Microsoft.MachineLearningServices workspaces/schedules 2022-12-01-preview
Bicep resource definition
The workspaces/schedules resource type can be deployed with operations that target:
- Resource groups - See resource group deployment commands
For a list of changed properties in each API version, see change log.
Resource format
To create a Microsoft.MachineLearningServices/workspaces/schedules resource, add the following Bicep to your template.
resource symbolicname 'Microsoft.MachineLearningServices/workspaces/schedules@2022-12-01-preview' = {
parent: resourceSymbolicName
name: 'string'
properties: {
action: {
actionType: 'string'
// For remaining properties, see ScheduleActionBase objects
}
description: 'string'
displayName: 'string'
isEnabled: bool
properties: {
{customized property}: 'string'
}
tags: {
{customized property}: 'string'
}
trigger: {
endTime: 'string'
startTime: 'string'
timeZone: 'string'
triggerType: 'string'
// For remaining properties, see TriggerBase objects
}
}
}
TriggerBase objects
Set the triggerType property to specify the type of object.
For Cron, use:
{
expression: 'string'
triggerType: 'Cron'
}
For Recurrence, use:
{
frequency: 'string'
interval: int
schedule: {
hours: [
int
]
minutes: [
int
]
monthDays: [
int
]
weekDays: [
'string'
]
}
triggerType: 'Recurrence'
}
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
]
}
JobBaseProperties objects
Set the jobType property to specify the type of object.
For AutoML, use:
{
environmentId: 'string'
environmentVariables: {
{customized property}: 'string'
}
jobType: 'AutoML'
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
resources: {
dockerArgs: 'string'
instanceCount: int
instanceType: 'string'
properties: {
{customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
}
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:
{
autologgerSettings: {
mlflowAutologger: 'string'
}
codeId: 'string'
command: 'string'
distribution: {
distributionType: 'string'
// For remaining properties, see DistributionConfiguration objects
}
environmentId: 'string'
environmentVariables: {
{customized property}: 'string'
}
inputs: {
{customized property}: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
}
jobType: 'Command'
limits: {
jobLimitsType: 'string'
timeout: 'string'
}
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
resources: {
dockerArgs: 'string'
instanceCount: int
instanceType: 'string'
properties: {
{customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
}
shmSize: 'string'
}
}
For Labeling, use:
{
dataConfiguration: {
dataId: 'string'
incrementalDataRefresh: 'string'
}
jobInstructions: {
uri: 'string'
}
jobType: 'Labeling'
labelCategories: {
{customized property}: {
classes: {
{customized property}: {
displayName: 'string'
subclasses: {
{customized property}: ...
}
}
}
displayName: 'string'
multiSelect: 'string'
}
}
labelingJobMediaProperties: {
mediaType: 'string'
// For remaining properties, see LabelingJobMediaProperties objects
}
mlAssistConfiguration: {
mlAssist: 'string'
// For remaining properties, see MLAssistConfiguration objects
}
}
For Pipeline, use:
{
inputs: {
{customized property}: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
}
jobs: {
{customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
}
jobType: 'Pipeline'
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
settings: any(Azure.Bicep.Types.Concrete.AnyType)
sourceJobId: 'string'
}
For Spark, use:
{
archives: [
'string'
]
args: 'string'
codeId: 'string'
conf: {
{customized property}: 'string'
}
entry: {
sparkJobEntryType: 'string'
// For remaining properties, see SparkJobEntry objects
}
environmentId: 'string'
files: [
'string'
]
inputs: {
{customized property}: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
}
jars: [
'string'
]
jobType: 'Spark'
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
pyFiles: [
'string'
]
resources: {
instanceType: 'string'
runtimeVersion: 'string'
}
}
For Sweep, use:
{
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
inputs: {
{customized property}: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
}
jobType: 'Sweep'
limits: {
jobLimitsType: 'string'
maxConcurrentTrials: int
maxTotalTrials: int
timeout: 'string'
trialTimeout: 'string'
}
objective: {
goal: 'string'
primaryMetric: 'string'
}
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
samplingAlgorithm: {
samplingAlgorithmType: 'string'
// For remaining properties, see SamplingAlgorithm objects
}
searchSpace: any(Azure.Bicep.Types.Concrete.AnyType)
trial: {
codeId: 'string'
command: 'string'
distribution: {
distributionType: 'string'
// For remaining properties, see DistributionConfiguration objects
}
environmentId: 'string'
environmentVariables: {
{customized property}: 'string'
}
resources: {
dockerArgs: 'string'
instanceCount: int
instanceType: 'string'
properties: {
{customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
}
shmSize: 'string'
}
}
}
IdentityConfiguration objects
Set the identityType property to specify the type of object.
For AMLToken, use:
{
identityType: 'AMLToken'
}
For Managed, use:
{
clientId: 'string'
identityType: 'Managed'
objectId: 'string'
resourceId: 'string'
}
For UserIdentity, use:
{
identityType: 'UserIdentity'
}
Nodes objects
Set the nodesValueType property to specify the type of object.
For All, use:
{
nodesValueType: 'All'
}
SparkJobEntry objects
Set the sparkJobEntryType property to specify the type of object.
For SparkJobPythonEntry, use:
{
file: 'string'
sparkJobEntryType: 'SparkJobPythonEntry'
}
For SparkJobScalaEntry, use:
{
className: 'string'
sparkJobEntryType: 'SparkJobScalaEntry'
}
TargetRollingWindowSize objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode: 'Auto'
}
For Custom, use:
{
mode: 'Custom'
value: int
}
LabelingJobMediaProperties objects
Set the mediaType property to specify the type of object.
For Image, use:
{
annotationType: 'string'
mediaType: 'Image'
}
For Text, use:
{
annotationType: 'string'
mediaType: 'Text'
}
JobOutput objects
Set the jobOutputType property to specify the type of object.
For custom_model, use:
{
assetName: 'string'
assetVersion: 'string'
jobOutputType: 'custom_model'
mode: 'string'
uri: 'string'
}
For mlflow_model, use:
{
assetName: 'string'
assetVersion: 'string'
jobOutputType: 'mlflow_model'
mode: 'string'
uri: 'string'
}
For mltable, use:
{
assetName: 'string'
assetVersion: 'string'
jobOutputType: 'mltable'
mode: 'string'
uri: 'string'
}
For triton_model, use:
{
assetName: 'string'
assetVersion: 'string'
jobOutputType: 'triton_model'
mode: 'string'
uri: 'string'
}
For uri_file, use:
{
assetName: 'string'
assetVersion: 'string'
jobOutputType: 'uri_file'
mode: 'string'
uri: 'string'
}
For uri_folder, use:
{
assetName: 'string'
assetVersion: 'string'
jobOutputType: 'uri_folder'
mode: 'string'
uri: 'string'
}
SamplingAlgorithm objects
Set the samplingAlgorithmType property to specify the type of object.
For Bayesian, use:
{
samplingAlgorithmType: 'Bayesian'
}
For Grid, use:
{
samplingAlgorithmType: 'Grid'
}
For Random, use:
{
logbase: 'string'
rule: 'string'
samplingAlgorithmType: 'Random'
seed: int
}
AutoMLVertical objects
Set the taskType property to specify the type of object.
For Classification, use:
{
cvSplitColumnNames: [
'string'
]
featurizationSettings: {
blockedTransformers: [
'string'
]
columnNameAndTypes: {
{customized property}: 'string'
}
datasetLanguage: 'string'
enableDnnFeaturization: bool
mode: 'string'
transformerParams: {
{customized property}: [
{
fields: [
'string'
]
parameters: any(Azure.Bicep.Types.Concrete.AnyType)
}
]
}
}
fixedParameters: {
booster: 'string'
boostingType: 'string'
growPolicy: 'string'
learningRate: int
maxBin: int
maxDepth: int
maxLeaves: int
minDataInLeaf: int
minSplitGain: int
modelName: 'string'
nEstimators: int
numLeaves: int
preprocessorName: 'string'
regAlpha: int
regLambda: int
subsample: int
subsampleFreq: int
treeMethod: 'string'
withMean: bool
withStd: bool
}
limitSettings: {
enableEarlyTermination: bool
exitScore: int
maxConcurrentTrials: int
maxCoresPerTrial: int
maxNodes: int
maxTrials: int
sweepConcurrentTrials: int
sweepTrials: int
timeout: 'string'
trialTimeout: 'string'
}
nCrossValidations: {
mode: 'string'
// For remaining properties, see NCrossValidations objects
}
positiveLabel: 'string'
primaryMetric: 'string'
searchSpace: [
{
booster: 'string'
boostingType: 'string'
growPolicy: 'string'
learningRate: 'string'
maxBin: 'string'
maxDepth: 'string'
maxLeaves: 'string'
minDataInLeaf: 'string'
minSplitGain: 'string'
modelName: 'string'
nEstimators: 'string'
numLeaves: 'string'
preprocessorName: 'string'
regAlpha: 'string'
regLambda: 'string'
subsample: 'string'
subsampleFreq: 'string'
treeMethod: 'string'
withMean: 'string'
withStd: 'string'
}
]
sweepSettings: {
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm: 'string'
}
taskType: 'Classification'
testData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
testDataSize: int
trainingSettings: {
allowedTrainingAlgorithms: [
'string'
]
blockedTrainingAlgorithms: [
'string'
]
enableDnnTraining: bool
enableModelExplainability: bool
enableOnnxCompatibleModels: bool
enableStackEnsemble: bool
enableVoteEnsemble: bool
ensembleModelDownloadTimeout: 'string'
stackEnsembleSettings: {
stackMetaLearnerKWargs: any(Azure.Bicep.Types.Concrete.AnyType)
stackMetaLearnerTrainPercentage: int
stackMetaLearnerType: 'string'
}
trainingMode: 'string'
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
weightColumnName: 'string'
}
For Forecasting, use:
{
cvSplitColumnNames: [
'string'
]
featurizationSettings: {
blockedTransformers: [
'string'
]
columnNameAndTypes: {
{customized property}: 'string'
}
datasetLanguage: 'string'
enableDnnFeaturization: bool
mode: 'string'
transformerParams: {
{customized property}: [
{
fields: [
'string'
]
parameters: any(Azure.Bicep.Types.Concrete.AnyType)
}
]
}
}
fixedParameters: {
booster: 'string'
boostingType: 'string'
growPolicy: 'string'
learningRate: int
maxBin: int
maxDepth: int
maxLeaves: int
minDataInLeaf: int
minSplitGain: int
modelName: 'string'
nEstimators: int
numLeaves: int
preprocessorName: 'string'
regAlpha: int
regLambda: int
subsample: int
subsampleFreq: int
treeMethod: 'string'
withMean: bool
withStd: bool
}
forecastingSettings: {
countryOrRegionForHolidays: 'string'
cvStepSize: int
featureLags: 'string'
forecastHorizon: {
mode: 'string'
// For remaining properties, see ForecastHorizon objects
}
frequency: 'string'
seasonality: {
mode: 'string'
// For remaining properties, see Seasonality objects
}
shortSeriesHandlingConfig: 'string'
targetAggregateFunction: 'string'
targetLags: {
mode: 'string'
// For remaining properties, see TargetLags objects
}
targetRollingWindowSize: {
mode: 'string'
// For remaining properties, see TargetRollingWindowSize objects
}
timeColumnName: 'string'
timeSeriesIdColumnNames: [
'string'
]
useStl: 'string'
}
limitSettings: {
enableEarlyTermination: bool
exitScore: int
maxConcurrentTrials: int
maxCoresPerTrial: int
maxNodes: int
maxTrials: int
sweepConcurrentTrials: int
sweepTrials: int
timeout: 'string'
trialTimeout: 'string'
}
nCrossValidations: {
mode: 'string'
// For remaining properties, see NCrossValidations objects
}
primaryMetric: 'string'
searchSpace: [
{
booster: 'string'
boostingType: 'string'
growPolicy: 'string'
learningRate: 'string'
maxBin: 'string'
maxDepth: 'string'
maxLeaves: 'string'
minDataInLeaf: 'string'
minSplitGain: 'string'
modelName: 'string'
nEstimators: 'string'
numLeaves: 'string'
preprocessorName: 'string'
regAlpha: 'string'
regLambda: 'string'
subsample: 'string'
subsampleFreq: 'string'
treeMethod: 'string'
withMean: 'string'
withStd: 'string'
}
]
sweepSettings: {
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm: 'string'
}
taskType: 'Forecasting'
testData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
testDataSize: int
trainingSettings: {
allowedTrainingAlgorithms: [
'string'
]
blockedTrainingAlgorithms: [
'string'
]
enableDnnTraining: bool
enableModelExplainability: bool
enableOnnxCompatibleModels: bool
enableStackEnsemble: bool
enableVoteEnsemble: bool
ensembleModelDownloadTimeout: 'string'
stackEnsembleSettings: {
stackMetaLearnerKWargs: any(Azure.Bicep.Types.Concrete.AnyType)
stackMetaLearnerTrainPercentage: int
stackMetaLearnerType: 'string'
}
trainingMode: 'string'
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
weightColumnName: 'string'
}
For ImageClassification, use:
{
limitSettings: {
maxConcurrentTrials: int
maxTrials: int
timeout: 'string'
}
modelSettings: {
advancedSettings: 'string'
amsGradient: bool
augmentations: 'string'
beta1: int
beta2: int
checkpointFrequency: int
checkpointModel: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
checkpointRunId: 'string'
distributed: bool
earlyStopping: bool
earlyStoppingDelay: int
earlyStoppingPatience: int
enableOnnxNormalization: bool
evaluationFrequency: int
gradientAccumulationStep: int
layersToFreeze: int
learningRate: int
learningRateScheduler: 'string'
modelName: 'string'
momentum: int
nesterov: bool
numberOfEpochs: int
numberOfWorkers: int
optimizer: 'string'
randomSeed: int
stepLRGamma: int
stepLRStepSize: int
trainingBatchSize: int
trainingCropSize: int
validationBatchSize: int
validationCropSize: int
validationResizeSize: int
warmupCosineLRCycles: int
warmupCosineLRWarmupEpochs: int
weightDecay: int
weightedLoss: int
}
primaryMetric: 'string'
searchSpace: [
{
amsGradient: 'string'
augmentations: 'string'
beta1: 'string'
beta2: 'string'
distributed: 'string'
earlyStopping: 'string'
earlyStoppingDelay: 'string'
earlyStoppingPatience: 'string'
enableOnnxNormalization: 'string'
evaluationFrequency: 'string'
gradientAccumulationStep: 'string'
layersToFreeze: 'string'
learningRate: 'string'
learningRateScheduler: 'string'
modelName: 'string'
momentum: 'string'
nesterov: 'string'
numberOfEpochs: 'string'
numberOfWorkers: 'string'
optimizer: 'string'
randomSeed: 'string'
stepLRGamma: 'string'
stepLRStepSize: 'string'
trainingBatchSize: 'string'
trainingCropSize: 'string'
validationBatchSize: 'string'
validationCropSize: 'string'
validationResizeSize: 'string'
warmupCosineLRCycles: 'string'
warmupCosineLRWarmupEpochs: 'string'
weightDecay: 'string'
weightedLoss: 'string'
}
]
sweepSettings: {
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm: 'string'
}
taskType: 'ImageClassification'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
}
For ImageClassificationMultilabel, use:
{
limitSettings: {
maxConcurrentTrials: int
maxTrials: int
timeout: 'string'
}
modelSettings: {
advancedSettings: 'string'
amsGradient: bool
augmentations: 'string'
beta1: int
beta2: int
checkpointFrequency: int
checkpointModel: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
checkpointRunId: 'string'
distributed: bool
earlyStopping: bool
earlyStoppingDelay: int
earlyStoppingPatience: int
enableOnnxNormalization: bool
evaluationFrequency: int
gradientAccumulationStep: int
layersToFreeze: int
learningRate: int
learningRateScheduler: 'string'
modelName: 'string'
momentum: int
nesterov: bool
numberOfEpochs: int
numberOfWorkers: int
optimizer: 'string'
randomSeed: int
stepLRGamma: int
stepLRStepSize: int
trainingBatchSize: int
trainingCropSize: int
validationBatchSize: int
validationCropSize: int
validationResizeSize: int
warmupCosineLRCycles: int
warmupCosineLRWarmupEpochs: int
weightDecay: int
weightedLoss: int
}
primaryMetric: 'string'
searchSpace: [
{
amsGradient: 'string'
augmentations: 'string'
beta1: 'string'
beta2: 'string'
distributed: 'string'
earlyStopping: 'string'
earlyStoppingDelay: 'string'
earlyStoppingPatience: 'string'
enableOnnxNormalization: 'string'
evaluationFrequency: 'string'
gradientAccumulationStep: 'string'
layersToFreeze: 'string'
learningRate: 'string'
learningRateScheduler: 'string'
modelName: 'string'
momentum: 'string'
nesterov: 'string'
numberOfEpochs: 'string'
numberOfWorkers: 'string'
optimizer: 'string'
randomSeed: 'string'
stepLRGamma: 'string'
stepLRStepSize: 'string'
trainingBatchSize: 'string'
trainingCropSize: 'string'
validationBatchSize: 'string'
validationCropSize: 'string'
validationResizeSize: 'string'
warmupCosineLRCycles: 'string'
warmupCosineLRWarmupEpochs: 'string'
weightDecay: 'string'
weightedLoss: 'string'
}
]
sweepSettings: {
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm: 'string'
}
taskType: 'ImageClassificationMultilabel'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
}
For ImageInstanceSegmentation, use:
{
limitSettings: {
maxConcurrentTrials: int
maxTrials: int
timeout: 'string'
}
modelSettings: {
advancedSettings: 'string'
amsGradient: bool
augmentations: 'string'
beta1: int
beta2: int
boxDetectionsPerImage: int
boxScoreThreshold: int
checkpointFrequency: int
checkpointModel: {
description: 'string'
jobInputType: 'string'
mode: 'string'
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: 'string'
searchSpace: [
{
amsGradient: 'string'
augmentations: 'string'
beta1: 'string'
beta2: 'string'
boxDetectionsPerImage: 'string'
boxScoreThreshold: 'string'
distributed: 'string'
earlyStopping: 'string'
earlyStoppingDelay: 'string'
earlyStoppingPatience: 'string'
enableOnnxNormalization: 'string'
evaluationFrequency: 'string'
gradientAccumulationStep: 'string'
imageSize: 'string'
layersToFreeze: 'string'
learningRate: 'string'
learningRateScheduler: 'string'
maxSize: 'string'
minSize: 'string'
modelName: 'string'
modelSize: 'string'
momentum: 'string'
multiScale: 'string'
nesterov: 'string'
nmsIouThreshold: 'string'
numberOfEpochs: 'string'
numberOfWorkers: 'string'
optimizer: 'string'
randomSeed: 'string'
stepLRGamma: 'string'
stepLRStepSize: 'string'
tileGridSize: 'string'
tileOverlapRatio: 'string'
tilePredictionsNmsThreshold: 'string'
trainingBatchSize: 'string'
validationBatchSize: 'string'
validationIouThreshold: 'string'
validationMetricType: 'string'
warmupCosineLRCycles: 'string'
warmupCosineLRWarmupEpochs: 'string'
weightDecay: 'string'
}
]
sweepSettings: {
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm: 'string'
}
taskType: 'ImageInstanceSegmentation'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
}
For ImageObjectDetection, use:
{
limitSettings: {
maxConcurrentTrials: int
maxTrials: int
timeout: 'string'
}
modelSettings: {
advancedSettings: 'string'
amsGradient: bool
augmentations: 'string'
beta1: int
beta2: int
boxDetectionsPerImage: int
boxScoreThreshold: int
checkpointFrequency: int
checkpointModel: {
description: 'string'
jobInputType: 'string'
mode: 'string'
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: 'string'
searchSpace: [
{
amsGradient: 'string'
augmentations: 'string'
beta1: 'string'
beta2: 'string'
boxDetectionsPerImage: 'string'
boxScoreThreshold: 'string'
distributed: 'string'
earlyStopping: 'string'
earlyStoppingDelay: 'string'
earlyStoppingPatience: 'string'
enableOnnxNormalization: 'string'
evaluationFrequency: 'string'
gradientAccumulationStep: 'string'
imageSize: 'string'
layersToFreeze: 'string'
learningRate: 'string'
learningRateScheduler: 'string'
maxSize: 'string'
minSize: 'string'
modelName: 'string'
modelSize: 'string'
momentum: 'string'
multiScale: 'string'
nesterov: 'string'
nmsIouThreshold: 'string'
numberOfEpochs: 'string'
numberOfWorkers: 'string'
optimizer: 'string'
randomSeed: 'string'
stepLRGamma: 'string'
stepLRStepSize: 'string'
tileGridSize: 'string'
tileOverlapRatio: 'string'
tilePredictionsNmsThreshold: 'string'
trainingBatchSize: 'string'
validationBatchSize: 'string'
validationIouThreshold: 'string'
validationMetricType: 'string'
warmupCosineLRCycles: 'string'
warmupCosineLRWarmupEpochs: 'string'
weightDecay: 'string'
}
]
sweepSettings: {
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm: 'string'
}
taskType: 'ImageObjectDetection'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
}
For Regression, use:
{
cvSplitColumnNames: [
'string'
]
featurizationSettings: {
blockedTransformers: [
'string'
]
columnNameAndTypes: {
{customized property}: 'string'
}
datasetLanguage: 'string'
enableDnnFeaturization: bool
mode: 'string'
transformerParams: {
{customized property}: [
{
fields: [
'string'
]
parameters: any(Azure.Bicep.Types.Concrete.AnyType)
}
]
}
}
fixedParameters: {
booster: 'string'
boostingType: 'string'
growPolicy: 'string'
learningRate: int
maxBin: int
maxDepth: int
maxLeaves: int
minDataInLeaf: int
minSplitGain: int
modelName: 'string'
nEstimators: int
numLeaves: int
preprocessorName: 'string'
regAlpha: int
regLambda: int
subsample: int
subsampleFreq: int
treeMethod: 'string'
withMean: bool
withStd: bool
}
limitSettings: {
enableEarlyTermination: bool
exitScore: int
maxConcurrentTrials: int
maxCoresPerTrial: int
maxNodes: int
maxTrials: int
sweepConcurrentTrials: int
sweepTrials: int
timeout: 'string'
trialTimeout: 'string'
}
nCrossValidations: {
mode: 'string'
// For remaining properties, see NCrossValidations objects
}
primaryMetric: 'string'
searchSpace: [
{
booster: 'string'
boostingType: 'string'
growPolicy: 'string'
learningRate: 'string'
maxBin: 'string'
maxDepth: 'string'
maxLeaves: 'string'
minDataInLeaf: 'string'
minSplitGain: 'string'
modelName: 'string'
nEstimators: 'string'
numLeaves: 'string'
preprocessorName: 'string'
regAlpha: 'string'
regLambda: 'string'
subsample: 'string'
subsampleFreq: 'string'
treeMethod: 'string'
withMean: 'string'
withStd: 'string'
}
]
sweepSettings: {
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm: 'string'
}
taskType: 'Regression'
testData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
testDataSize: int
trainingSettings: {
allowedTrainingAlgorithms: [
'string'
]
blockedTrainingAlgorithms: [
'string'
]
enableDnnTraining: bool
enableModelExplainability: bool
enableOnnxCompatibleModels: bool
enableStackEnsemble: bool
enableVoteEnsemble: bool
ensembleModelDownloadTimeout: 'string'
stackEnsembleSettings: {
stackMetaLearnerKWargs: any(Azure.Bicep.Types.Concrete.AnyType)
stackMetaLearnerTrainPercentage: int
stackMetaLearnerType: 'string'
}
trainingMode: 'string'
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
weightColumnName: 'string'
}
For TextClassification, use:
{
featurizationSettings: {
datasetLanguage: 'string'
}
fixedParameters: {
gradientAccumulationSteps: int
learningRate: int
learningRateScheduler: 'string'
modelName: 'string'
numberOfEpochs: int
trainingBatchSize: int
validationBatchSize: int
warmupRatio: int
weightDecay: int
}
limitSettings: {
maxConcurrentTrials: int
maxNodes: int
maxTrials: int
timeout: 'string'
trialTimeout: 'string'
}
primaryMetric: 'string'
searchSpace: [
{
gradientAccumulationSteps: 'string'
learningRate: 'string'
learningRateScheduler: 'string'
modelName: 'string'
numberOfEpochs: 'string'
trainingBatchSize: 'string'
validationBatchSize: 'string'
warmupRatio: 'string'
weightDecay: 'string'
}
]
sweepSettings: {
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm: 'string'
}
taskType: 'TextClassification'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
}
For TextClassificationMultilabel, use:
{
featurizationSettings: {
datasetLanguage: 'string'
}
fixedParameters: {
gradientAccumulationSteps: int
learningRate: int
learningRateScheduler: 'string'
modelName: 'string'
numberOfEpochs: int
trainingBatchSize: int
validationBatchSize: int
warmupRatio: int
weightDecay: int
}
limitSettings: {
maxConcurrentTrials: int
maxNodes: int
maxTrials: int
timeout: 'string'
trialTimeout: 'string'
}
searchSpace: [
{
gradientAccumulationSteps: 'string'
learningRate: 'string'
learningRateScheduler: 'string'
modelName: 'string'
numberOfEpochs: 'string'
trainingBatchSize: 'string'
validationBatchSize: 'string'
warmupRatio: 'string'
weightDecay: 'string'
}
]
sweepSettings: {
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm: 'string'
}
taskType: 'TextClassificationMultilabel'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
}
For TextNER, use:
{
featurizationSettings: {
datasetLanguage: 'string'
}
fixedParameters: {
gradientAccumulationSteps: int
learningRate: int
learningRateScheduler: 'string'
modelName: 'string'
numberOfEpochs: int
trainingBatchSize: int
validationBatchSize: int
warmupRatio: int
weightDecay: int
}
limitSettings: {
maxConcurrentTrials: int
maxNodes: int
maxTrials: int
timeout: 'string'
trialTimeout: 'string'
}
searchSpace: [
{
gradientAccumulationSteps: 'string'
learningRate: 'string'
learningRateScheduler: 'string'
modelName: 'string'
numberOfEpochs: 'string'
trainingBatchSize: 'string'
validationBatchSize: 'string'
warmupRatio: 'string'
weightDecay: 'string'
}
]
sweepSettings: {
earlyTermination: {
delayEvaluation: int
evaluationInterval: int
policyType: 'string'
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm: 'string'
}
taskType: 'TextNER'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
}
ForecastHorizon 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
}
NCrossValidations objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode: 'Auto'
}
For Custom, use:
{
mode: 'Custom'
value: int
}
DistributionConfiguration objects
Set the distributionType property to specify the type of object.
