize": "string",
"validationCropSize": "string",
"validationResizeSize": "string",
"warmupCosineLRCycles": "string",
"warmupCosineLRWarmupEpochs": "string",
"weightDecay": "string",
"weightedLoss": "string"
}
],
"sweepSettings": {
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"samplingAlgorithm": "string"
},
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int"
For ImageInstanceSegmentation, use:
"taskType": "ImageInstanceSegmentation",
"limitSettings": {
"maxConcurrentTrials": "int",
"maxTrials": "int",
"timeout": "string"
},
"modelSettings": {
"advancedSettings": "string",
"amsGradient": "bool",
"augmentations": "string",
"beta1": "int",
"beta2": "int",
"boxDetectionsPerImage": "int",
"boxScoreThreshold": "int",
"checkpointFrequency": "int",
"checkpointModel": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"checkpointRunId": "string",
"distributed": "bool",
"earlyStopping": "bool",
"earlyStoppingDelay": "int",
"earlyStoppingPatience": "int",
"enableOnnxNormalization": "bool",
"evaluationFrequency": "int",
"gradientAccumulationStep": "int",
"imageSize": "int",
"layersToFreeze": "int",
"learningRate": "int",
"learningRateScheduler": "string",
"maxSize": "int",
"minSize": "int",
"modelName": "string",
"modelSize": "string",
"momentum": "int",
"multiScale": "bool",
"nesterov": "bool",
"nmsIouThreshold": "int",
"numberOfEpochs": "int",
"numberOfWorkers": "int",
"optimizer": "string",
"randomSeed": "int",
"stepLRGamma": "int",
"stepLRStepSize": "int",
"tileGridSize": "string",
"tileOverlapRatio": "int",
"tilePredictionsNmsThreshold": "int",
"trainingBatchSize": "int",
"validationBatchSize": "int",
"validationIouThreshold": "int",
"validationMetricType": "string",
"warmupCosineLRCycles": "int",
"warmupCosineLRWarmupEpochs": "int",
"weightDecay": "int"
},
"primaryMetric": "MeanAveragePrecision",
"searchSpace": [
{
"amsGradient": "string",
"augmentations": "string",
"beta1": "string",
"beta2": "string",
"boxDetectionsPerImage": "string",
"boxScoreThreshold": "string",
"distributed": "string",
"earlyStopping": "string",
"earlyStoppingDelay": "string",
"earlyStoppingPatience": "string",
"enableOnnxNormalization": "string",
"evaluationFrequency": "string",
"gradientAccumulationStep": "string",
"imageSize": "string",
"layersToFreeze": "string",
"learningRate": "string",
"learningRateScheduler": "string",
"maxSize": "string",
"minSize": "string",
"modelName": "string",
"modelSize": "string",
"momentum": "string",
"multiScale": "string",
"nesterov": "string",
"nmsIouThreshold": "string",
"numberOfEpochs": "string",
"numberOfWorkers": "string",
"optimizer": "string",
"randomSeed": "string",
"stepLRGamma": "string",
"stepLRStepSize": "string",
"tileGridSize": "string",
"tileOverlapRatio": "string",
"tilePredictionsNmsThreshold": "string",
"trainingBatchSize": "string",
"validationBatchSize": "string",
"validationIouThreshold": "string",
"validationMetricType": "string",
"warmupCosineLRCycles": "string",
"warmupCosineLRWarmupEpochs": "string",
"weightDecay": "string"
}
],
"sweepSettings": {
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"samplingAlgorithm": "string"
},
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int"
For ImageObjectDetection, use:
"taskType": "ImageObjectDetection",
"limitSettings": {
"maxConcurrentTrials": "int",
"maxTrials": "int",
"timeout": "string"
},
"modelSettings": {
"advancedSettings": "string",
"amsGradient": "bool",
"augmentations": "string",
"beta1": "int",
"beta2": "int",
"boxDetectionsPerImage": "int",
"boxScoreThreshold": "int",
"checkpointFrequency": "int",
"checkpointModel": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"checkpointRunId": "string",
"distributed": "bool",
"earlyStopping": "bool",
"earlyStoppingDelay": "int",
"earlyStoppingPatience": "int",
"enableOnnxNormalization": "bool",
"evaluationFrequency": "int",
"gradientAccumulationStep": "int",
"imageSize": "int",
"layersToFreeze": "int",
"learningRate": "int",
"learningRateScheduler": "string",
"maxSize": "int",
"minSize": "int",
"modelName": "string",
"modelSize": "string",
"momentum": "int",
"multiScale": "bool",
"nesterov": "bool",
"nmsIouThreshold": "int",
"numberOfEpochs": "int",
"numberOfWorkers": "int",
"optimizer": "string",
"randomSeed": "int",
"stepLRGamma": "int",
"stepLRStepSize": "int",
"tileGridSize": "string",
"tileOverlapRatio": "int",
"tilePredictionsNmsThreshold": "int",
"trainingBatchSize": "int",
"validationBatchSize": "int",
"validationIouThreshold": "int",
"validationMetricType": "string",
"warmupCosineLRCycles": "int",
"warmupCosineLRWarmupEpochs": "int",
"weightDecay": "int"
},
"primaryMetric": "MeanAveragePrecision",
"searchSpace": [
{
"amsGradient": "string",
"augmentations": "string",
"beta1": "string",
"beta2": "string",
"boxDetectionsPerImage": "string",
"boxScoreThreshold": "string",
"distributed": "string",
"earlyStopping": "string",
"earlyStoppingDelay": "string",
"earlyStoppingPatience": "string",
"enableOnnxNormalization": "string",
"evaluationFrequency": "string",
"gradientAccumulationStep": "string",
"imageSize": "string",
"layersToFreeze": "string",
"learningRate": "string",
"learningRateScheduler": "string",
"maxSize": "string",
"minSize": "string",
"modelName": "string",
"modelSize": "string",
"momentum": "string",
"multiScale": "string",
"nesterov": "string",
"nmsIouThreshold": "string",
"numberOfEpochs": "string",
"numberOfWorkers": "string",
"optimizer": "string",
"randomSeed": "string",
"stepLRGamma": "string",
"stepLRStepSize": "string",
"tileGridSize": "string",
"tileOverlapRatio": "string",
"tilePredictionsNmsThreshold": "string",
"trainingBatchSize": "string",
"validationBatchSize": "string",
"validationIouThreshold": "string",
"validationMetricType": "string",
"warmupCosineLRCycles": "string",
"warmupCosineLRWarmupEpochs": "string",
"weightDecay": "string"
}
],
"sweepSettings": {
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"samplingAlgorithm": "string"
},
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int"
For Regression, use:
"taskType": "Regression",
"cvSplitColumnNames": [ "string" ],
"featurizationSettings": {
"blockedTransformers": [ "string" ],
"columnNameAndTypes": {
"{customized property}": "string"
},
"datasetLanguage": "string",
"enableDnnFeaturization": "bool",
"mode": "string",
"transformerParams": {
"{customized property}": [
{
"fields": [ "string" ],
"parameters": {}
}
]
}
},
"limitSettings": {
"enableEarlyTermination": "bool",
"exitScore": "int",
"maxConcurrentTrials": "int",
"maxCoresPerTrial": "int",
"maxTrials": "int",
"timeout": "string",
"trialTimeout": "string"
},
"nCrossValidations": {
"mode": "string"
// For remaining properties, see NCrossValidations objects
},
"primaryMetric": "string",
"testData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"testDataSize": "int",
"trainingSettings": {
"allowedTrainingAlgorithms": [ "string" ],
"blockedTrainingAlgorithms": [ "string" ],
"enableDnnTraining": "bool",
"enableModelExplainability": "bool",
"enableOnnxCompatibleModels": "bool",
"enableStackEnsemble": "bool",
"enableVoteEnsemble": "bool",
"ensembleModelDownloadTimeout": "string",
"stackEnsembleSettings": {
"stackMetaLearnerKWargs": {},
"stackMetaLearnerTrainPercentage": "int",
"stackMetaLearnerType": "string"
}
},
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int",
"weightColumnName": "string"
For TextClassification, use:
"taskType": "TextClassification",
"featurizationSettings": {
"datasetLanguage": "string"
},
"limitSettings": {
"maxConcurrentTrials": "int",
"maxTrials": "int",
"timeout": "string"
},
"primaryMetric": "string",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
}
For TextClassificationMultilabel, use:
"taskType": "TextClassificationMultilabel",
"featurizationSettings": {
"datasetLanguage": "string"
},
"limitSettings": {
"maxConcurrentTrials": "int",
"maxTrials": "int",
"timeout": "string"
},
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
}
For TextNER, use:
"taskType": "TextNER",
"featurizationSettings": {
"datasetLanguage": "string"
},
"limitSettings": {
"maxConcurrentTrials": "int",
"maxTrials": "int",
"timeout": "string"
},
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
}
NCrossValidations objects
Set the mode property to specify the type of object.
For Auto, use:
"mode": "Auto"
For Custom, use:
"mode": "Custom",
"value": "int"
ForecastHorizon objects
Set the mode property to specify the type of object.
For Auto, use:
"mode": "Auto"
For Custom, use:
"mode": "Custom",
"value": "int"
Seasonality objects
Set the mode property to specify the type of object.
For Auto, use:
"mode": "Auto"
For Custom, use:
"mode": "Custom",
"value": "int"
TargetLags objects
Set the mode property to specify the type of object.
For Auto, use:
"mode": "Auto"
For Custom, use:
"mode": "Custom",
"values": [ "int" ]
TargetRollingWindowSize objects
Set the mode property to specify the type of object.
For Auto, use:
"mode": "Auto"
For Custom, use:
"mode": "Custom",
"value": "int"
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"
DistributionConfiguration objects
Set the distributionType property to specify the type of object.
For Mpi, use:
"distributionType": "Mpi",
"processCountPerInstance": "int"
For PyTorch, use:
"distributionType": "PyTorch",
"processCountPerInstance": "int"
For TensorFlow, use:
"distributionType": "TensorFlow",
"parameterServerCount": "int",
"workerCount": "int"
JobInput objects
Set the jobInputType property to specify the type of object.
For custom_model, use:
"jobInputType": "custom_model",
"mode": "string",
"uri": "string"
For literal, use:
"jobInputType": "literal",
"value": "string"
For mlflow_model, use:
"jobInputType": "mlflow_model",
"mode": "string",
"uri": "string"
For mltable, use:
"jobInputType": "mltable",
"mode": "string",
"uri": "string"
For triton_model, use:
"jobInputType": "triton_model",
"mode": "string",
"uri": "string"
For uri_file, use:
"jobInputType": "uri_file",
"mode": "string",
"uri": "string"
For uri_folder, use:
"jobInputType": "uri_folder",
"mode": "string",
"uri": "string"
FineTuningVertical objects
Set the modelProvider property to specify the type of object.
For AzureOpenAI, use:
"modelProvider": "AzureOpenAI",
"hyperParameters": {
"batchSize": "int",
"learningRateMultiplier": "int",
"nEpochs": "int"
}
For Custom, use:
"modelProvider": "Custom",
"hyperParameters": {
"{customized property}": "string"
}
SparkJobEntry objects
Set the sparkJobEntryType property to specify the type of object.
For SparkJobPythonEntry, use:
"sparkJobEntryType": "SparkJobPythonEntry",
"file": "string"
For SparkJobScalaEntry, use:
"sparkJobEntryType": "SparkJobScalaEntry",
"className": "string"
SamplingAlgorithm objects
Set the samplingAlgorithmType property to specify the type of object.
For Bayesian, use:
"samplingAlgorithmType": "Bayesian"
For Grid, use:
"samplingAlgorithmType": "Grid"
For Random, use:
"samplingAlgorithmType": "Random",
"rule": "string",
"seed": "int"
MonitorComputeConfigurationBase objects
Set the computeType property to specify the type of object.
For ServerlessSpark, use:
"computeType": "ServerlessSpark",
"computeIdentity": {
"computeIdentityType": "string"
// For remaining properties, see MonitorComputeIdentityBase objects
},
"instanceType": "string",
"runtimeVersion": "string"
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}": {}
}
}
MonitoringSignalBase objects
Set the signalType property to specify the type of object.
For Custom, use:
"signalType": "Custom",
"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"
}
}
]
For DataDrift, use:
"signalType": "DataDrift",
"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
}
For DataQuality, use:
"signalType": "DataQuality",
"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
}
For FeatureAttributionDrift, use:
"signalType": "FeatureAttributionDrift",
"featureDataTypeOverride": {
"{customized property}": "string"
},
"featureImportanceSettings": {
"mode": "string",
"targetColumn": "string"
},
"metricThreshold": {
"metric": "NormalizedDiscountedCumulativeGain",
"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
}
For PredictionDrift, use:
"signalType": "PredictionDrift",
"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
}
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"
MonitoringFeatureFilterBase objects
Set the filterType property to specify the type of object.
