Microsoft.MachineLearningServices workspaces/jobs 2024-10-01-preview
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- 2024-10-01
- 2024-10-01-preview
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- 2024-04-01
- 2024-04-01-preview
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- 2023-10-01
- 2023-08-01-preview
- 2023-06-01-preview
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- 2022-12-01-preview
- 2022-10-01
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- 2022-06-01-preview
- 2022-05-01
- 2022-02-01-preview
- 2021-03-01-preview
Bicep resource definition
The workspaces/jobs resource type can be deployed with operations that target:
- Resource groups - See resource group deployment commands
For a list of changed properties in each API version, see change log.
Resource format
To create a Microsoft.MachineLearningServices/workspaces/jobs resource, add the following Bicep to your template.
resource symbolicname 'Microsoft.MachineLearningServices/workspaces/jobs@2024-10-01-preview' = {
parent: resourceSymbolicName
name: 'string'
properties: {
componentId: 'string'
computeId: 'string'
description: 'string'
displayName: 'string'
experimentName: 'string'
identity: {
identityType: 'string'
// For remaining properties, see IdentityConfiguration objects
}
isArchived: bool
notificationSetting: {
emailOn: [
'string'
]
emails: [
'string'
]
webhooks: {
{customized property}: {
eventType: 'string'
webhookType: 'string'
// For remaining properties, see Webhook objects
}
}
}
properties: {
{customized property}: 'string'
}
services: {
{customized property}: {
endpoint: 'string'
jobServiceType: 'string'
nodes: {
nodesValueType: 'string'
// For remaining properties, see Nodes objects
}
port: int
properties: {
{customized property}: 'string'
}
}
}
tags: {
{customized property}: 'string'
}
jobType: 'string'
// For remaining properties, see JobBaseProperties objects
}
}
JobBaseProperties objects
Set the jobType property to specify the type of object.
For AutoML, use:
{
environmentId: 'string'
environmentVariables: {
{customized property}: 'string'
}
jobType: 'AutoML'
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
queueSettings: {
jobTier: 'string'
}
resources: {
dockerArgs: 'string'
dockerArgsList: [
'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:
{
codeId: 'string'
command: 'string'
distribution: {
distributionType: 'string'
// For remaining properties, see DistributionConfiguration objects
}
environmentId: 'string'
environmentVariables: {
{customized property}: 'string'
}
inputs: {
{customized property}: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
}
jobType: 'Command'
limits: {
jobLimitsType: 'string'
timeout: 'string'
}
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
queueSettings: {
jobTier: 'string'
}
resources: {
dockerArgs: 'string'
dockerArgsList: [
'string'
]
instanceCount: int
instanceType: 'string'
properties: {
{customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
}
shmSize: 'string'
}
}
For FineTuning, use:
{
fineTuningDetails: {
model: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
taskType: 'string'
trainingData: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
validationData: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
modelProvider: 'string'
// For remaining properties, see FineTuningVertical objects
}
jobType: 'FineTuning'
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
queueSettings: {
jobTier: 'string'
}
resources: {
instanceTypes: [
'string'
]
}
}
For Pipeline, use:
{
inputs: {
{customized property}: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
}
jobs: {
{customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
}
jobType: 'Pipeline'
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
settings: any(Azure.Bicep.Types.Concrete.AnyType)
sourceJobId: 'string'
}
For Spark, use:
{
archives: [
'string'
]
args: 'string'
codeId: 'string'
conf: {
{customized property}: 'string'
}
entry: {
sparkJobEntryType: 'string'
// For remaining properties, see SparkJobEntry objects
}
environmentId: 'string'
environmentVariables: {
{customized property}: 'string'
}
files: [
'string'
]
inputs: {
{customized property}: {
description: 'string'
jobInputType: 'string'
// For remaining properties, see JobInput objects
}
}
jars: [
'string'
]
jobType: 'Spark'
outputs: {
{customized property}: {
description: 'string'
jobOutputType: 'string'
// For remaining properties, see JobOutput objects
}
}
pyFiles: [
'string'
]
queueSettings: {
jobTier: 'string'
}
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
}
}
queueSettings: {
jobTier: 'string'
}
samplingAlgorithm: {
samplingAlgorithmType: 'string'
// For remaining properties, see SamplingAlgorithm objects
}
searchSpace: any(Azure.Bicep.Types.Concrete.AnyType)
trial: {
codeId: 'string'
command: 'string'
distribution: {
distributionType: 'string'
// For remaining properties, see DistributionConfiguration objects
}
environmentId: 'string'
environmentVariables: {
{customized property}: 'string'
}
resources: {
dockerArgs: 'string'
dockerArgsList: [
'string'
]
instanceCount: int
instanceType: 'string'
properties: {
{customized property}: any(Azure.Bicep.Types.Concrete.AnyType)
}
shmSize: 'string'
}
}
}
Webhook objects
Set the webhookType property to specify the type of object.
For AzureDevOps, use:
{
webhookType: 'AzureDevOps'
}
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
}
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)
}
]
}
}
limitSettings: {
enableEarlyTermination: bool
exitScore: int
maxConcurrentTrials: int
maxCoresPerTrial: int
maxTrials: int
timeout: 'string'
trialTimeout: 'string'
}
nCrossValidations: {
mode: 'string'
// For remaining properties, see NCrossValidations objects
}
positiveLabel: 'string'
primaryMetric: 'string'
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'
}
}
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)
}
]
}
}
forecastingSettings: {
countryOrRegionForHolidays: 'string'
cvStepSize: int
featureLags: 'string'
forecastHorizon: {
mode: 'string'
// For remaining properties, see ForecastHorizon objects
}
frequency: 'string'
seasonality: {
mode: 'string'
// For remaining properties, see Seasonality objects
}
shortSeriesHandlingConfig: 'string'
targetAggregateFunction: 'string'
targetLags: {
mode: 'string'
// For remaining properties, see TargetLags objects
}
targetRollingWindowSize: {
mode: 'string'
// For remaining properties, see TargetRollingWindowSize objects
}
timeColumnName: 'string'
timeSeriesIdColumnNames: [
'string'
]
useStl: 'string'
}
limitSettings: {
enableEarlyTermination: bool
exitScore: int
maxConcurrentTrials: int
maxCoresPerTrial: int
maxTrials: int
timeout: 'string'
trialTimeout: 'string'
}
nCrossValidations: {
mode: 'string'
// For remaining properties, see NCrossValidations objects
}
primaryMetric: 'string'
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'
}
}
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)
}
]
}
}
limitSettings: {
enableEarlyTermination: bool
exitScore: int
maxConcurrentTrials: int
maxCoresPerTrial: int
maxTrials: int
timeout: 'string'
trialTimeout: 'string'
}
nCrossValidations: {
mode: 'string'
// For remaining properties, see NCrossValidations objects
}
primaryMetric: 'string'
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'
}
}
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
validationDataSize: int
weightColumnName: 'string'
}
For TextClassification, use:
{
featurizationSettings: {
datasetLanguage: 'string'
}
limitSettings: {
maxConcurrentTrials: int
maxTrials: int
timeout: 'string'
}
primaryMetric: 'string'
taskType: 'TextClassification'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
}
For TextClassificationMultilabel, use:
{
featurizationSettings: {
datasetLanguage: 'string'
}
limitSettings: {
maxConcurrentTrials: int
maxTrials: int
timeout: 'string'
}
taskType: 'TextClassificationMultilabel'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
}
For TextNER, use:
{
featurizationSettings: {
datasetLanguage: 'string'
}
limitSettings: {
maxConcurrentTrials: int
maxTrials: int
timeout: 'string'
}
taskType: 'TextNER'
validationData: {
description: 'string'
jobInputType: 'string'
mode: 'string'
uri: 'string'
}
}
Nodes objects
Set the nodesValueType property to specify the type of object.