For Mpi, use:
{
distributionType: 'Mpi'
processCountPerInstance: int
}
For PyTorch, use:
{
distributionType: 'PyTorch'
processCountPerInstance: int
}
For TensorFlow, use:
{
distributionType: 'TensorFlow'
parameterServerCount: int
workerCount: int
}
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
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: {
{customized property}: 'string'
}
jobType: 'string'
// For remaining properties, see JobBaseProperties objects
}
}
For InvokeBatchEndpoint, use:
{
actionType: 'InvokeBatchEndpoint'
endpointInvocationDefinition: any(Azure.Bicep.Types.Concrete.AnyType)
}
MLAssistConfiguration objects
Set the mlAssist property to specify the type of object.
For Disabled, use:
{
mlAssist: 'Disabled'
}
For Enabled, use:
{
inferencingComputeBinding: 'string'
mlAssist: 'Enabled'
trainingComputeBinding: 'string'
}
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'
}
Property values
AllNodes
| Name | Description | Value |
|---|---|---|
| nodesValueType | [Required] Type of the Nodes value | 'All' (required) |
AmlToken
| Name | Description | Value |
|---|---|---|
| identityType | [Required] Specifies the type of identity framework. | 'AMLToken' (required) |
AutoForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set forecast horizon value selection mode. | 'Auto' (required) |
AutologgerSettings
| Name | Description | Value |
|---|---|---|
| mlflowAutologger | [Required] Indicates whether mlflow autologger is enabled. | 'Disabled' 'Enabled' (required) |
AutoMLJob
| Name | Description | Value |
|---|---|---|
| environmentId | The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job. |
string |
| environmentVariables | Environment variables included in the job. | AutoMLJobEnvironmentVariables |
| jobType | [Required] Specifies the type of job. | 'AutoML' (required) |
| outputs | Mapping of output data bindings used in the job. | AutoMLJobOutputs |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
| taskDetails | [Required] This represents scenario which can be one of Tables/NLP/Image | AutoMLVertical (required) |
AutoMLJobEnvironmentVariables
| Name | Description | Value |
|---|
AutoMLJobOutputs
| Name | Description | Value |
|---|
AutoMLVertical
| Name | Description | Value |
|---|---|---|
| logVerbosity | Log verbosity for the job. | 'Critical' 'Debug' 'Error' 'Info' 'NotSet' 'Warning' |
| targetColumnName | Target column name: This is prediction values column. Also known as label column name in context of classification tasks. |
string |
| taskType | Set to 'Classification' for type Classification. Set to 'Forecasting' for type Forecasting. Set to 'ImageClassification' for type ImageClassification. Set to 'ImageClassificationMultilabel' for type ImageClassificationMultilabel. Set to 'ImageInstanceSegmentation' for type ImageInstanceSegmentation. Set to 'ImageObjectDetection' for type ImageObjectDetection. Set to 'Regression' for type Regression. Set to 'TextClassification' for type TextClassification. Set to 'TextClassificationMultilabel' for type TextClassificationMultilabel. Set to 'TextNER' for type TextNer. | 'Classification' 'Forecasting' 'ImageClassification' 'ImageClassificationMultilabel' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'Regression' 'TextClassification' 'TextClassificationMultilabel' 'TextNER' (required) |
| trainingData | [Required] Training data input. | MLTableJobInput (required) |
AutoNCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | [Required] Mode for determining N-Cross validations. | 'Auto' (required) |
AutoSeasonality
| Name | Description | Value |
|---|---|---|
| mode | [Required] Seasonality mode. | 'Auto' (required) |
AutoTargetLags
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set target lags mode - Auto/Custom | 'Auto' (required) |
AutoTargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | [Required] TargetRollingWindowSiz detection mode. | 'Auto' (required) |
BanditPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'Bandit' (required) |
| slackAmount | Absolute distance allowed from the best performing run. | int |
| slackFactor | Ratio of the allowed distance from the best performing run. | int |
BayesianSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Bayesian' (required) |
Classification
| Name | Description | Value |
|---|---|---|
| cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
| featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | TableFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
| nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
| positiveLabel | Positive label for binary metrics calculation. | string |
| primaryMetric | Primary metric for the task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | TableParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | TableSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'Classification' (required) |
| testData | Test data input. | MLTableJobInput |
| testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| trainingSettings | Inputs for training phase for an AutoML Job. | ClassificationTrainingSettings |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
ClassificationTrainingSettings
| Name | Description | Value |
|---|---|---|
| allowedTrainingAlgorithms | Allowed models for classification task. | String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier' |
| blockedTrainingAlgorithms | Blocked models for classification task. | String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier' |
| enableDnnTraining | Enable recommendation of DNN models. | bool |
| enableModelExplainability | Flag to turn on explainability on best model. | bool |
| enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
| enableStackEnsemble | Enable stack ensemble run. | bool |
| enableVoteEnsemble | Enable voting ensemble run. | bool |
| ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
| stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
| trainingMode | TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen. |
'Auto' 'Distributed' 'NonDistributed' |
ColumnTransformer
| Name | Description | Value |
|---|---|---|
| fields | Fields to apply transformer logic on. | string[] |
| parameters | Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format. |
any |
CommandJob
| Name | Description | Value |
|---|---|---|
| autologgerSettings | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | AutologgerSettings |
| codeId | ARM resource ID of the code asset. | string |
| command | [Required] The command to execute on startup of the job. eg. "python train.py" | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
| environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| environmentVariables | Environment variables included in the job. | CommandJobEnvironmentVariables |
| inputs | Mapping of input data bindings used in the job. | CommandJobInputs |
| jobType | [Required] Specifies the type of job. | 'Command' (required) |
| limits | Command Job limit. | CommandJobLimits |
| outputs | Mapping of output data bindings used in the job. | CommandJobOutputs |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
CommandJobEnvironmentVariables
| Name | Description | Value |
|---|
CommandJobInputs
| Name | Description | Value |
|---|
CommandJobLimits
| Name | Description | Value |
|---|---|---|
| jobLimitsType | [Required] JobLimit type. | 'Command' 'Sweep' (required) |
| timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
CommandJobOutputs
| Name | Description | Value |
|---|
CronTrigger
| Name | Description | Value |
|---|---|---|
| expression | [Required] Specifies cron expression of schedule. The expression should follow NCronTab format. |
string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| triggerType | [Required] | 'Cron' (required) |
CustomForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set forecast horizon value selection mode. | 'Custom' (required) |
| value | [Required] Forecast horizon value. | int (required) |
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 Constraints: Pattern = [a-zA-Z0-9_] (required) |
CustomModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'custom_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
CustomNCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | [Required] Mode for determining N-Cross validations. | 'Custom' (required) |
| value | [Required] N-Cross validations value. | int (required) |
CustomSeasonality
| Name | Description | Value |
|---|---|---|
| mode | [Required] Seasonality mode. | 'Custom' (required) |
| value | [Required] Seasonality value. | int (required) |
CustomTargetLags
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set target lags mode - Auto/Custom | 'Custom' (required) |
| values | [Required] Set target lags values. | int[] (required) |
CustomTargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | [Required] TargetRollingWindowSiz detection mode. | 'Custom' (required) |
| value | [Required] TargetRollingWindowSize value. | int (required) |
DistributionConfiguration
| Name | Description | Value |
|---|---|---|
| distributionType | Set to 'Mpi' for type Mpi. Set to 'PyTorch' for type PyTorch. Set to 'TensorFlow' for type TensorFlow. | 'Mpi' 'PyTorch' 'TensorFlow' (required) |
EarlyTerminationPolicy
| Name | Description | Value |
|---|---|---|
| delayEvaluation | Number of intervals by which to delay the first evaluation. | int |
| evaluationInterval | Interval (number of runs) between policy evaluations. | int |
| policyType | Set to 'Bandit' for type BanditPolicy. Set to 'MedianStopping' for type MedianStoppingPolicy. Set to 'TruncationSelection' for type TruncationSelectionPolicy. | 'Bandit' 'MedianStopping' 'TruncationSelection' (required) |
EndpointScheduleAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'InvokeBatchEndpoint' (required) |
| endpointInvocationDefinition | [Required] Defines Schedule action definition details. <see href="TBD" /> |
any (required) |
ForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoForecastHorizon. Set to 'Custom' for type CustomForecastHorizon. | 'Auto' 'Custom' (required) |
Forecasting
| Name | Description | Value |
|---|---|---|
| cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
| featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | TableFixedParameters |
| forecastingSettings | Forecasting task specific inputs. | ForecastingSettings |
| limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
| nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
| primaryMetric | Primary metric for forecasting task. | 'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | TableParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | TableSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'Forecasting' (required) |
| testData | Test data input. | MLTableJobInput |
| testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| trainingSettings | Inputs for training phase for an AutoML Job. | ForecastingTrainingSettings |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
ForecastingSettings
| Name | Description | Value |
|---|---|---|
| countryOrRegionForHolidays | Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'. |
string |
| cvStepSize | Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will bethree days apart. |
int |
| featureLags | Flag for generating lags for the numeric features with 'auto' or null. | 'Auto' 'None' |
| forecastHorizon | The desired maximum forecast horizon in units of time-series frequency. | ForecastHorizon |
| frequency | When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default. | string |
| seasonality | Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred. |
Seasonality |
| shortSeriesHandlingConfig | The parameter defining how if AutoML should handle short time series. | 'Auto' 'Drop' 'None' 'Pad' |
| targetAggregateFunction | The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean". |
'Max' 'Mean' 'Min' 'None' 'Sum' |
| targetLags | The number of past periods to lag from the target column. | TargetLags |
| targetRollingWindowSize | The number of past periods used to create a rolling window average of the target column. | TargetRollingWindowSize |
| timeColumnName | The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. | string |
| timeSeriesIdColumnNames | The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting. |
string[] |
| useStl | Configure STL Decomposition of the time-series target column. | 'None' 'Season' 'SeasonTrend' |
ForecastingTrainingSettings
| Name | Description | Value |
|---|---|---|
| allowedTrainingAlgorithms | Allowed models for forecasting task. | String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SeasonalAverage' 'SeasonalNaive' 'SGD' 'TCNForecaster' 'XGBoostRegressor' |
| blockedTrainingAlgorithms | Blocked models for forecasting task. | String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SeasonalAverage' 'SeasonalNaive' 'SGD' 'TCNForecaster' 'XGBoostRegressor' |
| enableDnnTraining | Enable recommendation of DNN models. | bool |
| enableModelExplainability | Flag to turn on explainability on best model. | bool |
| enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
| enableStackEnsemble | Enable stack ensemble run. | bool |
| enableVoteEnsemble | Enable voting ensemble run. | bool |
| ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
| stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
| trainingMode | TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen. |
'Auto' 'Distributed' 'NonDistributed' |
GridSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Grid' (required) |
IdentityConfiguration
| Name | Description | Value |
|---|---|---|
| identityType | Set to 'AMLToken' for type AmlToken. Set to 'Managed' for type ManagedIdentity. Set to 'UserIdentity' for type UserIdentity. | 'AMLToken' 'Managed' 'UserIdentity' (required) |
ImageClassification
| Name | Description | Value |
|---|---|---|
| limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
| modelSettings | Settings used for training the model. | ImageModelSettingsClassification |
| primaryMetric | Primary metric to optimize for this task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsClassification[] |
| sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'ImageClassification' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageClassificationMultilabel
| Name | Description | Value |
|---|---|---|
| limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
| modelSettings | Settings used for training the model. | ImageModelSettingsClassification |
| primaryMetric | Primary metric to optimize for this task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'IOU' 'NormMacroRecall' 'PrecisionScoreWeighted' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsClassification[] |
| sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'ImageClassificationMultilabel' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageInstanceSegmentation
| Name | Description | Value |
|---|---|---|
| limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
| modelSettings | Settings used for training the model. | ImageModelSettingsObjectDetection |
| primaryMetric | Primary metric to optimize for this task. | 'MeanAveragePrecision' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsObjectDetection[] |
| sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'ImageInstanceSegmentation' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageLimitSettings
| Name | Description | Value |
|---|---|---|
| maxConcurrentTrials | Maximum number of concurrent AutoML iterations. | int |
| maxTrials | Maximum number of AutoML iterations. | int |
| timeout | AutoML job timeout. | string |
ImageModelDistributionSettingsClassification
| Name | Description | Value |
|---|---|---|
| amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | string |
| augmentations | Settings for using Augmentations. | string |
| beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
| beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
| distributed | Whether to use distributer training. | string |
| earlyStopping | Enable early stopping logic during training. | string |
| earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
string |
| earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
string |
| enableOnnxNormalization | Enable normalization when exporting ONNX model. | string |
| evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | string |
| gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
string |
| layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| learningRate | Initial learning rate. Must be a float in the range [0, 1]. | string |
| learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | string |
| modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | string |
| nesterov | Enable nesterov when optimizer is 'sgd'. | string |
| numberOfEpochs | Number of training epochs. Must be a positive integer. | string |
| numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | string |
| optimizer | Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. | string |
| randomSeed | Random seed to be used when using deterministic training. | string |
| stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | string |
| stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | string |
| trainingBatchSize | Training batch size. Must be a positive integer. | string |
| trainingCropSize | Image crop size that is input to the neural network for the training dataset. Must be a positive integer. | string |
| validationBatchSize | Validation batch size. Must be a positive integer. | string |
| validationCropSize | Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. | string |
| validationResizeSize | Image size to which to resize before cropping for validation dataset. Must be a positive integer. | string |
| warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | string |
| warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | string |
| weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | string |
| weightedLoss | Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2. |
string |
ImageModelDistributionSettingsObjectDetection
| Name | Description | Value |
|---|---|---|
| amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | string |
| augmentations | Settings for using Augmentations. | string |
| beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
| beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
| boxDetectionsPerImage | Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| boxScoreThreshold | During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
string |
| distributed | Whether to use distributer training. | string |
| earlyStopping | Enable early stopping logic during training. | string |
| earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
string |
| earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
string |
| enableOnnxNormalization | Enable normalization when exporting ONNX model. | string |
| evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | string |
| gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
string |
| imageSize | Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
| layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| learningRate | Initial learning rate. Must be a float in the range [0, 1]. | string |
| learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | string |
| maxSize | Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| minSize | Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| modelSize | Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
| momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | string |
| multiScale | Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
| nesterov | Enable nesterov when optimizer is 'sgd'. | string |
| nmsIouThreshold | IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1]. | string |
| numberOfEpochs | Number of training epochs. Must be a positive integer. | string |
| numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | string |
| optimizer | Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. | string |
| randomSeed | Random seed to be used when using deterministic training. | string |
| stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | string |
| stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | string |
| tileGridSize | The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| tileOverlapRatio | Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| tilePredictionsNmsThreshold | The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression |
string |
| trainingBatchSize | Training batch size. Must be a positive integer. | string |
| validationBatchSize | Validation batch size. Must be a positive integer. | string |
| validationIouThreshold | IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. | string |
| validationMetricType | Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'. | string |
| warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | string |
| warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | string |
| weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | string |
ImageModelSettingsClassification
| Name | Description | Value |
|---|---|---|
| advancedSettings | Settings for advanced scenarios. | string |
| amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | bool |
| augmentations | Settings for using Augmentations. | string |
| beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
| beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
| checkpointFrequency | Frequency to store model checkpoints. Must be a positive integer. | int |
| checkpointModel | The pretrained checkpoint model for incremental training. | MLFlowModelJobInput |
| checkpointRunId | The id of a previous run that has a pretrained checkpoint for incremental training. | string |
| distributed | Whether to use distributed training. | bool |
| earlyStopping | Enable early stopping logic during training. | bool |
| earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
int |
| earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
int |
| enableOnnxNormalization | Enable normalization when exporting ONNX model. | bool |
| evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | int |
| gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
int |
| layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
int |
| learningRate | Initial learning rate. Must be a float in the range [0, 1]. | int |
| learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | 'None' 'Step' 'WarmupCosine' |
| modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | int |
| nesterov | Enable nesterov when optimizer is 'sgd'. | bool |
| numberOfEpochs | Number of training epochs. Must be a positive integer. | int |
| numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | int |
| optimizer | Type of optimizer. | 'Adam' 'Adamw' 'None' 'Sgd' |
| randomSeed | Random seed to be used when using deterministic training. | int |
| stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | int |
| stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | int |
| trainingBatchSize | Training batch size. Must be a positive integer. | int |
| trainingCropSize | Image crop size that is input to the neural network for the training dataset. Must be a positive integer. | int |
| validationBatchSize | Validation batch size. Must be a positive integer. | int |
| validationCropSize | Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. | int |
| validationResizeSize | Image size to which to resize before cropping for validation dataset. Must be a positive integer. | int |
| warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | int |
| warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | int |
| weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | int |
| weightedLoss | Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2. |
int |
ImageModelSettingsObjectDetection
| Name | Description | Value |
|---|---|---|
| advancedSettings | Settings for advanced scenarios. | string |
| amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | bool |
| augmentations | Settings for using Augmentations. | string |
| beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
| beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
| boxDetectionsPerImage | Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| boxScoreThreshold | During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
int |
| checkpointFrequency | Frequency to store model checkpoints. Must be a positive integer. | int |
| checkpointModel | The pretrained checkpoint model for incremental training. | MLFlowModelJobInput |
| checkpointRunId | The id of a previous run that has a pretrained checkpoint for incremental training. | string |
| distributed | Whether to use distributed training. | bool |
| earlyStopping | Enable early stopping logic during training. | bool |
| earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
int |
| earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
int |
| enableOnnxNormalization | Enable normalization when exporting ONNX model. | bool |
| evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | int |
| gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
int |
| imageSize | Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
int |
| layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
int |
| learningRate | Initial learning rate. Must be a float in the range [0, 1]. | int |
| learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | 'None' 'Step' 'WarmupCosine' |
| maxSize | Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| minSize | Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| modelSize | Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
'ExtraLarge' 'Large' 'Medium' 'None' 'Small' |
| momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | int |
| multiScale | Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm. |
bool |
| nesterov | Enable nesterov when optimizer is 'sgd'. | bool |
| nmsIouThreshold | IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1]. | int |
| numberOfEpochs | Number of training epochs. Must be a positive integer. | int |
| numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | int |
| optimizer | Type of optimizer. | 'Adam' 'Adamw' 'None' 'Sgd' |
| randomSeed | Random seed to be used when using deterministic training. | int |
| stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | int |
| stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | int |
| tileGridSize | The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| tileOverlapRatio | Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| tilePredictionsNmsThreshold | The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| trainingBatchSize | Training batch size. Must be a positive integer. | int |
| validationBatchSize | Validation batch size. Must be a positive integer. | int |
| validationIouThreshold | IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. | int |
| validationMetricType | Metric computation method to use for validation metrics. | 'Coco' 'CocoVoc' 'None' 'Voc' |
| warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | int |
| warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | int |
| weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | int |
ImageObjectDetection
| Name | Description | Value |
|---|---|---|
| limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
| modelSettings | Settings used for training the model. | ImageModelSettingsObjectDetection |
| primaryMetric | Primary metric to optimize for this task. | 'MeanAveragePrecision' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsObjectDetection[] |
| sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'ImageObjectDetection' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageSweepSettings
| Name | Description | Value |
|---|---|---|
| earlyTermination | Type of early termination policy. | EarlyTerminationPolicy |
| samplingAlgorithm | [Required] Type of the hyperparameter sampling algorithms. | 'Bayesian' 'Grid' 'Random' (required) |
JobBaseProperties
| Name | Description | Value |
|---|---|---|
| componentId | ARM resource ID of the component resource. | string |
| computeId | ARM resource ID of the compute resource. | string |
| description | The asset description text. | string |
| displayName | Display name of job. | string |
| experimentName | The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment. | string |
| identity | Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null. |
IdentityConfiguration |
| isArchived | Is the asset archived? | bool |
| jobType | Set to 'AutoML' for type AutoMLJob. Set to 'Command' for type CommandJob. Set to 'Labeling' for type LabelingJobProperties. Set to 'Pipeline' for type PipelineJob. Set to 'Spark' for type SparkJob. Set to 'Sweep' for type SweepJob. | 'AutoML' 'Command' 'Labeling' 'Pipeline' 'Spark' 'Sweep' (required) |
| 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. | ResourceBaseTags |
JobBaseServices
| Name | Description | Value |
|---|
JobInput
| Name | Description | Value |
|---|---|---|
| description | Description for the input. | string |
| jobInputType | Set to 'custom_model' for type CustomModelJobInput. Set to 'literal' for type LiteralJobInput. Set to 'mlflow_model' for type MLFlowModelJobInput. Set to 'mltable' for type MLTableJobInput. Set to 'triton_model' for type TritonModelJobInput. Set to 'uri_file' for type UriFileJobInput. Set to 'uri_folder' for type UriFolderJobInput. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
JobOutput
| Name | Description | Value |
|---|---|---|
| description | Description for the output. | string |
| jobOutputType | Set to 'custom_model' for type CustomModelJobOutput. Set to 'mlflow_model' for type MLFlowModelJobOutput. Set to 'mltable' for type MLTableJobOutput. Set to 'triton_model' for type TritonModelJobOutput. Set to 'uri_file' for type UriFileJobOutput. Set to 'uri_folder' for type UriFolderJobOutput. | 'custom_model' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
JobResourceConfiguration
| Name | Description | Value |
|---|---|---|
| dockerArgs | Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types. | string |
| instanceCount | Optional number of instances or nodes used by the compute target. | int |
| instanceType | Optional type of VM used as supported by the compute target. | string |
| properties | Additional properties bag. | ResourceConfigurationProperties |
| shmSize | Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes). | string Constraints: Pattern = \d+[bBkKmMgG] |
JobScheduleAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'CreateJob' (required) |
| jobDefinition | [Required] Defines Schedule action definition details. | JobBaseProperties (required) |
JobService
| Name | Description | Value |
|---|---|---|
| endpoint | Url for endpoint. | string |
| jobServiceType | Endpoint type. | string |
| nodes | Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node. |
Nodes |
| port | Port for endpoint set by user. | int |
| properties | Additional properties to set on the endpoint. | JobServiceProperties |
JobServiceProperties
| Name | Description | Value |
|---|
LabelCategory
| Name | Description | Value |
|---|---|---|
| classes | Dictionary of label classes in this category. | LabelCategoryClasses |
| displayName | Display name of the label category. | string |
| multiSelect | Indicates whether it is allowed to select multiple classes in this category. | 'Disabled' 'Enabled' |
LabelCategoryClasses
| Name | Description | Value |
|---|
LabelClass
| Name | Description | Value |
|---|---|---|
| displayName | Display name of the label class. | string |
| subclasses | Dictionary of subclasses of the label class. | LabelClassSubclasses |
LabelClassSubclasses
| Name | Description | Value |
|---|
LabelingDataConfiguration
| Name | Description | Value |
|---|---|---|
| dataId | Resource Id of the data asset to perform labeling. | string |
| incrementalDataRefresh | Indicates whether to enable incremental data refresh. | 'Disabled' 'Enabled' |
LabelingJobImageProperties
| Name | Description | Value |
|---|---|---|