For AllFeatures, use:
"filterType": "AllFeatures"
For FeatureSubset, use:
"filterType": "FeatureSubset",
"features": [ "string" ]
For TopNByAttribution, use:
"filterType": "TopNByAttribution",
"top": "int"
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"
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"
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:
"triggerType": "Cron",
"expression": "string"
For Recurrence, use:
"triggerType": "Recurrence",
"frequency": "string",
"interval": "int",
"schedule": {
"hours": [ "int" ],
"minutes": [ "int" ],
"monthDays": [ "int" ],
"weekDays": [ "string" ]
}
Property values
workspaces/schedules
Name | Description | Value |
---|---|---|
type | The resource type | 'Microsoft.MachineLearningServices/workspaces/schedules' |
apiVersion | The resource api version | '2024-07-01-preview' |
name | The resource name See how to set names and types for child resources in JSON ARM templates. |
string (required) |
properties | [Required] Additional attributes of the entity. | ScheduleProperties (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. | object |
trigger | [Required] Specifies the trigger details | TriggerBase (required) |
ScheduleActionBase
Name | Description | Value |
---|---|---|
actionType | Set the object type | CreateJob CreateMonitor InvokeBatchEndpoint (required) |
JobScheduleAction
Name | Description | Value |
---|---|---|
actionType | [Required] Specifies the action type of the schedule | 'CreateJob' (required) |
jobDefinition | [Required] Defines Schedule action definition details. | JobBaseProperties (required) |
JobBaseProperties
Name | Description | Value |
---|---|---|
componentId | ARM resource ID of the component resource. | string |
computeId | ARM resource ID of the compute resource. | string |
description | The asset description text. | string |
displayName | Display name of job. | string |
experimentName | The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment. | string |
identity | Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null. |
IdentityConfiguration |
isArchived | Is the asset archived? | bool |
notificationSetting | Notification setting for the job | NotificationSetting |
properties | The asset property dictionary. | ResourceBaseProperties |
services | List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject. |
JobBaseServices |
tags | Tag dictionary. Tags can be added, removed, and updated. | object |
jobType | Set the object type | AutoML Command FineTuning Pipeline Spark Sweep (required) |
IdentityConfiguration
Name | Description | Value |
---|---|---|
identityType | Set the object type | AMLToken Managed UserIdentity (required) |
AmlToken
Name | Description | Value |
---|---|---|
identityType | [Required] Specifies the type of identity framework. | 'AMLToken' (required) |
ManagedIdentity
Name | Description | Value |
---|---|---|
identityType | [Required] Specifies the type of identity framework. | 'Managed' (required) |
clientId | Specifies a user-assigned identity by client ID. For system-assigned, do not set this field. | string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$ |
objectId | Specifies a user-assigned identity by object ID. For system-assigned, do not set this field. | string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$ |
resourceId | Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field. | string |
UserIdentity
Name | Description | Value |
---|---|---|
identityType | [Required] Specifies the type of identity framework. | 'UserIdentity' (required) |
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 |
---|---|---|
{customized property} | Webhook |
Webhook
Name | Description | Value |
---|---|---|
eventType | Send callback on a specified notification event | string |
webhookType | Set the object type | AzureDevOps (required) |
AzureDevOpsWebhook
Name | Description | Value |
---|---|---|
webhookType | [Required] Specifies the type of service to send a callback | 'AzureDevOps' (required) |
ResourceBaseProperties
Name | Description | Value |
---|---|---|
{customized property} | string |
JobBaseServices
Name | Description | Value |
---|---|---|
{customized property} | JobService |
JobService
Name | Description | Value |
---|---|---|
endpoint | Url for endpoint. | string |
jobServiceType | Endpoint type. | string |
nodes | Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node. |
Nodes |
port | Port for endpoint. | int |
properties | Additional properties to set on the endpoint. | JobServiceProperties |
Nodes
Name | Description | Value |
---|---|---|
nodesValueType | Set the object type | All (required) |
AllNodes
Name | Description | Value |
---|---|---|
nodesValueType | [Required] Type of the Nodes value | 'All' (required) |
JobServiceProperties
Name | Description | Value |
---|---|---|
{customized property} | string |
AutoMLJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | 'AutoML' (required) |
environmentId | The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job. |
string |
environmentVariables | Environment variables included in the job. | AutoMLJobEnvironmentVariables |
outputs | Mapping of output data bindings used in the job. | AutoMLJobOutputs |
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 |
---|---|---|
{customized property} | string |
AutoMLJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
JobOutput
Name | Description | Value |
---|---|---|
description | Description for the output. | string |
jobOutputType | Set the object type | custom_model mlflow_model mltable triton_model uri_file uri_folder (required) |
CustomModelJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | 'custom_model' (required) |
mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
uri | Output Asset URI. | string |
MLFlowModelJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | 'mlflow_model' (required) |
mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
uri | Output Asset URI. | string |
MLTableJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | 'mltable' (required) |
mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
uri | Output Asset URI. | string |
TritonModelJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | 'triton_model' (required) |
mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
uri | Output Asset URI. | string |
UriFileJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | 'uri_file' (required) |
mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
uri | Output Asset URI. | string |
UriFolderJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | 'uri_folder' (required) |
mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
uri | Output Asset URI. | string |
QueueSettings
Name | Description | Value |
---|---|---|
jobTier | Controls the compute job tier | 'Basic' 'Null' 'Premium' 'Spot' 'Standard' |
JobResourceConfiguration
Name | Description | Value |
---|---|---|
dockerArgs | Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types. | string |
instanceCount | Optional number of instances or nodes used by the compute target. | int |
instanceType | Optional type of VM used as supported by the compute target. | string |
properties | Additional properties bag. | ResourceConfigurationProperties |
shmSize | Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes). | string Constraints: Pattern = \d+[bBkKmMgG] |
ResourceConfigurationProperties
Name | Description | Value |
---|---|---|
{customized property} |
AutoMLVertical
Name | Description | Value |
---|---|---|
logVerbosity | Log verbosity for the job. | 'Critical' 'Debug' 'Error' 'Info' 'NotSet' 'Warning' |
targetColumnName | Target column name: This is prediction values column. Also known as label column name in context of classification tasks. |
string |
trainingData | [Required] Training data input. | MLTableJobInput (required) |
taskType | Set the object type | Classification Forecasting ImageClassification ImageClassificationMultilabel ImageInstanceSegmentation ImageObjectDetection Regression TextClassification TextClassificationMultilabel TextNER (required) |
MLTableJobInput
Name | Description | Value |
---|---|---|
description | Description for the input. | string |
jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
Classification
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'Classification' (required) |
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
positiveLabel | Positive label for binary metrics calculation. | string |
primaryMetric | Primary metric for the task. | 'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
testData | Test data input. | MLTableJobInput |
testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
trainingSettings | Inputs for training phase for an AutoML Job. | ClassificationTrainingSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
TableVerticalFeaturizationSettings
Name | Description | Value |
---|---|---|
blockedTransformers | These transformers shall not be used in featurization. | String array containing any of: 'CatTargetEncoder' 'CountVectorizer' 'HashOneHotEncoder' 'LabelEncoder' 'NaiveBayes' 'OneHotEncoder' 'TextTargetEncoder' 'TfIdf' 'WoETargetEncoder' 'WordEmbedding' |
columnNameAndTypes | Dictionary of column name and its type (int, float, string, datetime etc). | TableVerticalFeaturizationSettingsColumnNameAndTypes |
datasetLanguage | Dataset language, useful for the text data. | string |
enableDnnFeaturization | Determines whether to use Dnn based featurizers for data featurization. | bool |
mode | Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done. |
'Auto' 'Custom' 'Off' |
transformerParams | User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor. | TableVerticalFeaturizationSettingsTransformerParams |
TableVerticalFeaturizationSettingsColumnNameAndTypes
Name | Description | Value |
---|---|---|
{customized property} | string |
TableVerticalFeaturizationSettingsTransformerParams
Name | Description | Value |
---|---|---|
{customized property} | ColumnTransformer[] |
ColumnTransformer
Name | Description | Value |
---|---|---|
fields | Fields to apply transformer logic on. | string[] |
parameters | Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format. |
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 |
maxTrials | Number of iterations. | int |
timeout | AutoML job timeout. | string |
trialTimeout | Iteration timeout. | string |
NCrossValidations
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoNCrossValidations
Name | Description | Value |
---|---|---|
mode | [Required] Mode for determining N-Cross validations. | 'Auto' (required) |
CustomNCrossValidations
Name | Description | Value |
---|---|---|
mode | [Required] Mode for determining N-Cross validations. | 'Custom' (required) |
value | [Required] N-Cross validations value. | int (required) |
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 |
StackEnsembleSettings
Name | Description | Value |
---|---|---|
stackMetaLearnerKWargs | Optional parameters to pass to the initializer of the meta-learner. | |
stackMetaLearnerTrainPercentage | Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2. | int |
stackMetaLearnerType | The meta-learner is a model trained on the output of the individual heterogeneous models. | 'ElasticNet' 'ElasticNetCV' 'LightGBMClassifier' 'LightGBMRegressor' 'LinearRegression' 'LogisticRegression' 'LogisticRegressionCV' 'None' |
Forecasting
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'Forecasting' (required) |
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
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' |
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' |
forecastHorizon | The desired maximum forecast horizon in units of time-series frequency. | ForecastHorizon |
frequency | When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default. | string |
seasonality | Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred. |
Seasonality |
shortSeriesHandlingConfig | The parameter defining how if AutoML should handle short time series. | 'Auto' 'Drop' 'None' 'Pad' |
targetAggregateFunction | The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean". |
'Max' 'Mean' 'Min' 'None' 'Sum' |
targetLags | The number of past periods to lag from the target column. | TargetLags |
targetRollingWindowSize | The number of past periods used to create a rolling window average of the target column. | TargetRollingWindowSize |
timeColumnName | The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. | string |
timeSeriesIdColumnNames | The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting. |
string[] |
useStl | Configure STL Decomposition of the time-series target column. | 'None' 'Season' 'SeasonTrend' |
ForecastHorizon
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoForecastHorizon
Name | Description | Value |
---|---|---|
mode | [Required] Set forecast horizon value selection mode. | 'Auto' (required) |
CustomForecastHorizon
Name | Description | Value |
---|---|---|
mode | [Required] Set forecast horizon value selection mode. | 'Custom' (required) |
value | [Required] Forecast horizon value. | int (required) |
Seasonality
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoSeasonality
Name | Description | Value |
---|---|---|
mode | [Required] Seasonality mode. | 'Auto' (required) |
CustomSeasonality
Name | Description | Value |
---|---|---|
mode | [Required] Seasonality mode. | 'Custom' (required) |
value | [Required] Seasonality value. | int (required) |
TargetLags
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoTargetLags
Name | Description | Value |
---|---|---|
mode | [Required] Set target lags mode - Auto/Custom | 'Auto' (required) |
CustomTargetLags
Name | Description | Value |
---|---|---|
mode | [Required] Set target lags mode - Auto/Custom | 'Custom' (required) |
values | [Required] Set target lags values. | int[] (required) |
TargetRollingWindowSize
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoTargetRollingWindowSize
Name | Description | Value |
---|---|---|
mode | [Required] TargetRollingWindowSiz detection mode. | 'Auto' (required) |
CustomTargetRollingWindowSize
Name | Description | Value |
---|---|---|
mode | [Required] TargetRollingWindowSiz detection mode. | 'Custom' (required) |
value | [Required] TargetRollingWindowSize value. | int (required) |
ForecastingTrainingSettings
Name | Description | Value |
---|---|---|
allowedTrainingAlgorithms | Allowed models for forecasting task. | String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SGD' 'SeasonalAverage' 'SeasonalNaive' 'TCNForecaster' 'XGBoostRegressor' |
blockedTrainingAlgorithms | Blocked models for forecasting task. | String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SGD' 'SeasonalAverage' 'SeasonalNaive' 'TCNForecaster' 'XGBoostRegressor' |
enableDnnTraining | Enable recommendation of DNN models. | bool |
enableModelExplainability | Flag to turn on explainability on best model. | bool |
enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
enableStackEnsemble | Enable stack ensemble run. | bool |
enableVoteEnsemble | Enable voting ensemble run. | bool |
ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
ImageClassification
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'ImageClassification' (required) |
limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
modelSettings | Settings used for training the model. | ImageModelSettingsClassification |
primaryMetric | Primary metric to optimize for this task. | 'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsClassification[] |
sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageLimitSettings
Name | Description | Value |
---|---|---|
maxConcurrentTrials | Maximum number of concurrent AutoML iterations. | int |
maxTrials | Maximum number of AutoML iterations. | int |
timeout | AutoML job timeout. | string |
ImageModelSettingsClassification
Name | Description | Value |
---|---|---|
advancedSettings | Settings for advanced scenarios. | string |
amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | bool |
augmentations | Settings for using Augmentations. | string |
beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
checkpointFrequency | Frequency to store model checkpoints. Must be a positive integer. | int |
checkpointModel | The pretrained checkpoint model for incremental training. | MLFlowModelJobInput |
checkpointRunId | The id of a previous run that has a pretrained checkpoint for incremental training. | string |
distributed | Whether to use distributed training. | bool |
earlyStopping | Enable early stopping logic during training. | bool |
earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
int |
earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
int |
enableOnnxNormalization | Enable normalization when exporting ONNX model. | bool |
evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | int |
gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
int |
layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
int |
learningRate | Initial learning rate. Must be a float in the range [0, 1]. | int |
learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | 'None' 'Step' 'WarmupCosine' |
modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | int |
nesterov | Enable nesterov when optimizer is 'sgd'. | bool |
numberOfEpochs | Number of training epochs. Must be a positive integer. | int |
numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | int |
optimizer | Type of optimizer. | 'Adam' 'Adamw' 'None' 'Sgd' |
randomSeed | Random seed to be used when using deterministic training. | int |
stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | int |
stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | int |
trainingBatchSize | Training batch size. Must be a positive integer. | int |
trainingCropSize | Image crop size that is input to the neural network for the training dataset. Must be a positive integer. | int |
validationBatchSize | Validation batch size. Must be a positive integer. | int |
validationCropSize | Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. | int |
validationResizeSize | Image size to which to resize before cropping for validation dataset. Must be a positive integer. | int |
warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | int |
warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | int |
weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | int |
weightedLoss | Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2. |
int |
MLFlowModelJobInput
Name | Description | Value |
---|---|---|
description | Description for the input. | string |
jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
ImageModelDistributionSettingsClassification
Name | Description | Value |
---|---|---|
amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | string |
augmentations | Settings for using Augmentations. | string |
beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
distributed | Whether to use distributer training. | string |
earlyStopping | Enable early stopping logic during training. | string |
earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
string |
earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
string |
enableOnnxNormalization | Enable normalization when exporting ONNX model. | string |
evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | string |
gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
string |
layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
string |
learningRate | Initial learning rate. Must be a float in the range [0, 1]. | string |
learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | string |
modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | string |
nesterov | Enable nesterov when optimizer is 'sgd'. | string |
numberOfEpochs | Number of training epochs. Must be a positive integer. | string |
numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | string |
optimizer | Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. | string |
randomSeed | Random seed to be used when using deterministic training. | string |
stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | string |
stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | string |
trainingBatchSize | Training batch size. Must be a positive integer. | string |
trainingCropSize | Image crop size that is input to the neural network for the training dataset. Must be a positive integer. | string |
validationBatchSize | Validation batch size. Must be a positive integer. | string |
validationCropSize | Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. | string |
validationResizeSize | Image size to which to resize before cropping for validation dataset. Must be a positive integer. | string |
warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | string |
warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | string |
weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | string |
weightedLoss | Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2. |
string |
ImageSweepSettings
Name | Description | Value |
---|---|---|
earlyTermination | Type of early termination policy. | EarlyTerminationPolicy |
samplingAlgorithm | [Required] Type of the hyperparameter sampling algorithms. | 'Bayesian' 'Grid' 'Random' (required) |
EarlyTerminationPolicy
Name | Description | Value |
---|---|---|
delayEvaluation | Number of intervals by which to delay the first evaluation. | int |
evaluationInterval | Interval (number of runs) between policy evaluations. | int |
policyType | Set the object type | Bandit MedianStopping TruncationSelection (required) |
BanditPolicy
Name | Description | Value |
---|---|---|
policyType | [Required] Name of policy configuration | 'Bandit' (required) |
slackAmount | Absolute distance allowed from the best performing run. | int |
slackFactor | Ratio of the allowed distance from the best performing run. | int |
MedianStoppingPolicy
Name | Description | Value |