For All, use:
{
nodesValueType: 'All'
}
ForecastHorizon objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode: 'Auto'
}
For Custom, use:
{
mode: 'Custom'
value: int
}
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'
}
FineTuningVertical objects
Set the modelProvider property to specify the type of object.
For AzureOpenAI, use:
{
hyperParameters: {
batchSize: int
learningRateMultiplier: int
nEpochs: int
}
modelProvider: 'AzureOpenAI'
}
For Custom, use:
{
hyperParameters: {
{customized property}: 'string'
}
modelProvider: 'Custom'
}
TargetLags objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode: 'Auto'
}
For Custom, use:
{
mode: 'Custom'
values: [
int
]
}
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:
{
rule: 'string'
samplingAlgorithmType: 'Random'
seed: int
}
Seasonality objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode: 'Auto'
}
For Custom, use:
{
mode: 'Custom'
value: int
}
JobOutput objects
Set the jobOutputType property to specify the type of object.
For custom_model, use:
{
assetName: 'string'
jobOutputType: 'custom_model'
mode: 'string'
uri: 'string'
}
For mlflow_model, use:
{
assetName: 'string'
jobOutputType: 'mlflow_model'
mode: 'string'
uri: 'string'
}
For mltable, use:
{
assetName: 'string'
jobOutputType: 'mltable'
mode: 'string'
uri: 'string'
}
For triton_model, use:
{
assetName: 'string'
jobOutputType: 'triton_model'
mode: 'string'
uri: 'string'
}
For uri_file, use:
{
assetName: 'string'
jobOutputType: 'uri_file'
mode: 'string'
uri: 'string'
}
For uri_folder, use:
{
assetName: 'string'
jobOutputType: 'uri_folder'
mode: 'string'
uri: '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'
}
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'
}
NCrossValidations objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode: 'Auto'
}
For Custom, use:
{
mode: 'Custom'
value: int
}
TargetRollingWindowSize objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode: 'Auto'
}
For Custom, use:
{
mode: 'Custom'
value: int
}
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
}
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) |
AutoMLJob
Name | Description | Value |
---|---|---|
environmentId | The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job. |
string |
environmentVariables | Environment variables included in the job. | AutoMLJobEnvironmentVariables |
jobType | [Required] Specifies the type of job. | 'AutoML' (required) |
outputs | Mapping of output data bindings used in the job. | AutoMLJobOutputs |
queueSettings | Queue settings for the job | QueueSettings |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
taskDetails | [Required] This represents scenario which can be one of Tables/NLP/Image | AutoMLVertical (required) |
AutoMLJobEnvironmentVariables
Name | Description | Value |
---|
AutoMLJobOutputs
Name | Description | Value |
---|
AutoMLVertical
Name | Description | Value |
---|---|---|
logVerbosity | Log verbosity for the job. | 'Critical' 'Debug' 'Error' 'Info' 'NotSet' 'Warning' |
targetColumnName | Target column name: This is prediction values column. Also known as label column name in context of classification tasks. |
string |
taskType | Set to 'Classification' for type Classification. Set to 'Forecasting' for type Forecasting. Set to 'ImageClassification' for type ImageClassification. Set to 'ImageClassificationMultilabel' for type ImageClassificationMultilabel. Set to 'ImageInstanceSegmentation' for type ImageInstanceSegmentation. Set to 'ImageObjectDetection' for type ImageObjectDetection. Set to 'Regression' for type Regression. Set to 'TextClassification' for type TextClassification. Set to 'TextClassificationMultilabel' for type TextClassificationMultilabel. Set to 'TextNER' for type TextNer. | 'Classification' 'Forecasting' 'ImageClassification' 'ImageClassificationMultilabel' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'Regression' 'TextClassification' 'TextClassificationMultilabel' 'TextNER' (required) |
trainingData | [Required] Training data input. | MLTableJobInput (required) |
AutoNCrossValidations
Name | Description | Value |
---|---|---|
mode | [Required] Mode for determining N-Cross validations. | 'Auto' (required) |
AutoSeasonality
Name | Description | Value |
---|---|---|
mode | [Required] Seasonality mode. | 'Auto' (required) |
AutoTargetLags
Name | Description | Value |
---|---|---|
mode | [Required] Set target lags mode - Auto/Custom | 'Auto' (required) |
AutoTargetRollingWindowSize
Name | Description | Value |
---|---|---|
mode | [Required] TargetRollingWindowSiz detection mode. | 'Auto' (required) |
AzureDevOpsWebhook
Name | Description | Value |
---|---|---|
webhookType | [Required] Specifies the type of service to send a callback | 'AzureDevOps' (required) |
AzureOpenAiFineTuning
Name | Description | Value |
---|---|---|
hyperParameters | HyperParameters for fine tuning Azure Open AI model. | AzureOpenAiHyperParameters |
modelProvider | [Required] Enum to determine the type of fine tuning. | 'AzureOpenAI' (required) |
AzureOpenAiHyperParameters
Name | Description | Value |
---|---|---|
batchSize | Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. | int |
learningRateMultiplier | Scaling factor for the learning rate. A smaller learning rate may be useful to avoid over fitting. | int |
nEpochs | The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. | int |
BanditPolicy
Name | Description | Value |
---|---|---|
policyType | [Required] Name of policy configuration | 'Bandit' (required) |
slackAmount | Absolute distance allowed from the best performing run. | int |
slackFactor | Ratio of the allowed distance from the best performing run. | int |
BayesianSamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Bayesian' (required) |
Classification
Name | Description | Value |
---|---|---|
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
positiveLabel | Positive label for binary metrics calculation. | string |
primaryMetric | Primary metric for the task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
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 |
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 |
---|---|---|
codeId | ARM resource ID of the code asset. | string |
command | [Required] The command to execute on startup of the job. eg. "python train.py" | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
environmentVariables | Environment variables included in the job. | CommandJobEnvironmentVariables |
inputs | Mapping of input data bindings used in the job. | CommandJobInputs |
jobType | [Required] Specifies the type of job. | 'Command' (required) |
limits | Command Job limit. | CommandJobLimits |
outputs | Mapping of output data bindings used in the job. | CommandJobOutputs |
queueSettings | Queue settings for the job | QueueSettings |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
CommandJobEnvironmentVariables
Name | Description | Value |
---|
CommandJobInputs
Name | Description | Value |
---|