| annotationType | Annotation type of image labeling job. | 'BoundingBox' 'Classification' 'InstanceSegmentation' |
| mediaType | [Required] Media type of the job. | 'Image' (required) |
LabelingJobInstructions
| Name | Description | Value |
|---|---|---|
| uri | The link to a page with detailed labeling instructions for labelers. | string |
LabelingJobLabelCategories
| Name | Description | Value |
|---|
LabelingJobMediaProperties
| Name | Description | Value |
|---|---|---|
| mediaType | Set to 'Image' for type LabelingJobImageProperties. Set to 'Text' for type LabelingJobTextProperties. | 'Image' 'Text' (required) |
LabelingJobProperties
| Name | Description | Value |
|---|---|---|
| dataConfiguration | Configuration of data used in the job. | LabelingDataConfiguration |
| jobInstructions | Labeling instructions of the job. | LabelingJobInstructions |
| jobType | [Required] Specifies the type of job. | 'Labeling' (required) |
| labelCategories | Label categories of the job. | LabelingJobLabelCategories |
| labelingJobMediaProperties | Media type specific properties in the job. | LabelingJobMediaProperties |
| mlAssistConfiguration | Configuration of MLAssist feature in the job. | MLAssistConfiguration |
LabelingJobTextProperties
| Name | Description | Value |
|---|---|---|
| annotationType | Annotation type of text labeling job. | 'Classification' 'NamedEntityRecognition' |
| mediaType | [Required] Media type of the job. | 'Text' (required) |
LiteralJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'literal' (required) |
| value | [Required] Literal value for the input. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
ManagedIdentity
| Name | Description | Value |
|---|---|---|
| clientId | Specifies a user-assigned identity by client ID. For system-assigned, do not set this field. | string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$ |
| identityType | [Required] Specifies the type of identity framework. | 'Managed' (required) |
| objectId | Specifies a user-assigned identity by object ID. For system-assigned, do not set this field. | string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$ |
| resourceId | Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field. | string |
MedianStoppingPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'MedianStopping' (required) |
Microsoft.MachineLearningServices/workspaces/schedules
| Name | Description | Value |
|---|---|---|
| name | The resource name | string Constraints: Pattern = ^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$ (required) |
| parent | In Bicep, you can specify the parent resource for a child resource. You only need to add this property when the child resource is declared outside of the parent resource. For more information, see Child resource outside parent resource. |
Symbolic name for resource of type: workspaces |
| properties | [Required] Additional attributes of the entity. | ScheduleProperties (required) |
MLAssistConfiguration
| Name | Description | Value |
|---|---|---|
| mlAssist | Set to 'Disabled' for type MLAssistConfigurationDisabled. Set to 'Enabled' for type MLAssistConfigurationEnabled. | 'Disabled' 'Enabled' (required) |
MLAssistConfigurationDisabled
| Name | Description | Value |
|---|---|---|
| mlAssist | [Required] Indicates whether MLAssist feature is enabled. | 'Disabled' (required) |
MLAssistConfigurationEnabled
| Name | Description | Value |
|---|---|---|
| inferencingComputeBinding | [Required] AML compute binding used in inferencing. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| mlAssist | [Required] Indicates whether MLAssist feature is enabled. | 'Enabled' (required) |
| trainingComputeBinding | [Required] AML compute binding used in training. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'mlflow_model' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobInput
| Name | Description | Value |
|---|---|---|
| description | Description for the input. | string |
| jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'mlflow_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
MLTableJobInput
| Name | Description | Value |
|---|---|---|
| description | Description for the input. | string |
| jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLTableJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'mltable' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLTableJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'mltable' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
Mpi
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'Mpi' (required) |
| processCountPerInstance | Number of processes per MPI node. | int |
NCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoNCrossValidations. Set to 'Custom' for type CustomNCrossValidations. | 'Auto' 'Custom' (required) |
NlpFixedParameters
| Name | Description | Value |
|---|---|---|
| gradientAccumulationSteps | Number of steps to accumulate gradients over before running a backward pass. | int |
| learningRate | The learning rate for the training procedure. | int |
| learningRateScheduler | The type of learning rate schedule to use during the training procedure. | 'Constant' 'ConstantWithWarmup' 'Cosine' 'CosineWithRestarts' 'Linear' 'None' 'Polynomial' |
| modelName | The name of the model to train. | string |
| numberOfEpochs | Number of training epochs. | int |
| trainingBatchSize | The batch size for the training procedure. | int |
| validationBatchSize | The batch size to be used during evaluation. | int |
| warmupRatio | The warmup ratio, used alongside LrSchedulerType. | int |
| weightDecay | The weight decay for the training procedure. | int |
NlpParameterSubspace
| Name | Description | Value |
|---|---|---|
| gradientAccumulationSteps | Number of steps to accumulate gradients over before running a backward pass. | string |
| learningRate | The learning rate for the training procedure. | string |
| learningRateScheduler | The type of learning rate schedule to use during the training procedure. | string |
| modelName | The name of the model to train. | string |
| numberOfEpochs | Number of training epochs. | string |
| trainingBatchSize | The batch size for the training procedure. | string |
| validationBatchSize | The batch size to be used during evaluation. | string |
| warmupRatio | The warmup ratio, used alongside LrSchedulerType. | string |
| weightDecay | The weight decay for the training procedure. | string |
NlpSweepSettings
| Name | Description | Value |
|---|---|---|
| earlyTermination | Type of early termination policy for the sweeping job. | EarlyTerminationPolicy |
| samplingAlgorithm | [Required] Type of sampling algorithm. | 'Bayesian' 'Grid' 'Random' (required) |
NlpVerticalFeaturizationSettings
| Name | Description | Value |
|---|---|---|
| datasetLanguage | Dataset language, useful for the text data. | string |
NlpVerticalLimitSettings
| Name | Description | Value |
|---|---|---|
| maxConcurrentTrials | Maximum Concurrent AutoML iterations. | int |
| maxNodes | Maximum nodes to use for the experiment. | int |
| maxTrials | Number of AutoML iterations. | int |
| timeout | AutoML job timeout. | string |
| trialTimeout | Timeout for individual HD trials. | string |
Nodes
| Name | Description | Value |
|---|---|---|
| nodesValueType | Set to 'All' for type AllNodes. | 'All' (required) |
Objective
| Name | Description | Value |
|---|---|---|
| goal | [Required] Defines supported metric goals for hyperparameter tuning | 'Maximize' 'Minimize' (required) |
| primaryMetric | [Required] Name of the metric to optimize. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
PipelineJob
| Name | Description | Value |
|---|---|---|
| inputs | Inputs for the pipeline job. | PipelineJobInputs |
| jobs | Jobs construct the Pipeline Job. | PipelineJobJobs |
| jobType | [Required] Specifies the type of job. | 'Pipeline' (required) |
| outputs | Outputs for the pipeline job | PipelineJobOutputs |
| settings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | any |
| sourceJobId | ARM resource ID of source job. | string |
PipelineJobInputs
| Name | Description | Value |
|---|
PipelineJobJobs
| Name | Description | Value |
|---|
PipelineJobOutputs
| Name | Description | Value |
|---|
PyTorch
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'PyTorch' (required) |
| processCountPerInstance | Number of processes per node. | int |
RandomSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| logbase | An optional positive number or e in string format to be used as base for log based random sampling | string |
| rule | The specific type of random algorithm | 'Random' 'Sobol' |
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Random' (required) |
| seed | An optional integer to use as the seed for random number generation | int |
RecurrenceSchedule
| Name | Description | Value |
|---|---|---|
| hours | [Required] List of hours for the schedule. | int[] (required) |
| minutes | [Required] List of minutes for the schedule. | int[] (required) |
| monthDays | List of month days for the schedule | int[] |
| weekDays | List of days for the schedule. | String array containing any of: 'Friday' 'Monday' 'Saturday' 'Sunday' 'Thursday' 'Tuesday' 'Wednesday' |
RecurrenceTrigger
| Name | Description | Value |
|---|---|---|
| frequency | [Required] The frequency to trigger schedule. | 'Day' 'Hour' 'Minute' 'Month' 'Week' (required) |
| interval | [Required] Specifies schedule interval in conjunction with frequency | int (required) |
| schedule | The recurrence schedule. | RecurrenceSchedule |
| triggerType | [Required] | 'Recurrence' (required) |
Regression
| Name | Description | Value |
|---|---|---|
| cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
| featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | TableFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
| nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
| primaryMetric | Primary metric for regression task. | 'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | TableParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | TableSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'Regression' (required) |
| testData | Test data input. | MLTableJobInput |
| testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| trainingSettings | Inputs for training phase for an AutoML Job. | RegressionTrainingSettings |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
RegressionTrainingSettings
| Name | Description | Value |
|---|---|---|
| allowedTrainingAlgorithms | Allowed models for regression task. | String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor' |
| blockedTrainingAlgorithms | Blocked models for regression task. | String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor' |
| enableDnnTraining | Enable recommendation of DNN models. | bool |
| enableModelExplainability | Flag to turn on explainability on best model. | bool |
| enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
| enableStackEnsemble | Enable stack ensemble run. | bool |
| enableVoteEnsemble | Enable voting ensemble run. | bool |
| ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
| stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
| trainingMode | TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen. |
'Auto' 'Distributed' 'NonDistributed' |
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceConfigurationProperties
| Name | Description | Value |
|---|
SamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | Set to 'Bayesian' for type BayesianSamplingAlgorithm. Set to 'Grid' for type GridSamplingAlgorithm. Set to 'Random' for type RandomSamplingAlgorithm. | 'Bayesian' 'Grid' 'Random' (required) |
ScheduleActionBase
| Name | Description | Value |
|---|---|---|
| actionType | Set to 'CreateJob' for type JobScheduleAction. Set to 'InvokeBatchEndpoint' for type EndpointScheduleAction. | 'CreateJob' 'InvokeBatchEndpoint' (required) |
ScheduleProperties
| Name | Description | Value |
|---|---|---|
| action | [Required] Specifies the action of the schedule | ScheduleActionBase (required) |
| description | The asset description text. | string |
| displayName | Display name of schedule. | string |
| isEnabled | Is the schedule enabled? | bool |
| properties | The asset property dictionary. | ResourceBaseProperties |
| tags | Tag dictionary. Tags can be added, removed, and updated. | ResourceBaseTags |
| trigger | [Required] Specifies the trigger details | TriggerBase (required) |
Seasonality
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoSeasonality. Set to 'Custom' for type CustomSeasonality. | 'Auto' 'Custom' (required) |
SparkJob
| Name | Description | Value |
|---|---|---|
| archives | Archive files used in the job. | string[] |
| args | Arguments for the job. | string |
| codeId | [Required] ARM resource ID of the code asset. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| conf | Spark configured properties. | SparkJobConf |
| entry | [Required] The entry to execute on startup of the job. | SparkJobEntry (required) |
| environmentId | The ARM resource ID of the Environment specification for the job. | string |
| files | Files used in the job. | string[] |
| inputs | Mapping of input data bindings used in the job. | SparkJobInputs |
| jars | Jar files used in the job. | string[] |
| jobType | [Required] Specifies the type of job. | 'Spark' (required) |
| outputs | Mapping of output data bindings used in the job. | SparkJobOutputs |
| pyFiles | Python files used in the job. | string[] |
| resources | Compute Resource configuration for the job. | SparkResourceConfiguration |
SparkJobConf
| Name | Description | Value |
|---|
SparkJobEntry
| Name | Description | Value |
|---|---|---|
| sparkJobEntryType | Set to 'SparkJobPythonEntry' for type SparkJobPythonEntry. Set to 'SparkJobScalaEntry' for type SparkJobScalaEntry. | 'SparkJobPythonEntry' 'SparkJobScalaEntry' (required) |
SparkJobInputs
| Name | Description | Value |
|---|
SparkJobOutputs
| Name | Description | Value |
|---|
SparkJobPythonEntry
| Name | Description | Value |
|---|---|---|
| file | [Required] Relative python file path for job entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobPythonEntry' (required) |
SparkJobScalaEntry
| Name | Description | Value |
|---|---|---|
| className | [Required] Scala class name used as entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobScalaEntry' (required) |
SparkResourceConfiguration
| Name | Description | Value |
|---|---|---|
| instanceType | Optional type of VM used as supported by the compute target. | string |
| runtimeVersion | Version of spark runtime used for the job. | string |
StackEnsembleSettings
| Name | Description | Value |
|---|---|---|
| stackMetaLearnerKWargs | Optional parameters to pass to the initializer of the meta-learner. | any |
| stackMetaLearnerTrainPercentage | Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2. | int |
| stackMetaLearnerType | The meta-learner is a model trained on the output of the individual heterogeneous models. | 'ElasticNet' 'ElasticNetCV' 'LightGBMClassifier' 'LightGBMRegressor' 'LinearRegression' 'LogisticRegression' 'LogisticRegressionCV' 'None' |
SweepJob
| Name | Description | Value |
|---|---|---|
| earlyTermination | Early termination policies enable canceling poor-performing runs before they complete | EarlyTerminationPolicy |
| inputs | Mapping of input data bindings used in the job. | SweepJobInputs |
| jobType | [Required] Specifies the type of job. | 'Sweep' (required) |
| limits | Sweep Job limit. | SweepJobLimits |
| objective | [Required] Optimization objective. | Objective (required) |
| outputs | Mapping of output data bindings used in the job. | SweepJobOutputs |
| samplingAlgorithm | [Required] The hyperparameter sampling algorithm | SamplingAlgorithm (required) |
| searchSpace | [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter | any (required) |
| trial | [Required] Trial component definition. | TrialComponent (required) |
SweepJobInputs
| Name | Description | Value |
|---|
SweepJobLimits
| Name | Description | Value |
|---|---|---|
| jobLimitsType | [Required] JobLimit type. | 'Command' 'Sweep' (required) |
| maxConcurrentTrials | Sweep Job max concurrent trials. | int |
| maxTotalTrials | Sweep Job max total trials. | int |
| timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
| trialTimeout | Sweep Job Trial timeout value. | string |
SweepJobOutputs
| Name | Description | Value |
|---|
TableFixedParameters
| Name | Description | Value |
|---|---|---|
| booster | Specify the boosting type, e.g gbdt for XGBoost. | string |
| boostingType | Specify the boosting type, e.g gbdt for LightGBM. | string |
| growPolicy | Specify the grow policy, which controls the way new nodes are added to the tree. | string |
| learningRate | The learning rate for the training procedure. | int |
| maxBin | Specify the Maximum number of discrete bins to bucket continuous features . | int |
| maxDepth | Specify the max depth to limit the tree depth explicitly. | int |
| maxLeaves | Specify the max leaves to limit the tree leaves explicitly. | int |
| minDataInLeaf | The minimum number of data per leaf. | int |
| minSplitGain | Minimum loss reduction required to make a further partition on a leaf node of the tree. | int |
| modelName | The name of the model to train. | string |
| nEstimators | Specify the number of trees (or rounds) in an model. | int |
| numLeaves | Specify the number of leaves. | int |
| preprocessorName | The name of the preprocessor to use. | string |
| regAlpha | L1 regularization term on weights. | int |
| regLambda | L2 regularization term on weights. | int |
| subsample | Subsample ratio of the training instance. | int |
| subsampleFreq | Frequency of subsample. | int |
| treeMethod | Specify the tree method. | string |
| withMean | If true, center before scaling the data with StandardScalar. | bool |
| withStd | If true, scaling the data with Unit Variance with StandardScalar. | bool |
TableParameterSubspace
| Name | Description | Value |
|---|---|---|
| booster | Specify the boosting type, e.g gbdt for XGBoost. | string |
| boostingType | Specify the boosting type, e.g gbdt for LightGBM. | string |
| growPolicy | Specify the grow policy, which controls the way new nodes are added to the tree. | string |
| learningRate | The learning rate for the training procedure. | string |
| maxBin | Specify the Maximum number of discrete bins to bucket continuous features . | string |
| maxDepth | Specify the max depth to limit the tree depth explicitly. | string |
| maxLeaves | Specify the max leaves to limit the tree leaves explicitly. | string |
| minDataInLeaf | The minimum number of data per leaf. | string |
| minSplitGain | Minimum loss reduction required to make a further partition on a leaf node of the tree. | string |
| modelName | The name of the model to train. | string |
| nEstimators | Specify the number of trees (or rounds) in an model. | string |
| numLeaves | Specify the number of leaves. | string |
| preprocessorName | The name of the preprocessor to use. | string |
| regAlpha | L1 regularization term on weights. | string |
| regLambda | L2 regularization term on weights. | string |
| subsample | Subsample ratio of the training instance. | string |
| subsampleFreq | Frequency of subsample | string |
| treeMethod | Specify the tree method. | string |
| withMean | If true, center before scaling the data with StandardScalar. | string |
| withStd | If true, scaling the data with Unit Variance with StandardScalar. | string |
TableSweepSettings
| Name | Description | Value |
|---|---|---|
| earlyTermination | Type of early termination policy for the sweeping job. | EarlyTerminationPolicy |
| samplingAlgorithm | [Required] Type of sampling algorithm. | 'Bayesian' 'Grid' 'Random' (required) |
TableVerticalFeaturizationSettings
| Name | Description | Value |
|---|---|---|
| blockedTransformers | These transformers shall not be used in featurization. | String array containing any of: 'CatTargetEncoder' 'CountVectorizer' 'HashOneHotEncoder' 'LabelEncoder' 'NaiveBayes' 'OneHotEncoder' 'TextTargetEncoder' 'TfIdf' 'WoETargetEncoder' 'WordEmbedding' |
| columnNameAndTypes | Dictionary of column name and its type (int, float, string, datetime etc). | TableVerticalFeaturizationSettingsColumnNameAndTypes |
| datasetLanguage | Dataset language, useful for the text data. | string |
| enableDnnFeaturization | Determines whether to use Dnn based featurizers for data featurization. | bool |
| mode | Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done. |
'Auto' 'Custom' 'Off' |
| transformerParams | User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor. | TableVerticalFeaturizationSettingsTransformerParams |
TableVerticalFeaturizationSettingsColumnNameAndTypes
| Name | Description | Value |
|---|
TableVerticalFeaturizationSettingsTransformerParams
| Name | Description | Value |
|---|
TableVerticalLimitSettings
| Name | Description | Value |
|---|---|---|
| enableEarlyTermination | Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations. | bool |
| exitScore | Exit score for the AutoML job. | int |
| maxConcurrentTrials | Maximum Concurrent iterations. | int |
| maxCoresPerTrial | Max cores per iteration. | int |
| maxNodes | Maximum nodes to use for the experiment. | int |
| maxTrials | Number of iterations. | int |
| sweepConcurrentTrials | Number of concurrent sweeping runs that user wants to trigger. | int |
| sweepTrials | Number of sweeping runs that user wants to trigger. | int |
| timeout | AutoML job timeout. | string |
| trialTimeout | Iteration timeout. | string |
TargetLags
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoTargetLags. Set to 'Custom' for type CustomTargetLags. | 'Auto' 'Custom' (required) |
TargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoTargetRollingWindowSize. Set to 'Custom' for type CustomTargetRollingWindowSize. | 'Auto' 'Custom' (required) |
TensorFlow
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'TensorFlow' (required) |
| parameterServerCount | Number of parameter server tasks. | int |
| workerCount | Number of workers. If not specified, will default to the instance count. | int |
TextClassification
| Name | Description | Value |
|---|---|---|
| featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | NlpFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
| primaryMetric | Primary metric for Text-Classification task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | NlpParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | NlpSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'TextClassification' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TextClassificationMultilabel
| Name | Description | Value |
|---|---|---|
| featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | NlpFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | NlpParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | NlpSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'TextClassificationMultilabel' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TextNer
| Name | Description | Value |
|---|---|---|
| featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | NlpFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | NlpParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | NlpSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'TextNER' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TrialComponent
| Name | Description | Value |
|---|---|---|
| codeId | ARM resource ID of the code asset. | string |
| command | [Required] The command to execute on startup of the job. eg. "python train.py" | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
| environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| environmentVariables | Environment variables included in the job. | TrialComponentEnvironmentVariables |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
TrialComponentEnvironmentVariables
| Name | Description | Value |
|---|
TriggerBase
| Name | Description | Value |
|---|---|---|
| endTime | Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely |
string |
| startTime | Specifies start time of schedule in ISO 8601 format, but without a UTC offset. | string |
| timeZone | Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones?view=windows-11 |
string |
| triggerType | Set to 'Cron' for type CronTrigger. Set to 'Recurrence' for type RecurrenceTrigger. | 'Cron' 'Recurrence' (required) |
TritonModelJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'triton_model' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
TritonModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'triton_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
TruncationSelectionPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'TruncationSelection' (required) |
| truncationPercentage | The percentage of runs to cancel at each evaluation interval. | int |
UriFileJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'uri_file' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
UriFileJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'uri_file' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
UriFolderJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'uri_folder' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
UriFolderJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'uri_folder' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
UserIdentity
| Name | Description | Value |
|---|---|---|
| identityType | [Required] Specifies the type of identity framework. | 'UserIdentity' (required) |
ARM template resource definition
The workspaces/schedules resource type can be deployed with operations that target:
- Resource groups - See resource group deployment commands
For a list of changed properties in each API version, see change log.
Resource format
To create a Microsoft.MachineLearningServices/workspaces/schedules resource, add the following JSON to your template.
{
"type": "Microsoft.MachineLearningServices/workspaces/schedules",
"apiVersion": "2022-12-01-preview",
"name": "string",
"properties": {
"action": {
"actionType": "string"
// For remaining properties, see ScheduleActionBase objects
},
"description": "string",
"displayName": "string",
"isEnabled": "bool",
"properties": {
"{customized property}": "string"
},
"tags": {
"{customized property}": "string"
},
"trigger": {
"endTime": "string",
"startTime": "string",
"timeZone": "string",
"triggerType": "string"
// For remaining properties, see TriggerBase objects
}
}
}
TriggerBase objects
Set the triggerType property to specify the type of object.
For Cron, use:
{
"expression": "string",
"triggerType": "Cron"
}
For Recurrence, use:
{
"frequency": "string",
"interval": "int",
"schedule": {
"hours": [ "int" ],
"minutes": [ "int" ],
"monthDays": [ "int" ],
"weekDays": [ "string" ]
},
"triggerType": "Recurrence"
}
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" ]
}
JobBaseProperties objects
Set the jobType property to specify the type of object.
For AutoML, use:
{
"environmentId": "string",
"environmentVariables": {
"{customized property}": "string"
},
"jobType": "AutoML",
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"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:
{
"autologgerSettings": {
"mlflowAutologger": "string"
},
"codeId": "string",
"command": "string",
"distribution": {
"distributionType": "string"
// For remaining properties, see DistributionConfiguration objects
},
"environmentId": "string",
"environmentVariables": {
"{customized property}": "string"
},
"inputs": {
"{customized property}": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
}
},
"jobType": "Command",
"limits": {
"jobLimitsType": "string",
"timeout": "string"
},
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"resources": {
"dockerArgs": "string",
"instanceCount": "int",
"instanceType": "string",
"properties": {
"{customized property}": {}
},
"shmSize": "string"
}
}
For Labeling, use:
{
"dataConfiguration": {
"dataId": "string",
"incrementalDataRefresh": "string"
},
"jobInstructions": {
"uri": "string"
},
"jobType": "Labeling",
"labelCategories": {
"{customized property}": {
"classes": {
"{customized property}": {
"displayName": "string",
"subclasses": {
"{customized property}": ...
}
}
},
"displayName": "string",
"multiSelect": "string"
}
},
"labelingJobMediaProperties": {
"mediaType": "string"
// For remaining properties, see LabelingJobMediaProperties objects
},
"mlAssistConfiguration": {
"mlAssist": "string"
// For remaining properties, see MLAssistConfiguration objects
}
}
For Pipeline, use:
{
"inputs": {
"{customized property}": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
}
},
"jobs": {
"{customized property}": {}
},
"jobType": "Pipeline",
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"settings": {},
"sourceJobId": "string"
}
For Spark, use:
{
"archives": [ "string" ],
"args": "string",
"codeId": "string",
"conf": {
"{customized property}": "string"
},
"entry": {
"sparkJobEntryType": "string"
// For remaining properties, see SparkJobEntry objects
},
"environmentId": "string",
"files": [ "string" ],
"inputs": {
"{customized property}": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
}
},
"jars": [ "string" ],
"jobType": "Spark",
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"pyFiles": [ "string" ],
"resources": {
"instanceType": "string",
"runtimeVersion": "string"
}
}
For Sweep, use:
{
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"inputs": {
"{customized property}": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
}
},
"jobType": "Sweep",
"limits": {
"jobLimitsType": "string",
"maxConcurrentTrials": "int",
"maxTotalTrials": "int",
"timeout": "string",
"trialTimeout": "string"
},
"objective": {
"goal": "string",
"primaryMetric": "string"
},
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"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:
{
"clientId": "string",
"identityType": "Managed",
"objectId": "string",
"resourceId": "string"
}
For UserIdentity, use:
{
"identityType": "UserIdentity"
}
Nodes objects
Set the nodesValueType property to specify the type of object.
For All, use:
{
"nodesValueType": "All"
}
SparkJobEntry objects
Set the sparkJobEntryType property to specify the type of object.
For SparkJobPythonEntry, use:
{
"file": "string",
"sparkJobEntryType": "SparkJobPythonEntry"
}
For SparkJobScalaEntry, use:
{
"className": "string",
"sparkJobEntryType": "SparkJobScalaEntry"
}
TargetRollingWindowSize objects
Set the mode property to specify the type of object.
For Auto, use:
{
"mode": "Auto"
}
For Custom, use:
{
"mode": "Custom",
"value": "int"
}
LabelingJobMediaProperties objects
Set the mediaType property to specify the type of object.
For Image, use:
{
"annotationType": "string",
"mediaType": "Image"
}
For Text, use:
{
"annotationType": "string",
"mediaType": "Text"
}
JobOutput objects
Set the jobOutputType property to specify the type of object.
For custom_model, use:
{
"assetName": "string",
"assetVersion": "string",
"jobOutputType": "custom_model",
"mode": "string",
"uri": "string"
}
For mlflow_model, use:
{
"assetName": "string",
"assetVersion": "string",
"jobOutputType": "mlflow_model",
"mode": "string",
"uri": "string"
}
For mltable, use:
{
"assetName": "string",
"assetVersion": "string",
"jobOutputType": "mltable",
"mode": "string",
"uri": "string"
}
For triton_model, use:
{
"assetName": "string",
"assetVersion": "string",
"jobOutputType": "triton_model",
"mode": "string",
"uri": "string"
}
For uri_file, use:
{
"assetName": "string",
"assetVersion": "string",
"jobOutputType": "uri_file",
"mode": "string",
"uri": "string"
}
For uri_folder, use:
{
"assetName": "string",
"assetVersion": "string",
"jobOutputType": "uri_folder",
"mode": "string",
"uri": "string"
}
SamplingAlgorithm objects
Set the samplingAlgorithmType property to specify the type of object.
For Bayesian, use:
{
"samplingAlgorithmType": "Bayesian"
}
For Grid, use:
{
"samplingAlgorithmType": "Grid"
}
For Random, use:
{
"logbase": "string",
"rule": "string",
"samplingAlgorithmType": "Random",
"seed": "int"
}
AutoMLVertical objects
Set the taskType property to specify the type of object.