---|---|---|
policyType | [Required] Name of policy configuration | 'MedianStopping' (required) |
TruncationSelectionPolicy
Name | Description | Value |
---|---|---|
policyType | [Required] Name of policy configuration | 'TruncationSelection' (required) |
truncationPercentage | The percentage of runs to cancel at each evaluation interval. | int |
ImageClassificationMultilabel
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'ImageClassificationMultilabel' (required) |
limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
modelSettings | Settings used for training the model. | ImageModelSettingsClassification |
primaryMetric | Primary metric to optimize for this task. | 'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'IOU' 'NormMacroRecall' 'PrecisionScoreWeighted' |
searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsClassification[] |
sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageInstanceSegmentation
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'ImageInstanceSegmentation' (required) |
limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
modelSettings | Settings used for training the model. | ImageModelSettingsObjectDetection |
primaryMetric | Primary metric to optimize for this task. | 'MeanAveragePrecision' |
searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsObjectDetection[] |
sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageModelSettingsObjectDetection
Name | Description | Value |
---|---|---|
advancedSettings | Settings for advanced scenarios. | string |
amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | bool |
augmentations | Settings for using Augmentations. | string |
beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
boxDetectionsPerImage | Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
boxScoreThreshold | During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
int |
checkpointFrequency | Frequency to store model checkpoints. Must be a positive integer. | int |
checkpointModel | The pretrained checkpoint model for incremental training. | MLFlowModelJobInput |
checkpointRunId | The id of a previous run that has a pretrained checkpoint for incremental training. | string |
distributed | Whether to use distributed training. | bool |
earlyStopping | Enable early stopping logic during training. | bool |
earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
int |
earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
int |
enableOnnxNormalization | Enable normalization when exporting ONNX model. | bool |
evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | int |
gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
int |
imageSize | Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
int |
layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
int |
learningRate | Initial learning rate. Must be a float in the range [0, 1]. | int |
learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | 'None' 'Step' 'WarmupCosine' |
maxSize | Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
minSize | Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
modelSize | Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
'ExtraLarge' 'Large' 'Medium' 'None' 'Small' |
momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | int |
multiScale | Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm. |
bool |
nesterov | Enable nesterov when optimizer is 'sgd'. | bool |
nmsIouThreshold | IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1]. | int |
numberOfEpochs | Number of training epochs. Must be a positive integer. | int |
numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | int |
optimizer | Type of optimizer. | 'Adam' 'Adamw' 'None' 'Sgd' |
randomSeed | Random seed to be used when using deterministic training. | int |
stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | int |
stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | int |
tileGridSize | The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
tileOverlapRatio | Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm. |
int |
tilePredictionsNmsThreshold | The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
trainingBatchSize | Training batch size. Must be a positive integer. | int |
validationBatchSize | Validation batch size. Must be a positive integer. | int |
validationIouThreshold | IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. | int |
validationMetricType | Metric computation method to use for validation metrics. | 'Coco' 'CocoVoc' 'None' 'Voc' |
warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | int |
warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | int |
weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | int |
ImageModelDistributionSettingsObjectDetection
Name | Description | Value |
---|---|---|
amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | string |
augmentations | Settings for using Augmentations. | string |
beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
boxDetectionsPerImage | Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
boxScoreThreshold | During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
string |
distributed | Whether to use distributer training. | string |
earlyStopping | Enable early stopping logic during training. | string |
earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
string |
earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
string |
enableOnnxNormalization | Enable normalization when exporting ONNX model. | string |
evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | string |
gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
string |
imageSize | Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
string |
learningRate | Initial learning rate. Must be a float in the range [0, 1]. | string |
learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | string |
maxSize | Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
minSize | Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
modelSize | Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | string |
multiScale | Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
nesterov | Enable nesterov when optimizer is 'sgd'. | string |
nmsIouThreshold | IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1]. | string |
numberOfEpochs | Number of training epochs. Must be a positive integer. | string |
numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | string |
optimizer | Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. | string |
randomSeed | Random seed to be used when using deterministic training. | string |
stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | string |
stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | string |
tileGridSize | The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
tileOverlapRatio | Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm. |
string |
tilePredictionsNmsThreshold | The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression |
string |
trainingBatchSize | Training batch size. Must be a positive integer. | string |
validationBatchSize | Validation batch size. Must be a positive integer. | string |
validationIouThreshold | IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. | string |
validationMetricType | Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'. | string |
warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | string |
warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | string |
weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | string |
ImageObjectDetection
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'ImageObjectDetection' (required) |
limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
modelSettings | Settings used for training the model. | ImageModelSettingsObjectDetection |
primaryMetric | Primary metric to optimize for this task. | 'MeanAveragePrecision' |
searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsObjectDetection[] |
sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
Regression
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'Regression' (required) |
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
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' |
testData | Test data input. | MLTableJobInput |
testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
trainingSettings | Inputs for training phase for an AutoML Job. | RegressionTrainingSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
RegressionTrainingSettings
Name | Description | Value |
---|---|---|
allowedTrainingAlgorithms | Allowed models for regression task. | String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor' |
blockedTrainingAlgorithms | Blocked models for regression task. | String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor' |
enableDnnTraining | Enable recommendation of DNN models. | bool |
enableModelExplainability | Flag to turn on explainability on best model. | bool |
enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
enableStackEnsemble | Enable stack ensemble run. | bool |
enableVoteEnsemble | Enable voting ensemble run. | bool |
ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
TextClassification
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'TextClassification' (required) |
featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
primaryMetric | Primary metric for Text-Classification task. | 'AUCWeighted' 'Accuracy' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
validationData | Validation data inputs. | MLTableJobInput |
NlpVerticalFeaturizationSettings
Name | Description | Value |
---|---|---|
datasetLanguage | Dataset language, useful for the text data. | string |
NlpVerticalLimitSettings
Name | Description | Value |
---|---|---|
maxConcurrentTrials | Maximum Concurrent AutoML iterations. | int |
maxTrials | Number of AutoML iterations. | int |
timeout | AutoML job timeout. | string |
TextClassificationMultilabel
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'TextClassificationMultilabel' (required) |
featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
validationData | Validation data inputs. | MLTableJobInput |
TextNer
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | 'TextNER' (required) |
featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
validationData | Validation data inputs. | MLTableJobInput |
CommandJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | 'Command' (required) |
codeId | ARM resource ID of the code asset. | string |
command | [Required] The command to execute on startup of the job. eg. "python train.py" | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
environmentVariables | Environment variables included in the job. | CommandJobEnvironmentVariables |
inputs | Mapping of input data bindings used in the job. | CommandJobInputs |
limits | Command Job limit. | CommandJobLimits |
outputs | Mapping of output data bindings used in the job. | CommandJobOutputs |
queueSettings | Queue settings for the job | QueueSettings |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
DistributionConfiguration
Name | Description | Value |
---|---|---|
distributionType | Set the object type | Mpi PyTorch TensorFlow (required) |
Mpi
Name | Description | Value |
---|---|---|
distributionType | [Required] Specifies the type of distribution framework. | 'Mpi' (required) |
processCountPerInstance | Number of processes per MPI node. | int |
PyTorch
Name | Description | Value |
---|---|---|
distributionType | [Required] Specifies the type of distribution framework. | 'PyTorch' (required) |
processCountPerInstance | Number of processes per node. | int |
TensorFlow
Name | Description | Value |
---|---|---|
distributionType | [Required] Specifies the type of distribution framework. | 'TensorFlow' (required) |
parameterServerCount | Number of parameter server tasks. | int |
workerCount | Number of workers. If not specified, will default to the instance count. | int |
CommandJobEnvironmentVariables
Name | Description | Value |
---|---|---|
{customized property} | string |
CommandJobInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
JobInput
Name | Description | Value |
---|---|---|
description | Description for the input. | string |
jobInputType | Set the object type | custom_model literal mlflow_model mltable triton_model uri_file uri_folder (required) |
CustomModelJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'custom_model' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
LiteralJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'literal' (required) |
value | [Required] Literal value for the input. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
TritonModelJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'triton_model' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
UriFileJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'uri_file' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
UriFolderJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'uri_folder' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
CommandJobLimits
Name | Description | Value |
---|---|---|
jobLimitsType | [Required] JobLimit type. | 'Command' 'Sweep' (required) |
timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
CommandJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
FineTuningJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | 'FineTuning' (required) |
fineTuningDetails | [Required] | FineTuningVertical (required) |
outputs | [Required] | FineTuningJobOutputs (required) |
queueSettings | Queue settings for the job | QueueSettings |
resources | Instance types and other resources for the job | JobResources |
FineTuningVertical
Name | Description | Value |
---|---|---|
model | [Required] Input model for fine tuning. | MLFlowModelJobInput (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 |
modelProvider | Set the object type | AzureOpenAI Custom (required) |
AzureOpenAiFineTuning
Name | Description | Value |
---|---|---|
modelProvider | [Required] Enum to determine the type of fine tuning. | 'AzureOpenAI' (required) |
hyperParameters | HyperParameters for fine tuning Azure Open AI model. | AzureOpenAiHyperParameters |
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 |
CustomModelFineTuning
Name | Description | Value |
---|---|---|
modelProvider | [Required] Enum to determine the type of fine tuning. | 'Custom' (required) |
hyperParameters | HyperParameters for fine tuning custom model. | CustomModelFineTuningHyperParameters |
CustomModelFineTuningHyperParameters
Name | Description | Value |
---|---|---|
{customized property} | string |
FineTuningJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
JobResources
Name | Description | Value |
---|---|---|
instanceTypes | List of instance types to choose from. | string[] |
PipelineJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | 'Pipeline' (required) |
inputs | Inputs for the pipeline job. | PipelineJobInputs |
jobs | Jobs construct the Pipeline Job. | PipelineJobJobs |
outputs | Outputs for the pipeline job | PipelineJobOutputs |
settings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | |
sourceJobId | ARM resource ID of source job. | string |
PipelineJobInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
PipelineJobJobs
Name | Description | Value |
---|---|---|
{customized property} |
PipelineJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
SparkJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | 'Spark' (required) |
archives | Archive files used in the job. | string[] |
args | Arguments for the job. | string |
codeId | [Required] arm-id of the code asset. | string (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[] |
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 |
---|---|---|
{customized property} | string |
SparkJobEntry
Name | Description | Value |
---|---|---|
sparkJobEntryType | Set the object type | SparkJobPythonEntry SparkJobScalaEntry (required) |
SparkJobPythonEntry
Name | Description | Value |
---|---|---|
sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobPythonEntry' (required) |
file | [Required] Relative python file path for job entry point. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
SparkJobScalaEntry
Name | Description | Value |
---|---|---|
sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobScalaEntry' (required) |
className | [Required] Scala class name used as entry point. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
SparkJobEnvironmentVariables
Name | Description | Value |
---|---|---|
{customized property} | string |
SparkJobInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
SparkJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
SparkResourceConfiguration
Name | Description | Value |
---|---|---|
instanceType | Optional type of VM used as supported by the compute target. | string |
runtimeVersion | Version of spark runtime used for the job. | string |
SweepJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | 'Sweep' (required) |
earlyTermination | Early termination policies enable canceling poor-performing runs before they complete | EarlyTerminationPolicy |
inputs | Mapping of input data bindings used in the job. | SweepJobInputs |
limits | Sweep Job limit. | SweepJobLimits |
objective | [Required] Optimization objective. | Objective (required) |
outputs | Mapping of output data bindings used in the job. | SweepJobOutputs |
queueSettings | Queue settings for the job | QueueSettings |
samplingAlgorithm | [Required] The hyperparameter sampling algorithm | SamplingAlgorithm (required) |
searchSpace | [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter | |
trial | [Required] Trial component definition. | TrialComponent (required) |
SweepJobInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
SweepJobLimits
Name | Description | Value |
---|---|---|
jobLimitsType | [Required] JobLimit type. | 'Command' 'Sweep' (required) |
maxConcurrentTrials | Sweep Job max concurrent trials. | int |
maxTotalTrials | Sweep Job max total trials. | int |
timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
trialTimeout | Sweep Job Trial timeout value. | string |
Objective
Name | Description | Value |
---|---|---|
goal | [Required] Defines supported metric goals for hyperparameter tuning | 'Maximize' 'Minimize' (required) |
primaryMetric | [Required] Name of the metric to optimize. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
SweepJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
SamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | Set the object type | Bayesian Grid Random (required) |
BayesianSamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Bayesian' (required) |
GridSamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Grid' (required) |
RandomSamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Random' (required) |
rule | The specific type of random algorithm | 'Random' 'Sobol' |
seed | An optional integer to use as the seed for random number generation | int |
TrialComponent
Name | Description | Value |
---|---|---|
codeId | ARM resource ID of the code asset. | string |
command | [Required] The command to execute on startup of the job. eg. "python train.py" | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
environmentVariables | Environment variables included in the job. | TrialComponentEnvironmentVariables |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
TrialComponentEnvironmentVariables
Name | Description | Value |
---|---|---|
{customized property} | string |
CreateMonitorAction
Name | Description | Value |
---|---|---|
actionType | [Required] Specifies the action type of the schedule | 'CreateMonitor' (required) |
monitorDefinition | [Required] Defines the monitor. | MonitorDefinition (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 entities targeted by the monitor. | MonitoringTarget |
signals | [Required] The signals to monitor. | MonitorDefinitionSignals (required) |
MonitorNotificationSettings
Name | Description | Value |
---|---|---|
emailNotificationSettings | The AML notification email settings. | MonitorEmailNotificationSettings |
MonitorEmailNotificationSettings
Name | Description | Value |
---|---|---|
emails | The email recipient list which has a limitation of 499 characters in total. | string[] |
MonitorComputeConfigurationBase
Name | Description | Value |
---|---|---|
computeType | Set the object type | ServerlessSpark (required) |
MonitorServerlessSparkCompute
Name | Description | Value |
---|---|---|
computeType | [Required] Specifies the type of signal to monitor. | 'ServerlessSpark' (required) |
computeIdentity | [Required] The identity scheme leveraged to by the spark jobs running on serverless Spark. | MonitorComputeIdentityBase (required) |
instanceType | [Required] The instance type running the Spark job. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
runtimeVersion | [Required] The Spark runtime version. | string (required) Constraints: Min length = 1 Pattern = ^[0-9]+\.[0-9]+$ |
MonitorComputeIdentityBase
Name | Description | Value |
---|---|---|
computeIdentityType | Set the object type | AmlToken ManagedIdentity (required) |
AmlTokenComputeIdentity
Name | Description | Value |
---|---|---|
computeIdentityType | [Required] Specifies the type of identity to use within the monitoring jobs. | 'AmlToken' (required) |
ManagedComputeIdentity
Name | Description | Value |
---|---|---|
computeIdentityType | [Required] Specifies the type of identity to use within the monitoring jobs. | 'ManagedIdentity' (required) |
identity | The identity which will be leveraged by the monitoring jobs. | ManagedServiceIdentity |
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 |
UserAssignedIdentities
Name | Description | Value |
---|---|---|
{customized property} | UserAssignedIdentity |
UserAssignedIdentity
This object doesn't contain any properties to set during deployment. All properties are ReadOnly.