CommandJobLimits
Name | Description | Value |
---|---|---|
jobLimitsType | [Required] JobLimit type. | 'Command' 'Sweep' (required) |
timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
CommandJobOutputs
Name | Description | Value |
---|
CustomForecastHorizon
Name | Description | Value |
---|---|---|
mode | [Required] Set forecast horizon value selection mode. | 'Custom' (required) |
value | [Required] Forecast horizon value. | int (required) |
CustomModelFineTuning
Name | Description | Value |
---|---|---|
hyperParameters | HyperParameters for fine tuning custom model. | CustomModelFineTuningHyperParameters |
modelProvider | [Required] Enum to determine the type of fine tuning. | 'Custom' (required) |
CustomModelFineTuningHyperParameters
Name | Description | Value |
---|
CustomModelJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'custom_model' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
CustomModelJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | string |
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) |
FineTuningJob
Name | Description | Value |
---|---|---|
fineTuningDetails | [Required] | FineTuningVertical (required) |
jobType | [Required] Specifies the type of job. | 'FineTuning' (required) |
outputs | [Required] | FineTuningJobOutputs (required) |
queueSettings | Queue settings for the job | QueueSettings |
resources | Instance types and other resources for the job | JobResources |
FineTuningJobOutputs
Name | Description | Value |
---|
FineTuningVertical
Name | Description | Value |
---|---|---|
model | [Required] Input model for fine tuning. | JobInput (required) |
modelProvider | Set to 'AzureOpenAI' for type AzureOpenAiFineTuning. Set to 'Custom' for type CustomModelFineTuning. | 'AzureOpenAI' 'Custom' (required) |
taskType | [Required] Fine tuning task type. | 'ChatCompletion' 'ImageClassification' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'QuestionAnswering' 'TextClassification' 'TextCompletion' 'TextSummarization' 'TextTranslation' 'TokenClassification' 'VideoMultiObjectTracking' (required) |
trainingData | [Required] Training data for fine tuning. | JobInput (required) |
validationData | Validation data for fine tuning. | JobInput |
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 |
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' |
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 |
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 'FineTuning' for type FineTuningJob. Set to 'Pipeline' for type PipelineJob. Set to 'Spark' for type SparkJob. Set to 'Sweep' for type SweepJob. | 'AutoML' 'Command' 'FineTuning' 'Pipeline' 'Spark' 'Sweep' (required) |
notificationSetting | Notification setting for the job | NotificationSetting |
properties | The asset property dictionary. | ResourceBaseProperties |
services | List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject. |
JobBaseServices |
tags | Tag dictionary. Tags can be added, removed, and updated. | 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 |
dockerArgsList | Extra arguments to pass to the Docker run command, as a collection. 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] |
JobResources
Name | Description | Value |
---|---|---|
instanceTypes | List of instance types to choose from. | string[] |
JobService
Name | Description | Value |
---|---|---|
endpoint | Url for endpoint. | string |
jobServiceType | Endpoint type. | string |
nodes | Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node. |
Nodes |
port | Port for endpoint. | int |
properties | Additional properties to set on the endpoint. | JobServiceProperties |
JobServiceProperties
Name | Description | Value |
---|
LiteralJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'literal' (required) |
value | [Required] Literal value for the input. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
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/jobs
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. | JobBaseProperties (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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobInput
Name | Description | Value |
---|---|---|
description | Description for the input. | string |
jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | string |
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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLTableJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'mltable' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLTableJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | string |
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) |
NlpVerticalFeaturizationSettings
Name | Description | Value |
---|---|---|
datasetLanguage | Dataset language, useful for the text data. | string |
NlpVerticalLimitSettings
Name | Description | Value |
---|---|---|
maxConcurrentTrials | Maximum Concurrent AutoML iterations. | int |
maxTrials | Number of AutoML iterations. | int |
timeout | AutoML job timeout. | string |
Nodes
Name | Description | Value |
---|---|---|
nodesValueType | Set to 'All' for type AllNodes. | 'All' (required) |
NotificationSetting
Name | Description | Value |
---|---|---|
emailOn | Send email notification to user on specified notification type | String array containing any of: 'JobCancelled' 'JobCompleted' 'JobFailed' |
emails | This is the email recipient list which has a limitation of 499 characters in total concat with comma separator | string[] |
webhooks | Send webhook callback to a service. Key is a user-provided name for the webhook. | NotificationSettingWebhooks |
NotificationSettingWebhooks
Name | Description | Value |
---|
Objective
Name | Description | Value |
---|---|---|
goal | [Required] Defines supported metric goals for hyperparameter tuning | 'Maximize' 'Minimize' (required) |
primaryMetric | [Required] Name of the metric to optimize. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
PipelineJob
Name | Description | Value |
---|---|---|
inputs | Inputs for the pipeline job. | PipelineJobInputs |
jobs | Jobs construct the Pipeline Job. | PipelineJobJobs |
jobType | [Required] Specifies the type of job. | 'Pipeline' (required) |
outputs | Outputs for the pipeline job | PipelineJobOutputs |
settings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | any |
sourceJobId | ARM resource ID of source job. | string |
PipelineJobInputs
Name | Description | Value |
---|
PipelineJobJobs
Name | Description | Value |
---|
PipelineJobOutputs
Name | Description | Value |
---|
PyTorch
Name | Description | Value |
---|---|---|
distributionType | [Required] Specifies the type of distribution framework. | 'PyTorch' (required) |
processCountPerInstance | Number of processes per node. | int |
QueueSettings
Name | Description | Value |
---|---|---|
jobTier | Controls the compute job tier | 'Basic' 'Null' 'Premium' 'Spot' 'Standard' |
RandomSamplingAlgorithm
Name | Description | Value |
---|---|---|
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 |