For Classification, use:
{
"cvSplitColumnNames": [ "string" ],
"featurizationSettings": {
"blockedTransformers": [ "string" ],
"columnNameAndTypes": {
"{customized property}": "string"
},
"datasetLanguage": "string",
"enableDnnFeaturization": "bool",
"mode": "string",
"transformerParams": {
"{customized property}": [
{
"fields": [ "string" ],
"parameters": {}
}
]
}
},
"fixedParameters": {
"booster": "string",
"boostingType": "string",
"growPolicy": "string",
"learningRate": "int",
"maxBin": "int",
"maxDepth": "int",
"maxLeaves": "int",
"minDataInLeaf": "int",
"minSplitGain": "int",
"modelName": "string",
"nEstimators": "int",
"numLeaves": "int",
"preprocessorName": "string",
"regAlpha": "int",
"regLambda": "int",
"subsample": "int",
"subsampleFreq": "int",
"treeMethod": "string",
"withMean": "bool",
"withStd": "bool"
},
"limitSettings": {
"enableEarlyTermination": "bool",
"exitScore": "int",
"maxConcurrentTrials": "int",
"maxCoresPerTrial": "int",
"maxNodes": "int",
"maxTrials": "int",
"sweepConcurrentTrials": "int",
"sweepTrials": "int",
"timeout": "string",
"trialTimeout": "string"
},
"nCrossValidations": {
"mode": "string"
// For remaining properties, see NCrossValidations objects
},
"positiveLabel": "string",
"primaryMetric": "string",
"searchSpace": [
{
"booster": "string",
"boostingType": "string",
"growPolicy": "string",
"learningRate": "string",
"maxBin": "string",
"maxDepth": "string",
"maxLeaves": "string",
"minDataInLeaf": "string",
"minSplitGain": "string",
"modelName": "string",
"nEstimators": "string",
"numLeaves": "string",
"preprocessorName": "string",
"regAlpha": "string",
"regLambda": "string",
"subsample": "string",
"subsampleFreq": "string",
"treeMethod": "string",
"withMean": "string",
"withStd": "string"
}
],
"sweepSettings": {
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"samplingAlgorithm": "string"
},
"taskType": "Classification",
"testData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"testDataSize": "int",
"trainingSettings": {
"allowedTrainingAlgorithms": [ "string" ],
"blockedTrainingAlgorithms": [ "string" ],
"enableDnnTraining": "bool",
"enableModelExplainability": "bool",
"enableOnnxCompatibleModels": "bool",
"enableStackEnsemble": "bool",
"enableVoteEnsemble": "bool",
"ensembleModelDownloadTimeout": "string",
"stackEnsembleSettings": {
"stackMetaLearnerKWargs": {},
"stackMetaLearnerTrainPercentage": "int",
"stackMetaLearnerType": "string"
},
"trainingMode": "string"
},
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int",
"weightColumnName": "string"
}
For Forecasting, use:
{
"cvSplitColumnNames": [ "string" ],
"featurizationSettings": {
"blockedTransformers": [ "string" ],
"columnNameAndTypes": {
"{customized property}": "string"
},
"datasetLanguage": "string",
"enableDnnFeaturization": "bool",
"mode": "string",
"transformerParams": {
"{customized property}": [
{
"fields": [ "string" ],
"parameters": {}
}
]
}
},
"fixedParameters": {
"booster": "string",
"boostingType": "string",
"growPolicy": "string",
"learningRate": "int",
"maxBin": "int",
"maxDepth": "int",
"maxLeaves": "int",
"minDataInLeaf": "int",
"minSplitGain": "int",
"modelName": "string",
"nEstimators": "int",
"numLeaves": "int",
"preprocessorName": "string",
"regAlpha": "int",
"regLambda": "int",
"subsample": "int",
"subsampleFreq": "int",
"treeMethod": "string",
"withMean": "bool",
"withStd": "bool"
},
"forecastingSettings": {
"countryOrRegionForHolidays": "string",
"cvStepSize": "int",
"featureLags": "string",
"forecastHorizon": {
"mode": "string"
// For remaining properties, see ForecastHorizon objects
},
"frequency": "string",
"seasonality": {
"mode": "string"
// For remaining properties, see Seasonality objects
},
"shortSeriesHandlingConfig": "string",
"targetAggregateFunction": "string",
"targetLags": {
"mode": "string"
// For remaining properties, see TargetLags objects
},
"targetRollingWindowSize": {
"mode": "string"
// For remaining properties, see TargetRollingWindowSize objects
},
"timeColumnName": "string",
"timeSeriesIdColumnNames": [ "string" ],
"useStl": "string"
},
"limitSettings": {
"enableEarlyTermination": "bool",
"exitScore": "int",
"maxConcurrentTrials": "int",
"maxCoresPerTrial": "int",
"maxNodes": "int",
"maxTrials": "int",
"sweepConcurrentTrials": "int",
"sweepTrials": "int",
"timeout": "string",
"trialTimeout": "string"
},
"nCrossValidations": {
"mode": "string"
// For remaining properties, see NCrossValidations objects
},
"primaryMetric": "string",
"searchSpace": [
{
"booster": "string",
"boostingType": "string",
"growPolicy": "string",
"learningRate": "string",
"maxBin": "string",
"maxDepth": "string",
"maxLeaves": "string",
"minDataInLeaf": "string",
"minSplitGain": "string",
"modelName": "string",
"nEstimators": "string",
"numLeaves": "string",
"preprocessorName": "string",
"regAlpha": "string",
"regLambda": "string",
"subsample": "string",
"subsampleFreq": "string",
"treeMethod": "string",
"withMean": "string",
"withStd": "string"
}
],
"sweepSettings": {
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"samplingAlgorithm": "string"
},
"taskType": "Forecasting",
"testData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"testDataSize": "int",
"trainingSettings": {
"allowedTrainingAlgorithms": [ "string" ],
"blockedTrainingAlgorithms": [ "string" ],
"enableDnnTraining": "bool",
"enableModelExplainability": "bool",
"enableOnnxCompatibleModels": "bool",
"enableStackEnsemble": "bool",
"enableVoteEnsemble": "bool",
"ensembleModelDownloadTimeout": "string",
"stackEnsembleSettings": {
"stackMetaLearnerKWargs": {},
"stackMetaLearnerTrainPercentage": "int",
"stackMetaLearnerType": "string"
},
"trainingMode": "string"
},
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int",
"weightColumnName": "string"
}
For ImageClassification, use:
{
"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"
},
"taskType": "ImageClassification",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int"
}
For ImageClassificationMultilabel, use:
{
"limitSettings": {
"maxConcurrentTrials": "int",
"maxTrials": "int",
"timeout": "string"
},
"modelSettings": {
"advancedSettings": "string",
"amsGradient": "bool",
"augmentations": "string",
"beta1": "int",
"beta2": "int",
"checkpointFrequency": "int",
"checkpointModel": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"checkpointRunId": "string",
"distributed": "bool",
"earlyStopping": "bool",
"earlyStoppingDelay": "int",
"earlyStoppingPatience": "int",
"enableOnnxNormalization": "bool",
"evaluationFrequency": "int",
"gradientAccumulationStep": "int",
"layersToFreeze": "int",
"learningRate": "int",
"learningRateScheduler": "string",
"modelName": "string",
"momentum": "int",
"nesterov": "bool",
"numberOfEpochs": "int",
"numberOfWorkers": "int",
"optimizer": "string",
"randomSeed": "int",
"stepLRGamma": "int",
"stepLRStepSize": "int",
"trainingBatchSize": "int",
"trainingCropSize": "int",
"validationBatchSize": "int",
"validationCropSize": "int",
"validationResizeSize": "int",
"warmupCosineLRCycles": "int",
"warmupCosineLRWarmupEpochs": "int",
"weightDecay": "int",
"weightedLoss": "int"
},
"primaryMetric": "string",
"searchSpace": [
{
"amsGradient": "string",
"augmentations": "string",
"beta1": "string",
"beta2": "string",
"distributed": "string",
"earlyStopping": "string",
"earlyStoppingDelay": "string",
"earlyStoppingPatience": "string",
"enableOnnxNormalization": "string",
"evaluationFrequency": "string",
"gradientAccumulationStep": "string",
"layersToFreeze": "string",
"learningRate": "string",
"learningRateScheduler": "string",
"modelName": "string",
"momentum": "string",
"nesterov": "string",
"numberOfEpochs": "string",
"numberOfWorkers": "string",
"optimizer": "string",
"randomSeed": "string",
"stepLRGamma": "string",
"stepLRStepSize": "string",
"trainingBatchSize": "string",
"trainingCropSize": "string",
"validationBatchSize": "string",
"validationCropSize": "string",
"validationResizeSize": "string",
"warmupCosineLRCycles": "string",
"warmupCosineLRWarmupEpochs": "string",
"weightDecay": "string",
"weightedLoss": "string"
}
],
"sweepSettings": {
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"samplingAlgorithm": "string"
},
"taskType": "ImageClassificationMultilabel",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int"
}
For ImageInstanceSegmentation, use:
{
"limitSettings": {
"maxConcurrentTrials": "int",
"maxTrials": "int",
"timeout": "string"
},
"modelSettings": {
"advancedSettings": "string",
"amsGradient": "bool",
"augmentations": "string",
"beta1": "int",
"beta2": "int",
"boxDetectionsPerImage": "int",
"boxScoreThreshold": "int",
"checkpointFrequency": "int",
"checkpointModel": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"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": "string",
"searchSpace": [
{
"amsGradient": "string",
"augmentations": "string",
"beta1": "string",
"beta2": "string",
"boxDetectionsPerImage": "string",
"boxScoreThreshold": "string",
"distributed": "string",
"earlyStopping": "string",
"earlyStoppingDelay": "string",
"earlyStoppingPatience": "string",
"enableOnnxNormalization": "string",
"evaluationFrequency": "string",
"gradientAccumulationStep": "string",
"imageSize": "string",
"layersToFreeze": "string",
"learningRate": "string",
"learningRateScheduler": "string",
"maxSize": "string",
"minSize": "string",
"modelName": "string",
"modelSize": "string",
"momentum": "string",
"multiScale": "string",
"nesterov": "string",
"nmsIouThreshold": "string",
"numberOfEpochs": "string",
"numberOfWorkers": "string",
"optimizer": "string",
"randomSeed": "string",
"stepLRGamma": "string",
"stepLRStepSize": "string",
"tileGridSize": "string",
"tileOverlapRatio": "string",
"tilePredictionsNmsThreshold": "string",
"trainingBatchSize": "string",
"validationBatchSize": "string",
"validationIouThreshold": "string",
"validationMetricType": "string",
"warmupCosineLRCycles": "string",
"warmupCosineLRWarmupEpochs": "string",
"weightDecay": "string"
}
],
"sweepSettings": {
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"samplingAlgorithm": "string"
},
"taskType": "ImageInstanceSegmentation",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int"
}
For ImageObjectDetection, use:
{
"limitSettings": {
"maxConcurrentTrials": "int",
"maxTrials": "int",
"timeout": "string"
},
"modelSettings": {
"advancedSettings": "string",
"amsGradient": "bool",
"augmentations": "string",
"beta1": "int",
"beta2": "int",
"boxDetectionsPerImage": "int",
"boxScoreThreshold": "int",
"checkpointFrequency": "int",
"checkpointModel": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"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": "string",
"searchSpace": [
{
"amsGradient": "string",
"augmentations": "string",
"beta1": "string",
"beta2": "string",
"boxDetectionsPerImage": "string",
"boxScoreThreshold": "string",
"distributed": "string",
"earlyStopping": "string",
"earlyStoppingDelay": "string",
"earlyStoppingPatience": "string",
"enableOnnxNormalization": "string",
"evaluationFrequency": "string",
"gradientAccumulationStep": "string",
"imageSize": "string",
"layersToFreeze": "string",
"learningRate": "string",
"learningRateScheduler": "string",
"maxSize": "string",
"minSize": "string",
"modelName": "string",
"modelSize": "string",
"momentum": "string",
"multiScale": "string",
"nesterov": "string",
"nmsIouThreshold": "string",
"numberOfEpochs": "string",
"numberOfWorkers": "string",
"optimizer": "string",
"randomSeed": "string",
"stepLRGamma": "string",
"stepLRStepSize": "string",
"tileGridSize": "string",
"tileOverlapRatio": "string",
"tilePredictionsNmsThreshold": "string",
"trainingBatchSize": "string",
"validationBatchSize": "string",
"validationIouThreshold": "string",
"validationMetricType": "string",
"warmupCosineLRCycles": "string",
"warmupCosineLRWarmupEpochs": "string",
"weightDecay": "string"
}
],
"sweepSettings": {
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"samplingAlgorithm": "string"
},
"taskType": "ImageObjectDetection",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int"
}
For Regression, use:
{
"cvSplitColumnNames": [ "string" ],
"featurizationSettings": {
"blockedTransformers": [ "string" ],
"columnNameAndTypes": {
"{customized property}": "string"
},
"datasetLanguage": "string",
"enableDnnFeaturization": "bool",
"mode": "string",
"transformerParams": {
"{customized property}": [
{
"fields": [ "string" ],
"parameters": {}
}
]
}
},
"fixedParameters": {
"booster": "string",
"boostingType": "string",
"growPolicy": "string",
"learningRate": "int",
"maxBin": "int",
"maxDepth": "int",
"maxLeaves": "int",
"minDataInLeaf": "int",
"minSplitGain": "int",
"modelName": "string",
"nEstimators": "int",
"numLeaves": "int",
"preprocessorName": "string",
"regAlpha": "int",
"regLambda": "int",
"subsample": "int",
"subsampleFreq": "int",
"treeMethod": "string",
"withMean": "bool",
"withStd": "bool"
},
"limitSettings": {
"enableEarlyTermination": "bool",
"exitScore": "int",
"maxConcurrentTrials": "int",
"maxCoresPerTrial": "int",
"maxNodes": "int",
"maxTrials": "int",
"sweepConcurrentTrials": "int",
"sweepTrials": "int",
"timeout": "string",
"trialTimeout": "string"
},
"nCrossValidations": {
"mode": "string"
// For remaining properties, see NCrossValidations objects
},
"primaryMetric": "string",
"searchSpace": [
{
"booster": "string",
"boostingType": "string",
"growPolicy": "string",
"learningRate": "string",
"maxBin": "string",
"maxDepth": "string",
"maxLeaves": "string",
"minDataInLeaf": "string",
"minSplitGain": "string",
"modelName": "string",
"nEstimators": "string",
"numLeaves": "string",
"preprocessorName": "string",
"regAlpha": "string",
"regLambda": "string",
"subsample": "string",
"subsampleFreq": "string",
"treeMethod": "string",
"withMean": "string",
"withStd": "string"
}
],
"sweepSettings": {
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"samplingAlgorithm": "string"
},
"taskType": "Regression",
"testData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"testDataSize": "int",
"trainingSettings": {
"allowedTrainingAlgorithms": [ "string" ],
"blockedTrainingAlgorithms": [ "string" ],
"enableDnnTraining": "bool",
"enableModelExplainability": "bool",
"enableOnnxCompatibleModels": "bool",
"enableStackEnsemble": "bool",
"enableVoteEnsemble": "bool",
"ensembleModelDownloadTimeout": "string",
"stackEnsembleSettings": {
"stackMetaLearnerKWargs": {},
"stackMetaLearnerTrainPercentage": "int",
"stackMetaLearnerType": "string"
},
"trainingMode": "string"
},
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int",
"weightColumnName": "string"
}
For TextClassification, use:
{
"featurizationSettings": {
"datasetLanguage": "string"
},
"fixedParameters": {
"gradientAccumulationSteps": "int",
"learningRate": "int",
"learningRateScheduler": "string",
"modelName": "string",
"numberOfEpochs": "int",
"trainingBatchSize": "int",
"validationBatchSize": "int",
"warmupRatio": "int",
"weightDecay": "int"
},
"limitSettings": {
"maxConcurrentTrials": "int",
"maxNodes": "int",
"maxTrials": "int",
"timeout": "string",
"trialTimeout": "string"
},
"primaryMetric": "string",
"searchSpace": [
{
"gradientAccumulationSteps": "string",
"learningRate": "string",
"learningRateScheduler": "string",
"modelName": "string",
"numberOfEpochs": "string",
"trainingBatchSize": "string",
"validationBatchSize": "string",
"warmupRatio": "string",
"weightDecay": "string"
}
],
"sweepSettings": {
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"samplingAlgorithm": "string"
},
"taskType": "TextClassification",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
}
}
For TextClassificationMultilabel, use:
{
"featurizationSettings": {
"datasetLanguage": "string"
},
"fixedParameters": {
"gradientAccumulationSteps": "int",
"learningRate": "int",
"learningRateScheduler": "string",
"modelName": "string",
"numberOfEpochs": "int",
"trainingBatchSize": "int",
"validationBatchSize": "int",
"warmupRatio": "int",
"weightDecay": "int"
},
"limitSettings": {
"maxConcurrentTrials": "int",
"maxNodes": "int",
"maxTrials": "int",
"timeout": "string",
"trialTimeout": "string"
},
"searchSpace": [
{
"gradientAccumulationSteps": "string",
"learningRate": "string",
"learningRateScheduler": "string",
"modelName": "string",
"numberOfEpochs": "string",
"trainingBatchSize": "string",
"validationBatchSize": "string",
"warmupRatio": "string",
"weightDecay": "string"
}
],
"sweepSettings": {
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"samplingAlgorithm": "string"
},
"taskType": "TextClassificationMultilabel",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
}
}
For TextNER, use:
{
"featurizationSettings": {
"datasetLanguage": "string"
},
"fixedParameters": {
"gradientAccumulationSteps": "int",
"learningRate": "int",
"learningRateScheduler": "string",
"modelName": "string",
"numberOfEpochs": "int",
"trainingBatchSize": "int",
"validationBatchSize": "int",
"warmupRatio": "int",
"weightDecay": "int"
},
"limitSettings": {
"maxConcurrentTrials": "int",
"maxNodes": "int",
"maxTrials": "int",
"timeout": "string",
"trialTimeout": "string"
},
"searchSpace": [
{
"gradientAccumulationSteps": "string",
"learningRate": "string",
"learningRateScheduler": "string",
"modelName": "string",
"numberOfEpochs": "string",
"trainingBatchSize": "string",
"validationBatchSize": "string",
"warmupRatio": "string",
"weightDecay": "string"
}
],
"sweepSettings": {
"earlyTermination": {
"delayEvaluation": "int",
"evaluationInterval": "int",
"policyType": "string"
// For remaining properties, see EarlyTerminationPolicy objects
},
"samplingAlgorithm": "string"
},
"taskType": "TextNER",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
}
}
ForecastHorizon 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"
}
NCrossValidations objects
Set the mode property to specify the type of object.
For Auto, use:
{
"mode": "Auto"
}
For Custom, use:
{
"mode": "Custom",
"value": "int"
}
DistributionConfiguration objects
Set the distributionType property to specify the type of object.
For Mpi, use:
{
"distributionType": "Mpi",
"processCountPerInstance": "int"
}
For PyTorch, use:
{
"distributionType": "PyTorch",
"processCountPerInstance": "int"
}
For TensorFlow, use:
{
"distributionType": "TensorFlow",
"parameterServerCount": "int",
"workerCount": "int"
}
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",
"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": {
"{customized property}": "string"
},
"jobType": "string"
// For remaining properties, see JobBaseProperties objects
}
}
For InvokeBatchEndpoint, use:
{
"actionType": "InvokeBatchEndpoint",
"endpointInvocationDefinition": {}
}
MLAssistConfiguration objects
Set the mlAssist property to specify the type of object.
For Disabled, use:
{
"mlAssist": "Disabled"
}
For Enabled, use:
{
"inferencingComputeBinding": "string",
"mlAssist": "Enabled",
"trainingComputeBinding": "string"
}
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"
}
Property values
AllNodes
| Name | Description | Value |
|---|---|---|
| nodesValueType | [Required] Type of the Nodes value | 'All' (required) |
AmlToken
| Name | Description | Value |
|---|---|---|
| identityType | [Required] Specifies the type of identity framework. | 'AMLToken' (required) |
AutoForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set forecast horizon value selection mode. | 'Auto' (required) |
AutologgerSettings
| Name | Description | Value |
|---|---|---|
| mlflowAutologger | [Required] Indicates whether mlflow autologger is enabled. | 'Disabled' 'Enabled' (required) |
AutoMLJob
| Name | Description | Value |
|---|---|---|
| environmentId | The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job. |
string |
| environmentVariables | Environment variables included in the job. | AutoMLJobEnvironmentVariables |
| jobType | [Required] Specifies the type of job. | 'AutoML' (required) |
| outputs | Mapping of output data bindings used in the job. | AutoMLJobOutputs |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
| taskDetails | [Required] This represents scenario which can be one of Tables/NLP/Image | AutoMLVertical (required) |
AutoMLJobEnvironmentVariables
| Name | Description | Value |
|---|
AutoMLJobOutputs
| Name | Description | Value |
|---|
AutoMLVertical
| Name | Description | Value |
|---|---|---|
| logVerbosity | Log verbosity for the job. | 'Critical' 'Debug' 'Error' 'Info' 'NotSet' 'Warning' |
| targetColumnName | Target column name: This is prediction values column. Also known as label column name in context of classification tasks. |
string |
| taskType | Set to 'Classification' for type Classification. Set to 'Forecasting' for type Forecasting. Set to 'ImageClassification' for type ImageClassification. Set to 'ImageClassificationMultilabel' for type ImageClassificationMultilabel. Set to 'ImageInstanceSegmentation' for type ImageInstanceSegmentation. Set to 'ImageObjectDetection' for type ImageObjectDetection. Set to 'Regression' for type Regression. Set to 'TextClassification' for type TextClassification. Set to 'TextClassificationMultilabel' for type TextClassificationMultilabel. Set to 'TextNER' for type TextNer. | 'Classification' 'Forecasting' 'ImageClassification' 'ImageClassificationMultilabel' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'Regression' 'TextClassification' 'TextClassificationMultilabel' 'TextNER' (required) |
| trainingData | [Required] Training data input. | MLTableJobInput (required) |
AutoNCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | [Required] Mode for determining N-Cross validations. | 'Auto' (required) |
AutoSeasonality
| Name | Description | Value |
|---|---|---|
| mode | [Required] Seasonality mode. | 'Auto' (required) |
AutoTargetLags
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set target lags mode - Auto/Custom | 'Auto' (required) |
AutoTargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | [Required] TargetRollingWindowSiz detection mode. | 'Auto' (required) |
BanditPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'Bandit' (required) |
| slackAmount | Absolute distance allowed from the best performing run. | int |
| slackFactor | Ratio of the allowed distance from the best performing run. | int |
BayesianSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Bayesian' (required) |
Classification
| Name | Description | Value |
|---|---|---|
| cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
| featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | TableFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
| nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
| positiveLabel | Positive label for binary metrics calculation. | string |
| primaryMetric | Primary metric for the task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | TableParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | TableSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'Classification' (required) |
| testData | Test data input. | MLTableJobInput |
| testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| trainingSettings | Inputs for training phase for an AutoML Job. | ClassificationTrainingSettings |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
ClassificationTrainingSettings
| Name | Description | Value |
|---|---|---|
| allowedTrainingAlgorithms | Allowed models for classification task. | String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier' |
| blockedTrainingAlgorithms | Blocked models for classification task. | String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier' |
| enableDnnTraining | Enable recommendation of DNN models. | bool |
| enableModelExplainability | Flag to turn on explainability on best model. | bool |
| enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
| enableStackEnsemble | Enable stack ensemble run. | bool |
| enableVoteEnsemble | Enable voting ensemble run. | bool |
| ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
| stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
| trainingMode | TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen. |
'Auto' 'Distributed' 'NonDistributed' |
ColumnTransformer
| Name | Description | Value |
|---|---|---|
| fields | Fields to apply transformer logic on. | string[] |
| parameters | Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format. |
any |
CommandJob
| Name | Description | Value |
|---|---|---|
| autologgerSettings | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | AutologgerSettings |
| codeId | ARM resource ID of the code asset. | string |
| command | [Required] The command to execute on startup of the job. eg. "python train.py" | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
| environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| environmentVariables | Environment variables included in the job. | CommandJobEnvironmentVariables |
| inputs | Mapping of input data bindings used in the job. | CommandJobInputs |
| jobType | [Required] Specifies the type of job. | 'Command' (required) |
| limits | Command Job limit. | CommandJobLimits |
| outputs | Mapping of output data bindings used in the job. | CommandJobOutputs |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
CommandJobEnvironmentVariables
| Name | Description | Value |
|---|
CommandJobInputs
| Name | Description | Value |
|---|
CommandJobLimits
| Name | Description | Value |
|---|---|---|
| jobLimitsType | [Required] JobLimit type. | 'Command' 'Sweep' (required) |
| timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
CommandJobOutputs
| Name | Description | Value |
|---|
CronTrigger
| Name | Description | Value |
|---|---|---|
| expression | [Required] Specifies cron expression of schedule. The expression should follow NCronTab format. |
string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| triggerType | [Required] | 'Cron' (required) |
CustomForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set forecast horizon value selection mode. | 'Custom' (required) |
| value | [Required] Forecast horizon value. | int (required) |
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 Constraints: Pattern = [a-zA-Z0-9_] (required) |
CustomModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'custom_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
CustomNCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | [Required] Mode for determining N-Cross validations. | 'Custom' (required) |
| value | [Required] N-Cross validations value. | int (required) |
CustomSeasonality
| Name | Description | Value |
|---|---|---|
| mode | [Required] Seasonality mode. | 'Custom' (required) |
| value | [Required] Seasonality value. | int (required) |
CustomTargetLags
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set target lags mode - Auto/Custom | 'Custom' (required) |
| values | [Required] Set target lags values. | int[] (required) |
CustomTargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | [Required] TargetRollingWindowSiz detection mode. | 'Custom' (required) |
| value | [Required] TargetRollingWindowSize value. | int (required) |
DistributionConfiguration
| Name | Description | Value |
|---|---|---|
| distributionType | Set to 'Mpi' for type Mpi. Set to 'PyTorch' for type PyTorch. Set to 'TensorFlow' for type TensorFlow. | 'Mpi' 'PyTorch' 'TensorFlow' (required) |
EarlyTerminationPolicy
| Name | Description | Value |
|---|---|---|
| delayEvaluation | Number of intervals by which to delay the first evaluation. | int |
| evaluationInterval | Interval (number of runs) between policy evaluations. | int |
| policyType | Set to 'Bandit' for type BanditPolicy. Set to 'MedianStopping' for type MedianStoppingPolicy. Set to 'TruncationSelection' for type TruncationSelectionPolicy. | 'Bandit' 'MedianStopping' 'TruncationSelection' (required) |
EndpointScheduleAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'InvokeBatchEndpoint' (required) |
| endpointInvocationDefinition | [Required] Defines Schedule action definition details. <see href="TBD" /> |
any (required) |
ForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoForecastHorizon. Set to 'Custom' for type CustomForecastHorizon. | 'Auto' 'Custom' (required) |
Forecasting
| Name | Description | Value |
|---|---|---|
| cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
| featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | TableFixedParameters |
| forecastingSettings | Forecasting task specific inputs. | ForecastingSettings |
| limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
| nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
| primaryMetric | Primary metric for forecasting task. | 'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | TableParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | TableSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'Forecasting' (required) |
| testData | Test data input. | MLTableJobInput |
| testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| trainingSettings | Inputs for training phase for an AutoML Job. | ForecastingTrainingSettings |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
ForecastingSettings
| Name | Description | Value |
|---|---|---|
| countryOrRegionForHolidays | Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'. |
string |
| cvStepSize | Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will bethree days apart. |
int |
| featureLags | Flag for generating lags for the numeric features with 'auto' or null. | 'Auto' 'None' |
| forecastHorizon | The desired maximum forecast horizon in units of time-series frequency. | ForecastHorizon |
| frequency | When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default. | string |
| seasonality | Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred. |
Seasonality |
| shortSeriesHandlingConfig | The parameter defining how if AutoML should handle short time series. | 'Auto' 'Drop' 'None' 'Pad' |
| targetAggregateFunction | The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean". |
'Max' 'Mean' 'Min' 'None' 'Sum' |
| targetLags | The number of past periods to lag from the target column. | TargetLags |
| targetRollingWindowSize | The number of past periods used to create a rolling window average of the target column. | TargetRollingWindowSize |
| timeColumnName | The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. | string |
| timeSeriesIdColumnNames | The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting. |
string[] |
| useStl | Configure STL Decomposition of the time-series target column. | 'None' 'Season' 'SeasonTrend' |
ForecastingTrainingSettings
| Name | Description | Value |
|---|---|---|
| allowedTrainingAlgorithms | Allowed models for forecasting task. | String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SeasonalAverage' 'SeasonalNaive' 'SGD' 'TCNForecaster' 'XGBoostRegressor' |
| blockedTrainingAlgorithms | Blocked models for forecasting task. | String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SeasonalAverage' 'SeasonalNaive' 'SGD' 'TCNForecaster' 'XGBoostRegressor' |
| enableDnnTraining | Enable recommendation of DNN models. | bool |
| enableModelExplainability | Flag to turn on explainability on best model. | bool |
| enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
| enableStackEnsemble | Enable stack ensemble run. | bool |
| enableVoteEnsemble | Enable voting ensemble run. | bool |
| ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
| stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
| trainingMode | TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen. |
'Auto' 'Distributed' 'NonDistributed' |
GridSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Grid' (required) |
IdentityConfiguration
| Name | Description | Value |
|---|---|---|
| identityType | Set to 'AMLToken' for type AmlToken. Set to 'Managed' for type ManagedIdentity. Set to 'UserIdentity' for type UserIdentity. | 'AMLToken' 'Managed' 'UserIdentity' (required) |
ImageClassification
| Name | Description | Value |
|---|---|---|
| limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
| modelSettings | Settings used for training the model. | ImageModelSettingsClassification |
| primaryMetric | Primary metric to optimize for this task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsClassification[] |
| sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'ImageClassification' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageClassificationMultilabel
| Name | Description | Value |
|---|---|---|
| limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
| modelSettings | Settings used for training the model. | ImageModelSettingsClassification |
| primaryMetric | Primary metric to optimize for this task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'IOU' 'NormMacroRecall' 'PrecisionScoreWeighted' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsClassification[] |
| sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'ImageClassificationMultilabel' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageInstanceSegmentation
| Name | Description | Value |
|---|---|---|
| limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
| modelSettings | Settings used for training the model. | ImageModelSettingsObjectDetection |
| primaryMetric | Primary metric to optimize for this task. | 'MeanAveragePrecision' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsObjectDetection[] |
| sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'ImageInstanceSegmentation' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageLimitSettings
| Name | Description | Value |
|---|---|---|
| maxConcurrentTrials | Maximum number of concurrent AutoML iterations. | int |
| maxTrials | Maximum number of AutoML iterations. | int |
| timeout | AutoML job timeout. | string |
ImageModelDistributionSettingsClassification
| Name | Description | Value |
|---|---|---|
| amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | string |
| augmentations | Settings for using Augmentations. | string |
| beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
| beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
| distributed | Whether to use distributer training. | string |
| earlyStopping | Enable early stopping logic during training. | string |
| earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
string |
| earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
string |
| enableOnnxNormalization | Enable normalization when exporting ONNX model. | string |
| evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | string |
| gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
string |
| layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| learningRate | Initial learning rate. Must be a float in the range [0, 1]. | string |
| learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | string |
| modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | string |
| nesterov | Enable nesterov when optimizer is 'sgd'. | string |
| numberOfEpochs | Number of training epochs. Must be a positive integer. | string |
| numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | string |
| optimizer | Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. | string |
| randomSeed | Random seed to be used when using deterministic training. | string |
| stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | string |
| stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | string |
| trainingBatchSize | Training batch size. Must be a positive integer. | string |
| trainingCropSize | Image crop size that is input to the neural network for the training dataset. Must be a positive integer. | string |
| validationBatchSize | Validation batch size. Must be a positive integer. | string |
| validationCropSize | Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. | string |
| validationResizeSize | Image size to which to resize before cropping for validation dataset. Must be a positive integer. | string |
| warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | string |
| warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | string |
| weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | string |
| weightedLoss | Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2. |
string |
ImageModelDistributionSettingsObjectDetection
| Name | Description | Value |
|---|---|---|
| amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | string |
| augmentations | Settings for using Augmentations. | string |
| beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
| beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
| boxDetectionsPerImage | Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| boxScoreThreshold | During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
string |
| distributed | Whether to use distributer training. | string |
| earlyStopping | Enable early stopping logic during training. | string |
| earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
string |
| earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
string |
| enableOnnxNormalization | Enable normalization when exporting ONNX model. | string |
| evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | string |
| gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
string |
| imageSize | Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
| layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| learningRate | Initial learning rate. Must be a float in the range [0, 1]. | string |
| learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | string |
| maxSize | Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| minSize | Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| modelSize | Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
| momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | string |
| multiScale | Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
| nesterov | Enable nesterov when optimizer is 'sgd'. | string |
| nmsIouThreshold | IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1]. | string |
| numberOfEpochs | Number of training epochs. Must be a positive integer. | string |
| numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | string |
| optimizer | Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. | string |
| randomSeed | Random seed to be used when using deterministic training. | string |
| stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | string |
| stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | string |
| tileGridSize | The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| tileOverlapRatio | Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| tilePredictionsNmsThreshold | The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression |
string |
| trainingBatchSize | Training batch size. Must be a positive integer. | string |
| validationBatchSize | Validation batch size. Must be a positive integer. | string |
| validationIouThreshold | IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. | string |
| validationMetricType | Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'. | string |
| warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | string |
| warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | string |
| weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | string |
ImageModelSettingsClassification
| Name | Description | Value |
|---|---|---|
| advancedSettings | Settings for advanced scenarios. | string |
| amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | bool |
| augmentations | Settings for using Augmentations. | string |
| beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
| beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
| checkpointFrequency | Frequency to store model checkpoints. Must be a positive integer. | int |
| checkpointModel | The pretrained checkpoint model for incremental training. | MLFlowModelJobInput |
| checkpointRunId | The id of a previous run that has a pretrained checkpoint for incremental training. | string |
| distributed | Whether to use distributed training. | bool |
| earlyStopping | Enable early stopping logic during training. | bool |
| earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
int |
| earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
int |
| enableOnnxNormalization | Enable normalization when exporting ONNX model. | bool |
| evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | int |
| gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
int |
| layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
int |
| learningRate | Initial learning rate. Must be a float in the range [0, 1]. | int |
| learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | 'None' 'Step' 'WarmupCosine' |
| modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | int |
| nesterov | Enable nesterov when optimizer is 'sgd'. | bool |
| numberOfEpochs | Number of training epochs. Must be a positive integer. | int |
| numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | int |
| optimizer | Type of optimizer. | 'Adam' 'Adamw' 'None' 'Sgd' |
| randomSeed | Random seed to be used when using deterministic training. | int |
| stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | int |
| stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | int |
| trainingBatchSize | Training batch size. Must be a positive integer. | int |
| trainingCropSize | Image crop size that is input to the neural network for the training dataset. Must be a positive integer. | int |
| validationBatchSize | Validation batch size. Must be a positive integer. | int |
| validationCropSize | Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. | int |
| validationResizeSize | Image size to which to resize before cropping for validation dataset. Must be a positive integer. | int |
| warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | int |
| warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | int |
| weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | int |
| weightedLoss | Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2. |
int |
ImageModelSettingsObjectDetection
| Name | Description | Value |
|---|---|---|
| advancedSettings | Settings for advanced scenarios. | string |
| amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | bool |
| augmentations | Settings for using Augmentations. | string |
| beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
| beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
| boxDetectionsPerImage | Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| boxScoreThreshold | During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
int |
| checkpointFrequency | Frequency to store model checkpoints. Must be a positive integer. | int |
| checkpointModel | The pretrained checkpoint model for incremental training. | MLFlowModelJobInput |
| checkpointRunId | The id of a previous run that has a pretrained checkpoint for incremental training. | string |
| distributed | Whether to use distributed training. | bool |
| earlyStopping | Enable early stopping logic during training. | bool |
| earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
int |
| earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
int |
| enableOnnxNormalization | Enable normalization when exporting ONNX model. | bool |
| evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | int |
| gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
int |
| imageSize | Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
int |
| layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
int |
| learningRate | Initial learning rate. Must be a float in the range [0, 1]. | int |
| learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | 'None' 'Step' 'WarmupCosine' |
| maxSize | Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| minSize | Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| modelSize | Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
'ExtraLarge' 'Large' 'Medium' 'None' 'Small' |
| momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | int |
| multiScale | Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm. |
bool |
| nesterov | Enable nesterov when optimizer is 'sgd'. | bool |
| nmsIouThreshold | IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1]. | int |
| numberOfEpochs | Number of training epochs. Must be a positive integer. | int |
| numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | int |
| optimizer | Type of optimizer. | 'Adam' 'Adamw' 'None' 'Sgd' |
| randomSeed | Random seed to be used when using deterministic training. | int |
| stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | int |
| stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | int |
| tileGridSize | The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| tileOverlapRatio | Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| tilePredictionsNmsThreshold | The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| trainingBatchSize | Training batch size. Must be a positive integer. | int |
| validationBatchSize | Validation batch size. Must be a positive integer. | int |
| validationIouThreshold | IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. | int |
| validationMetricType | Metric computation method to use for validation metrics. | 'Coco' 'CocoVoc' 'None' 'Voc' |
| warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | int |
| warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | int |
| weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | int |
ImageObjectDetection
| Name | Description | Value |
|---|---|---|
| limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
| modelSettings | Settings used for training the model. | ImageModelSettingsObjectDetection |
| primaryMetric | Primary metric to optimize for this task. | 'MeanAveragePrecision' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsObjectDetection[] |
| sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'ImageObjectDetection' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageSweepSettings
| Name | Description | Value |
|---|---|---|
| earlyTermination | Type of early termination policy. | EarlyTerminationPolicy |
| samplingAlgorithm | [Required] Type of the hyperparameter sampling algorithms. | 'Bayesian' 'Grid' 'Random' (required) |
JobBaseProperties
| Name | Description | Value |
|---|---|---|
| componentId | ARM resource ID of the component resource. | string |
| computeId | ARM resource ID of the compute resource. | string |
| description | The asset description text. | string |
| displayName | Display name of job. | string |
| experimentName | The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment. | string |
| identity | Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null. |
IdentityConfiguration |
| isArchived | Is the asset archived? | bool |
| jobType | Set to 'AutoML' for type AutoMLJob. Set to 'Command' for type CommandJob. Set to 'Labeling' for type LabelingJobProperties. Set to 'Pipeline' for type PipelineJob. Set to 'Spark' for type SparkJob. Set to 'Sweep' for type SweepJob. | 'AutoML' 'Command' 'Labeling' 'Pipeline' 'Spark' 'Sweep' (required) |
| 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. | ResourceBaseTags |
JobBaseServices
| Name | Description | Value |
|---|
JobInput
| Name | Description | Value |
|---|---|---|
| description | Description for the input. | string |
| jobInputType | Set to 'custom_model' for type CustomModelJobInput. Set to 'literal' for type LiteralJobInput. Set to 'mlflow_model' for type MLFlowModelJobInput. Set to 'mltable' for type MLTableJobInput. Set to 'triton_model' for type TritonModelJobInput. Set to 'uri_file' for type UriFileJobInput. Set to 'uri_folder' for type UriFolderJobInput. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
JobOutput
| Name | Description | Value |
|---|---|---|
| description | Description for the output. | string |
| jobOutputType | Set to 'custom_model' for type CustomModelJobOutput. Set to 'mlflow_model' for type MLFlowModelJobOutput. Set to 'mltable' for type MLTableJobOutput. Set to 'triton_model' for type TritonModelJobOutput. Set to 'uri_file' for type UriFileJobOutput. Set to 'uri_folder' for type UriFolderJobOutput. | 'custom_model' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
JobResourceConfiguration
| Name | Description | Value |
|---|---|---|
| dockerArgs | Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types. | string |
| instanceCount | Optional number of instances or nodes used by the compute target. | int |
| instanceType | Optional type of VM used as supported by the compute target. | string |
| properties | Additional properties bag. | ResourceConfigurationProperties |
| shmSize | Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes). | string Constraints: Pattern = \d+[bBkKmMgG] |
JobScheduleAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'CreateJob' (required) |
| jobDefinition | [Required] Defines Schedule action definition details. | JobBaseProperties (required) |
JobService
| Name | Description | Value |
|---|---|---|
| endpoint | Url for endpoint. | string |
| jobServiceType | Endpoint type. | string |
| nodes | Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node. |
Nodes |
| port | Port for endpoint set by user. | int |
| properties | Additional properties to set on the endpoint. | JobServiceProperties |
JobServiceProperties
| Name | Description | Value |
|---|
LabelCategory
| Name | Description | Value |
|---|---|---|
| classes | Dictionary of label classes in this category. | LabelCategoryClasses |
| displayName | Display name of the label category. | string |
| multiSelect | Indicates whether it is allowed to select multiple classes in this category. | 'Disabled' 'Enabled' |
LabelCategoryClasses
| Name | Description | Value |
|---|
LabelClass
| Name | Description | Value |
|---|---|---|
| displayName | Display name of the label class. | string |
| subclasses | Dictionary of subclasses of the label class. | LabelClassSubclasses |
LabelClassSubclasses
| Name | Description | Value |
|---|
LabelingDataConfiguration
| Name | Description | Value |
|---|---|---|
| dataId | Resource Id of the data asset to perform labeling. | string |
| incrementalDataRefresh | Indicates whether to enable incremental data refresh. | 'Disabled' 'Enabled' |
LabelingJobImageProperties
| Name | Description | Value |
|---|---|---|
| annotationType | Annotation type of image labeling job. | 'BoundingBox' 'Classification' 'InstanceSegmentation' |
| mediaType | [Required] Media type of the job. | 'Image' (required) |
LabelingJobInstructions
| Name | Description | Value |
|---|---|---|
| uri | The link to a page with detailed labeling instructions for labelers. | string |
LabelingJobLabelCategories
| Name | Description | Value |
|---|
LabelingJobMediaProperties
| Name | Description | Value |
|---|---|---|
| mediaType | Set to 'Image' for type LabelingJobImageProperties. Set to 'Text' for type LabelingJobTextProperties. | 'Image' 'Text' (required) |
LabelingJobProperties
| Name | Description | Value |
|---|---|---|
| dataConfiguration | Configuration of data used in the job. | LabelingDataConfiguration |
| jobInstructions | Labeling instructions of the job. | LabelingJobInstructions |
| jobType | [Required] Specifies the type of job. | 'Labeling' (required) |
| labelCategories | Label categories of the job. | LabelingJobLabelCategories |
| labelingJobMediaProperties | Media type specific properties in the job. | LabelingJobMediaProperties |
| mlAssistConfiguration | Configuration of MLAssist feature in the job. | MLAssistConfiguration |
LabelingJobTextProperties
| Name | Description | Value |
|---|---|---|
| annotationType | Annotation type of text labeling job. | 'Classification' 'NamedEntityRecognition' |
| mediaType | [Required] Media type of the job. | 'Text' (required) |
LiteralJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'literal' (required) |
| value | [Required] Literal value for the input. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
ManagedIdentity
| Name | Description | Value |
|---|---|---|
| clientId | Specifies a user-assigned identity by client ID. For system-assigned, do not set this field. | string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$ |
| identityType | [Required] Specifies the type of identity framework. | 'Managed' (required) |
| objectId | Specifies a user-assigned identity by object ID. For system-assigned, do not set this field. | string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$ |
| resourceId | Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field. | string |
MedianStoppingPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'MedianStopping' (required) |
Microsoft.MachineLearningServices/workspaces/schedules
| Name | Description | Value |
|---|---|---|
| apiVersion | The api version | '2022-12-01-preview' |
| name | The resource name | string Constraints: Pattern = ^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$ (required) |
| properties | [Required] Additional attributes of the entity. | ScheduleProperties (required) |
| type | The resource type | 'Microsoft.MachineLearningServices/workspaces/schedules' |
MLAssistConfiguration
| Name | Description | Value |
|---|---|---|
| mlAssist | Set to 'Disabled' for type MLAssistConfigurationDisabled. Set to 'Enabled' for type MLAssistConfigurationEnabled. | 'Disabled' 'Enabled' (required) |
MLAssistConfigurationDisabled
| Name | Description | Value |
|---|---|---|
| mlAssist | [Required] Indicates whether MLAssist feature is enabled. | 'Disabled' (required) |
MLAssistConfigurationEnabled
| Name | Description | Value |
|---|---|---|
| inferencingComputeBinding | [Required] AML compute binding used in inferencing. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| mlAssist | [Required] Indicates whether MLAssist feature is enabled. | 'Enabled' (required) |
| trainingComputeBinding | [Required] AML compute binding used in training. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'mlflow_model' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobInput
| Name | Description | Value |
|---|---|---|
| description | Description for the input. | string |
| jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'mlflow_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
MLTableJobInput
| Name | Description | Value |
|---|---|---|
| description | Description for the input. | string |
| jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLTableJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'mltable' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLTableJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'mltable' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
Mpi
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'Mpi' (required) |
| processCountPerInstance | Number of processes per MPI node. | int |
NCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoNCrossValidations. Set to 'Custom' for type CustomNCrossValidations. | 'Auto' 'Custom' (required) |
NlpFixedParameters
| Name | Description | Value |
|---|---|---|
| gradientAccumulationSteps | Number of steps to accumulate gradients over before running a backward pass. | int |
| learningRate | The learning rate for the training procedure. | int |
| learningRateScheduler | The type of learning rate schedule to use during the training procedure. | 'Constant' 'ConstantWithWarmup' 'Cosine' 'CosineWithRestarts' 'Linear' 'None' 'Polynomial' |
| modelName | The name of the model to train. | string |
| numberOfEpochs | Number of training epochs. | int |
| trainingBatchSize | The batch size for the training procedure. | int |
| validationBatchSize | The batch size to be used during evaluation. | int |
| warmupRatio | The warmup ratio, used alongside LrSchedulerType. | int |
| weightDecay | The weight decay for the training procedure. | int |
NlpParameterSubspace
| Name | Description | Value |
|---|---|---|
| gradientAccumulationSteps | Number of steps to accumulate gradients over before running a backward pass. | string |
| learningRate | The learning rate for the training procedure. | string |
| learningRateScheduler | The type of learning rate schedule to use during the training procedure. | string |
| modelName | The name of the model to train. | string |
| numberOfEpochs | Number of training epochs. | string |
| trainingBatchSize | The batch size for the training procedure. | string |
| validationBatchSize | The batch size to be used during evaluation. | string |
| warmupRatio | The warmup ratio, used alongside LrSchedulerType. | string |
| weightDecay | The weight decay for the training procedure. | string |
NlpSweepSettings
| Name | Description | Value |
|---|---|---|
| earlyTermination | Type of early termination policy for the sweeping job. | EarlyTerminationPolicy |
| samplingAlgorithm | [Required] Type of sampling algorithm. | 'Bayesian' 'Grid' 'Random' (required) |
NlpVerticalFeaturizationSettings
| Name | Description | Value |
|---|---|---|
| datasetLanguage | Dataset language, useful for the text data. | string |
NlpVerticalLimitSettings
| Name | Description | Value |
|---|---|---|
| maxConcurrentTrials | Maximum Concurrent AutoML iterations. | int |
| maxNodes | Maximum nodes to use for the experiment. | int |
| maxTrials | Number of AutoML iterations. | int |
| timeout | AutoML job timeout. | string |
| trialTimeout | Timeout for individual HD trials. | string |
Nodes
| Name | Description | Value |
|---|---|---|
| nodesValueType | Set to 'All' for type AllNodes. | 'All' (required) |
Objective
| Name | Description | Value |
|---|---|---|
| goal | [Required] Defines supported metric goals for hyperparameter tuning | 'Maximize' 'Minimize' (required) |
| primaryMetric | [Required] Name of the metric to optimize. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
PipelineJob
| Name | Description | Value |
|---|---|---|
| inputs | Inputs for the pipeline job. | PipelineJobInputs |
| jobs | Jobs construct the Pipeline Job. | PipelineJobJobs |
| jobType | [Required] Specifies the type of job. | 'Pipeline' (required) |
| outputs | Outputs for the pipeline job | PipelineJobOutputs |
| settings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | any |
| sourceJobId | ARM resource ID of source job. | string |
PipelineJobInputs
| Name | Description | Value |
|---|
PipelineJobJobs
| Name | Description | Value |
|---|
PipelineJobOutputs
| Name | Description | Value |
|---|
PyTorch
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'PyTorch' (required) |
| processCountPerInstance | Number of processes per node. | int |
RandomSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| logbase | An optional positive number or e in string format to be used as base for log based random sampling | string |
| rule | The specific type of random algorithm | 'Random' 'Sobol' |
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Random' (required) |
| seed | An optional integer to use as the seed for random number generation | int |
RecurrenceSchedule
| Name | Description | Value |
|---|---|---|
| hours | [Required] List of hours for the schedule. | int[] (required) |
| minutes | [Required] List of minutes for the schedule. | int[] (required) |
| monthDays | List of month days for the schedule | int[] |
| weekDays | List of days for the schedule. | String array containing any of: 'Friday' 'Monday' 'Saturday' 'Sunday' 'Thursday' 'Tuesday' 'Wednesday' |
RecurrenceTrigger
| Name | Description | Value |
|---|---|---|
| frequency | [Required] The frequency to trigger schedule. | 'Day' 'Hour' 'Minute' 'Month' 'Week' (required) |
| interval | [Required] Specifies schedule interval in conjunction with frequency | int (required) |
| schedule | The recurrence schedule. | RecurrenceSchedule |
| triggerType | [Required] | 'Recurrence' (required) |
Regression
| Name | Description | Value |
|---|---|---|
| cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
| featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | TableFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
| nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
| primaryMetric | Primary metric for regression task. | 'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | TableParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | TableSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'Regression' (required) |
| testData | Test data input. | MLTableJobInput |
| testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| trainingSettings | Inputs for training phase for an AutoML Job. | RegressionTrainingSettings |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
RegressionTrainingSettings
| Name | Description | Value |
|---|---|---|
| allowedTrainingAlgorithms | Allowed models for regression task. | String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor' |
| blockedTrainingAlgorithms | Blocked models for regression task. | String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor' |
| enableDnnTraining | Enable recommendation of DNN models. | bool |
| enableModelExplainability | Flag to turn on explainability on best model. | bool |
| enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
| enableStackEnsemble | Enable stack ensemble run. | bool |
| enableVoteEnsemble | Enable voting ensemble run. | bool |
| ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
| stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
| trainingMode | TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen. |
'Auto' 'Distributed' 'NonDistributed' |
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceConfigurationProperties
| Name | Description | Value |
|---|
SamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | Set to 'Bayesian' for type BayesianSamplingAlgorithm. Set to 'Grid' for type GridSamplingAlgorithm. Set to 'Random' for type RandomSamplingAlgorithm. | 'Bayesian' 'Grid' 'Random' (required) |
ScheduleActionBase
| Name | Description | Value |
|---|---|---|
| actionType | Set to 'CreateJob' for type JobScheduleAction. Set to 'InvokeBatchEndpoint' for type EndpointScheduleAction. | 'CreateJob' 'InvokeBatchEndpoint' (required) |
ScheduleProperties
| Name | Description | Value |
|---|---|---|
| action | [Required] Specifies the action of the schedule | ScheduleActionBase (required) |
| description | The asset description text. | string |
| displayName | Display name of schedule. | string |
| isEnabled | Is the schedule enabled? | bool |
| properties | The asset property dictionary. | ResourceBaseProperties |
| tags | Tag dictionary. Tags can be added, removed, and updated. | ResourceBaseTags |
| trigger | [Required] Specifies the trigger details | TriggerBase (required) |
Seasonality
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoSeasonality. Set to 'Custom' for type CustomSeasonality. | 'Auto' 'Custom' (required) |
SparkJob
| Name | Description | Value |
|---|---|---|
| archives | Archive files used in the job. | string[] |
| args | Arguments for the job. | string |
| codeId | [Required] ARM resource ID of the code asset. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| conf | Spark configured properties. | SparkJobConf |
| entry | [Required] The entry to execute on startup of the job. | SparkJobEntry (required) |
| environmentId | The ARM resource ID of the Environment specification for the job. | string |
| files | Files used in the job. | string[] |
| inputs | Mapping of input data bindings used in the job. | SparkJobInputs |
| jars | Jar files used in the job. | string[] |
| jobType | [Required] Specifies the type of job. | 'Spark' (required) |
| outputs | Mapping of output data bindings used in the job. | SparkJobOutputs |
| pyFiles | Python files used in the job. | string[] |
| resources | Compute Resource configuration for the job. | SparkResourceConfiguration |
SparkJobConf
| Name | Description | Value |
|---|
SparkJobEntry
| Name | Description | Value |
|---|---|---|
| sparkJobEntryType | Set to 'SparkJobPythonEntry' for type SparkJobPythonEntry. Set to 'SparkJobScalaEntry' for type SparkJobScalaEntry. | 'SparkJobPythonEntry' 'SparkJobScalaEntry' (required) |
SparkJobInputs
| Name | Description | Value |
|---|
SparkJobOutputs
| Name | Description | Value |
|---|
SparkJobPythonEntry
| Name | Description | Value |
|---|---|---|
| file | [Required] Relative python file path for job entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobPythonEntry' (required) |
SparkJobScalaEntry
| Name | Description | Value |
|---|---|---|
| className | [Required] Scala class name used as entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobScalaEntry' (required) |
SparkResourceConfiguration
| Name | Description | Value |
|---|---|---|
| instanceType | Optional type of VM used as supported by the compute target. | string |
| runtimeVersion | Version of spark runtime used for the job. | string |
StackEnsembleSettings
| Name | Description | Value |
|---|---|---|
| stackMetaLearnerKWargs | Optional parameters to pass to the initializer of the meta-learner. | any |
| stackMetaLearnerTrainPercentage | Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2. | int |
| stackMetaLearnerType | The meta-learner is a model trained on the output of the individual heterogeneous models. | 'ElasticNet' 'ElasticNetCV' 'LightGBMClassifier' 'LightGBMRegressor' 'LinearRegression' 'LogisticRegression' 'LogisticRegressionCV' 'None' |
SweepJob
| Name | Description | Value |
|---|---|---|
| earlyTermination | Early termination policies enable canceling poor-performing runs before they complete | EarlyTerminationPolicy |
| inputs | Mapping of input data bindings used in the job. | SweepJobInputs |
| jobType | [Required] Specifies the type of job. | 'Sweep' (required) |
| limits | Sweep Job limit. | SweepJobLimits |
| objective | [Required] Optimization objective. | Objective (required) |
| outputs | Mapping of output data bindings used in the job. | SweepJobOutputs |
| samplingAlgorithm | [Required] The hyperparameter sampling algorithm | SamplingAlgorithm (required) |
| searchSpace | [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter | any (required) |
| trial | [Required] Trial component definition. | TrialComponent (required) |
SweepJobInputs
| Name | Description | Value |
|---|
SweepJobLimits
| Name | Description | Value |
|---|---|---|
| jobLimitsType | [Required] JobLimit type. | 'Command' 'Sweep' (required) |
| maxConcurrentTrials | Sweep Job max concurrent trials. | int |
| maxTotalTrials | Sweep Job max total trials. | int |
| timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
| trialTimeout | Sweep Job Trial timeout value. | string |
SweepJobOutputs
| Name | Description | Value |
|---|
TableFixedParameters
| Name | Description | Value |
|---|---|---|
| booster | Specify the boosting type, e.g gbdt for XGBoost. | string |
| boostingType | Specify the boosting type, e.g gbdt for LightGBM. | string |
| growPolicy | Specify the grow policy, which controls the way new nodes are added to the tree. | string |
| learningRate | The learning rate for the training procedure. | int |
| maxBin | Specify the Maximum number of discrete bins to bucket continuous features . | int |
| maxDepth | Specify the max depth to limit the tree depth explicitly. | int |
| maxLeaves | Specify the max leaves to limit the tree leaves explicitly. | int |
| minDataInLeaf | The minimum number of data per leaf. | int |
| minSplitGain | Minimum loss reduction required to make a further partition on a leaf node of the tree. | int |
| modelName | The name of the model to train. | string |
| nEstimators | Specify the number of trees (or rounds) in an model. | int |
| numLeaves | Specify the number of leaves. | int |
| preprocessorName | The name of the preprocessor to use. | string |
| regAlpha | L1 regularization term on weights. | int |
| regLambda | L2 regularization term on weights. | int |
| subsample | Subsample ratio of the training instance. | int |
| subsampleFreq | Frequency of subsample. | int |
| treeMethod | Specify the tree method. | string |
| withMean | If true, center before scaling the data with StandardScalar. | bool |
| withStd | If true, scaling the data with Unit Variance with StandardScalar. | bool |
TableParameterSubspace
| Name | Description | Value |
|---|---|---|
| booster | Specify the boosting type, e.g gbdt for XGBoost. | string |
| boostingType | Specify the boosting type, e.g gbdt for LightGBM. | string |
| growPolicy | Specify the grow policy, which controls the way new nodes are added to the tree. | string |
| learningRate | The learning rate for the training procedure. | string |
| maxBin | Specify the Maximum number of discrete bins to bucket continuous features . | string |
| maxDepth | Specify the max depth to limit the tree depth explicitly. | string |
| maxLeaves | Specify the max leaves to limit the tree leaves explicitly. | string |
| minDataInLeaf | The minimum number of data per leaf. | string |
| minSplitGain | Minimum loss reduction required to make a further partition on a leaf node of the tree. | string |
| modelName | The name of the model to train. | string |
| nEstimators | Specify the number of trees (or rounds) in an model. | string |
| numLeaves | Specify the number of leaves. | string |
| preprocessorName | The name of the preprocessor to use. | string |
| regAlpha | L1 regularization term on weights. | string |
| regLambda | L2 regularization term on weights. | string |
| subsample | Subsample ratio of the training instance. | string |
| subsampleFreq | Frequency of subsample | string |
| treeMethod | Specify the tree method. | string |
| withMean | If true, center before scaling the data with StandardScalar. | string |
| withStd | If true, scaling the data with Unit Variance with StandardScalar. | string |
TableSweepSettings
| Name | Description | Value |
|---|---|---|
| earlyTermination | Type of early termination policy for the sweeping job. | EarlyTerminationPolicy |
| samplingAlgorithm | [Required] Type of sampling algorithm. | 'Bayesian' 'Grid' 'Random' (required) |
TableVerticalFeaturizationSettings
| Name | Description | Value |
|---|---|---|
| blockedTransformers | These transformers shall not be used in featurization. | String array containing any of: 'CatTargetEncoder' 'CountVectorizer' 'HashOneHotEncoder' 'LabelEncoder' 'NaiveBayes' 'OneHotEncoder' 'TextTargetEncoder' 'TfIdf' 'WoETargetEncoder' 'WordEmbedding' |
| columnNameAndTypes | Dictionary of column name and its type (int, float, string, datetime etc). | TableVerticalFeaturizationSettingsColumnNameAndTypes |
| datasetLanguage | Dataset language, useful for the text data. | string |
| enableDnnFeaturization | Determines whether to use Dnn based featurizers for data featurization. | bool |
| mode | Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done. |
'Auto' 'Custom' 'Off' |
| transformerParams | User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor. | TableVerticalFeaturizationSettingsTransformerParams |
TableVerticalFeaturizationSettingsColumnNameAndTypes
| Name | Description | Value |
|---|
TableVerticalFeaturizationSettingsTransformerParams
| Name | Description | Value |
|---|
TableVerticalLimitSettings
| Name | Description | Value |
|---|---|---|
| enableEarlyTermination | Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations. | bool |
| exitScore | Exit score for the AutoML job. | int |
| maxConcurrentTrials | Maximum Concurrent iterations. | int |
| maxCoresPerTrial | Max cores per iteration. | int |
| maxNodes | Maximum nodes to use for the experiment. | int |
| maxTrials | Number of iterations. | int |
| sweepConcurrentTrials | Number of concurrent sweeping runs that user wants to trigger. | int |
| sweepTrials | Number of sweeping runs that user wants to trigger. | int |
| timeout | AutoML job timeout. | string |
| trialTimeout | Iteration timeout. | string |
TargetLags
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoTargetLags. Set to 'Custom' for type CustomTargetLags. | 'Auto' 'Custom' (required) |
TargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoTargetRollingWindowSize. Set to 'Custom' for type CustomTargetRollingWindowSize. | 'Auto' 'Custom' (required) |
TensorFlow
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'TensorFlow' (required) |
| parameterServerCount | Number of parameter server tasks. | int |
| workerCount | Number of workers. If not specified, will default to the instance count. | int |
TextClassification
| Name | Description | Value |
|---|---|---|
| featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | NlpFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
| primaryMetric | Primary metric for Text-Classification task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | NlpParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | NlpSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'TextClassification' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TextClassificationMultilabel
| Name | Description | Value |
|---|---|---|
| featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | NlpFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | NlpParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | NlpSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'TextClassificationMultilabel' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TextNer
| Name | Description | Value |
|---|---|---|
| featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | NlpFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | NlpParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | NlpSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'TextNER' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TrialComponent
| Name | Description | Value |
|---|---|---|
| codeId | ARM resource ID of the code asset. | string |
| command | [Required] The command to execute on startup of the job. eg. "python train.py" | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
| environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| environmentVariables | Environment variables included in the job. | TrialComponentEnvironmentVariables |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
TrialComponentEnvironmentVariables
| Name | Description | Value |
|---|
TriggerBase
| Name | Description | Value |
|---|---|---|
| endTime | Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely |
string |
| startTime | Specifies start time of schedule in ISO 8601 format, but without a UTC offset. | string |
| timeZone | Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones?view=windows-11 |
string |
| triggerType | Set to 'Cron' for type CronTrigger. Set to 'Recurrence' for type RecurrenceTrigger. | 'Cron' 'Recurrence' (required) |
TritonModelJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'triton_model' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
TritonModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'triton_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
TruncationSelectionPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'TruncationSelection' (required) |
| truncationPercentage | The percentage of runs to cancel at each evaluation interval. | int |
UriFileJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'uri_file' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
UriFileJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'uri_file' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
UriFolderJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'uri_folder' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
UriFolderJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'uri_folder' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
UserIdentity
| Name | Description | Value |
|---|---|---|
| identityType | [Required] Specifies the type of identity framework. | 'UserIdentity' (required) |
Terraform (AzAPI provider) resource definition
The workspaces/schedules resource type can be deployed with operations that target:
- Resource groups
For a list of changed properties in each API version, see change log.