MonitoringTarget
Name | Description | Value |
---|---|---|
deploymentId | Reference to the deployment asset targeted by this monitor. | string |
modelId | Reference to the model asset targeted by this monitor. | string |
taskType | [Required] The machine learning task type of the monitored model. | 'Classification' 'Regression' (required) |
MonitorDefinitionSignals
Name | Description | Value |
---|---|---|
{customized property} | MonitoringSignalBase |
MonitoringSignalBase
Name | Description | Value |
---|---|---|
notificationTypes | The current notification mode for this signal. | String array containing any of: 'AmlNotification' |
properties | Property dictionary. Properties can be added, but not removed or altered. | MonitoringSignalBaseProperties |
signalType | Set the object type | Custom DataDrift DataQuality FeatureAttributionDrift PredictionDrift (required) |
MonitoringSignalBaseProperties
Name | Description | Value |
---|---|---|
{customized property} | string |
CustomMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | 'Custom' (required) |
componentId | [Required] Reference to the component asset used to calculate the custom metrics. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
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) |
CustomMonitoringSignalInputAssets
Name | Description | Value |
---|---|---|
{customized property} | MonitoringInputDataBase |
MonitoringInputDataBase
Name | Description | Value |
---|---|---|
columns | Mapping of column names to special uses. | MonitoringInputDataBaseColumns |
dataContext | The context metadata of the data source. | string |
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 (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
inputDataType | Set the object type | Fixed Rolling Static (required) |
MonitoringInputDataBaseColumns
Name | Description | Value |
---|---|---|
{customized property} | string |
FixedInputData
Name | Description | Value |
---|---|---|
inputDataType | [Required] Specifies the type of signal to monitor. | 'Fixed' (required) |
RollingInputData
Name | Description | Value |
---|---|---|
inputDataType | [Required] Specifies the type of signal to monitor. | 'Rolling' (required) |
preprocessingComponentId | Reference to the component asset 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 rolling data window. | string (required) |
StaticInputData
Name | Description | Value |
---|---|---|
inputDataType | [Required] Specifies the type of signal to monitor. | 'Static' (required) |
preprocessingComponentId | Reference to the component asset 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) |
CustomMonitoringSignalInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
CustomMetricThreshold
Name | Description | Value |
---|---|---|
metric | [Required] The user-defined metric to calculate. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
MonitoringThreshold
Name | Description | Value |
---|---|---|
value | The threshold value. If null, the set default is dependent on the metric type. | int |
DataDriftMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | 'DataDrift' (required) |
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) |
DataDriftMonitoringSignalFeatureDataTypeOverride
Name | Description | Value |
---|---|---|
{customized property} | 'Categorical' 'Numerical' |
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 |
MonitoringFeatureFilterBase
Name | Description | Value |
---|---|---|
filterType | Set the object type | AllFeatures FeatureSubset TopNByAttribution (required) |
AllFeatures
Name | Description | Value |
---|---|---|
filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | 'AllFeatures' (required) |
FeatureSubset
Name | Description | Value |
---|---|---|
filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | 'FeatureSubset' (required) |
features | [Required] The list of features to include. | string[] (required) |
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 |
DataDriftMetricThresholdBase
Name | Description | Value |
---|---|---|
threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
dataType | Set the object type | Categorical Numerical (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) |
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) |
DataQualityMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | 'DataQuality' (required) |
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) |
DataQualityMonitoringSignalFeatureDataTypeOverride
Name | Description | Value |
---|---|---|
{customized property} | 'Categorical' 'Numerical' |
DataQualityMetricThresholdBase
Name | Description | Value |
---|---|---|
threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
dataType | Set the object type | Categorical Numerical (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) |
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) |
FeatureAttributionDriftMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | 'FeatureAttributionDrift' (required) |
featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | FeatureAttributionDriftMonitoringSignalFeatureDataTy... |
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) |
FeatureAttributionDriftMonitoringSignalFeatureDataTy...
Name | Description | Value |
---|---|---|
{customized property} | 'Categorical' 'Numerical' |
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 |
PredictionDriftMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | 'PredictionDrift' (required) |
featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | PredictionDriftMonitoringSignalFeatureDataTypeOverri... |
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) |
PredictionDriftMonitoringSignalFeatureDataTypeOverri...
Name | Description | Value |
---|---|---|
{customized property} | 'Categorical' 'Numerical' |
PredictionDriftMetricThresholdBase
Name | Description | Value |
---|---|---|
threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
dataType | Set the object type | Categorical Numerical (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) |
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) |
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" /} |
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 /> |
string |
triggerType | Set the object type | Cron Recurrence (required) |
CronTrigger
Name | Description | Value |
---|---|---|
triggerType | [Required] | 'Cron' (required) |
expression | [Required] Specifies cron expression of schedule. The expression should follow NCronTab format. |
string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
RecurrenceTrigger
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 |
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 |
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 |
string |
triggerType | [Required] | 'Cron' 'Recurrence' (required) |
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' |
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@2024-07-01-preview"
name = "string"
parent_id = "string"
body = jsonencode({
properties = {
action = {
actionType = "string"
// For remaining properties, see ScheduleActionBase objects
}
description = "string"
displayName = "string"
isEnabled = bool
properties = {
{customized property} = "string"
}
tags = {}
trigger = {
endTime = "string"
startTime = "string"
timeZone = "string"
triggerType = "string"
// For remaining properties, see TriggerBase objects
}
}
})
}
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"
}
services = {
{customized property} = {
endpoint = "string"
jobServiceType = "string"
nodes = {
nodesValueType = "string"
// For remaining properties, see Nodes objects
}
port = int
properties = {
{customized property} = "string"
}
}
}
tags = {}
jobType = "string"
// For remaining properties, see JobBaseProperties objects
}
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 = "AmlNotification"
properties = {
{customized property} = "string"
}
signalType = "string"
// For remaining properties, see MonitoringSignalBase objects
}
}
}
For InvokeBatchEndpoint, use:
actionType = "InvokeBatchEndpoint"
JobBaseProperties objects
Set the jobType property to specify the type of object.
For AutoML, use:
jobType = "AutoML"
environmentId = "string"
environmentVariables = {
{customized property} = "string"
}
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
queueSettings = {
jobTier = "string"
}
resources = {
dockerArgs = "string"
instanceCount = int
instanceType = "string"
properties = {}
shmSize = "string"
}
taskDetails = {
logVerbosity = "string"
targetColumnName = "string"
trainingData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
taskType = "string"
// For remaining properties, see AutoMLVertical objects
}
For Command, use:
jobType = "Command"
codeId = "string"
command = "string"
distribution = {
distributionType = "string"
// For remaining properties, see DistributionConfiguration objects
}
environmentId = "string"
environmentVariables = {
{customized property} = "string"
}
inputs = {
{customized property} = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
}
limits = {
jobLimitsType = "string"
timeout = "string"
}
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
queueSettings = {
jobTier = "string"
}
resources = {
dockerArgs = "string"
instanceCount = int
instanceType = "string"
properties = {}
shmSize = "string"
}
For FineTuning, use:
jobType = "FineTuning"
fineTuningDetails = {
model = {
description = "string"
jobInputType = "string"
mode = "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
}
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
queueSettings = {
jobTier = "string"
}
resources = {
instanceTypes = [
"string"
]
}
For Pipeline, use:
jobType = "Pipeline"
inputs = {
{customized property} = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
}
jobs = {}
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
sourceJobId = "string"
For Spark, use:
jobType = "Spark"
archives = [
"string"
]
args = "string"
codeId = "string"
conf = {
{customized property} = "string"
}
entry = {
sparkJobEntryType = "string"
// For remaining properties, see SparkJobEntry objects
}
environmentId = "string"
environmentVariables = {
{customized property} = "string"
}
files = [
"string"
]
inputs = {
{customized property} = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
}
jars = [
"string"
]
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
pyFiles = [
"string"
]
queueSettings = {
jobTier = "string"
}
resources = {
instanceType = "string"
runtimeVersion = "string"
}
For Sweep, use:
jobType = "Sweep"
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
inputs = {
{customized property} = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
}
limits = {
jobLimitsType = "string"
maxConcurrentTrials = int
maxTotalTrials = int
timeout = "string"
trialTimeout = "string"
}
objective = {
goal = "string"
primaryMetric = "string"
}
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
queueSettings = {
jobTier = "string"
}
samplingAlgorithm = {
samplingAlgorithmType = "string"
// For remaining properties, see SamplingAlgorithm objects
}
trial = {
codeId = "string"
command = "string"
distribution = {
distributionType = "string"
// For remaining properties, see DistributionConfiguration objects
}
environmentId = "string"
environmentVariables = {
{customized property} = "string"
}
resources = {
dockerArgs = "string"
instanceCount = int
instanceType = "string"
properties = {}
shmSize = "string"
}
}
IdentityConfiguration objects
Set the identityType property to specify the type of object.
For AMLToken, use:
identityType = "AMLToken"
For Managed, use:
identityType = "Managed"
clientId = "string"
objectId = "string"
resourceId = "string"
For UserIdentity, use:
identityType = "UserIdentity"
Webhook objects
Set the webhookType property to specify the type of object.
For AzureDevOps, use:
webhookType = "AzureDevOps"
Nodes objects
Set the nodesValueType property to specify the type of object.
For All, use:
nodesValueType = "All"
JobOutput objects
Set the jobOutputType property to specify the type of object.
For custom_model, use:
jobOutputType = "custom_model"
mode = "string"
uri = "string"
For mlflow_model, use:
jobOutputType = "mlflow_model"
mode = "string"
uri = "string"
For mltable, use:
jobOutputType = "mltable"
mode = "string"
uri = "string"
For triton_model, use:
jobOutputType = "triton_model"
mode = "string"
uri = "string"
For uri_file, use:
jobOutputType = "uri_file"
mode = "string"
uri = "string"
For uri_folder, use:
jobOutputType = "uri_folder"
mode = "string"
uri = "string"
AutoMLVertical objects
Set the taskType property to specify the type of object.