Regression
Name | Description | Value |
---|---|---|
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
primaryMetric | Primary metric for regression task. | 'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation' |
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 |
ResourceBaseProperties
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) |
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-id of the code asset. | string (required) |
conf | Spark configured properties. | SparkJobConf |
entry | [Required] The entry to execute on startup of the job. | SparkJobEntry (required) |
environmentId | The ARM resource ID of the Environment specification for the job. | string |
environmentVariables | Environment variables included in the job. | SparkJobEnvironmentVariables |
files | Files used in the job. | string[] |
inputs | Mapping of input data bindings used in the job. | SparkJobInputs |
jars | Jar files used in the job. | string[] |
jobType | [Required] Specifies the type of job. | 'Spark' (required) |
outputs | Mapping of output data bindings used in the job. | SparkJobOutputs |
pyFiles | Python files used in the job. | string[] |
queueSettings | Queue settings for the job | QueueSettings |
resources | Compute Resource configuration for the job. | SparkResourceConfiguration |
SparkJobConf
Name | Description | Value |
---|
SparkJobEntry
Name | Description | Value |
---|---|---|
sparkJobEntryType | Set to 'SparkJobPythonEntry' for type SparkJobPythonEntry. Set to 'SparkJobScalaEntry' for type SparkJobScalaEntry. | 'SparkJobPythonEntry' 'SparkJobScalaEntry' (required) |
SparkJobEnvironmentVariables
Name | Description | Value |
---|
SparkJobInputs
Name | Description | Value |
---|
SparkJobOutputs
Name | Description | Value |
---|
SparkJobPythonEntry
Name | Description | Value |
---|---|---|
file | [Required] Relative python file path for job entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobPythonEntry' (required) |
SparkJobScalaEntry
Name | Description | Value |
---|---|---|
className | [Required] Scala class name used as entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobScalaEntry' (required) |
SparkResourceConfiguration
Name | Description | Value |
---|---|---|
instanceType | Optional type of VM used as supported by the compute target. | string |
runtimeVersion | Version of spark runtime used for the job. | string |
StackEnsembleSettings
Name | Description | Value |
---|---|---|
stackMetaLearnerKWargs | Optional parameters to pass to the initializer of the meta-learner. | any |
stackMetaLearnerTrainPercentage | Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2. | int |
stackMetaLearnerType | The meta-learner is a model trained on the output of the individual heterogeneous models. | 'ElasticNet' 'ElasticNetCV' 'LightGBMClassifier' 'LightGBMRegressor' 'LinearRegression' 'LogisticRegression' 'LogisticRegressionCV' 'None' |
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 |
queueSettings | Queue settings for the job | QueueSettings |
samplingAlgorithm | [Required] The hyperparameter sampling algorithm | SamplingAlgorithm (required) |
searchSpace | [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter | 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 |
---|
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 |
maxTrials | Number of iterations. | 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 |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
primaryMetric | Primary metric for Text-Classification task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
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 |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
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 |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
environmentVariables | Environment variables included in the job. | TrialComponentEnvironmentVariables |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
TrialComponentEnvironmentVariables
Name | Description | Value |
---|
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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
TritonModelJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | 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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
UriFileJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | 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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
UriFolderJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | 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) |
Webhook
Name | Description | Value |
---|---|---|
eventType | Send callback on a specified notification event | string |
webhookType | Set to 'AzureDevOps' for type AzureDevOpsWebhook. | 'AzureDevOps' (required) |
Quickstart samples
The following quickstart samples deploy this resource type.
Bicep File | Description |
---|---|
Create an Azure Machine Learning AutoML classification job | This template creates an Azure Machine Learning AutoML classification job to find out the best model for predicting if a client will subscribe to a fixed term deposit with a financial institution. |
Create an Azure Machine Learning Command job | This template creates an Azure Machine Learning Command job with a basic hello_world script |
Create an Azure Machine Learning Sweep job | This template creates an Azure Machine Learning Sweep job for hyperparameter tuning. |
ARM template resource definition
The workspaces/jobs resource type can be deployed with operations that target:
- Resource groups - See resource group deployment commands
For a list of changed properties in each API version, see change log.
Resource format
To create a Microsoft.MachineLearningServices/workspaces/jobs resource, add the following JSON to your template.
{
"type": "Microsoft.MachineLearningServices/workspaces/jobs",
"apiVersion": "2024-10-01-preview",
"name": "string",
"properties": {
"componentId": "string",
"computeId": "string",
"description": "string",
"displayName": "string",
"experimentName": "string",
"identity": {
"identityType": "string"
// For remaining properties, see IdentityConfiguration objects
},
"isArchived": "bool",
"notificationSetting": {
"emailOn": [ "string" ],
"emails": [ "string" ],
"webhooks": {
"{customized property}": {
"eventType": "string",
"webhookType": "string"
// For remaining properties, see Webhook objects
}
}
},
"properties": {
"{customized property}": "string"
},
"services": {
"{customized property}": {
"endpoint": "string",
"jobServiceType": "string",
"nodes": {
"nodesValueType": "string"
// For remaining properties, see Nodes objects
},
"port": "int",
"properties": {
"{customized property}": "string"
}
}
},
"tags": {
"{customized property}": "string"
},
"jobType": "string"
// For remaining properties, see JobBaseProperties objects
}
}
JobBaseProperties objects
Set the jobType property to specify the type of object.