Resource format
To create a Microsoft.MachineLearningServices/workspaces/schedules resource, add the following Terraform to your template.
resource "azapi_resource" "symbolicname" {
type = "Microsoft.MachineLearningServices/workspaces/schedules@2022-12-01-preview"
name = "string"
body = jsonencode({
properties = {
action = {
actionType = "string"
// For remaining properties, see ScheduleActionBase objects
}
description = "string"
displayName = "string"
isEnabled = bool
properties = {
{customized property} = "string"
}
tags = {
{customized property} = "string"
}
trigger = {
endTime = "string"
startTime = "string"
timeZone = "string"
triggerType = "string"
// For remaining properties, see TriggerBase objects
}
}
})
}
TriggerBase objects
Set the triggerType property to specify the type of object.
For Cron, use:
{
expression = "string"
triggerType = "Cron"
}
For Recurrence, use:
{
frequency = "string"
interval = int
schedule = {
hours = [
int
]
minutes = [
int
]
monthDays = [
int
]
weekDays = [
"string"
]
}
triggerType = "Recurrence"
}
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
]
}
JobBaseProperties objects
Set the jobType property to specify the type of object.
For AutoML, use:
{
environmentId = "string"
environmentVariables = {
{customized property} = "string"
}
jobType = "AutoML"
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
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:
{
autologgerSettings = {
mlflowAutologger = "string"
}
codeId = "string"
command = "string"
distribution = {
distributionType = "string"
// For remaining properties, see DistributionConfiguration objects
}
environmentId = "string"
environmentVariables = {
{customized property} = "string"
}
inputs = {
{customized property} = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
}
jobType = "Command"
limits = {
jobLimitsType = "string"
timeout = "string"
}
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
resources = {
dockerArgs = "string"
instanceCount = int
instanceType = "string"
properties = {
{customized property} = ?
}
shmSize = "string"
}
}
For Labeling, use:
{
dataConfiguration = {
dataId = "string"
incrementalDataRefresh = "string"
}
jobInstructions = {
uri = "string"
}
jobType = "Labeling"
labelCategories = {
{customized property} = {
classes = {
{customized property} = {
displayName = "string"
subclasses = {
{customized property} = ...
}
}
}
displayName = "string"
multiSelect = "string"
}
}
labelingJobMediaProperties = {
mediaType = "string"
// For remaining properties, see LabelingJobMediaProperties objects
}
mlAssistConfiguration = {
mlAssist = "string"
// For remaining properties, see MLAssistConfiguration objects
}
}
For Pipeline, use:
{
inputs = {
{customized property} = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
}
jobs = {
{customized property} = ?
}
jobType = "Pipeline"
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
settings = ?
sourceJobId = "string"
}
For Spark, use:
{
archives = [
"string"
]
args = "string"
codeId = "string"
conf = {
{customized property} = "string"
}
entry = {
sparkJobEntryType = "string"
// For remaining properties, see SparkJobEntry objects
}
environmentId = "string"
files = [
"string"
]
inputs = {
{customized property} = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
}
jars = [
"string"
]
jobType = "Spark"
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
pyFiles = [
"string"
]
resources = {
instanceType = "string"
runtimeVersion = "string"
}
}
For Sweep, use:
{
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
inputs = {
{customized property} = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
}
jobType = "Sweep"
limits = {
jobLimitsType = "string"
maxConcurrentTrials = int
maxTotalTrials = int
timeout = "string"
trialTimeout = "string"
}
objective = {
goal = "string"
primaryMetric = "string"
}
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
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:
{
clientId = "string"
identityType = "Managed"
objectId = "string"
resourceId = "string"
}
For UserIdentity, use:
{
identityType = "UserIdentity"
}
Nodes objects
Set the nodesValueType property to specify the type of object.
For All, use:
{
nodesValueType = "All"
}
SparkJobEntry objects
Set the sparkJobEntryType property to specify the type of object.
For SparkJobPythonEntry, use:
{
file = "string"
sparkJobEntryType = "SparkJobPythonEntry"
}
For SparkJobScalaEntry, use:
{
className = "string"
sparkJobEntryType = "SparkJobScalaEntry"
}
TargetRollingWindowSize objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode = "Auto"
}
For Custom, use:
{
mode = "Custom"
value = int
}
LabelingJobMediaProperties objects
Set the mediaType property to specify the type of object.
For Image, use:
{
annotationType = "string"
mediaType = "Image"
}
For Text, use:
{
annotationType = "string"
mediaType = "Text"
}
JobOutput objects
Set the jobOutputType property to specify the type of object.
For custom_model, use:
{
assetName = "string"
assetVersion = "string"
jobOutputType = "custom_model"
mode = "string"
uri = "string"
}
For mlflow_model, use:
{
assetName = "string"
assetVersion = "string"
jobOutputType = "mlflow_model"
mode = "string"
uri = "string"
}
For mltable, use:
{
assetName = "string"
assetVersion = "string"
jobOutputType = "mltable"
mode = "string"
uri = "string"
}
For triton_model, use:
{
assetName = "string"
assetVersion = "string"
jobOutputType = "triton_model"
mode = "string"
uri = "string"
}
For uri_file, use:
{
assetName = "string"
assetVersion = "string"
jobOutputType = "uri_file"
mode = "string"
uri = "string"
}
For uri_folder, use:
{
assetName = "string"
assetVersion = "string"
jobOutputType = "uri_folder"
mode = "string"
uri = "string"
}
SamplingAlgorithm objects
Set the samplingAlgorithmType property to specify the type of object.
For Bayesian, use:
{
samplingAlgorithmType = "Bayesian"
}
For Grid, use:
{
samplingAlgorithmType = "Grid"
}
For Random, use:
{
logbase = "string"
rule = "string"
samplingAlgorithmType = "Random"
seed = int
}
AutoMLVertical objects
Set the taskType property to specify the type of object.
For Classification, use:
{
cvSplitColumnNames = [
"string"
]
featurizationSettings = {
blockedTransformers = [
"string"
]
columnNameAndTypes = {
{customized property} = "string"
}
datasetLanguage = "string"
enableDnnFeaturization = bool
mode = "string"
transformerParams = {
{customized property} = [
{
fields = [
"string"
]
parameters = ?
}
]
}
}
fixedParameters = {
booster = "string"
boostingType = "string"
growPolicy = "string"
learningRate = int
maxBin = int
maxDepth = int
maxLeaves = int
minDataInLeaf = int
minSplitGain = int
modelName = "string"
nEstimators = int
numLeaves = int
preprocessorName = "string"
regAlpha = int
regLambda = int
subsample = int
subsampleFreq = int
treeMethod = "string"
withMean = bool
withStd = bool
}
limitSettings = {
enableEarlyTermination = bool
exitScore = int
maxConcurrentTrials = int
maxCoresPerTrial = int
maxNodes = int
maxTrials = int
sweepConcurrentTrials = int
sweepTrials = int
timeout = "string"
trialTimeout = "string"
}
nCrossValidations = {
mode = "string"
// For remaining properties, see NCrossValidations objects
}
positiveLabel = "string"
primaryMetric = "string"
searchSpace = [
{
booster = "string"
boostingType = "string"
growPolicy = "string"
learningRate = "string"
maxBin = "string"
maxDepth = "string"
maxLeaves = "string"
minDataInLeaf = "string"
minSplitGain = "string"
modelName = "string"
nEstimators = "string"
numLeaves = "string"
preprocessorName = "string"
regAlpha = "string"
regLambda = "string"
subsample = "string"
subsampleFreq = "string"
treeMethod = "string"
withMean = "string"
withStd = "string"
}
]
sweepSettings = {
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm = "string"
}
taskType = "Classification"
testData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
testDataSize = int
trainingSettings = {
allowedTrainingAlgorithms = [
"string"
]
blockedTrainingAlgorithms = [
"string"
]
enableDnnTraining = bool
enableModelExplainability = bool
enableOnnxCompatibleModels = bool
enableStackEnsemble = bool
enableVoteEnsemble = bool
ensembleModelDownloadTimeout = "string"
stackEnsembleSettings = {
stackMetaLearnerKWargs = ?
stackMetaLearnerTrainPercentage = int
stackMetaLearnerType = "string"
}
trainingMode = "string"
}
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
weightColumnName = "string"
}
For Forecasting, use:
{
cvSplitColumnNames = [
"string"
]
featurizationSettings = {
blockedTransformers = [
"string"
]
columnNameAndTypes = {
{customized property} = "string"
}
datasetLanguage = "string"
enableDnnFeaturization = bool
mode = "string"
transformerParams = {
{customized property} = [
{
fields = [
"string"
]
parameters = ?
}
]
}
}
fixedParameters = {
booster = "string"
boostingType = "string"
growPolicy = "string"
learningRate = int
maxBin = int
maxDepth = int
maxLeaves = int
minDataInLeaf = int
minSplitGain = int
modelName = "string"
nEstimators = int
numLeaves = int
preprocessorName = "string"
regAlpha = int
regLambda = int
subsample = int
subsampleFreq = int
treeMethod = "string"
withMean = bool
withStd = bool
}
forecastingSettings = {
countryOrRegionForHolidays = "string"
cvStepSize = int
featureLags = "string"
forecastHorizon = {
mode = "string"
// For remaining properties, see ForecastHorizon objects
}
frequency = "string"
seasonality = {
mode = "string"
// For remaining properties, see Seasonality objects
}
shortSeriesHandlingConfig = "string"
targetAggregateFunction = "string"
targetLags = {
mode = "string"
// For remaining properties, see TargetLags objects
}
targetRollingWindowSize = {
mode = "string"
// For remaining properties, see TargetRollingWindowSize objects
}
timeColumnName = "string"
timeSeriesIdColumnNames = [
"string"
]
useStl = "string"
}
limitSettings = {
enableEarlyTermination = bool
exitScore = int
maxConcurrentTrials = int
maxCoresPerTrial = int
maxNodes = int
maxTrials = int
sweepConcurrentTrials = int
sweepTrials = int
timeout = "string"
trialTimeout = "string"
}
nCrossValidations = {
mode = "string"
// For remaining properties, see NCrossValidations objects
}
primaryMetric = "string"
searchSpace = [
{
booster = "string"
boostingType = "string"
growPolicy = "string"
learningRate = "string"
maxBin = "string"
maxDepth = "string"
maxLeaves = "string"
minDataInLeaf = "string"
minSplitGain = "string"
modelName = "string"
nEstimators = "string"
numLeaves = "string"
preprocessorName = "string"
regAlpha = "string"
regLambda = "string"
subsample = "string"
subsampleFreq = "string"
treeMethod = "string"
withMean = "string"
withStd = "string"
}
]
sweepSettings = {
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm = "string"
}
taskType = "Forecasting"
testData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
testDataSize = int
trainingSettings = {
allowedTrainingAlgorithms = [
"string"
]
blockedTrainingAlgorithms = [
"string"
]
enableDnnTraining = bool
enableModelExplainability = bool
enableOnnxCompatibleModels = bool
enableStackEnsemble = bool
enableVoteEnsemble = bool
ensembleModelDownloadTimeout = "string"
stackEnsembleSettings = {
stackMetaLearnerKWargs = ?
stackMetaLearnerTrainPercentage = int
stackMetaLearnerType = "string"
}
trainingMode = "string"
}
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
weightColumnName = "string"
}
For ImageClassification, use:
{
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"
}
taskType = "ImageClassification"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
}
For ImageClassificationMultilabel, use:
{
limitSettings = {
maxConcurrentTrials = int
maxTrials = int
timeout = "string"
}
modelSettings = {
advancedSettings = "string"
amsGradient = bool
augmentations = "string"
beta1 = int
beta2 = int
checkpointFrequency = int
checkpointModel = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
checkpointRunId = "string"
distributed = bool
earlyStopping = bool
earlyStoppingDelay = int
earlyStoppingPatience = int
enableOnnxNormalization = bool
evaluationFrequency = int
gradientAccumulationStep = int
layersToFreeze = int
learningRate = int
learningRateScheduler = "string"
modelName = "string"
momentum = int
nesterov = bool
numberOfEpochs = int
numberOfWorkers = int
optimizer = "string"
randomSeed = int
stepLRGamma = int
stepLRStepSize = int
trainingBatchSize = int
trainingCropSize = int
validationBatchSize = int
validationCropSize = int
validationResizeSize = int
warmupCosineLRCycles = int
warmupCosineLRWarmupEpochs = int
weightDecay = int
weightedLoss = int
}
primaryMetric = "string"
searchSpace = [
{
amsGradient = "string"
augmentations = "string"
beta1 = "string"
beta2 = "string"
distributed = "string"
earlyStopping = "string"
earlyStoppingDelay = "string"
earlyStoppingPatience = "string"
enableOnnxNormalization = "string"
evaluationFrequency = "string"
gradientAccumulationStep = "string"
layersToFreeze = "string"
learningRate = "string"
learningRateScheduler = "string"
modelName = "string"
momentum = "string"
nesterov = "string"
numberOfEpochs = "string"
numberOfWorkers = "string"
optimizer = "string"
randomSeed = "string"
stepLRGamma = "string"
stepLRStepSize = "string"
trainingBatchSize = "string"
trainingCropSize = "string"
validationBatchSize = "string"
validationCropSize = "string"
validationResizeSize = "string"
warmupCosineLRCycles = "string"
warmupCosineLRWarmupEpochs = "string"
weightDecay = "string"
weightedLoss = "string"
}
]
sweepSettings = {
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm = "string"
}
taskType = "ImageClassificationMultilabel"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
}
For ImageInstanceSegmentation, use:
{
limitSettings = {
maxConcurrentTrials = int
maxTrials = int
timeout = "string"
}
modelSettings = {
advancedSettings = "string"
amsGradient = bool
augmentations = "string"
beta1 = int
beta2 = int
boxDetectionsPerImage = int
boxScoreThreshold = int
checkpointFrequency = int
checkpointModel = {
description = "string"
jobInputType = "string"
mode = "string"
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 = "string"
searchSpace = [
{
amsGradient = "string"
augmentations = "string"
beta1 = "string"
beta2 = "string"
boxDetectionsPerImage = "string"
boxScoreThreshold = "string"
distributed = "string"
earlyStopping = "string"
earlyStoppingDelay = "string"
earlyStoppingPatience = "string"
enableOnnxNormalization = "string"
evaluationFrequency = "string"
gradientAccumulationStep = "string"
imageSize = "string"
layersToFreeze = "string"
learningRate = "string"
learningRateScheduler = "string"
maxSize = "string"
minSize = "string"
modelName = "string"
modelSize = "string"
momentum = "string"
multiScale = "string"
nesterov = "string"
nmsIouThreshold = "string"
numberOfEpochs = "string"
numberOfWorkers = "string"
optimizer = "string"
randomSeed = "string"
stepLRGamma = "string"
stepLRStepSize = "string"
tileGridSize = "string"
tileOverlapRatio = "string"
tilePredictionsNmsThreshold = "string"
trainingBatchSize = "string"
validationBatchSize = "string"
validationIouThreshold = "string"
validationMetricType = "string"
warmupCosineLRCycles = "string"
warmupCosineLRWarmupEpochs = "string"
weightDecay = "string"
}
]
sweepSettings = {
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm = "string"
}
taskType = "ImageInstanceSegmentation"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
}
For ImageObjectDetection, use:
{
limitSettings = {
maxConcurrentTrials = int
maxTrials = int
timeout = "string"
}
modelSettings = {
advancedSettings = "string"
amsGradient = bool
augmentations = "string"
beta1 = int
beta2 = int
boxDetectionsPerImage = int
boxScoreThreshold = int
checkpointFrequency = int
checkpointModel = {
description = "string"
jobInputType = "string"
mode = "string"
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 = "string"
searchSpace = [
{
amsGradient = "string"
augmentations = "string"
beta1 = "string"
beta2 = "string"
boxDetectionsPerImage = "string"
boxScoreThreshold = "string"
distributed = "string"
earlyStopping = "string"
earlyStoppingDelay = "string"
earlyStoppingPatience = "string"
enableOnnxNormalization = "string"
evaluationFrequency = "string"
gradientAccumulationStep = "string"
imageSize = "string"
layersToFreeze = "string"
learningRate = "string"
learningRateScheduler = "string"
maxSize = "string"
minSize = "string"
modelName = "string"
modelSize = "string"
momentum = "string"
multiScale = "string"
nesterov = "string"
nmsIouThreshold = "string"
numberOfEpochs = "string"
numberOfWorkers = "string"
optimizer = "string"
randomSeed = "string"
stepLRGamma = "string"
stepLRStepSize = "string"
tileGridSize = "string"
tileOverlapRatio = "string"
tilePredictionsNmsThreshold = "string"
trainingBatchSize = "string"
validationBatchSize = "string"
validationIouThreshold = "string"
validationMetricType = "string"
warmupCosineLRCycles = "string"
warmupCosineLRWarmupEpochs = "string"
weightDecay = "string"
}
]
sweepSettings = {
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm = "string"
}
taskType = "ImageObjectDetection"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
}
For Regression, use:
{
cvSplitColumnNames = [
"string"
]
featurizationSettings = {
blockedTransformers = [
"string"
]
columnNameAndTypes = {
{customized property} = "string"
}
datasetLanguage = "string"
enableDnnFeaturization = bool
mode = "string"
transformerParams = {
{customized property} = [
{
fields = [
"string"
]
parameters = ?
}
]
}
}
fixedParameters = {
booster = "string"
boostingType = "string"
growPolicy = "string"
learningRate = int
maxBin = int
maxDepth = int
maxLeaves = int
minDataInLeaf = int
minSplitGain = int
modelName = "string"
nEstimators = int
numLeaves = int
preprocessorName = "string"
regAlpha = int
regLambda = int
subsample = int
subsampleFreq = int
treeMethod = "string"
withMean = bool
withStd = bool
}
limitSettings = {
enableEarlyTermination = bool
exitScore = int
maxConcurrentTrials = int
maxCoresPerTrial = int
maxNodes = int
maxTrials = int
sweepConcurrentTrials = int
sweepTrials = int
timeout = "string"
trialTimeout = "string"
}
nCrossValidations = {
mode = "string"
// For remaining properties, see NCrossValidations objects
}
primaryMetric = "string"
searchSpace = [
{
booster = "string"
boostingType = "string"
growPolicy = "string"
learningRate = "string"
maxBin = "string"
maxDepth = "string"
maxLeaves = "string"
minDataInLeaf = "string"
minSplitGain = "string"
modelName = "string"
nEstimators = "string"
numLeaves = "string"
preprocessorName = "string"
regAlpha = "string"
regLambda = "string"
subsample = "string"
subsampleFreq = "string"
treeMethod = "string"
withMean = "string"
withStd = "string"
}
]
sweepSettings = {
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm = "string"
}
taskType = "Regression"
testData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
testDataSize = int
trainingSettings = {
allowedTrainingAlgorithms = [
"string"
]
blockedTrainingAlgorithms = [
"string"
]
enableDnnTraining = bool
enableModelExplainability = bool
enableOnnxCompatibleModels = bool
enableStackEnsemble = bool
enableVoteEnsemble = bool
ensembleModelDownloadTimeout = "string"
stackEnsembleSettings = {
stackMetaLearnerKWargs = ?
stackMetaLearnerTrainPercentage = int
stackMetaLearnerType = "string"
}
trainingMode = "string"
}
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
weightColumnName = "string"
}
For TextClassification, use:
{
featurizationSettings = {
datasetLanguage = "string"
}
fixedParameters = {
gradientAccumulationSteps = int
learningRate = int
learningRateScheduler = "string"
modelName = "string"
numberOfEpochs = int
trainingBatchSize = int
validationBatchSize = int
warmupRatio = int
weightDecay = int
}
limitSettings = {
maxConcurrentTrials = int
maxNodes = int
maxTrials = int
timeout = "string"
trialTimeout = "string"
}
primaryMetric = "string"
searchSpace = [
{
gradientAccumulationSteps = "string"
learningRate = "string"
learningRateScheduler = "string"
modelName = "string"
numberOfEpochs = "string"
trainingBatchSize = "string"
validationBatchSize = "string"
warmupRatio = "string"
weightDecay = "string"
}
]
sweepSettings = {
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm = "string"
}
taskType = "TextClassification"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
}
For TextClassificationMultilabel, use:
{
featurizationSettings = {
datasetLanguage = "string"
}
fixedParameters = {
gradientAccumulationSteps = int
learningRate = int
learningRateScheduler = "string"
modelName = "string"
numberOfEpochs = int
trainingBatchSize = int
validationBatchSize = int
warmupRatio = int
weightDecay = int
}
limitSettings = {
maxConcurrentTrials = int
maxNodes = int
maxTrials = int
timeout = "string"
trialTimeout = "string"
}
searchSpace = [
{
gradientAccumulationSteps = "string"
learningRate = "string"
learningRateScheduler = "string"
modelName = "string"
numberOfEpochs = "string"
trainingBatchSize = "string"
validationBatchSize = "string"
warmupRatio = "string"
weightDecay = "string"
}
]
sweepSettings = {
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm = "string"
}
taskType = "TextClassificationMultilabel"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
}
For TextNER, use:
{
featurizationSettings = {
datasetLanguage = "string"
}
fixedParameters = {
gradientAccumulationSteps = int
learningRate = int
learningRateScheduler = "string"
modelName = "string"
numberOfEpochs = int
trainingBatchSize = int
validationBatchSize = int
warmupRatio = int
weightDecay = int
}
limitSettings = {
maxConcurrentTrials = int
maxNodes = int
maxTrials = int
timeout = "string"
trialTimeout = "string"
}
searchSpace = [
{
gradientAccumulationSteps = "string"
learningRate = "string"
learningRateScheduler = "string"
modelName = "string"
numberOfEpochs = "string"
trainingBatchSize = "string"
validationBatchSize = "string"
warmupRatio = "string"
weightDecay = "string"
}
]
sweepSettings = {
earlyTermination = {
delayEvaluation = int
evaluationInterval = int
policyType = "string"
// For remaining properties, see EarlyTerminationPolicy objects
}
samplingAlgorithm = "string"
}
taskType = "TextNER"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
}
ForecastHorizon 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
}
NCrossValidations objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode = "Auto"
}
For Custom, use:
{
mode = "Custom"
value = int
}
DistributionConfiguration objects
Set the distributionType property to specify the type of object.