For Classification, use:
taskType = "Classification"
cvSplitColumnNames = [
"string"
]
featurizationSettings = {
blockedTransformers = [
"string"
]
columnNameAndTypes = {
{customized property} = "string"
}
datasetLanguage = "string"
enableDnnFeaturization = bool
mode = "string"
transformerParams = {
{customized property} = [
{
fields = [
"string"
]
}
]
}
}
limitSettings = {
enableEarlyTermination = bool
exitScore = int
maxConcurrentTrials = int
maxCoresPerTrial = int
maxTrials = int
timeout = "string"
trialTimeout = "string"
}
nCrossValidations = {
mode = "string"
// For remaining properties, see NCrossValidations objects
}
positiveLabel = "string"
primaryMetric = "string"
testData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
testDataSize = int
trainingSettings = {
allowedTrainingAlgorithms = [
"string"
]
blockedTrainingAlgorithms = [
"string"
]
enableDnnTraining = bool
enableModelExplainability = bool
enableOnnxCompatibleModels = bool
enableStackEnsemble = bool
enableVoteEnsemble = bool
ensembleModelDownloadTimeout = "string"
stackEnsembleSettings = {
stackMetaLearnerTrainPercentage = int
stackMetaLearnerType = "string"
}
}
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
weightColumnName = "string"
For Forecasting, use:
taskType = "Forecasting"
cvSplitColumnNames = [
"string"
]
featurizationSettings = {
blockedTransformers = [
"string"
]
columnNameAndTypes = {
{customized property} = "string"
}
datasetLanguage = "string"
enableDnnFeaturization = bool
mode = "string"
transformerParams = {
{customized property} = [
{
fields = [
"string"
]
}
]
}
}
forecastingSettings = {
countryOrRegionForHolidays = "string"
cvStepSize = int
featureLags = "string"
forecastHorizon = {
mode = "string"
// For remaining properties, see ForecastHorizon objects
}
frequency = "string"
seasonality = {
mode = "string"
// For remaining properties, see Seasonality objects
}
shortSeriesHandlingConfig = "string"
targetAggregateFunction = "string"
targetLags = {
mode = "string"
// For remaining properties, see TargetLags objects
}
targetRollingWindowSize = {
mode = "string"
// For remaining properties, see TargetRollingWindowSize objects
}
timeColumnName = "string"
timeSeriesIdColumnNames = [
"string"
]
useStl = "string"
}
limitSettings = {
enableEarlyTermination = bool
exitScore = int
maxConcurrentTrials = int
maxCoresPerTrial = int
maxTrials = int
timeout = "string"
trialTimeout = "string"
}
nCrossValidations = {
mode = "string"
// For remaining properties, see NCrossValidations objects
}
primaryMetric = "string"
testData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
testDataSize = int
trainingSettings = {
allowedTrainingAlgorithms = [
"string"
]
blockedTrainingAlgorithms = [
"string"
]
enableDnnTraining = bool
enableModelExplainability = bool
enableOnnxCompatibleModels = bool
enableStackEnsemble = bool
enableVoteEnsemble = bool
ensembleModelDownloadTimeout = "string"
stackEnsembleSettings = {
stackMetaLearnerTrainPercentage = int
stackMetaLearnerType = "string"
}
}
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
weightColumnName = "string"
For ImageClassification, use:
taskType = "ImageClassification"
limitSettings = {
maxConcurrentTrials = int
maxTrials = int
timeout = "string"
}
modelSettings = {
advancedSettings = "string"
amsGradient = bool
augmentations = "string"
beta1 = int
beta2 = int
checkpointFrequency = int
checkpointModel = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
checkpointRunId = "string"
distributed = bool
earlyStopping = bool
earlyStoppingDelay = int
earlyStoppingPatience = int
enableOnnxNormalization = bool
evaluationFrequency = int
gradientAccumulationStep = int
layersToFreeze = int
learningRate = int
learningRateScheduler = "string"
modelName = "string"
momentum = int
nesterov = bool
numberOfEpochs = int
numberOfWorkers = int
optimizer = "string"
randomSeed = int
stepLRGamma = int
stepLRStepSize = int
trainingBatchSize = int
trainingCropSize = int
validationBatchSize = int
validationCropSize = int
validationResizeSize = int
warmupCosineLRCycles = int
warmupCosineLRWarmupEpochs = int
weightDecay = int
weightedLoss = int
}
primaryMetric = "string"
searchSpace = [
{
amsGradient = "string"
augmentations = "string"
beta1 = "string"
beta2 = "string"
distributed = "string"
earlyStopping = "string"
earlyStoppingDelay = "string"
earlyStoppingPatience = "string"
enableOnnxNormalization = "string"
evaluationFrequency = "string"
gradientAccumulationStep = "string"
layersToFreeze = "string"
learningRate = "string"
learningRateScheduler = "string"
modelName = "string"
momentum = "string"
nesterov = "string"
numberOfEpochs = "string"
numberOfWorkers = "string"
optimizer = "string"
randomSeed = "string"
stepLRGamma = "string"
stepLRStepSize = "string"
trainingBatchSize = "string"
trainingCropSize = "string"
validationBatchSize = "string"
validationCropSize = "string"
validationResizeSize = "string"
warmupCosineLRCycles = "string"
warmupCosineLRWarmupEpochs = "string"
weightDecay = "string"
weightedLoss = "string"
}
]
sweepSettings = {
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm = "string"
}
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
For ImageClassificationMultilabel, use:
taskType = "ImageClassificationMultilabel"
limitSettings = {
maxConcurrentTrials = int
maxTrials = int
timeout = "string"
}
modelSettings = {
advancedSettings = "string"
amsGradient = bool
augmentations = "string"
beta1 = int
beta2 = int
checkpointFrequency = int
checkpointModel = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
checkpointRunId = "string"
distributed = bool
earlyStopping = bool
earlyStoppingDelay = int
earlyStoppingPatience = int
enableOnnxNormalization = bool
evaluationFrequency = int
gradientAccumulationStep = int
layersToFreeze = int
learningRate = int
learningRateScheduler = "string"
modelName = "string"
momentum = int
nesterov = bool
numberOfEpochs = int
numberOfWorkers = int
optimizer = "string"
randomSeed = int
stepLRGamma = int
stepLRStepSize = int
trainingBatchSize = int
trainingCropSize = int
validationBatchSize = int
validationCropSize = int
validationResizeSize = int
warmupCosineLRCycles = int
warmupCosineLRWarmupEpochs = int
weightDecay = int
weightedLoss = int
}
primaryMetric = "string"
searchSpace = [
{
amsGradient = "string"
augmentations = "string"
beta1 = "string"
beta2 = "string"
distributed = "string"
earlyStopping = "string"
earlyStoppingDelay = "string"
earlyStoppingPatience = "string"
enableOnnxNormalization = "string"
evaluationFrequency = "string"
gradientAccumulationStep = "string"
layersToFreeze = "string"
learningRate = "string"
learningRateScheduler = "string"
modelName = "string"
momentum = "string"
nesterov = "string"
numberOfEpochs = "string"
numberOfWorkers = "string"
optimizer = "string"
randomSeed = "string"
stepLRGamma = "string"
stepLRStepSize = "string"
trainingBatchSize = "string"
trainingCropSize = "string"
validationBatchSize = "string"
validationCropSize = "string"
validationResizeSize = "string"
warmupCosineLRCycles = "string"
warmupCosineLRWarmupEpochs = "string"
weightDecay = "string"
weightedLoss = "string"
}
]
sweepSettings = {
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm = "string"
}
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
For ImageInstanceSegmentation, use:
taskType = "ImageInstanceSegmentation"
limitSettings = {
maxConcurrentTrials = int
maxTrials = int
timeout = "string"
}
modelSettings = {
advancedSettings = "string"
amsGradient = bool
augmentations = "string"
beta1 = int
beta2 = int
boxDetectionsPerImage = int
boxScoreThreshold = int
checkpointFrequency = int
checkpointModel = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
checkpointRunId = "string"
distributed = bool
earlyStopping = bool
earlyStoppingDelay = int
earlyStoppingPatience = int
enableOnnxNormalization = bool
evaluationFrequency = int
gradientAccumulationStep = int
imageSize = int
layersToFreeze = int
learningRate = int
learningRateScheduler = "string"
maxSize = int
minSize = int
modelName = "string"
modelSize = "string"
momentum = int
multiScale = bool
nesterov = bool
nmsIouThreshold = int
numberOfEpochs = int
numberOfWorkers = int
optimizer = "string"
randomSeed = int
stepLRGamma = int
stepLRStepSize = int
tileGridSize = "string"
tileOverlapRatio = int
tilePredictionsNmsThreshold = int
trainingBatchSize = int
validationBatchSize = int
validationIouThreshold = int
validationMetricType = "string"
warmupCosineLRCycles = int
warmupCosineLRWarmupEpochs = int
weightDecay = int
}
primaryMetric = "MeanAveragePrecision"
searchSpace = [
{
amsGradient = "string"
augmentations = "string"
beta1 = "string"
beta2 = "string"
boxDetectionsPerImage = "string"
boxScoreThreshold = "string"
distributed = "string"
earlyStopping = "string"
earlyStoppingDelay = "string"
earlyStoppingPatience = "string"
enableOnnxNormalization = "string"
evaluationFrequency = "string"
gradientAccumulationStep = "string"
imageSize = "string"
layersToFreeze = "string"
learningRate = "string"
learningRateScheduler = "string"
maxSize = "string"
minSize = "string"
modelName = "string"
modelSize = "string"
momentum = "string"
multiScale = "string"
nesterov = "string"
nmsIouThreshold = "string"
numberOfEpochs = "string"
numberOfWorkers = "string"
optimizer = "string"
randomSeed = "string"
stepLRGamma = "string"
stepLRStepSize = "string"
tileGridSize = "string"
tileOverlapRatio = "string"
tilePredictionsNmsThreshold = "string"
trainingBatchSize = "string"
validationBatchSize = "string"
validationIouThreshold = "string"
validationMetricType = "string"
warmupCosineLRCycles = "string"
warmupCosineLRWarmupEpochs = "string"
weightDecay = "string"
}
]
sweepSettings = {
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm = "string"
}
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
For ImageObjectDetection, use:
taskType = "ImageObjectDetection"
limitSettings = {
maxConcurrentTrials = int
maxTrials = int
timeout = "string"
}
modelSettings = {
advancedSettings = "string"
amsGradient = bool
augmentations = "string"
beta1 = int
beta2 = int
boxDetectionsPerImage = int
boxScoreThreshold = int
checkpointFrequency = int
checkpointModel = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
checkpointRunId = "string"
distributed = bool
earlyStopping = bool
earlyStoppingDelay = int
earlyStoppingPatience = int
enableOnnxNormalization = bool
evaluationFrequency = int
gradientAccumulationStep = int
imageSize = int
layersToFreeze = int
learningRate = int
learningRateScheduler = "string"
maxSize = int
minSize = int
modelName = "string"
modelSize = "string"
momentum = int
multiScale = bool
nesterov = bool
nmsIouThreshold = int
numberOfEpochs = int
numberOfWorkers = int
optimizer = "string"
randomSeed = int
stepLRGamma = int
stepLRStepSize = int
tileGridSize = "string"
tileOverlapRatio = int
tilePredictionsNmsThreshold = int
trainingBatchSize = int
validationBatchSize = int
validationIouThreshold = int
validationMetricType = "string"
warmupCosineLRCycles = int
warmupCosineLRWarmupEpochs = int
weightDecay = int
}
primaryMetric = "MeanAveragePrecision"
searchSpace = [
{
amsGradient = "string"
augmentations = "string"
beta1 = "string"
beta2 = "string"
boxDetectionsPerImage = "string"
boxScoreThreshold = "string"
distributed = "string"
earlyStopping = "string"
earlyStoppingDelay = "string"
earlyStoppingPatience = "string"
enableOnnxNormalization = "string"
evaluationFrequency = "string"
gradientAccumulationStep = "string"
imageSize = "string"
layersToFreeze = "string"
learningRate = "string"
learningRateScheduler = "string"
maxSize = "string"
minSize = "string"
modelName = "string"
modelSize = "string"
momentum = "string"
multiScale = "string"
nesterov = "string"
nmsIouThreshold = "string"
numberOfEpochs = "string"
numberOfWorkers = "string"
optimizer = "string"
randomSeed = "string"
stepLRGamma = "string"
stepLRStepSize = "string"
tileGridSize = "string"
tileOverlapRatio = "string"
tilePredictionsNmsThreshold = "string"
trainingBatchSize = "string"
validationBatchSize = "string"
validationIouThreshold = "string"
validationMetricType = "string"
warmupCosineLRCycles = "string"
warmupCosineLRWarmupEpochs = "string"
weightDecay = "string"
}
]
sweepSettings = {
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm = "string"
}
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
For Regression, use:
taskType = "Regression"
cvSplitColumnNames = [
"string"
]
featurizationSettings = {
blockedTransformers = [
"string"
]
columnNameAndTypes = {
{customized property} = "string"
}
datasetLanguage = "string"
enableDnnFeaturization = bool
mode = "string"
transformerParams = {
{customized property} = [
{
fields = [
"string"
]
}
]
}
}
limitSettings = {
enableEarlyTermination = bool
exitScore = int
maxConcurrentTrials = int
maxCoresPerTrial = int
maxTrials = int
timeout = "string"
trialTimeout = "string"
}
nCrossValidations = {
mode = "string"
// For remaining properties, see NCrossValidations objects
}
primaryMetric = "string"
testData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
testDataSize = int
trainingSettings = {
allowedTrainingAlgorithms = [
"string"
]
blockedTrainingAlgorithms = [
"string"
]
enableDnnTraining = bool
enableModelExplainability = bool
enableOnnxCompatibleModels = bool
enableStackEnsemble = bool
enableVoteEnsemble = bool
ensembleModelDownloadTimeout = "string"
stackEnsembleSettings = {
stackMetaLearnerTrainPercentage = int
stackMetaLearnerType = "string"
}
}
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
weightColumnName = "string"
For TextClassification, use:
taskType = "TextClassification"
featurizationSettings = {
datasetLanguage = "string"
}
limitSettings = {
maxConcurrentTrials = int
maxTrials = int
timeout = "string"
}
primaryMetric = "string"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
For TextClassificationMultilabel, use:
taskType = "TextClassificationMultilabel"
featurizationSettings = {
datasetLanguage = "string"
}
limitSettings = {
maxConcurrentTrials = int
maxTrials = int
timeout = "string"
}
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
For TextNER, use:
taskType = "TextNER"
featurizationSettings = {
datasetLanguage = "string"
}
limitSettings = {
maxConcurrentTrials = int
maxTrials = int
timeout = "string"
}
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
NCrossValidations objects
Set the mode property to specify the type of object.
For Auto, use:
mode = "Auto"
For Custom, use:
mode = "Custom"
value = int
ForecastHorizon objects
Set the mode property to specify the type of object.
For Auto, use:
mode = "Auto"
For Custom, use:
mode = "Custom"
value = int
Seasonality objects
Set the mode property to specify the type of object.
For Auto, use:
mode = "Auto"
For Custom, use:
mode = "Custom"
value = int
TargetLags objects
Set the mode property to specify the type of object.
For Auto, use:
mode = "Auto"
For Custom, use:
mode = "Custom"
values = [
int
]
TargetRollingWindowSize objects
Set the mode property to specify the type of object.
For Auto, use:
mode = "Auto"
For Custom, use:
mode = "Custom"
value = int
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
DistributionConfiguration objects
Set the distributionType property to specify the type of object.
For Mpi, use:
distributionType = "Mpi"
processCountPerInstance = int
For PyTorch, use:
distributionType = "PyTorch"
processCountPerInstance = int
For TensorFlow, use:
distributionType = "TensorFlow"
parameterServerCount = int
workerCount = int
JobInput objects
Set the jobInputType property to specify the type of object.
For custom_model, use:
jobInputType = "custom_model"
mode = "string"
uri = "string"
For literal, use:
jobInputType = "literal"
value = "string"
For mlflow_model, use:
jobInputType = "mlflow_model"
mode = "string"
uri = "string"
For mltable, use:
jobInputType = "mltable"
mode = "string"
uri = "string"
For triton_model, use:
jobInputType = "triton_model"
mode = "string"
uri = "string"
For uri_file, use:
jobInputType = "uri_file"
mode = "string"
uri = "string"
For uri_folder, use:
jobInputType = "uri_folder"
mode = "string"
uri = "string"
FineTuningVertical objects
Set the modelProvider property to specify the type of object.