For AutoML, use:
{
"environmentId": "string",
"environmentVariables": {
"{customized property}": "string"
},
"jobType": "AutoML",
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"queueSettings": {
"jobTier": "string"
},
"resources": {
"dockerArgs": "string",
"dockerArgsList": [ "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:
{
"codeId": "string",
"command": "string",
"distribution": {
"distributionType": "string"
// For remaining properties, see DistributionConfiguration objects
},
"environmentId": "string",
"environmentVariables": {
"{customized property}": "string"
},
"inputs": {
"{customized property}": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
}
},
"jobType": "Command",
"limits": {
"jobLimitsType": "string",
"timeout": "string"
},
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"queueSettings": {
"jobTier": "string"
},
"resources": {
"dockerArgs": "string",
"dockerArgsList": [ "string" ],
"instanceCount": "int",
"instanceType": "string",
"properties": {
"{customized property}": {}
},
"shmSize": "string"
}
}
For FineTuning, use:
{
"fineTuningDetails": {
"model": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
},
"taskType": "string",
"trainingData": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
},
"validationData": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
},
"modelProvider": "string"
// For remaining properties, see FineTuningVertical objects
},
"jobType": "FineTuning",
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"queueSettings": {
"jobTier": "string"
},
"resources": {
"instanceTypes": [ "string" ]
}
}
For Pipeline, use:
{
"inputs": {
"{customized property}": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
}
},
"jobs": {
"{customized property}": {}
},
"jobType": "Pipeline",
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"settings": {},
"sourceJobId": "string"
}
For Spark, use:
{
"archives": [ "string" ],
"args": "string",
"codeId": "string",
"conf": {
"{customized property}": "string"
},
"entry": {
"sparkJobEntryType": "string"
// For remaining properties, see SparkJobEntry objects
},
"environmentId": "string",
"environmentVariables": {
"{customized property}": "string"
},
"files": [ "string" ],
"inputs": {
"{customized property}": {
"description": "string",
"jobInputType": "string"
// For remaining properties, see JobInput objects
}
},
"jars": [ "string" ],
"jobType": "Spark",
"outputs": {
"{customized property}": {
"description": "string",
"jobOutputType": "string"
// For remaining properties, see JobOutput objects
}
},
"pyFiles": [ "string" ],
"queueSettings": {
"jobTier": "string"
},
"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
}
},
"queueSettings": {
"jobTier": "string"
},
"samplingAlgorithm": {
"samplingAlgorithmType": "string"
// For remaining properties, see SamplingAlgorithm objects
},
"searchSpace": {},
"trial": {
"codeId": "string",
"command": "string",
"distribution": {
"distributionType": "string"
// For remaining properties, see DistributionConfiguration objects
},
"environmentId": "string",
"environmentVariables": {
"{customized property}": "string"
},
"resources": {
"dockerArgs": "string",
"dockerArgsList": [ "string" ],
"instanceCount": "int",
"instanceType": "string",
"properties": {
"{customized property}": {}
},
"shmSize": "string"
}
}
}
Webhook objects
Set the webhookType property to specify the type of object.
For AzureDevOps, use:
{
"webhookType": "AzureDevOps"
}
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"
}
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": {}
}
]
}
},
"limitSettings": {
"enableEarlyTermination": "bool",
"exitScore": "int",
"maxConcurrentTrials": "int",
"maxCoresPerTrial": "int",
"maxTrials": "int",
"timeout": "string",
"trialTimeout": "string"
},
"nCrossValidations": {
"mode": "string"
// For remaining properties, see NCrossValidations objects
},
"positiveLabel": "string",
"primaryMetric": "string",
"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"
}
},
"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": {}
}
]
}
},
"forecastingSettings": {
"countryOrRegionForHolidays": "string",
"cvStepSize": "int",
"featureLags": "string",
"forecastHorizon": {
"mode": "string"
// For remaining properties, see ForecastHorizon objects
},
"frequency": "string",
"seasonality": {
"mode": "string"
// For remaining properties, see Seasonality objects
},
"shortSeriesHandlingConfig": "string",
"targetAggregateFunction": "string",
"targetLags": {
"mode": "string"
// For remaining properties, see TargetLags objects
},
"targetRollingWindowSize": {
"mode": "string"
// For remaining properties, see TargetRollingWindowSize objects
},
"timeColumnName": "string",
"timeSeriesIdColumnNames": [ "string" ],
"useStl": "string"
},
"limitSettings": {
"enableEarlyTermination": "bool",
"exitScore": "int",
"maxConcurrentTrials": "int",
"maxCoresPerTrial": "int",
"maxTrials": "int",
"timeout": "string",
"trialTimeout": "string"
},
"nCrossValidations": {
"mode": "string"
// For remaining properties, see NCrossValidations objects
},
"primaryMetric": "string",
"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"
}
},
"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": {}
}
]
}
},
"limitSettings": {
"enableEarlyTermination": "bool",
"exitScore": "int",
"maxConcurrentTrials": "int",
"maxCoresPerTrial": "int",
"maxTrials": "int",
"timeout": "string",
"trialTimeout": "string"
},
"nCrossValidations": {
"mode": "string"
// For remaining properties, see NCrossValidations objects
},
"primaryMetric": "string",
"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"
}
},
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
},
"validationDataSize": "int",
"weightColumnName": "string"
}
For TextClassification, use:
{
"featurizationSettings": {
"datasetLanguage": "string"
},
"limitSettings": {
"maxConcurrentTrials": "int",
"maxTrials": "int",
"timeout": "string"
},
"primaryMetric": "string",
"taskType": "TextClassification",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
}
}
For TextClassificationMultilabel, use:
{
"featurizationSettings": {
"datasetLanguage": "string"
},
"limitSettings": {
"maxConcurrentTrials": "int",
"maxTrials": "int",
"timeout": "string"
},
"taskType": "TextClassificationMultilabel",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
}
}
For TextNER, use:
{
"featurizationSettings": {
"datasetLanguage": "string"
},
"limitSettings": {
"maxConcurrentTrials": "int",
"maxTrials": "int",
"timeout": "string"
},
"taskType": "TextNER",
"validationData": {
"description": "string",
"jobInputType": "string",
"mode": "string",
"uri": "string"
}
}
Nodes objects
Set the nodesValueType property to specify the type of object.
For All, use:
{
"nodesValueType": "All"
}
ForecastHorizon objects
Set the mode property to specify the type of object.
For Auto, use:
{
"mode": "Auto"
}
For Custom, use:
{
"mode": "Custom",
"value": "int"
}
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"
}
FineTuningVertical objects
Set the modelProvider property to specify the type of object.
For AzureOpenAI, use:
{
"hyperParameters": {
"batchSize": "int",
"learningRateMultiplier": "int",
"nEpochs": "int"
},
"modelProvider": "AzureOpenAI"
}
For Custom, use:
{
"hyperParameters": {
"{customized property}": "string"
},
"modelProvider": "Custom"
}
TargetLags objects
Set the mode property to specify the type of object.
For Auto, use:
{
"mode": "Auto"
}
For Custom, use:
{
"mode": "Custom",
"values": [ "int" ]
}
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:
{
"rule": "string",
"samplingAlgorithmType": "Random",
"seed": "int"
}
Seasonality objects
Set the mode property to specify the type of object.
For Auto, use:
{
"mode": "Auto"
}
For Custom, use:
{
"mode": "Custom",
"value": "int"
}
JobOutput objects
Set the jobOutputType property to specify the type of object.
For custom_model, use:
{
"assetName": "string",
"jobOutputType": "custom_model",
"mode": "string",
"uri": "string"
}
For mlflow_model, use:
{
"assetName": "string",
"jobOutputType": "mlflow_model",
"mode": "string",
"uri": "string"
}
For mltable, use:
{
"assetName": "string",
"jobOutputType": "mltable",
"mode": "string",
"uri": "string"
}
For triton_model, use:
{
"assetName": "string",
"jobOutputType": "triton_model",
"mode": "string",
"uri": "string"
}
For uri_file, use:
{
"assetName": "string",
"jobOutputType": "uri_file",
"mode": "string",
"uri": "string"
}
For uri_folder, use:
{
"assetName": "string",
"jobOutputType": "uri_folder",
"mode": "string",
"uri": "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"
}
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"
}
NCrossValidations objects
Set the mode property to specify the type of object.