For Mpi, use:
{
distributionType = "Mpi"
processCountPerInstance = int
}
For PyTorch, use:
{
distributionType = "PyTorch"
processCountPerInstance = int
}
For TensorFlow, use:
{
distributionType = "TensorFlow"
parameterServerCount = int
workerCount = int
}
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
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 = {
{customized property} = "string"
}
jobType = "string"
// For remaining properties, see JobBaseProperties objects
}
}
For InvokeBatchEndpoint, use:
{
actionType = "InvokeBatchEndpoint"
endpointInvocationDefinition = ?
}
MLAssistConfiguration objects
Set the mlAssist property to specify the type of object.
For Disabled, use:
{
mlAssist = "Disabled"
}
For Enabled, use:
{
inferencingComputeBinding = "string"
mlAssist = "Enabled"
trainingComputeBinding = "string"
}
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"
}
Property values
AllNodes
| Name | Description | Value |
|---|---|---|
| nodesValueType | [Required] Type of the Nodes value | 'All' (required) |
AmlToken
| Name | Description | Value |
|---|---|---|
| identityType | [Required] Specifies the type of identity framework. | 'AMLToken' (required) |
AutoForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set forecast horizon value selection mode. | 'Auto' (required) |
AutologgerSettings
| Name | Description | Value |
|---|---|---|
| mlflowAutologger | [Required] Indicates whether mlflow autologger is enabled. | 'Disabled' 'Enabled' (required) |
AutoMLJob
| Name | Description | Value |
|---|---|---|
| environmentId | The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job. |
string |
| environmentVariables | Environment variables included in the job. | AutoMLJobEnvironmentVariables |
| jobType | [Required] Specifies the type of job. | 'AutoML' (required) |
| outputs | Mapping of output data bindings used in the job. | AutoMLJobOutputs |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
| taskDetails | [Required] This represents scenario which can be one of Tables/NLP/Image | AutoMLVertical (required) |
AutoMLJobEnvironmentVariables
| Name | Description | Value |
|---|
AutoMLJobOutputs
| Name | Description | Value |
|---|
AutoMLVertical
| Name | Description | Value |
|---|---|---|
| logVerbosity | Log verbosity for the job. | 'Critical' 'Debug' 'Error' 'Info' 'NotSet' 'Warning' |
| targetColumnName | Target column name: This is prediction values column. Also known as label column name in context of classification tasks. |
string |
| taskType | Set to 'Classification' for type Classification. Set to 'Forecasting' for type Forecasting. Set to 'ImageClassification' for type ImageClassification. Set to 'ImageClassificationMultilabel' for type ImageClassificationMultilabel. Set to 'ImageInstanceSegmentation' for type ImageInstanceSegmentation. Set to 'ImageObjectDetection' for type ImageObjectDetection. Set to 'Regression' for type Regression. Set to 'TextClassification' for type TextClassification. Set to 'TextClassificationMultilabel' for type TextClassificationMultilabel. Set to 'TextNER' for type TextNer. | 'Classification' 'Forecasting' 'ImageClassification' 'ImageClassificationMultilabel' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'Regression' 'TextClassification' 'TextClassificationMultilabel' 'TextNER' (required) |
| trainingData | [Required] Training data input. | MLTableJobInput (required) |
AutoNCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | [Required] Mode for determining N-Cross validations. | 'Auto' (required) |
AutoSeasonality
| Name | Description | Value |
|---|---|---|
| mode | [Required] Seasonality mode. | 'Auto' (required) |
AutoTargetLags
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set target lags mode - Auto/Custom | 'Auto' (required) |
AutoTargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | [Required] TargetRollingWindowSiz detection mode. | 'Auto' (required) |
BanditPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'Bandit' (required) |
| slackAmount | Absolute distance allowed from the best performing run. | int |
| slackFactor | Ratio of the allowed distance from the best performing run. | int |
BayesianSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Bayesian' (required) |
Classification
| Name | Description | Value |
|---|---|---|
| cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
| featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | TableFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
| nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
| positiveLabel | Positive label for binary metrics calculation. | string |
| primaryMetric | Primary metric for the task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | TableParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | TableSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'Classification' (required) |
| testData | Test data input. | MLTableJobInput |
| testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| trainingSettings | Inputs for training phase for an AutoML Job. | ClassificationTrainingSettings |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
ClassificationTrainingSettings
| Name | Description | Value |
|---|---|---|
| allowedTrainingAlgorithms | Allowed models for classification task. | String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier' |
| blockedTrainingAlgorithms | Blocked models for classification task. | String array containing any of: 'BernoulliNaiveBayes' 'DecisionTree' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LightGBM' 'LinearSVM' 'LogisticRegression' 'MultinomialNaiveBayes' 'RandomForest' 'SGD' 'SVM' 'XGBoostClassifier' |
| enableDnnTraining | Enable recommendation of DNN models. | bool |
| enableModelExplainability | Flag to turn on explainability on best model. | bool |
| enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
| enableStackEnsemble | Enable stack ensemble run. | bool |
| enableVoteEnsemble | Enable voting ensemble run. | bool |
| ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
| stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
| trainingMode | TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen. |
'Auto' 'Distributed' 'NonDistributed' |
ColumnTransformer
| Name | Description | Value |
|---|---|---|
| fields | Fields to apply transformer logic on. | string[] |
| parameters | Different properties to be passed to transformer. Input expected is dictionary of key,value pairs in JSON format. |
any |
CommandJob
| Name | Description | Value |
|---|---|---|
| autologgerSettings | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | AutologgerSettings |
| codeId | ARM resource ID of the code asset. | string |
| command | [Required] The command to execute on startup of the job. eg. "python train.py" | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
| environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| environmentVariables | Environment variables included in the job. | CommandJobEnvironmentVariables |
| inputs | Mapping of input data bindings used in the job. | CommandJobInputs |
| jobType | [Required] Specifies the type of job. | 'Command' (required) |
| limits | Command Job limit. | CommandJobLimits |
| outputs | Mapping of output data bindings used in the job. | CommandJobOutputs |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
CommandJobEnvironmentVariables
| Name | Description | Value |
|---|
CommandJobInputs
| Name | Description | Value |
|---|
CommandJobLimits
| Name | Description | Value |
|---|---|---|
| jobLimitsType | [Required] JobLimit type. | 'Command' 'Sweep' (required) |
| timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
CommandJobOutputs
| Name | Description | Value |
|---|
CronTrigger
| Name | Description | Value |
|---|---|---|
| expression | [Required] Specifies cron expression of schedule. The expression should follow NCronTab format. |
string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| triggerType | [Required] | 'Cron' (required) |
CustomForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set forecast horizon value selection mode. | 'Custom' (required) |
| value | [Required] Forecast horizon value. | int (required) |
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 Constraints: Pattern = [a-zA-Z0-9_] (required) |
CustomModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'custom_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
CustomNCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | [Required] Mode for determining N-Cross validations. | 'Custom' (required) |
| value | [Required] N-Cross validations value. | int (required) |
CustomSeasonality
| Name | Description | Value |
|---|---|---|
| mode | [Required] Seasonality mode. | 'Custom' (required) |
| value | [Required] Seasonality value. | int (required) |
CustomTargetLags
| Name | Description | Value |
|---|---|---|
| mode | [Required] Set target lags mode - Auto/Custom | 'Custom' (required) |
| values | [Required] Set target lags values. | int[] (required) |
CustomTargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | [Required] TargetRollingWindowSiz detection mode. | 'Custom' (required) |
| value | [Required] TargetRollingWindowSize value. | int (required) |
DistributionConfiguration
| Name | Description | Value |
|---|---|---|
| distributionType | Set to 'Mpi' for type Mpi. Set to 'PyTorch' for type PyTorch. Set to 'TensorFlow' for type TensorFlow. | 'Mpi' 'PyTorch' 'TensorFlow' (required) |
EarlyTerminationPolicy
| Name | Description | Value |
|---|---|---|
| delayEvaluation | Number of intervals by which to delay the first evaluation. | int |
| evaluationInterval | Interval (number of runs) between policy evaluations. | int |
| policyType | Set to 'Bandit' for type BanditPolicy. Set to 'MedianStopping' for type MedianStoppingPolicy. Set to 'TruncationSelection' for type TruncationSelectionPolicy. | 'Bandit' 'MedianStopping' 'TruncationSelection' (required) |
EndpointScheduleAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'InvokeBatchEndpoint' (required) |
| endpointInvocationDefinition | [Required] Defines Schedule action definition details. <see href="TBD" /> |
any (required) |
ForecastHorizon
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoForecastHorizon. Set to 'Custom' for type CustomForecastHorizon. | 'Auto' 'Custom' (required) |
Forecasting
| Name | Description | Value |
|---|---|---|
| cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
| featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | TableFixedParameters |
| forecastingSettings | Forecasting task specific inputs. | ForecastingSettings |
| limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
| nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
| primaryMetric | Primary metric for forecasting task. | 'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | TableParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | TableSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'Forecasting' (required) |
| testData | Test data input. | MLTableJobInput |
| testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| trainingSettings | Inputs for training phase for an AutoML Job. | ForecastingTrainingSettings |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
ForecastingSettings
| Name | Description | Value |
|---|---|---|
| countryOrRegionForHolidays | Country or region for holidays for forecasting tasks. These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'. |
string |
| cvStepSize | Number of periods between the origin time of one CV fold and the next fold. For example, if CVStepSize = 3 for daily data, the origin time for each fold will bethree days apart. |
int |
| featureLags | Flag for generating lags for the numeric features with 'auto' or null. | 'Auto' 'None' |
| forecastHorizon | The desired maximum forecast horizon in units of time-series frequency. | ForecastHorizon |
| frequency | When forecasting, this parameter represents the period with which the forecast is desired, for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default. | string |
| seasonality | Set time series seasonality as an integer multiple of the series frequency. If seasonality is set to 'auto', it will be inferred. |
Seasonality |
| shortSeriesHandlingConfig | The parameter defining how if AutoML should handle short time series. | 'Auto' 'Drop' 'None' 'Pad' |
| targetAggregateFunction | The function to be used to aggregate the time series target column to conform to a user specified frequency. If the TargetAggregateFunction is set i.e. not 'None', but the freq parameter is not set, the error is raised. The possible target aggregation functions are: "sum", "max", "min" and "mean". |
'Max' 'Mean' 'Min' 'None' 'Sum' |
| targetLags | The number of past periods to lag from the target column. | TargetLags |
| targetRollingWindowSize | The number of past periods used to create a rolling window average of the target column. | TargetRollingWindowSize |
| timeColumnName | The name of the time column. This parameter is required when forecasting to specify the datetime column in the input data used for building the time series and inferring its frequency. | string |
| timeSeriesIdColumnNames | The names of columns used to group a timeseries. It can be used to create multiple series. If grain is not defined, the data set is assumed to be one time-series. This parameter is used with task type forecasting. |
string[] |
| useStl | Configure STL Decomposition of the time-series target column. | 'None' 'Season' 'SeasonTrend' |
ForecastingTrainingSettings
| Name | Description | Value |
|---|---|---|
| allowedTrainingAlgorithms | Allowed models for forecasting task. | String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SeasonalAverage' 'SeasonalNaive' 'SGD' 'TCNForecaster' 'XGBoostRegressor' |
| blockedTrainingAlgorithms | Blocked models for forecasting task. | String array containing any of: 'Arimax' 'AutoArima' 'Average' 'DecisionTree' 'ElasticNet' 'ExponentialSmoothing' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'Naive' 'Prophet' 'RandomForest' 'SeasonalAverage' 'SeasonalNaive' 'SGD' 'TCNForecaster' 'XGBoostRegressor' |
| enableDnnTraining | Enable recommendation of DNN models. | bool |
| enableModelExplainability | Flag to turn on explainability on best model. | bool |
| enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
| enableStackEnsemble | Enable stack ensemble run. | bool |
| enableVoteEnsemble | Enable voting ensemble run. | bool |
| ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
| stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
| trainingMode | TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen. |
'Auto' 'Distributed' 'NonDistributed' |
GridSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Grid' (required) |
IdentityConfiguration
| Name | Description | Value |
|---|---|---|
| identityType | Set to 'AMLToken' for type AmlToken. Set to 'Managed' for type ManagedIdentity. Set to 'UserIdentity' for type UserIdentity. | 'AMLToken' 'Managed' 'UserIdentity' (required) |
ImageClassification
| Name | Description | Value |
|---|---|---|
| limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
| modelSettings | Settings used for training the model. | ImageModelSettingsClassification |
| primaryMetric | Primary metric to optimize for this task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsClassification[] |
| sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'ImageClassification' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageClassificationMultilabel
| Name | Description | Value |
|---|---|---|
| limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
| modelSettings | Settings used for training the model. | ImageModelSettingsClassification |
| primaryMetric | Primary metric to optimize for this task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'IOU' 'NormMacroRecall' 'PrecisionScoreWeighted' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsClassification[] |
| sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'ImageClassificationMultilabel' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageInstanceSegmentation
| Name | Description | Value |
|---|---|---|
| limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
| modelSettings | Settings used for training the model. | ImageModelSettingsObjectDetection |
| primaryMetric | Primary metric to optimize for this task. | 'MeanAveragePrecision' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsObjectDetection[] |
| sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'ImageInstanceSegmentation' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageLimitSettings
| Name | Description | Value |
|---|---|---|
| maxConcurrentTrials | Maximum number of concurrent AutoML iterations. | int |
| maxTrials | Maximum number of AutoML iterations. | int |
| timeout | AutoML job timeout. | string |
ImageModelDistributionSettingsClassification
| Name | Description | Value |
|---|---|---|
| amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | string |
| augmentations | Settings for using Augmentations. | string |
| beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
| beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
| distributed | Whether to use distributer training. | string |
| earlyStopping | Enable early stopping logic during training. | string |
| earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
string |
| earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
string |
| enableOnnxNormalization | Enable normalization when exporting ONNX model. | string |
| evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | string |
| gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
string |
| layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| learningRate | Initial learning rate. Must be a float in the range [0, 1]. | string |
| learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | string |
| modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | string |
| nesterov | Enable nesterov when optimizer is 'sgd'. | string |
| numberOfEpochs | Number of training epochs. Must be a positive integer. | string |
| numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | string |
| optimizer | Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. | string |
| randomSeed | Random seed to be used when using deterministic training. | string |
| stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | string |
| stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | string |
| trainingBatchSize | Training batch size. Must be a positive integer. | string |
| trainingCropSize | Image crop size that is input to the neural network for the training dataset. Must be a positive integer. | string |
| validationBatchSize | Validation batch size. Must be a positive integer. | string |
| validationCropSize | Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. | string |
| validationResizeSize | Image size to which to resize before cropping for validation dataset. Must be a positive integer. | string |
| warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | string |
| warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | string |
| weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | string |
| weightedLoss | Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2. |
string |
ImageModelDistributionSettingsObjectDetection
| Name | Description | Value |
|---|---|---|
| amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | string |
| augmentations | Settings for using Augmentations. | string |
| beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
| beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | string |
| boxDetectionsPerImage | Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| boxScoreThreshold | During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
string |
| distributed | Whether to use distributer training. | string |
| earlyStopping | Enable early stopping logic during training. | string |
| earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
string |
| earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
string |
| enableOnnxNormalization | Enable normalization when exporting ONNX model. | string |
| evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | string |
| gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
string |
| imageSize | Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
| layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| learningRate | Initial learning rate. Must be a float in the range [0, 1]. | string |
| learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | string |
| maxSize | Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| minSize | Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| modelSize | Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
| momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | string |
| multiScale | Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm. |
string |
| nesterov | Enable nesterov when optimizer is 'sgd'. | string |
| nmsIouThreshold | IOU threshold used during inference in NMS post processing. Must be float in the range [0, 1]. | string |
| numberOfEpochs | Number of training epochs. Must be a positive integer. | string |
| numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | string |
| optimizer | Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'. | string |
| randomSeed | Random seed to be used when using deterministic training. | string |
| stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | string |
| stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | string |
| tileGridSize | The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| tileOverlapRatio | Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| tilePredictionsNmsThreshold | The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. NMS: Non-maximum suppression |
string |
| trainingBatchSize | Training batch size. Must be a positive integer. | string |
| validationBatchSize | Validation batch size. Must be a positive integer. | string |
| validationIouThreshold | IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. | string |
| validationMetricType | Metric computation method to use for validation metrics. Must be 'none', 'coco', 'voc', or 'coco_voc'. | string |
| warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | string |
| warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | string |
| weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | string |
ImageModelSettingsClassification
| Name | Description | Value |
|---|---|---|
| advancedSettings | Settings for advanced scenarios. | string |
| amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | bool |
| augmentations | Settings for using Augmentations. | string |
| beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
| beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
| checkpointFrequency | Frequency to store model checkpoints. Must be a positive integer. | int |
| checkpointModel | The pretrained checkpoint model for incremental training. | MLFlowModelJobInput |
| checkpointRunId | The id of a previous run that has a pretrained checkpoint for incremental training. | string |
| distributed | Whether to use distributed training. | bool |
| earlyStopping | Enable early stopping logic during training. | bool |
| earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
int |
| earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
int |
| enableOnnxNormalization | Enable normalization when exporting ONNX model. | bool |
| evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | int |
| gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
int |
| layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
int |
| learningRate | Initial learning rate. Must be a float in the range [0, 1]. | int |
| learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | 'None' 'Step' 'WarmupCosine' |
| modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | int |
| nesterov | Enable nesterov when optimizer is 'sgd'. | bool |
| numberOfEpochs | Number of training epochs. Must be a positive integer. | int |
| numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | int |
| optimizer | Type of optimizer. | 'Adam' 'Adamw' 'None' 'Sgd' |
| randomSeed | Random seed to be used when using deterministic training. | int |
| stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | int |
| stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | int |
| trainingBatchSize | Training batch size. Must be a positive integer. | int |
| trainingCropSize | Image crop size that is input to the neural network for the training dataset. Must be a positive integer. | int |
| validationBatchSize | Validation batch size. Must be a positive integer. | int |
| validationCropSize | Image crop size that is input to the neural network for the validation dataset. Must be a positive integer. | int |
| validationResizeSize | Image size to which to resize before cropping for validation dataset. Must be a positive integer. | int |
| warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | int |
| warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | int |
| weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | int |
| weightedLoss | Weighted loss. The accepted values are 0 for no weighted loss. 1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be 0 or 1 or 2. |
int |
ImageModelSettingsObjectDetection
| Name | Description | Value |
|---|---|---|
| advancedSettings | Settings for advanced scenarios. | string |
| amsGradient | Enable AMSGrad when optimizer is 'adam' or 'adamw'. | bool |
| augmentations | Settings for using Augmentations. | string |
| beta1 | Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
| beta2 | Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range [0, 1]. | int |
| boxDetectionsPerImage | Maximum number of detections per image, for all classes. Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| boxScoreThreshold | During inference, only return proposals with a classification score greater than BoxScoreThreshold. Must be a float in the range[0, 1]. |
int |
| checkpointFrequency | Frequency to store model checkpoints. Must be a positive integer. | int |
| checkpointModel | The pretrained checkpoint model for incremental training. | MLFlowModelJobInput |
| checkpointRunId | The id of a previous run that has a pretrained checkpoint for incremental training. | string |
| distributed | Whether to use distributed training. | bool |
| earlyStopping | Enable early stopping logic during training. | bool |
| earlyStoppingDelay | Minimum number of epochs or validation evaluations to wait before primary metric improvement is tracked for early stopping. Must be a positive integer. |
int |
| earlyStoppingPatience | Minimum number of epochs or validation evaluations with no primary metric improvement before the run is stopped. Must be a positive integer. |
int |
| enableOnnxNormalization | Enable normalization when exporting ONNX model. | bool |
| evaluationFrequency | Frequency to evaluate validation dataset to get metric scores. Must be a positive integer. | int |
| gradientAccumulationStep | Gradient accumulation means running a configured number of "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps, and then using the accumulated gradients to compute the weight updates. Must be a positive integer. |
int |
| imageSize | Image size for train and validation. Must be a positive integer. Note: The training run may get into CUDA OOM if the size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
int |
| layersToFreeze | Number of layers to freeze for the model. Must be a positive integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1. For a full list of models supported and details on layer freeze, please see: /azure/machine-learning/how-to-auto-train-image-models. |
int |
| learningRate | Initial learning rate. Must be a float in the range [0, 1]. | int |
| learningRateScheduler | Type of learning rate scheduler. Must be 'warmup_cosine' or 'step'. | 'None' 'Step' 'WarmupCosine' |
| maxSize | Maximum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| minSize | Minimum size of the image to be rescaled before feeding it to the backbone. Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| modelName | Name of the model to use for training. For more information on the available models please visit the official documentation: /azure/machine-learning/how-to-auto-train-image-models. |
string |
| modelSize | Model size. Must be 'small', 'medium', 'large', or 'xlarge'. Note: training run may get into CUDA OOM if the model size is too big. Note: This settings is only supported for the 'yolov5' algorithm. |
'ExtraLarge' 'Large' 'Medium' 'None' 'Small' |
| momentum | Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1]. | int |
| multiScale | Enable multi-scale image by varying image size by +/- 50%. Note: training run may get into CUDA OOM if no sufficient GPU memory. Note: This settings is only supported for the 'yolov5' algorithm. |
bool |
| nesterov | Enable nesterov when optimizer is 'sgd'. | bool |
| nmsIouThreshold | IOU threshold used during inference in NMS post processing. Must be a float in the range [0, 1]. | int |
| numberOfEpochs | Number of training epochs. Must be a positive integer. | int |
| numberOfWorkers | Number of data loader workers. Must be a non-negative integer. | int |
| optimizer | Type of optimizer. | 'Adam' 'Adamw' 'None' 'Sgd' |
| randomSeed | Random seed to be used when using deterministic training. | int |
| stepLRGamma | Value of gamma when learning rate scheduler is 'step'. Must be a float in the range [0, 1]. | int |
| stepLRStepSize | Value of step size when learning rate scheduler is 'step'. Must be a positive integer. | int |
| tileGridSize | The grid size to use for tiling each image. Note: TileGridSize must not be None to enable small object detection logic. A string containing two integers in mxn format. Note: This settings is not supported for the 'yolov5' algorithm. |
string |
| tileOverlapRatio | Overlap ratio between adjacent tiles in each dimension. Must be float in the range [0, 1). Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| tilePredictionsNmsThreshold | The IOU threshold to use to perform NMS while merging predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1]. Note: This settings is not supported for the 'yolov5' algorithm. |
int |
| trainingBatchSize | Training batch size. Must be a positive integer. | int |
| validationBatchSize | Validation batch size. Must be a positive integer. | int |
| validationIouThreshold | IOU threshold to use when computing validation metric. Must be float in the range [0, 1]. | int |
| validationMetricType | Metric computation method to use for validation metrics. | 'Coco' 'CocoVoc' 'None' 'Voc' |
| warmupCosineLRCycles | Value of cosine cycle when learning rate scheduler is 'warmup_cosine'. Must be a float in the range [0, 1]. | int |
| warmupCosineLRWarmupEpochs | Value of warmup epochs when learning rate scheduler is 'warmup_cosine'. Must be a positive integer. | int |
| weightDecay | Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be a float in the range[0, 1]. | int |
ImageObjectDetection
| Name | Description | Value |
|---|---|---|
| limitSettings | [Required] Limit settings for the AutoML job. | ImageLimitSettings (required) |
| modelSettings | Settings used for training the model. | ImageModelSettingsObjectDetection |
| primaryMetric | Primary metric to optimize for this task. | 'MeanAveragePrecision' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | ImageModelDistributionSettingsObjectDetection[] |
| sweepSettings | Model sweeping and hyperparameter sweeping related settings. | ImageSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'ImageObjectDetection' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
ImageSweepSettings
| Name | Description | Value |
|---|---|---|
| earlyTermination | Type of early termination policy. | EarlyTerminationPolicy |
| samplingAlgorithm | [Required] Type of the hyperparameter sampling algorithms. | 'Bayesian' 'Grid' 'Random' (required) |
JobBaseProperties
| Name | Description | Value |
|---|---|---|
| componentId | ARM resource ID of the component resource. | string |
| computeId | ARM resource ID of the compute resource. | string |
| description | The asset description text. | string |
| displayName | Display name of job. | string |
| experimentName | The name of the experiment the job belongs to. If not set, the job is placed in the "Default" experiment. | string |