For AzureOpenAI, use:
modelProvider = "AzureOpenAI"
hyperParameters = {
batchSize = int
learningRateMultiplier = int
nEpochs = int
}
For Custom, use:
modelProvider = "Custom"
hyperParameters = {
{customized property} = "string"
}
SparkJobEntry objects
Set the sparkJobEntryType property to specify the type of object.
For SparkJobPythonEntry, use:
sparkJobEntryType = "SparkJobPythonEntry"
file = "string"
For SparkJobScalaEntry, use:
sparkJobEntryType = "SparkJobScalaEntry"
className = "string"
SamplingAlgorithm objects
Set the samplingAlgorithmType property to specify the type of object.
For Bayesian, use:
samplingAlgorithmType = "Bayesian"
For Grid, use:
samplingAlgorithmType = "Grid"
For Random, use:
samplingAlgorithmType = "Random"
rule = "string"
seed = int
MonitorComputeConfigurationBase objects
Set the computeType property to specify the type of object.
For ServerlessSpark, use:
computeType = "ServerlessSpark"
computeIdentity = {
computeIdentityType = "string"
// For remaining properties, see MonitorComputeIdentityBase objects
}
instanceType = "string"
runtimeVersion = "string"
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"
identity_ids = []
}
MonitoringSignalBase objects
Set the signalType property to specify the type of object.
For Custom, use:
signalType = "Custom"
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
}
}
]
For DataDrift, use:
signalType = "DataDrift"
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
}
For DataQuality, use:
signalType = "DataQuality"
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
}
For FeatureAttributionDrift, use:
signalType = "FeatureAttributionDrift"
featureDataTypeOverride = {
{customized property} = "string"
}
featureImportanceSettings = {
mode = "string"
targetColumn = "string"
}
metricThreshold = {
metric = "NormalizedDiscountedCumulativeGain"
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
}
For PredictionDrift, use:
signalType = "PredictionDrift"
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
}
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"
MonitoringFeatureFilterBase objects
Set the filterType property to specify the type of object.
For AllFeatures, use:
filterType = "AllFeatures"
For FeatureSubset, use:
filterType = "FeatureSubset"
features = [
"string"
]
For TopNByAttribution, use:
filterType = "TopNByAttribution"
top = int
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"
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"
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:
triggerType = "Cron"
expression = "string"
For Recurrence, use:
triggerType = "Recurrence"
frequency = "string"
interval = int
schedule = {
hours = [
int
]
minutes = [
int
]
monthDays = [
int
]
weekDays = [
"string"
]
}
Property values
workspaces/schedules
Name | Description | Value |
---|---|---|
type | The resource type | "Microsoft.MachineLearningServices/workspaces/schedules@2024-07-01-preview" |
name | The resource name | string (required) |
parent_id | The ID of the resource that is the parent for this resource. | ID for resource of type: workspaces |
properties | [Required] Additional attributes of the entity. | ScheduleProperties (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. | object |
trigger | [Required] Specifies the trigger details | TriggerBase (required) |
ScheduleActionBase
Name | Description | Value |
---|---|---|
actionType | Set the object type | CreateJob CreateMonitor InvokeBatchEndpoint (required) |
JobScheduleAction
Name | Description | Value |
---|---|---|
actionType | [Required] Specifies the action type of the schedule | "CreateJob" (required) |
jobDefinition | [Required] Defines Schedule action definition details. | JobBaseProperties (required) |
JobBaseProperties
Name | Description | Value |
---|---|---|
componentId | ARM resource ID of the component resource. | string |
computeId | ARM resource ID of the compute resource. | string |
description | The asset description text. | string |
displayName | Display name of job. | string |
experimentName | The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment. | string |
identity | Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null. |
IdentityConfiguration |
isArchived | Is the asset archived? | bool |
notificationSetting | Notification setting for the job | NotificationSetting |
properties | The asset property dictionary. | ResourceBaseProperties |
services | List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject. |
JobBaseServices |
tags | Tag dictionary. Tags can be added, removed, and updated. | object |
jobType | Set the object type | AutoML Command FineTuning Pipeline Spark Sweep (required) |
IdentityConfiguration
Name | Description | Value |
---|---|---|
identityType | Set the object type | AMLToken Managed UserIdentity (required) |
AmlToken
Name | Description | Value |
---|---|---|
identityType | [Required] Specifies the type of identity framework. | "AMLToken" (required) |
ManagedIdentity
Name | Description | Value |
---|---|---|
identityType | [Required] Specifies the type of identity framework. | "Managed" (required) |
clientId | Specifies a user-assigned identity by client ID. For system-assigned, do not set this field. | string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$ |
objectId | Specifies a user-assigned identity by object ID. For system-assigned, do not set this field. | string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$ |
resourceId | Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field. | string |
UserIdentity
Name | Description | Value |
---|---|---|
identityType | [Required] Specifies the type of identity framework. | "UserIdentity" (required) |
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 |
---|---|---|
{customized property} | Webhook |
Webhook
Name | Description | Value |
---|---|---|
eventType | Send callback on a specified notification event | string |
webhookType | Set the object type | AzureDevOps (required) |
AzureDevOpsWebhook
Name | Description | Value |
---|---|---|
webhookType | [Required] Specifies the type of service to send a callback | "AzureDevOps" (required) |
ResourceBaseProperties
Name | Description | Value |
---|---|---|
{customized property} | string |
JobBaseServices
Name | Description | Value |
---|---|---|
{customized property} | JobService |
JobService
Name | Description | Value |
---|---|---|
endpoint | Url for endpoint. | string |
jobServiceType | Endpoint type. | string |
nodes | Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node. |
Nodes |
port | Port for endpoint. | int |
properties | Additional properties to set on the endpoint. | JobServiceProperties |
Nodes
Name | Description | Value |
---|---|---|
nodesValueType | Set the object type | All (required) |
AllNodes
Name | Description | Value |
---|---|---|
nodesValueType | [Required] Type of the Nodes value | "All" (required) |
JobServiceProperties
Name | Description | Value |
---|---|---|
{customized property} | string |
AutoMLJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | "AutoML" (required) |
environmentId | The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job. |
string |
environmentVariables | Environment variables included in the job. | AutoMLJobEnvironmentVariables |
outputs | Mapping of output data bindings used in the job. | AutoMLJobOutputs |
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 |
---|---|---|
{customized property} | string |
AutoMLJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
JobOutput
Name | Description | Value |
---|---|---|
description | Description for the output. | string |
jobOutputType | Set the object type | custom_model mlflow_model mltable triton_model uri_file uri_folder (required) |
CustomModelJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | "custom_model" (required) |
mode | Output Asset Delivery Mode. | "Direct" "ReadWriteMount" "Upload" |
uri | Output Asset URI. | string |
MLFlowModelJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | "mlflow_model" (required) |
mode | Output Asset Delivery Mode. | "Direct" "ReadWriteMount" "Upload" |
uri | Output Asset URI. | string |
MLTableJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | "mltable" (required) |
mode | Output Asset Delivery Mode. | "Direct" "ReadWriteMount" "Upload" |
uri | Output Asset URI. | string |
TritonModelJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | "triton_model" (required) |
mode | Output Asset Delivery Mode. | "Direct" "ReadWriteMount" "Upload" |
uri | Output Asset URI. | string |
UriFileJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | "uri_file" (required) |
mode | Output Asset Delivery Mode. | "Direct" "ReadWriteMount" "Upload" |
uri | Output Asset URI. | string |
UriFolderJobOutput
Name | Description | Value |
---|---|---|
jobOutputType | [Required] Specifies the type of job. | "uri_folder" (required) |
mode | Output Asset Delivery Mode. | "Direct" "ReadWriteMount" "Upload" |
uri | Output Asset URI. | string |
QueueSettings
Name | Description | Value |
---|---|---|
jobTier | Controls the compute job tier | "Basic" "Null" "Premium" "Spot" "Standard" |
JobResourceConfiguration
Name | Description | Value |
---|---|---|
dockerArgs | Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types. | string |
instanceCount | Optional number of instances or nodes used by the compute target. | int |
instanceType | Optional type of VM used as supported by the compute target. | string |
properties | Additional properties bag. | ResourceConfigurationProperties |
shmSize | Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes). | string Constraints: Pattern = \d+[bBkKmMgG] |
ResourceConfigurationProperties
Name | Description | Value |
---|---|---|
{customized property} |
AutoMLVertical
Name | Description | Value |
---|---|---|
logVerbosity | Log verbosity for the job. | "Critical" "Debug" "Error" "Info" "NotSet" "Warning" |
targetColumnName | Target column name: This is prediction values column. Also known as label column name in context of classification tasks. |
string |
trainingData | [Required] Training data input. | MLTableJobInput (required) |
taskType | Set the object type | Classification Forecasting ImageClassification ImageClassificationMultilabel ImageInstanceSegmentation ImageObjectDetection Regression TextClassification TextClassificationMultilabel TextNER (required) |
MLTableJobInput
Name | Description | Value |
---|---|---|
description | Description for the input. | string |
jobInputType | [Required] Specifies the type of job. | "custom_model" "literal" "mlflow_model" "mltable" "triton_model" "uri_file" "uri_folder" (required) |
mode | Input Asset Delivery Mode. | "Direct" "Download" "EvalDownload" "EvalMount" "ReadOnlyMount" "ReadWriteMount" |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
Classification
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | "Classification" (required) |
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
positiveLabel | Positive label for binary metrics calculation. | string |
primaryMetric | Primary metric for the task. | "AUCWeighted" "Accuracy" "AveragePrecisionScoreWeighted" "NormMacroRecall" "PrecisionScoreWeighted" |
testData | Test data input. | MLTableJobInput |
testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
trainingSettings | Inputs for training phase for an AutoML Job. | ClassificationTrainingSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
TableVerticalFeaturizationSettings
Name | Description | Value |
---|---|---|
blockedTransformers | These transformers shall not be used in featurization. | String array containing any of: "CatTargetEncoder" "CountVectorizer" "HashOneHotEncoder" "LabelEncoder" "NaiveBayes" "OneHotEncoder" "TextTargetEncoder" "TfIdf" "WoETargetEncoder" "WordEmbedding" |
columnNameAndTypes | Dictionary of column name and its type (int, float, string, datetime etc). | TableVerticalFeaturizationSettingsColumnNameAndTypes |
datasetLanguage | Dataset language, useful for the text data. | string |
enableDnnFeaturization | Determines whether to use Dnn based featurizers for data featurization. | bool |
mode | Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done. |
"Auto" "Custom" "Off" |
transformerParams | User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor. | TableVerticalFeaturizationSettingsTransformerParams |
TableVerticalFeaturizationSettingsColumnNameAndTypes
Name | Description | Value |
---|---|---|
{customized property} | string |
TableVerticalFeaturizationSettingsTransformerParams
Name | Description | Value |
---|---|---|
{customized property} | ColumnTransformer[] |
ColumnTransformer
Name | Description | Value |
---|---|---|
fields | Fields to apply transformer logic on. | string[] |
parameters | Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format. |
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 |
maxTrials | Number of iterations. | int |
timeout | AutoML job timeout. | string |
trialTimeout | Iteration timeout. | string |
NCrossValidations
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoNCrossValidations
Name | Description | Value |
---|---|---|
mode | [Required] Mode for determining N-Cross validations. | "Auto" (required) |
CustomNCrossValidations
Name | Description | Value |
---|---|---|
mode | [Required] Mode for determining N-Cross validations. | "Custom" (required) |
value | [Required] N-Cross validations value. | int (required) |
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 |
StackEnsembleSettings
Name | Description | Value |
---|---|---|
stackMetaLearnerKWargs | Optional parameters to pass to the initializer of the meta-learner. | |
stackMetaLearnerTrainPercentage | Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2. | int |
stackMetaLearnerType | The meta-learner is a model trained on the output of the individual heterogeneous models. | "ElasticNet" "ElasticNetCV" "LightGBMClassifier" "LightGBMRegressor" "LinearRegression" "LogisticRegression" "LogisticRegressionCV" "None" |
Forecasting
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | "Forecasting" (required) |
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
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" |
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" |
forecastHorizon | The desired maximum forecast horizon in units of time-series frequency. | ForecastHorizon |
frequency | When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default. | string |
seasonality | Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred. |
Seasonality |
shortSeriesHandlingConfig | The parameter defining how if AutoML should handle short time series. | "Auto" "Drop" "None" "Pad" |
targetAggregateFunction | The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean". |
"Max" "Mean" "Min" "None" "Sum" |
targetLags | The number of past periods to lag from the target column. | TargetLags |
targetRollingWindowSize | The number of past periods used to create a rolling window average of the target column. | TargetRollingWindowSize |
timeColumnName | The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. | string |
timeSeriesIdColumnNames | The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting. |
string[] |
useStl | Configure STL Decomposition of the time-series target column. | "None" "Season" "SeasonTrend" |
ForecastHorizon
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoForecastHorizon
Name | Description | Value |
---|---|---|
mode | [Required] Set forecast horizon value selection mode. | "Auto" (required) |
CustomForecastHorizon
Name | Description | Value |
---|---|---|
mode | [Required] Set forecast horizon value selection mode. | "Custom" (required) |
value | [Required] Forecast horizon value. | int (required) |
Seasonality
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoSeasonality
Name | Description | Value |
---|---|---|
mode | [Required] Seasonality mode. | "Auto" (required) |
CustomSeasonality
Name | Description | Value |
---|---|---|
mode | [Required] Seasonality mode. | "Custom" (required) |
value | [Required] Seasonality value. | int (required) |
TargetLags
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoTargetLags
Name | Description | Value |
---|---|---|
mode | [Required] Set target lags mode - Auto/Custom | "Auto" (required) |
CustomTargetLags
Name | Description | Value |
---|---|---|
mode | [Required] Set target lags mode - Auto/Custom | "Custom" (required) |
values | [Required] Set target lags values. | int[] (required) |
TargetRollingWindowSize
Name | Description | Value |
---|---|---|
mode | Set the object type | Auto Custom (required) |
AutoTargetRollingWindowSize
Name | Description | Value |
---|---|---|
mode | [Required] TargetRollingWindowSiz detection mode. | "Auto" (required) |
CustomTargetRollingWindowSize
Name | Description | Value |
---|---|---|
mode | [Required] TargetRollingWindowSiz detection mode. | "Custom" (required) |
value | [Required] TargetRollingWindowSize value. | int (required) |
ForecastingTrainingSettings
Name | Description | Value |
---|---|---|
allowedTrainingAlgorithms | Allowed models for forecasting task. | String array containing any of: "Arimax" "AutoArima" "Average" "DecisionTree" "ElasticNet" "ExponentialSmoothing" "ExtremeRandomTrees" "GradientBoosting" "KNN" "LassoLars" "LightGBM" "Naive" "Prophet" "RandomForest" "SGD" "SeasonalAverage" "SeasonalNaive" "TCNForecaster" "XGBoostRegressor" |
blockedTrainingAlgorithms | Blocked models for forecasting task. | String array containing any of: "Arimax" "AutoArima" "Average" "DecisionTree" "ElasticNet" "ExponentialSmoothing" "ExtremeRandomTrees" "GradientBoosting" "KNN" "LassoLars" "LightGBM" "Naive" "Prophet" "RandomForest" "SGD" "SeasonalAverage" "SeasonalNaive" "TCNForecaster" "XGBoostRegressor" |
enableDnnTraining | Enable recommendation of DNN models. | bool |
enableModelExplainability | Flag to turn on explainability on best model. | bool |
enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
enableStackEnsemble | Enable stack ensemble run. | bool |
enableVoteEnsemble | Enable voting ensemble run. | bool |
ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
ImageClassification
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | "ImageClassification" (required) |
limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
modelSettings | Settings used for training the model. | ImageModelSettingsClassification |
primaryMetric | Primary metric to optimize for this task. | "AUCWeighted" "Accuracy" "AveragePrecisionScoreWeighted" "NormMacroRecall" "PrecisionScoreWeighted" |
searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsClassification[] |
sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageLimitSettings
Name | Description | Value |
---|---|---|
maxConcurrentTrials | Maximum number of concurrent AutoML iterations. | int |
maxTrials | Maximum number of AutoML iterations. | int |
timeout | AutoML job timeout. | string |
ImageModelSettingsClassification
Name | Description | Value |
---|---|---|
advancedSettings | Settings for advanced scenarios. | string |
amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | bool |
augmentations | Settings for using Augmentations. | string |
beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
checkpointFrequency | Frequency to store model checkpoints. Must be a positive integer. | int |
checkpointModel | The pretrained checkpoint model for incremental training. | MLFlowModelJobInput |
checkpointRunId | The id of a previous run that has a pretrained checkpoint for incremental training. | string |
distributed | Whether to use distributed training. | bool |
earlyStopping | Enable early stopping logic during training. | bool |
earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
int |
earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
int |
enableOnnxNormalization | Enable normalization when exporting ONNX model. | bool |
evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | int |
gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
int |
layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
int |
learningRate | Initial learning rate. Must be a float in the range [0, 1]. | int |
learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | "None" "Step" "WarmupCosine" |
modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | int |
nesterov | Enable nesterov when optimizer is 'sgd'. | bool |
numberOfEpochs | Number of training epochs. Must be a positive integer. | int |
numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | int |
optimizer | Type of optimizer. | "Adam" "Adamw" "None" "Sgd" |
randomSeed | Random seed to be used when using deterministic training. | int |
stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | int |
stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | int |
trainingBatchSize | Training batch size. Must be a positive integer. | int |
trainingCropSize | Image crop size that is input to the neural network for the training dataset. Must be a positive integer. | int |
validationBatchSize | Validation batch size. Must be a positive integer. | int |
validationCropSize | Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. | int |
validationResizeSize | Image size to which to resize before cropping for validation dataset. Must be a positive integer. | int |
warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | int |
warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | int |
weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | int |
weightedLoss | Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2. |
int |
MLFlowModelJobInput
Name | Description | Value |
---|---|---|
description | Description for the input. | string |
jobInputType | [Required] Specifies the type of job. | "custom_model" "literal" "mlflow_model" "mltable" "triton_model" "uri_file" "uri_folder" (required) |
mode | Input Asset Delivery Mode. | "Direct" "Download" "EvalDownload" "EvalMount" "ReadOnlyMount" "ReadWriteMount" |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
ImageModelDistributionSettingsClassification
Name | Description | Value |
---|---|---|
amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | string |
augmentations | Settings for using Augmentations. | string |
beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
distributed | Whether to use distributer training. | string |
earlyStopping | Enable early stopping logic during training. | string |
earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
string |
earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
string |
enableOnnxNormalization | Enable normalization when exporting ONNX model. | string |
evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | string |
gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
string |
layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
string |
learningRate | Initial learning rate. Must be a float in the range [0, 1]. | string |
learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | string |
modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | string |
nesterov | Enable nesterov when optimizer is 'sgd'. | string |
numberOfEpochs | Number of training epochs. Must be a positive integer. | string |
numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | string |
optimizer | Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. | string |
randomSeed | Random seed to be used when using deterministic training. | string |
stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | string |
stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | string |
trainingBatchSize | Training batch size. Must be a positive integer. | string |
trainingCropSize | Image crop size that is input to the neural network for the training dataset. Must be a positive integer. | string |
validationBatchSize | Validation batch size. Must be a positive integer. | string |
validationCropSize | Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. | string |
validationResizeSize | Image size to which to resize before cropping for validation dataset. Must be a positive integer. | string |
warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | string |
warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | string |
weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | string |
weightedLoss | Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2. |
string |
ImageSweepSettings
Name | Description | Value |
---|---|---|
earlyTermination | Type of early termination policy. | EarlyTerminationPolicy |
samplingAlgorithm | [Required] Type of the hyperparameter sampling algorithms. | "Bayesian" "Grid" "Random" (required) |
EarlyTerminationPolicy
Name | Description | Value |
---|---|---|
delayEvaluation | Number of intervals by which to delay the first evaluation. | int |
evaluationInterval | Interval (number of runs) between policy evaluations. | int |
policyType | Set the object type | Bandit MedianStopping TruncationSelection (required) |
BanditPolicy
Name | Description | Value |
---|---|---|
policyType | [Required] Name of policy configuration | "Bandit" (required) |
slackAmount | Absolute distance allowed from the best performing run. | int |
slackFactor | Ratio of the allowed distance from the best performing run. | int |
MedianStoppingPolicy
Name | Description | Value |
---|---|---|
policyType | [Required] Name of policy configuration | "MedianStopping" (required) |
TruncationSelectionPolicy
Name | Description | Value |
---|---|---|
policyType | [Required] Name of policy configuration | "TruncationSelection" (required) |
truncationPercentage | The percentage of runs to cancel at each evaluation interval. | int |
ImageClassificationMultilabel
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | "ImageClassificationMultilabel" (required) |
limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
modelSettings | Settings used for training the model. | ImageModelSettingsClassification |
primaryMetric | Primary metric to optimize for this task. | "AUCWeighted" "Accuracy" "AveragePrecisionScoreWeighted" "IOU" "NormMacroRecall" "PrecisionScoreWeighted" |
searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsClassification[] |
sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageInstanceSegmentation
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | "ImageInstanceSegmentation" (required) |
limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
modelSettings | Settings used for training the model. | ImageModelSettingsObjectDetection |
primaryMetric | Primary metric to optimize for this task. | "MeanAveragePrecision" |
searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsObjectDetection[] |
sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageModelSettingsObjectDetection
Name | Description | Value |
---|---|---|
advancedSettings | Settings for advanced scenarios. | string |
amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | bool |
augmentations | Settings for using Augmentations. | string |
beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
boxDetectionsPerImage | Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
boxScoreThreshold | During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
int |
checkpointFrequency | Frequency to store model checkpoints. Must be a positive integer. | int |
checkpointModel | The pretrained checkpoint model for incremental training. | MLFlowModelJobInput |
checkpointRunId | The id of a previous run that has a pretrained checkpoint for incremental training. | string |
distributed | Whether to use distributed training. | bool |
earlyStopping | Enable early stopping logic during training. | bool |
earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
int |
earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
int |
enableOnnxNormalization | Enable normalization when exporting ONNX model. | bool |
evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | int |
gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
int |
imageSize | Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
int |
layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
int |
learningRate | Initial learning rate. Must be a float in the range [0, 1]. | int |
learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | "None" "Step" "WarmupCosine" |
maxSize | Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
minSize | Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
modelSize | Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
"ExtraLarge" "Large" "Medium" "None" "Small" |
momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | int |
multiScale | Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm. |
bool |
nesterov | Enable nesterov when optimizer is 'sgd'. | bool |
nmsIouThreshold | IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1]. | int |
numberOfEpochs | Number of training epochs. Must be a positive integer. | int |
numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | int |
optimizer | Type of optimizer. | "Adam" "Adamw" "None" "Sgd" |
randomSeed | Random seed to be used when using deterministic training. | int |
stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | int |
stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | int |
tileGridSize | The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
tileOverlapRatio | Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm. |
int |
tilePredictionsNmsThreshold | The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
trainingBatchSize | Training batch size. Must be a positive integer. | int |
validationBatchSize | Validation batch size. Must be a positive integer. | int |
validationIouThreshold | IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. | int |
validationMetricType | Metric computation method to use for validation metrics. | "Coco" "CocoVoc" "None" "Voc" |
warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | int |
warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | int |
weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | int |
ImageModelDistributionSettingsObjectDetection
Name | Description | Value |
---|---|---|
amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | string |
augmentations | Settings for using Augmentations. | string |
beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
boxDetectionsPerImage | Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
boxScoreThreshold | During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
string |
distributed | Whether to use distributer training. | string |
earlyStopping | Enable early stopping logic during training. | string |
earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
string |
earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
string |
enableOnnxNormalization | Enable normalization when exporting ONNX model. | string |
evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | string |
gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
string |
imageSize | Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
string |
learningRate | Initial learning rate. Must be a float in the range [0, 1]. | string |
learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | string |
maxSize | Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
minSize | Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
modelSize | Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | string |
multiScale | Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
nesterov | Enable nesterov when optimizer is 'sgd'. | string |
nmsIouThreshold | IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1]. | string |
numberOfEpochs | Number of training epochs. Must be a positive integer. | string |
numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | string |
optimizer | Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. | string |
randomSeed | Random seed to be used when using deterministic training. | string |
stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | string |
stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | string |
tileGridSize | The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
tileOverlapRatio | Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm. |
string |
tilePredictionsNmsThreshold | The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression |
string |
trainingBatchSize | Training batch size. Must be a positive integer. | string |
validationBatchSize | Validation batch size. Must be a positive integer. | string |
validationIouThreshold | IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. | string |
validationMetricType | Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'. | string |
warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | string |
warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | string |
weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | string |
ImageObjectDetection
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | "ImageObjectDetection" (required) |
limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
modelSettings | Settings used for training the model. | ImageModelSettingsObjectDetection |
primaryMetric | Primary metric to optimize for this task. | "MeanAveragePrecision" |
searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsObjectDetection[] |
sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
Regression
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | "Regression" (required) |
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
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" |
testData | Test data input. | MLTableJobInput |
testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
trainingSettings | Inputs for training phase for an AutoML Job. | RegressionTrainingSettings |
validationData | Validation data inputs. | MLTableJobInput |
validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
RegressionTrainingSettings
Name | Description | Value |
---|---|---|
allowedTrainingAlgorithms | Allowed models for regression task. | String array containing any of: "DecisionTree" "ElasticNet" "ExtremeRandomTrees" "GradientBoosting" "KNN" "LassoLars" "LightGBM" "RandomForest" "SGD" "XGBoostRegressor" |
blockedTrainingAlgorithms | Blocked models for regression task. | String array containing any of: "DecisionTree" "ElasticNet" "ExtremeRandomTrees" "GradientBoosting" "KNN" "LassoLars" "LightGBM" "RandomForest" "SGD" "XGBoostRegressor" |
enableDnnTraining | Enable recommendation of DNN models. | bool |
enableModelExplainability | Flag to turn on explainability on best model. | bool |
enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
enableStackEnsemble | Enable stack ensemble run. | bool |
enableVoteEnsemble | Enable voting ensemble run. | bool |
ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
TextClassification
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | "TextClassification" (required) |
featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
primaryMetric | Primary metric for Text-Classification task. | "AUCWeighted" "Accuracy" "AveragePrecisionScoreWeighted" "NormMacroRecall" "PrecisionScoreWeighted" |
validationData | Validation data inputs. | MLTableJobInput |
NlpVerticalFeaturizationSettings
Name | Description | Value |
---|---|---|
datasetLanguage | Dataset language, useful for the text data. | string |
NlpVerticalLimitSettings
Name | Description | Value |
---|---|---|
maxConcurrentTrials | Maximum Concurrent AutoML iterations. | int |
maxTrials | Number of AutoML iterations. | int |
timeout | AutoML job timeout. | string |
TextClassificationMultilabel
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | "TextClassificationMultilabel" (required) |
featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
validationData | Validation data inputs. | MLTableJobInput |
TextNer
Name | Description | Value |
---|---|---|
taskType | [Required] Task type for AutoMLJob. | "TextNER" (required) |
featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
validationData | Validation data inputs. | MLTableJobInput |
CommandJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | "Command" (required) |
codeId | ARM resource ID of the code asset. | string |
command | [Required] The command to execute on startup of the job. eg. "python train.py" | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
environmentVariables | Environment variables included in the job. | CommandJobEnvironmentVariables |
inputs | Mapping of input data bindings used in the job. | CommandJobInputs |
limits | Command Job limit. | CommandJobLimits |
outputs | Mapping of output data bindings used in the job. | CommandJobOutputs |
queueSettings | Queue settings for the job | QueueSettings |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
DistributionConfiguration
Name | Description | Value |
---|---|---|
distributionType | Set the object type | Mpi PyTorch TensorFlow (required) |
Mpi
Name | Description | Value |
---|---|---|
distributionType | [Required] Specifies the type of distribution framework. | "Mpi" (required) |
processCountPerInstance | Number of processes per MPI node. | int |
PyTorch
Name | Description | Value |
---|---|---|