For Auto, use:
{
"mode": "Auto"
}
For Custom, use:
{
"mode": "Custom",
"value": "int"
}
TargetRollingWindowSize objects
Set the mode property to specify the type of object.
For Auto, use:
{
"mode": "Auto"
}
For Custom, use:
{
"mode": "Custom",
"value": "int"
}
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"
}
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) |
AutoMLJob
Name | Description | Value |
---|---|---|
environmentId | The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job. |
string |
environmentVariables | Environment variables included in the job. | AutoMLJobEnvironmentVariables |
jobType | [Required] Specifies the type of job. | 'AutoML' (required) |
outputs | Mapping of output data bindings used in the job. | AutoMLJobOutputs |
queueSettings | Queue settings for the job | QueueSettings |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
taskDetails | [Required] This represents scenario which can be one of Tables/NLP/Image | AutoMLVertical (required) |
AutoMLJobEnvironmentVariables
Name | Description | Value |
---|
AutoMLJobOutputs
Name | Description | Value |
---|
AutoMLVertical
Name | Description | Value |
---|---|---|
logVerbosity | Log verbosity for the job. | 'Critical' 'Debug' 'Error' 'Info' 'NotSet' 'Warning' |
targetColumnName | Target column name: This is prediction values column. Also known as label column name in context of classification tasks. |
string |
taskType | Set to 'Classification' for type Classification. Set to 'Forecasting' for type Forecasting. Set to 'ImageClassification' for type ImageClassification. Set to 'ImageClassificationMultilabel' for type ImageClassificationMultilabel. Set to 'ImageInstanceSegmentation' for type ImageInstanceSegmentation. Set to 'ImageObjectDetection' for type ImageObjectDetection. Set to 'Regression' for type Regression. Set to 'TextClassification' for type TextClassification. Set to 'TextClassificationMultilabel' for type TextClassificationMultilabel. Set to 'TextNER' for type TextNer. | 'Classification' 'Forecasting' 'ImageClassification' 'ImageClassificationMultilabel' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'Regression' 'TextClassification' 'TextClassificationMultilabel' 'TextNER' (required) |
trainingData | [Required] Training data input. | MLTableJobInput (required) |
AutoNCrossValidations
Name | Description | Value |
---|---|---|
mode | [Required] Mode for determining N-Cross validations. | 'Auto' (required) |
AutoSeasonality
Name | Description | Value |
---|---|---|
mode | [Required] Seasonality mode. | 'Auto' (required) |
AutoTargetLags
Name | Description | Value |
---|---|---|
mode | [Required] Set target lags mode - Auto/Custom | 'Auto' (required) |
AutoTargetRollingWindowSize
Name | Description | Value |
---|---|---|
mode | [Required] TargetRollingWindowSiz detection mode. | 'Auto' (required) |
AzureDevOpsWebhook
Name | Description | Value |
---|---|---|
webhookType | [Required] Specifies the type of service to send a callback | 'AzureDevOps' (required) |
AzureOpenAiFineTuning
Name | Description | Value |
---|---|---|
hyperParameters | HyperParameters for fine tuning Azure Open AI model. | AzureOpenAiHyperParameters |
modelProvider | [Required] Enum to determine the type of fine tuning. | 'AzureOpenAI' (required) |
AzureOpenAiHyperParameters
Name | Description | Value |
---|---|---|
batchSize | Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. | int |
learningRateMultiplier | Scaling factor for the learning rate. A smaller learning rate may be useful to avoid over fitting. | int |
nEpochs | The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. | int |
BanditPolicy
Name | Description | Value |
---|---|---|
policyType | [Required] Name of policy configuration | 'Bandit' (required) |
slackAmount | Absolute distance allowed from the best performing run. | int |
slackFactor | Ratio of the allowed distance from the best performing run. | int |
BayesianSamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Bayesian' (required) |
Classification
Name | Description | Value |
---|---|---|
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
positiveLabel | Positive label for binary metrics calculation. | string |
primaryMetric | Primary metric for the task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
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 |
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 |
---|---|---|
codeId | ARM resource ID of the code asset. | string |
command | [Required] The command to execute on startup of the job. eg. "python train.py" | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
environmentVariables | Environment variables included in the job. | CommandJobEnvironmentVariables |
inputs | Mapping of input data bindings used in the job. | CommandJobInputs |
jobType | [Required] Specifies the type of job. | 'Command' (required) |
limits | Command Job limit. | CommandJobLimits |
outputs | Mapping of output data bindings used in the job. | CommandJobOutputs |
queueSettings | Queue settings for the job | QueueSettings |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
CommandJobEnvironmentVariables
Name | Description | Value |
---|
CommandJobInputs
Name | Description | Value |
---|
CommandJobLimits
Name | Description | Value |
---|---|---|
jobLimitsType | [Required] JobLimit type. | 'Command' 'Sweep' (required) |
timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
CommandJobOutputs
Name | Description | Value |
---|
CustomForecastHorizon
Name | Description | Value |
---|---|---|
mode | [Required] Set forecast horizon value selection mode. | 'Custom' (required) |
value | [Required] Forecast horizon value. | int (required) |
CustomModelFineTuning
Name | Description | Value |
---|---|---|
hyperParameters | HyperParameters for fine tuning custom model. | CustomModelFineTuningHyperParameters |
modelProvider | [Required] Enum to determine the type of fine tuning. | 'Custom' (required) |
CustomModelFineTuningHyperParameters
Name | Description | Value |
---|
CustomModelJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'custom_model' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
CustomModelJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | string |
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) |
FineTuningJob
Name | Description | Value |
---|---|---|
fineTuningDetails | [Required] | FineTuningVertical (required) |
jobType | [Required] Specifies the type of job. | 'FineTuning' (required) |
outputs | [Required] | FineTuningJobOutputs (required) |
queueSettings | Queue settings for the job | QueueSettings |
resources | Instance types and other resources for the job | JobResources |
FineTuningJobOutputs
Name | Description | Value |
---|
FineTuningVertical
Name | Description | Value |
---|---|---|
model | [Required] Input model for fine tuning. | JobInput (required) |
modelProvider | Set to 'AzureOpenAI' for type AzureOpenAiFineTuning. Set to 'Custom' for type CustomModelFineTuning. | 'AzureOpenAI' 'Custom' (required) |
taskType | [Required] Fine tuning task type. | 'ChatCompletion' 'ImageClassification' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'QuestionAnswering' 'TextClassification' 'TextCompletion' 'TextSummarization' 'TextTranslation' 'TokenClassification' 'VideoMultiObjectTracking' (required) |
trainingData | [Required] Training data for fine tuning. | JobInput (required) |
validationData | Validation data for fine tuning. | JobInput |
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 |
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' |
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 |
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 'FineTuning' for type FineTuningJob. Set to 'Pipeline' for type PipelineJob. Set to 'Spark' for type SparkJob. Set to 'Sweep' for type SweepJob. | 'AutoML' 'Command' 'FineTuning' 'Pipeline' 'Spark' 'Sweep' (required) |
notificationSetting | Notification setting for the job | NotificationSetting |
properties | The asset property dictionary. | ResourceBaseProperties |
services | List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject. |
JobBaseServices |
tags | Tag dictionary. Tags can be added, removed, and updated. | 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 |
dockerArgsList | Extra arguments to pass to the Docker run command, as a collection. 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] |
JobResources
Name | Description | Value |
---|---|---|
instanceTypes | List of instance types to choose from. | string[] |
JobService
Name | Description | Value |
---|---|---|
endpoint | Url for endpoint. | string |
jobServiceType | Endpoint type. | string |