| identity | Identity configuration. If set, this should be one of AmlToken, ManagedIdentity, UserIdentity or null. Defaults to AmlToken if null. |
IdentityConfiguration |
| isArchived | Is the asset archived? | bool |
| jobType | Set to 'AutoML' for type AutoMLJob. Set to 'Command' for type CommandJob. Set to 'Labeling' for type LabelingJobProperties. Set to 'Pipeline' for type PipelineJob. Set to 'Spark' for type SparkJob. Set to 'Sweep' for type SweepJob. | 'AutoML' 'Command' 'Labeling' 'Pipeline' 'Spark' 'Sweep' (required) |
| 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. | ResourceBaseTags |
JobBaseServices
| Name | Description | Value |
|---|
JobInput
| Name | Description | Value |
|---|---|---|
| description | Description for the input. | string |
| jobInputType | Set to 'custom_model' for type CustomModelJobInput. Set to 'literal' for type LiteralJobInput. Set to 'mlflow_model' for type MLFlowModelJobInput. Set to 'mltable' for type MLTableJobInput. Set to 'triton_model' for type TritonModelJobInput. Set to 'uri_file' for type UriFileJobInput. Set to 'uri_folder' for type UriFolderJobInput. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
JobOutput
| Name | Description | Value |
|---|---|---|
| description | Description for the output. | string |
| jobOutputType | Set to 'custom_model' for type CustomModelJobOutput. Set to 'mlflow_model' for type MLFlowModelJobOutput. Set to 'mltable' for type MLTableJobOutput. Set to 'triton_model' for type TritonModelJobOutput. Set to 'uri_file' for type UriFileJobOutput. Set to 'uri_folder' for type UriFolderJobOutput. | 'custom_model' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
JobResourceConfiguration
| Name | Description | Value |
|---|---|---|
| dockerArgs | Extra arguments to pass to the Docker run command. This would override any parameters that have already been set by the system, or in this section. This parameter is only supported for Azure ML compute types. | string |
| instanceCount | Optional number of instances or nodes used by the compute target. | int |
| instanceType | Optional type of VM used as supported by the compute target. | string |
| properties | Additional properties bag. | ResourceConfigurationProperties |
| shmSize | Size of the docker container's shared memory block. This should be in the format of (number)(unit) where number as to be greater than 0 and the unit can be one of b(bytes), k(kilobytes), m(megabytes), or g(gigabytes). | string Constraints: Pattern = \d+[bBkKmMgG] |
JobScheduleAction
| Name | Description | Value |
|---|---|---|
| actionType | [Required] Specifies the action type of the schedule | 'CreateJob' (required) |
| jobDefinition | [Required] Defines Schedule action definition details. | JobBaseProperties (required) |
JobService
| Name | Description | Value |
|---|---|---|
| endpoint | Url for endpoint. | string |
| jobServiceType | Endpoint type. | string |
| nodes | Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node. |
Nodes |
| port | Port for endpoint set by user. | int |
| properties | Additional properties to set on the endpoint. | JobServiceProperties |
JobServiceProperties
| Name | Description | Value |
|---|
LabelCategory
| Name | Description | Value |
|---|---|---|
| classes | Dictionary of label classes in this category. | LabelCategoryClasses |
| displayName | Display name of the label category. | string |
| multiSelect | Indicates whether it is allowed to select multiple classes in this category. | 'Disabled' 'Enabled' |
LabelCategoryClasses
| Name | Description | Value |
|---|
LabelClass
| Name | Description | Value |
|---|---|---|
| displayName | Display name of the label class. | string |
| subclasses | Dictionary of subclasses of the label class. | LabelClassSubclasses |
LabelClassSubclasses
| Name | Description | Value |
|---|
LabelingDataConfiguration
| Name | Description | Value |
|---|---|---|
| dataId | Resource Id of the data asset to perform labeling. | string |
| incrementalDataRefresh | Indicates whether to enable incremental data refresh. | 'Disabled' 'Enabled' |
LabelingJobImageProperties
| Name | Description | Value |
|---|---|---|
| annotationType | Annotation type of image labeling job. | 'BoundingBox' 'Classification' 'InstanceSegmentation' |
| mediaType | [Required] Media type of the job. | 'Image' (required) |
LabelingJobInstructions
| Name | Description | Value |
|---|---|---|
| uri | The link to a page with detailed labeling instructions for labelers. | string |
LabelingJobLabelCategories
| Name | Description | Value |
|---|
LabelingJobMediaProperties
| Name | Description | Value |
|---|---|---|
| mediaType | Set to 'Image' for type LabelingJobImageProperties. Set to 'Text' for type LabelingJobTextProperties. | 'Image' 'Text' (required) |
LabelingJobProperties
| Name | Description | Value |
|---|---|---|
| dataConfiguration | Configuration of data used in the job. | LabelingDataConfiguration |
| jobInstructions | Labeling instructions of the job. | LabelingJobInstructions |
| jobType | [Required] Specifies the type of job. | 'Labeling' (required) |
| labelCategories | Label categories of the job. | LabelingJobLabelCategories |
| labelingJobMediaProperties | Media type specific properties in the job. | LabelingJobMediaProperties |
| mlAssistConfiguration | Configuration of MLAssist feature in the job. | MLAssistConfiguration |
LabelingJobTextProperties
| Name | Description | Value |
|---|---|---|
| annotationType | Annotation type of text labeling job. | 'Classification' 'NamedEntityRecognition' |
| mediaType | [Required] Media type of the job. | 'Text' (required) |
LiteralJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'literal' (required) |
| value | [Required] Literal value for the input. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
ManagedIdentity
| Name | Description | Value |
|---|---|---|
| clientId | Specifies a user-assigned identity by client ID. For system-assigned, do not set this field. | string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$ |
| identityType | [Required] Specifies the type of identity framework. | 'Managed' (required) |
| objectId | Specifies a user-assigned identity by object ID. For system-assigned, do not set this field. | string Constraints: Min length = 36 Max length = 36 Pattern = ^[0-9a-fA-F]{8}-([0-9a-fA-F]{4}-){3}[0-9a-fA-F]{12}$ |
| resourceId | Specifies a user-assigned identity by ARM resource ID. For system-assigned, do not set this field. | string |
MedianStoppingPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'MedianStopping' (required) |
Microsoft.MachineLearningServices/workspaces/schedules
| Name | Description | Value |
|---|---|---|
| name | The resource name | string Constraints: Pattern = ^[a-zA-Z0-9][a-zA-Z0-9\-_]{0,254}$ (required) |
| parent_id | The ID of the resource that is the parent for this resource. | ID for resource of type: workspaces |
| properties | [Required] Additional attributes of the entity. | ScheduleProperties (required) |
| type | The resource type | "Microsoft.MachineLearningServices/workspaces/schedules@2022-12-01-preview" |
MLAssistConfiguration
| Name | Description | Value |
|---|---|---|
| mlAssist | Set to 'Disabled' for type MLAssistConfigurationDisabled. Set to 'Enabled' for type MLAssistConfigurationEnabled. | 'Disabled' 'Enabled' (required) |
MLAssistConfigurationDisabled
| Name | Description | Value |
|---|---|---|
| mlAssist | [Required] Indicates whether MLAssist feature is enabled. | 'Disabled' (required) |
MLAssistConfigurationEnabled
| Name | Description | Value |
|---|---|---|
| inferencingComputeBinding | [Required] AML compute binding used in inferencing. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| mlAssist | [Required] Indicates whether MLAssist feature is enabled. | 'Enabled' (required) |
| trainingComputeBinding | [Required] AML compute binding used in training. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'mlflow_model' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobInput
| Name | Description | Value |
|---|---|---|
| description | Description for the input. | string |
| jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'mlflow_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
MLTableJobInput
| Name | Description | Value |
|---|---|---|
| description | Description for the input. | string |
| jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLTableJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'mltable' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
MLTableJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'mltable' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
Mpi
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'Mpi' (required) |
| processCountPerInstance | Number of processes per MPI node. | int |
NCrossValidations
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoNCrossValidations. Set to 'Custom' for type CustomNCrossValidations. | 'Auto' 'Custom' (required) |
NlpFixedParameters
| Name | Description | Value |
|---|---|---|
| gradientAccumulationSteps | Number of steps to accumulate gradients over before running a backward pass. | int |
| learningRate | The learning rate for the training procedure. | int |
| learningRateScheduler | The type of learning rate schedule to use during the training procedure. | 'Constant' 'ConstantWithWarmup' 'Cosine' 'CosineWithRestarts' 'Linear' 'None' 'Polynomial' |
| modelName | The name of the model to train. | string |
| numberOfEpochs | Number of training epochs. | int |
| trainingBatchSize | The batch size for the training procedure. | int |
| validationBatchSize | The batch size to be used during evaluation. | int |
| warmupRatio | The warmup ratio, used alongside LrSchedulerType. | int |
| weightDecay | The weight decay for the training procedure. | int |
NlpParameterSubspace
| Name | Description | Value |
|---|---|---|
| gradientAccumulationSteps | Number of steps to accumulate gradients over before running a backward pass. | string |
| learningRate | The learning rate for the training procedure. | string |
| learningRateScheduler | The type of learning rate schedule to use during the training procedure. | string |
| modelName | The name of the model to train. | string |
| numberOfEpochs | Number of training epochs. | string |
| trainingBatchSize | The batch size for the training procedure. | string |
| validationBatchSize | The batch size to be used during evaluation. | string |
| warmupRatio | The warmup ratio, used alongside LrSchedulerType. | string |
| weightDecay | The weight decay for the training procedure. | string |
NlpSweepSettings
| Name | Description | Value |
|---|---|---|
| earlyTermination | Type of early termination policy for the sweeping job. | EarlyTerminationPolicy |
| samplingAlgorithm | [Required] Type of sampling algorithm. | 'Bayesian' 'Grid' 'Random' (required) |
NlpVerticalFeaturizationSettings
| Name | Description | Value |
|---|---|---|
| datasetLanguage | Dataset language, useful for the text data. | string |
NlpVerticalLimitSettings
| Name | Description | Value |
|---|---|---|
| maxConcurrentTrials | Maximum Concurrent AutoML iterations. | int |
| maxNodes | Maximum nodes to use for the experiment. | int |
| maxTrials | Number of AutoML iterations. | int |
| timeout | AutoML job timeout. | string |
| trialTimeout | Timeout for individual HD trials. | string |
Nodes
| Name | Description | Value |
|---|---|---|
| nodesValueType | Set to 'All' for type AllNodes. | 'All' (required) |
Objective
| Name | Description | Value |
|---|---|---|
| goal | [Required] Defines supported metric goals for hyperparameter tuning | 'Maximize' 'Minimize' (required) |
| primaryMetric | [Required] Name of the metric to optimize. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
PipelineJob
| Name | Description | Value |
|---|---|---|
| inputs | Inputs for the pipeline job. | PipelineJobInputs |
| jobs | Jobs construct the Pipeline Job. | PipelineJobJobs |
| jobType | [Required] Specifies the type of job. | 'Pipeline' (required) |
| outputs | Outputs for the pipeline job | PipelineJobOutputs |
| settings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | any |
| sourceJobId | ARM resource ID of source job. | string |
PipelineJobInputs
| Name | Description | Value |
|---|
PipelineJobJobs
| Name | Description | Value |
|---|
PipelineJobOutputs
| Name | Description | Value |
|---|
PyTorch
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'PyTorch' (required) |
| processCountPerInstance | Number of processes per node. | int |
RandomSamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| logbase | An optional positive number or e in string format to be used as base for log based random sampling | string |
| rule | The specific type of random algorithm | 'Random' 'Sobol' |
| samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Random' (required) |
| seed | An optional integer to use as the seed for random number generation | int |
RecurrenceSchedule
| Name | Description | Value |
|---|---|---|
| hours | [Required] List of hours for the schedule. | int[] (required) |
| minutes | [Required] List of minutes for the schedule. | int[] (required) |
| monthDays | List of month days for the schedule | int[] |
| weekDays | List of days for the schedule. | String array containing any of: 'Friday' 'Monday' 'Saturday' 'Sunday' 'Thursday' 'Tuesday' 'Wednesday' |
RecurrenceTrigger
| Name | Description | Value |
|---|---|---|
| frequency | [Required] The frequency to trigger schedule. | 'Day' 'Hour' 'Minute' 'Month' 'Week' (required) |
| interval | [Required] Specifies schedule interval in conjunction with frequency | int (required) |
| schedule | The recurrence schedule. | RecurrenceSchedule |
| triggerType | [Required] | 'Recurrence' (required) |
Regression
| Name | Description | Value |
|---|---|---|
| cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
| featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | TableFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
| nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
| primaryMetric | Primary metric for regression task. | 'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | TableParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | TableSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'Regression' (required) |
| testData | Test data input. | MLTableJobInput |
| testDataSize | The fraction of test dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| trainingSettings | Inputs for training phase for an AutoML Job. | RegressionTrainingSettings |
| validationData | Validation data inputs. | MLTableJobInput |
| validationDataSize | The fraction of training dataset that needs to be set aside for validation purpose. Values between (0.0 , 1.0) Applied when validation dataset is not provided. |
int |
| weightColumnName | The name of the sample weight column. Automated ML supports a weighted column as an input, causing rows in the data to be weighted up or down. | string |
RegressionTrainingSettings
| Name | Description | Value |
|---|---|---|
| allowedTrainingAlgorithms | Allowed models for regression task. | String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor' |
| blockedTrainingAlgorithms | Blocked models for regression task. | String array containing any of: 'DecisionTree' 'ElasticNet' 'ExtremeRandomTrees' 'GradientBoosting' 'KNN' 'LassoLars' 'LightGBM' 'RandomForest' 'SGD' 'XGBoostRegressor' |
| enableDnnTraining | Enable recommendation of DNN models. | bool |
| enableModelExplainability | Flag to turn on explainability on best model. | bool |
| enableOnnxCompatibleModels | Flag for enabling onnx compatible models. | bool |
| enableStackEnsemble | Enable stack ensemble run. | bool |
| enableVoteEnsemble | Enable voting ensemble run. | bool |
| ensembleModelDownloadTimeout | During VotingEnsemble and StackEnsemble model generation, multiple fitted models from the previous child runs are downloaded. Configure this parameter with a higher value than 300 secs, if more time is needed. |
string |
| stackEnsembleSettings | Stack ensemble settings for stack ensemble run. | StackEnsembleSettings |
| trainingMode | TrainingMode mode - Setting to 'auto' is same as setting it to 'non-distributed' for now, however in the future may result in mixed mode or heuristics based mode selection. Default is 'auto'. If 'Distributed' then only distributed featurization is used and distributed algorithms are chosen. If 'NonDistributed' then only non distributed algorithms are chosen. |
'Auto' 'Distributed' 'NonDistributed' |
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseProperties
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceBaseTags
| Name | Description | Value |
|---|
ResourceConfigurationProperties
| Name | Description | Value |
|---|
SamplingAlgorithm
| Name | Description | Value |
|---|---|---|
| samplingAlgorithmType | Set to 'Bayesian' for type BayesianSamplingAlgorithm. Set to 'Grid' for type GridSamplingAlgorithm. Set to 'Random' for type RandomSamplingAlgorithm. | 'Bayesian' 'Grid' 'Random' (required) |
ScheduleActionBase
| Name | Description | Value |
|---|---|---|
| actionType | Set to 'CreateJob' for type JobScheduleAction. Set to 'InvokeBatchEndpoint' for type EndpointScheduleAction. | 'CreateJob' 'InvokeBatchEndpoint' (required) |
ScheduleProperties
| Name | Description | Value |
|---|---|---|
| action | [Required] Specifies the action of the schedule | ScheduleActionBase (required) |
| description | The asset description text. | string |
| displayName | Display name of schedule. | string |
| isEnabled | Is the schedule enabled? | bool |
| properties | The asset property dictionary. | ResourceBaseProperties |
| tags | Tag dictionary. Tags can be added, removed, and updated. | ResourceBaseTags |
| trigger | [Required] Specifies the trigger details | TriggerBase (required) |
Seasonality
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoSeasonality. Set to 'Custom' for type CustomSeasonality. | 'Auto' 'Custom' (required) |
SparkJob
| Name | Description | Value |
|---|---|---|
| archives | Archive files used in the job. | string[] |
| args | Arguments for the job. | string |
| codeId | [Required] ARM resource ID of the code asset. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| conf | Spark configured properties. | SparkJobConf |
| entry | [Required] The entry to execute on startup of the job. | SparkJobEntry (required) |
| environmentId | The ARM resource ID of the Environment specification for the job. | string |
| files | Files used in the job. | string[] |
| inputs | Mapping of input data bindings used in the job. | SparkJobInputs |
| jars | Jar files used in the job. | string[] |
| jobType | [Required] Specifies the type of job. | 'Spark' (required) |
| outputs | Mapping of output data bindings used in the job. | SparkJobOutputs |
| pyFiles | Python files used in the job. | string[] |
| resources | Compute Resource configuration for the job. | SparkResourceConfiguration |
SparkJobConf
| Name | Description | Value |
|---|
SparkJobEntry
| Name | Description | Value |
|---|---|---|
| sparkJobEntryType | Set to 'SparkJobPythonEntry' for type SparkJobPythonEntry. Set to 'SparkJobScalaEntry' for type SparkJobScalaEntry. | 'SparkJobPythonEntry' 'SparkJobScalaEntry' (required) |
SparkJobInputs
| Name | Description | Value |
|---|
SparkJobOutputs
| Name | Description | Value |
|---|
SparkJobPythonEntry
| Name | Description | Value |
|---|---|---|
| file | [Required] Relative python file path for job entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobPythonEntry' (required) |
SparkJobScalaEntry
| Name | Description | Value |
|---|---|---|
| className | [Required] Scala class name used as entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobScalaEntry' (required) |
SparkResourceConfiguration
| Name | Description | Value |
|---|---|---|
| instanceType | Optional type of VM used as supported by the compute target. | string |
| runtimeVersion | Version of spark runtime used for the job. | string |
StackEnsembleSettings
| Name | Description | Value |
|---|---|---|
| stackMetaLearnerKWargs | Optional parameters to pass to the initializer of the meta-learner. | any |
| stackMetaLearnerTrainPercentage | Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2. | int |
| stackMetaLearnerType | The meta-learner is a model trained on the output of the individual heterogeneous models. | 'ElasticNet' 'ElasticNetCV' 'LightGBMClassifier' 'LightGBMRegressor' 'LinearRegression' 'LogisticRegression' 'LogisticRegressionCV' 'None' |
SweepJob
| Name | Description | Value |
|---|---|---|
| earlyTermination | Early termination policies enable canceling poor-performing runs before they complete | EarlyTerminationPolicy |
| inputs | Mapping of input data bindings used in the job. | SweepJobInputs |
| jobType | [Required] Specifies the type of job. | 'Sweep' (required) |
| limits | Sweep Job limit. | SweepJobLimits |
| objective | [Required] Optimization objective. | Objective (required) |
| outputs | Mapping of output data bindings used in the job. | SweepJobOutputs |
| samplingAlgorithm | [Required] The hyperparameter sampling algorithm | SamplingAlgorithm (required) |
| searchSpace | [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter | any (required) |
| trial | [Required] Trial component definition. | TrialComponent (required) |
SweepJobInputs
| Name | Description | Value |
|---|
SweepJobLimits
| Name | Description | Value |
|---|---|---|
| jobLimitsType | [Required] JobLimit type. | 'Command' 'Sweep' (required) |
| maxConcurrentTrials | Sweep Job max concurrent trials. | int |
| maxTotalTrials | Sweep Job max total trials. | int |
| timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
| trialTimeout | Sweep Job Trial timeout value. | string |
SweepJobOutputs
| Name | Description | Value |
|---|
TableFixedParameters
| Name | Description | Value |
|---|---|---|
| booster | Specify the boosting type, e.g gbdt for XGBoost. | string |
| boostingType | Specify the boosting type, e.g gbdt for LightGBM. | string |
| growPolicy | Specify the grow policy, which controls the way new nodes are added to the tree. | string |
| learningRate | The learning rate for the training procedure. | int |
| maxBin | Specify the Maximum number of discrete bins to bucket continuous features . | int |
| maxDepth | Specify the max depth to limit the tree depth explicitly. | int |
| maxLeaves | Specify the max leaves to limit the tree leaves explicitly. | int |
| minDataInLeaf | The minimum number of data per leaf. | int |
| minSplitGain | Minimum loss reduction required to make a further partition on a leaf node of the tree. | int |
| modelName | The name of the model to train. | string |
| nEstimators | Specify the number of trees (or rounds) in an model. | int |
| numLeaves | Specify the number of leaves. | int |
| preprocessorName | The name of the preprocessor to use. | string |
| regAlpha | L1 regularization term on weights. | int |
| regLambda | L2 regularization term on weights. | int |
| subsample | Subsample ratio of the training instance. | int |
| subsampleFreq | Frequency of subsample. | int |
| treeMethod | Specify the tree method. | string |
| withMean | If true, center before scaling the data with StandardScalar. | bool |
| withStd | If true, scaling the data with Unit Variance with StandardScalar. | bool |
TableParameterSubspace
| Name | Description | Value |
|---|---|---|
| booster | Specify the boosting type, e.g gbdt for XGBoost. | string |
| boostingType | Specify the boosting type, e.g gbdt for LightGBM. | string |
| growPolicy | Specify the grow policy, which controls the way new nodes are added to the tree. | string |
| learningRate | The learning rate for the training procedure. | string |
| maxBin | Specify the Maximum number of discrete bins to bucket continuous features . | string |
| maxDepth | Specify the max depth to limit the tree depth explicitly. | string |
| maxLeaves | Specify the max leaves to limit the tree leaves explicitly. | string |
| minDataInLeaf | The minimum number of data per leaf. | string |
| minSplitGain | Minimum loss reduction required to make a further partition on a leaf node of the tree. | string |
| modelName | The name of the model to train. | string |
| nEstimators | Specify the number of trees (or rounds) in an model. | string |
| numLeaves | Specify the number of leaves. | string |
| preprocessorName | The name of the preprocessor to use. | string |
| regAlpha | L1 regularization term on weights. | string |
| regLambda | L2 regularization term on weights. | string |
| subsample | Subsample ratio of the training instance. | string |
| subsampleFreq | Frequency of subsample | string |
| treeMethod | Specify the tree method. | string |
| withMean | If true, center before scaling the data with StandardScalar. | string |
| withStd | If true, scaling the data with Unit Variance with StandardScalar. | string |
TableSweepSettings
| Name | Description | Value |
|---|---|---|
| earlyTermination | Type of early termination policy for the sweeping job. | EarlyTerminationPolicy |
| samplingAlgorithm | [Required] Type of sampling algorithm. | 'Bayesian' 'Grid' 'Random' (required) |
TableVerticalFeaturizationSettings
| Name | Description | Value |
|---|---|---|
| blockedTransformers | These transformers shall not be used in featurization. | String array containing any of: 'CatTargetEncoder' 'CountVectorizer' 'HashOneHotEncoder' 'LabelEncoder' 'NaiveBayes' 'OneHotEncoder' 'TextTargetEncoder' 'TfIdf' 'WoETargetEncoder' 'WordEmbedding' |
| columnNameAndTypes | Dictionary of column name and its type (int, float, string, datetime etc). | TableVerticalFeaturizationSettingsColumnNameAndTypes |
| datasetLanguage | Dataset language, useful for the text data. | string |
| enableDnnFeaturization | Determines whether to use Dnn based featurizers for data featurization. | bool |
| mode | Featurization mode - User can keep the default 'Auto' mode and AutoML will take care of necessary transformation of the data in featurization phase. If 'Off' is selected then no featurization is done. If 'Custom' is selected then user can specify additional inputs to customize how featurization is done. |
'Auto' 'Custom' 'Off' |
| transformerParams | User can specify additional transformers to be used along with the columns to which it would be applied and parameters for the transformer constructor. | TableVerticalFeaturizationSettingsTransformerParams |
TableVerticalFeaturizationSettingsColumnNameAndTypes
| Name | Description | Value |
|---|
TableVerticalFeaturizationSettingsTransformerParams
| Name | Description | Value |
|---|
TableVerticalLimitSettings
| Name | Description | Value |
|---|---|---|
| enableEarlyTermination | Enable early termination, determines whether or not if AutoMLJob will terminate early if there is no score improvement in last 20 iterations. | bool |
| exitScore | Exit score for the AutoML job. | int |
| maxConcurrentTrials | Maximum Concurrent iterations. | int |
| maxCoresPerTrial | Max cores per iteration. | int |
| maxNodes | Maximum nodes to use for the experiment. | int |
| maxTrials | Number of iterations. | int |
| sweepConcurrentTrials | Number of concurrent sweeping runs that user wants to trigger. | int |
| sweepTrials | Number of sweeping runs that user wants to trigger. | int |
| timeout | AutoML job timeout. | string |
| trialTimeout | Iteration timeout. | string |
TargetLags
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoTargetLags. Set to 'Custom' for type CustomTargetLags. | 'Auto' 'Custom' (required) |
TargetRollingWindowSize
| Name | Description | Value |
|---|---|---|
| mode | Set to 'Auto' for type AutoTargetRollingWindowSize. Set to 'Custom' for type CustomTargetRollingWindowSize. | 'Auto' 'Custom' (required) |
TensorFlow
| Name | Description | Value |
|---|---|---|
| distributionType | [Required] Specifies the type of distribution framework. | 'TensorFlow' (required) |
| parameterServerCount | Number of parameter server tasks. | int |
| workerCount | Number of workers. If not specified, will default to the instance count. | int |
TextClassification
| Name | Description | Value |
|---|---|---|
| featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | NlpFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
| primaryMetric | Primary metric for Text-Classification task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | NlpParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | NlpSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'TextClassification' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TextClassificationMultilabel
| Name | Description | Value |
|---|---|---|
| featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | NlpFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | NlpParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | NlpSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'TextClassificationMultilabel' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TextNer
| Name | Description | Value |
|---|---|---|
| featurizationSettings | Featurization inputs needed for AutoML job. | NlpVerticalFeaturizationSettings |
| fixedParameters | Model/training parameters that will remain constant throughout training. | NlpFixedParameters |
| limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
| searchSpace | Search space for sampling different combinations of models and their hyperparameters. | NlpParameterSubspace[] |
| sweepSettings | Settings for model sweeping and hyperparameter tuning. | NlpSweepSettings |
| taskType | [Required] Task type for AutoMLJob. | 'TextNER' (required) |
| validationData | Validation data inputs. | MLTableJobInput |
TrialComponent
| Name | Description | Value |
|---|---|---|
| codeId | ARM resource ID of the code asset. | string |
| command | [Required] The command to execute on startup of the job. eg. "python train.py" | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
| distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
| environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
| environmentVariables | Environment variables included in the job. | TrialComponentEnvironmentVariables |
| resources | Compute Resource configuration for the job. | JobResourceConfiguration |
TrialComponentEnvironmentVariables
| Name | Description | Value |
|---|
TriggerBase
| Name | Description | Value |
|---|---|---|
| endTime | Specifies end time of schedule in ISO 8601, but without a UTC offset. Refer https://en.wikipedia.org/wiki/ISO_8601. Recommented format would be "2022-06-01T00:00:01" If not present, the schedule will run indefinitely |
string |
| startTime | Specifies start time of schedule in ISO 8601 format, but without a UTC offset. | string |
| timeZone | Specifies time zone in which the schedule runs. TimeZone should follow Windows time zone format. Refer: /windows-hardware/manufacture/desktop/default-time-zones?view=windows-11 |
string |
| triggerType | Set to 'Cron' for type CronTrigger. Set to 'Recurrence' for type RecurrenceTrigger. | 'Cron' 'Recurrence' (required) |
TritonModelJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'triton_model' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
TritonModelJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'triton_model' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
TruncationSelectionPolicy
| Name | Description | Value |
|---|---|---|
| policyType | [Required] Name of policy configuration | 'TruncationSelection' (required) |
| truncationPercentage | The percentage of runs to cancel at each evaluation interval. | int |
UriFileJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'uri_file' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
UriFileJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'uri_file' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
UriFolderJobInput
| Name | Description | Value |
|---|---|---|
| jobInputType | [Required] Specifies the type of job. | 'uri_folder' (required) |
| mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
| uri | [Required] Input Asset URI. | string Constraints: Pattern = [a-zA-Z0-9_] (required) |
UriFolderJobOutput
| Name | Description | Value |
|---|---|---|
| assetName | Output Asset Name. | string |
| assetVersion | Output Asset Version. | string |
| jobOutputType | [Required] Specifies the type of job. | 'uri_folder' (required) |
| mode | Output Asset Delivery Mode. | 'Direct' 'ReadWriteMount' 'Upload' |
| uri | Output Asset URI. | string |
UserIdentity
| Name | Description | Value |
|---|---|---|
| identityType | [Required] Specifies the type of identity framework. | 'UserIdentity' (required) |