distributionType | [Required] Specifies the type of distribution framework. | "PyTorch" (required) |
processCountPerInstance | Number of processes per node. | int |
TensorFlow
Name | Description | Value |
---|---|---|
distributionType | [Required] Specifies the type of distribution framework. | "TensorFlow" (required) |
parameterServerCount | Number of parameter server tasks. | int |
workerCount | Number of workers. If not specified, will default to the instance count. | int |
CommandJobEnvironmentVariables
Name | Description | Value |
---|---|---|
{customized property} | string |
CommandJobInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
JobInput
Name | Description | Value |
---|---|---|
description | Description for the input. | string |
jobInputType | Set the object type | custom_model literal mlflow_model mltable triton_model uri_file uri_folder (required) |
CustomModelJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | "custom_model" (required) |
mode | Input Asset Delivery Mode. | "Direct" "Download" "EvalDownload" "EvalMount" "ReadOnlyMount" "ReadWriteMount" |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
LiteralJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | "literal" (required) |
value | [Required] Literal value for the input. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
TritonModelJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | "triton_model" (required) |
mode | Input Asset Delivery Mode. | "Direct" "Download" "EvalDownload" "EvalMount" "ReadOnlyMount" "ReadWriteMount" |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
UriFileJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | "uri_file" (required) |
mode | Input Asset Delivery Mode. | "Direct" "Download" "EvalDownload" "EvalMount" "ReadOnlyMount" "ReadWriteMount" |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
UriFolderJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | "uri_folder" (required) |
mode | Input Asset Delivery Mode. | "Direct" "Download" "EvalDownload" "EvalMount" "ReadOnlyMount" "ReadWriteMount" |
uri | [Required] Input Asset URI. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
CommandJobLimits
Name | Description | Value |
---|---|---|
jobLimitsType | [Required] JobLimit type. | "Command" "Sweep" (required) |
timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
CommandJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
FineTuningJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | "FineTuning" (required) |
fineTuningDetails | [Required] | FineTuningVertical (required) |
outputs | [Required] | FineTuningJobOutputs (required) |
queueSettings | Queue settings for the job | QueueSettings |
resources | Instance types and other resources for the job | JobResources |
FineTuningVertical
Name | Description | Value |
---|---|---|
model | [Required] Input model for fine tuning. | MLFlowModelJobInput (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 |
modelProvider | Set the object type | AzureOpenAI Custom (required) |
AzureOpenAiFineTuning
Name | Description | Value |
---|---|---|
modelProvider | [Required] Enum to determine the type of fine tuning. | "AzureOpenAI" (required) |
hyperParameters | HyperParameters for fine tuning Azure Open AI model. | AzureOpenAiHyperParameters |
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 |
CustomModelFineTuning
Name | Description | Value |
---|---|---|
modelProvider | [Required] Enum to determine the type of fine tuning. | "Custom" (required) |
hyperParameters | HyperParameters for fine tuning custom model. | CustomModelFineTuningHyperParameters |
CustomModelFineTuningHyperParameters
Name | Description | Value |
---|---|---|
{customized property} | string |
FineTuningJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
JobResources
Name | Description | Value |
---|---|---|
instanceTypes | List of instance types to choose from. | string[] |
PipelineJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | "Pipeline" (required) |
inputs | Inputs for the pipeline job. | PipelineJobInputs |
jobs | Jobs construct the Pipeline Job. | PipelineJobJobs |
outputs | Outputs for the pipeline job | PipelineJobOutputs |
settings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | |
sourceJobId | ARM resource ID of source job. | string |
PipelineJobInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
PipelineJobJobs
Name | Description | Value |
---|---|---|
{customized property} |
PipelineJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
SparkJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | "Spark" (required) |
archives | Archive files used in the job. | string[] |
args | Arguments for the job. | string |
codeId | [Required] arm-id of the code asset. | string (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[] |
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 |
---|---|---|
{customized property} | string |
SparkJobEntry
Name | Description | Value |
---|---|---|
sparkJobEntryType | Set the object type | SparkJobPythonEntry SparkJobScalaEntry (required) |
SparkJobPythonEntry
Name | Description | Value |
---|---|---|
sparkJobEntryType | [Required] Type of the job's entry point. | "SparkJobPythonEntry" (required) |
file | [Required] Relative python file path for job entry point. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
SparkJobScalaEntry
Name | Description | Value |
---|---|---|
sparkJobEntryType | [Required] Type of the job's entry point. | "SparkJobScalaEntry" (required) |
className | [Required] Scala class name used as entry point. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
SparkJobEnvironmentVariables
Name | Description | Value |
---|---|---|
{customized property} | string |
SparkJobInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
SparkJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
SparkResourceConfiguration
Name | Description | Value |
---|---|---|
instanceType | Optional type of VM used as supported by the compute target. | string |
runtimeVersion | Version of spark runtime used for the job. | string |
SweepJob
Name | Description | Value |
---|---|---|
jobType | [Required] Specifies the type of job. | "Sweep" (required) |
earlyTermination | Early termination policies enable canceling poor-performing runs before they complete | EarlyTerminationPolicy |
inputs | Mapping of input data bindings used in the job. | SweepJobInputs |
limits | Sweep Job limit. | SweepJobLimits |
objective | [Required] Optimization objective. | Objective (required) |
outputs | Mapping of output data bindings used in the job. | SweepJobOutputs |
queueSettings | Queue settings for the job | QueueSettings |
samplingAlgorithm | [Required] The hyperparameter sampling algorithm | SamplingAlgorithm (required) |
searchSpace | [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter | |
trial | [Required] Trial component definition. | TrialComponent (required) |
SweepJobInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
SweepJobLimits
Name | Description | Value |
---|---|---|
jobLimitsType | [Required] JobLimit type. | "Command" "Sweep" (required) |
maxConcurrentTrials | Sweep Job max concurrent trials. | int |
maxTotalTrials | Sweep Job max total trials. | int |
timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
trialTimeout | Sweep Job Trial timeout value. | string |
Objective
Name | Description | Value |
---|---|---|
goal | [Required] Defines supported metric goals for hyperparameter tuning | "Maximize" "Minimize" (required) |
primaryMetric | [Required] Name of the metric to optimize. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
SweepJobOutputs
Name | Description | Value |
---|---|---|
{customized property} | JobOutput |
SamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | Set the object type | Bayesian Grid Random (required) |
BayesianSamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | "Bayesian" (required) |
GridSamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | "Grid" (required) |
RandomSamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | "Random" (required) |
rule | The specific type of random algorithm | "Random" "Sobol" |
seed | An optional integer to use as the seed for random number generation | int |
TrialComponent
Name | Description | Value |
---|---|---|
codeId | ARM resource ID of the code asset. | string |
command | [Required] The command to execute on startup of the job. eg. "python train.py" | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
environmentVariables | Environment variables included in the job. | TrialComponentEnvironmentVariables |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
TrialComponentEnvironmentVariables
Name | Description | Value |
---|---|---|
{customized property} | string |
CreateMonitorAction
Name | Description | Value |
---|---|---|
actionType | [Required] Specifies the action type of the schedule | "CreateMonitor" (required) |
monitorDefinition | [Required] Defines the monitor. | MonitorDefinition (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 entities targeted by the monitor. | MonitoringTarget |
signals | [Required] The signals to monitor. | MonitorDefinitionSignals (required) |
MonitorNotificationSettings
Name | Description | Value |
---|---|---|
emailNotificationSettings | The AML notification email settings. | MonitorEmailNotificationSettings |
MonitorEmailNotificationSettings
Name | Description | Value |
---|---|---|
emails | The email recipient list which has a limitation of 499 characters in total. | string[] |
MonitorComputeConfigurationBase
Name | Description | Value |
---|---|---|
computeType | Set the object type | ServerlessSpark (required) |
MonitorServerlessSparkCompute
Name | Description | Value |
---|---|---|
computeType | [Required] Specifies the type of signal to monitor. | "ServerlessSpark" (required) |
computeIdentity | [Required] The identity scheme leveraged to by the spark jobs running on serverless Spark. | MonitorComputeIdentityBase (required) |
instanceType | [Required] The instance type running the Spark job. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
runtimeVersion | [Required] The Spark runtime version. | string (required) Constraints: Min length = 1 Pattern = ^[0-9]+\.[0-9]+$ |
MonitorComputeIdentityBase
Name | Description | Value |
---|---|---|
computeIdentityType | Set the object type | AmlToken ManagedIdentity (required) |
AmlTokenComputeIdentity
Name | Description | Value |
---|---|---|
computeIdentityType | [Required] Specifies the type of identity to use within the monitoring jobs. | "AmlToken" (required) |
ManagedComputeIdentity
Name | Description | Value |
---|---|---|
computeIdentityType | [Required] Specifies the type of identity to use within the monitoring jobs. | "ManagedIdentity" (required) |
identity | The identity which will be leveraged by the monitoring jobs. | ManagedServiceIdentity |
ManagedServiceIdentity
Name | Description | Value |
---|---|---|
type | Type of managed service identity (where both SystemAssigned and UserAssigned types are allowed). | "SystemAssigned" "SystemAssigned,UserAssigned" "UserAssigned" (required) |
identity_ids | 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. | Array of user identity IDs. |
UserAssignedIdentities
Name | Description | Value |
---|---|---|
{customized property} | UserAssignedIdentity |
UserAssignedIdentity
This object doesn't contain any properties to set during deployment. All properties are ReadOnly.
MonitoringTarget
Name | Description | Value |
---|---|---|
deploymentId | Reference to the deployment asset targeted by this monitor. | string |
modelId | Reference to the model asset targeted by this monitor. | string |
taskType | [Required] The machine learning task type of the monitored model. | "Classification" "Regression" (required) |
MonitorDefinitionSignals
Name | Description | Value |
---|---|---|
{customized property} | MonitoringSignalBase |
MonitoringSignalBase
Name | Description | Value |
---|---|---|
notificationTypes | The current notification mode for this signal. | String array containing any of: "AmlNotification" |
properties | Property dictionary. Properties can be added, but not removed or altered. | MonitoringSignalBaseProperties |
signalType | Set the object type | Custom DataDrift DataQuality FeatureAttributionDrift PredictionDrift (required) |
MonitoringSignalBaseProperties
Name | Description | Value |
---|---|---|
{customized property} | string |
CustomMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | "Custom" (required) |
componentId | [Required] Reference to the component asset used to calculate the custom metrics. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
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) |
CustomMonitoringSignalInputAssets
Name | Description | Value |
---|---|---|
{customized property} | MonitoringInputDataBase |
MonitoringInputDataBase
Name | Description | Value |
---|---|---|
columns | Mapping of column names to special uses. | MonitoringInputDataBaseColumns |
dataContext | The context metadata of the data source. | string |
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 (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
inputDataType | Set the object type | Fixed Rolling Static (required) |
MonitoringInputDataBaseColumns
Name | Description | Value |
---|---|---|
{customized property} | string |
FixedInputData
Name | Description | Value |
---|---|---|
inputDataType | [Required] Specifies the type of signal to monitor. | "Fixed" (required) |
RollingInputData
Name | Description | Value |
---|---|---|
inputDataType | [Required] Specifies the type of signal to monitor. | "Rolling" (required) |
preprocessingComponentId | Reference to the component asset 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 rolling data window. | string (required) |
StaticInputData
Name | Description | Value |
---|---|---|
inputDataType | [Required] Specifies the type of signal to monitor. | "Static" (required) |
preprocessingComponentId | Reference to the component asset 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) |
CustomMonitoringSignalInputs
Name | Description | Value |
---|---|---|
{customized property} | JobInput |
CustomMetricThreshold
Name | Description | Value |
---|---|---|
metric | [Required] The user-defined metric to calculate. | string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
MonitoringThreshold
Name | Description | Value |
---|---|---|
value | The threshold value. If null, the set default is dependent on the metric type. | int |
DataDriftMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | "DataDrift" (required) |
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) |
DataDriftMonitoringSignalFeatureDataTypeOverride
Name | Description | Value |
---|---|---|
{customized property} | "Categorical" "Numerical" |
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 |
MonitoringFeatureFilterBase
Name | Description | Value |
---|---|---|
filterType | Set the object type | AllFeatures FeatureSubset TopNByAttribution (required) |
AllFeatures
Name | Description | Value |
---|---|---|
filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | "AllFeatures" (required) |
FeatureSubset
Name | Description | Value |
---|---|---|
filterType | [Required] Specifies the feature filter to leverage when selecting features to calculate metrics over. | "FeatureSubset" (required) |
features | [Required] The list of features to include. | string[] (required) |
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 |
DataDriftMetricThresholdBase
Name | Description | Value |
---|---|---|
threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
dataType | Set the object type | Categorical Numerical (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) |
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) |
DataQualityMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | "DataQuality" (required) |
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) |
DataQualityMonitoringSignalFeatureDataTypeOverride
Name | Description | Value |
---|---|---|
{customized property} | "Categorical" "Numerical" |
DataQualityMetricThresholdBase
Name | Description | Value |
---|---|---|
threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
dataType | Set the object type | Categorical Numerical (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) |
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) |
FeatureAttributionDriftMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | "FeatureAttributionDrift" (required) |
featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | FeatureAttributionDriftMonitoringSignalFeatureDataTy... |
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) |
FeatureAttributionDriftMonitoringSignalFeatureDataTy...
Name | Description | Value |
---|---|---|
{customized property} | "Categorical" "Numerical" |
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 |
PredictionDriftMonitoringSignal
Name | Description | Value |
---|---|---|
signalType | [Required] Specifies the type of signal to monitor. | "PredictionDrift" (required) |
featureDataTypeOverride | A dictionary that maps feature names to their respective data types. | PredictionDriftMonitoringSignalFeatureDataTypeOverri... |
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) |
PredictionDriftMonitoringSignalFeatureDataTypeOverri...
Name | Description | Value |
---|---|---|
{customized property} | "Categorical" "Numerical" |
PredictionDriftMetricThresholdBase
Name | Description | Value |
---|---|---|
threshold | The threshold value. If null, a default value will be set depending on the selected metric. | MonitoringThreshold |
dataType | Set the object type | Categorical Numerical (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) |
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) |
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" /} |
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 /> |
string |
triggerType | Set the object type | Cron Recurrence (required) |
CronTrigger
Name | Description | Value |
---|---|---|
triggerType | [Required] | "Cron" (required) |
expression | [Required] Specifies cron expression of schedule. The expression should follow NCronTab format. |
string (required) Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] |
RecurrenceTrigger
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 |
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 |
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 |
string |
triggerType | [Required] | "Cron" "Recurrence" (required) |
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" |