nodes | Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node. |
Nodes |
port | Port for endpoint. | int |
properties | Additional properties to set on the endpoint. | JobServiceProperties |
JobServiceProperties
Name | Description | Value |
---|
LiteralJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'literal' (required) |
value | [Required] Literal value for the input. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
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/jobs
Name | Description | Value |
---|---|---|
apiVersion | The api version | '2024-10-01-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. | JobBaseProperties (required) |
type | The resource type | 'Microsoft.MachineLearningServices/workspaces/jobs' |
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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobInput
Name | Description | Value |
---|---|---|
description | Description for the input. | string |
jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | string |
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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLTableJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'mltable' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLTableJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | string |
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) |
NlpVerticalFeaturizationSettings
Name | Description | Value |
---|---|---|
datasetLanguage | Dataset language, useful for the text data. | string |
NlpVerticalLimitSettings
Name | Description | Value |
---|---|---|
maxConcurrentTrials | Maximum Concurrent AutoML iterations. | int |
maxTrials | Number of AutoML iterations. | int |
timeout | AutoML job timeout. | string |
Nodes
Name | Description | Value |
---|---|---|
nodesValueType | Set to 'All' for type AllNodes. | 'All' (required) |
NotificationSetting
Name | Description | Value |
---|---|---|
emailOn | Send email notification to user on specified notification type | String array containing any of: 'JobCancelled' 'JobCompleted' 'JobFailed' |
emails | This is the email recipient list which has a limitation of 499 characters in total concat with comma separator | string[] |
webhooks | Send webhook callback to a service. Key is a user-provided name for the webhook. | NotificationSettingWebhooks |
NotificationSettingWebhooks
Name | Description | Value |
---|
Objective
Name | Description | Value |
---|---|---|
goal | [Required] Defines supported metric goals for hyperparameter tuning | 'Maximize' 'Minimize' (required) |
primaryMetric | [Required] Name of the metric to optimize. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
PipelineJob
Name | Description | Value |
---|---|---|
inputs | Inputs for the pipeline job. | PipelineJobInputs |
jobs | Jobs construct the Pipeline Job. | PipelineJobJobs |
jobType | [Required] Specifies the type of job. | 'Pipeline' (required) |
outputs | Outputs for the pipeline job | PipelineJobOutputs |
settings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | any |
sourceJobId | ARM resource ID of source job. | string |
PipelineJobInputs
Name | Description | Value |
---|
PipelineJobJobs
Name | Description | Value |
---|
PipelineJobOutputs
Name | Description | Value |
---|
PyTorch
Name | Description | Value |
---|---|---|
distributionType | [Required] Specifies the type of distribution framework. | 'PyTorch' (required) |
processCountPerInstance | Number of processes per node. | int |
QueueSettings
Name | Description | Value |
---|---|---|
jobTier | Controls the compute job tier | 'Basic' 'Null' 'Premium' 'Spot' 'Standard' |
RandomSamplingAlgorithm
Name | Description | Value |
---|---|---|
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 |
Regression
Name | Description | Value |
---|---|---|
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
primaryMetric | Primary metric for regression task. | 'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation' |
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 |
ResourceBaseProperties
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) |
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-id of the code asset. | string (required) |
conf | Spark configured properties. | SparkJobConf |
entry | [Required] The entry to execute on startup of the job. | SparkJobEntry (required) |
environmentId | The ARM resource ID of the Environment specification for the job. | string |
environmentVariables | Environment variables included in the job. | SparkJobEnvironmentVariables |
files | Files used in the job. | string[] |
inputs | Mapping of input data bindings used in the job. | SparkJobInputs |
jars | Jar files used in the job. | string[] |
jobType | [Required] Specifies the type of job. | 'Spark' (required) |
outputs | Mapping of output data bindings used in the job. | SparkJobOutputs |
pyFiles | Python files used in the job. | string[] |
queueSettings | Queue settings for the job | QueueSettings |
resources | Compute Resource configuration for the job. | SparkResourceConfiguration |
SparkJobConf
Name | Description | Value |
---|
SparkJobEntry
Name | Description | Value |
---|---|---|
sparkJobEntryType | Set to 'SparkJobPythonEntry' for type SparkJobPythonEntry. Set to 'SparkJobScalaEntry' for type SparkJobScalaEntry. | 'SparkJobPythonEntry' 'SparkJobScalaEntry' (required) |
SparkJobEnvironmentVariables
Name | Description | Value |
---|
SparkJobInputs
Name | Description | Value |
---|
SparkJobOutputs
Name | Description | Value |
---|
SparkJobPythonEntry
Name | Description | Value |
---|---|---|
file | [Required] Relative python file path for job entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobPythonEntry' (required) |
SparkJobScalaEntry
Name | Description | Value |
---|---|---|
className | [Required] Scala class name used as entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobScalaEntry' (required) |
SparkResourceConfiguration
Name | Description | Value |
---|---|---|
instanceType | Optional type of VM used as supported by the compute target. | string |
runtimeVersion | Version of spark runtime used for the job. | string |
StackEnsembleSettings
Name | Description | Value |
---|---|---|
stackMetaLearnerKWargs | Optional parameters to pass to the initializer of the meta-learner. | any |
stackMetaLearnerTrainPercentage | Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2. | int |
stackMetaLearnerType | The meta-learner is a model trained on the output of the individual heterogeneous models. | 'ElasticNet' 'ElasticNetCV' 'LightGBMClassifier' 'LightGBMRegressor' 'LinearRegression' 'LogisticRegression' 'LogisticRegressionCV' 'None' |
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 |
queueSettings | Queue settings for the job | QueueSettings |
samplingAlgorithm | [Required] The hyperparameter sampling algorithm | SamplingAlgorithm (required) |
searchSpace | [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter | 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 |
---|
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 |
maxTrials | Number of iterations. | 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 |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
primaryMetric | Primary metric for Text-Classification task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
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 |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
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 |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
environmentVariables | Environment variables included in the job. | TrialComponentEnvironmentVariables |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
TrialComponentEnvironmentVariables
Name | Description | Value |
---|
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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
TritonModelJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | 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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
UriFileJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | 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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
UriFolderJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | 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) |
Webhook
Name | Description | Value |
---|---|---|
eventType | Send callback on a specified notification event | string |
webhookType | Set to 'AzureDevOps' for type AzureDevOpsWebhook. | 'AzureDevOps' (required) |
Quickstart templates
The following quickstart templates deploy this resource type.
Template | Description |
---|---|
Create an Azure Machine Learning AutoML classification job |
This template creates an Azure Machine Learning AutoML classification job to find out the best model for predicting if a client will subscribe to a fixed term deposit with a financial institution. |
Create an Azure Machine Learning Command job |
This template creates an Azure Machine Learning Command job with a basic hello_world script |
Create an Azure Machine Learning Sweep job |
This template creates an Azure Machine Learning Sweep job for hyperparameter tuning. |
Terraform (AzAPI provider) resource definition
The workspaces/jobs resource type can be deployed with operations that target:
- Resource groups
For a list of changed properties in each API version, see change log.
Resource format
To create a Microsoft.MachineLearningServices/workspaces/jobs resource, add the following Terraform to your template.
resource "azapi_resource" "symbolicname" {
type = "Microsoft.MachineLearningServices/workspaces/jobs@2024-10-01-preview"
name = "string"
body = jsonencode({
properties = {
componentId = "string"
computeId = "string"
description = "string"
displayName = "string"
experimentName = "string"
identity = {
identityType = "string"
// For remaining properties, see IdentityConfiguration objects
}
isArchived = bool
notificationSetting = {
emailOn = [
"string"
]
emails = [
"string"
]
webhooks = {
{customized property} = {
eventType = "string"
webhookType = "string"
// For remaining properties, see Webhook objects
}
}
}
properties = {
{customized property} = "string"
}
services = {
{customized property} = {
endpoint = "string"
jobServiceType = "string"
nodes = {
nodesValueType = "string"
// For remaining properties, see Nodes objects
}
port = int
properties = {
{customized property} = "string"
}
}
}
tags = {
{customized property} = "string"
}
jobType = "string"
// For remaining properties, see JobBaseProperties objects
}
})
}
JobBaseProperties objects
Set the jobType property to specify the type of object.
For AutoML, use:
{
environmentId = "string"
environmentVariables = {
{customized property} = "string"
}
jobType = "AutoML"
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
queueSettings = {
jobTier = "string"
}
resources = {
dockerArgs = "string"
dockerArgsList = [
"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:
{
codeId = "string"
command = "string"
distribution = {
distributionType = "string"
// For remaining properties, see DistributionConfiguration objects
}
environmentId = "string"
environmentVariables = {
{customized property} = "string"
}
inputs = {
{customized property} = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
}
jobType = "Command"
limits = {
jobLimitsType = "string"
timeout = "string"
}
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
queueSettings = {
jobTier = "string"
}
resources = {
dockerArgs = "string"
dockerArgsList = [
"string"
]
instanceCount = int
instanceType = "string"
properties = {
{customized property} = ?
}
shmSize = "string"
}
}
For FineTuning, use:
{
fineTuningDetails = {
model = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
taskType = "string"
trainingData = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
validationData = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
modelProvider = "string"
// For remaining properties, see FineTuningVertical objects
}
jobType = "FineTuning"
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
queueSettings = {
jobTier = "string"
}
resources = {
instanceTypes = [
"string"
]
}
}
For Pipeline, use:
{
inputs = {
{customized property} = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
}
jobs = {
{customized property} = ?
}
jobType = "Pipeline"
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
settings = ?
sourceJobId = "string"
}
For Spark, use:
{
archives = [
"string"
]
args = "string"
codeId = "string"
conf = {
{customized property} = "string"
}
entry = {
sparkJobEntryType = "string"
// For remaining properties, see SparkJobEntry objects
}
environmentId = "string"
environmentVariables = {
{customized property} = "string"
}
files = [
"string"
]
inputs = {
{customized property} = {
description = "string"
jobInputType = "string"
// For remaining properties, see JobInput objects
}
}
jars = [
"string"
]
jobType = "Spark"
outputs = {
{customized property} = {
description = "string"
jobOutputType = "string"
// For remaining properties, see JobOutput objects
}
}
pyFiles = [
"string"
]
queueSettings = {
jobTier = "string"
}
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
}
}
queueSettings = {
jobTier = "string"
}
samplingAlgorithm = {
samplingAlgorithmType = "string"
// For remaining properties, see SamplingAlgorithm objects
}
searchSpace = ?
trial = {
codeId = "string"
command = "string"
distribution = {
distributionType = "string"
// For remaining properties, see DistributionConfiguration objects
}
environmentId = "string"
environmentVariables = {
{customized property} = "string"
}
resources = {
dockerArgs = "string"
dockerArgsList = [
"string"
]
instanceCount = int
instanceType = "string"
properties = {
{customized property} = ?
}
shmSize = "string"
}
}
}
Webhook objects
Set the webhookType property to specify the type of object.
For AzureDevOps, use:
{
webhookType = "AzureDevOps"
}
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
}
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 = ?
}
]
}
}
limitSettings = {
enableEarlyTermination = bool
exitScore = int
maxConcurrentTrials = int
maxCoresPerTrial = int
maxTrials = int
timeout = "string"
trialTimeout = "string"
}
nCrossValidations = {
mode = "string"
// For remaining properties, see NCrossValidations objects
}
positiveLabel = "string"
primaryMetric = "string"
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"
}
}
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 = ?
}
]
}
}
forecastingSettings = {
countryOrRegionForHolidays = "string"
cvStepSize = int
featureLags = "string"
forecastHorizon = {
mode = "string"
// For remaining properties, see ForecastHorizon objects
}
frequency = "string"
seasonality = {
mode = "string"
// For remaining properties, see Seasonality objects
}
shortSeriesHandlingConfig = "string"
targetAggregateFunction = "string"
targetLags = {
mode = "string"
// For remaining properties, see TargetLags objects
}
targetRollingWindowSize = {
mode = "string"
// For remaining properties, see TargetRollingWindowSize objects
}
timeColumnName = "string"
timeSeriesIdColumnNames = [
"string"
]
useStl = "string"
}
limitSettings = {
enableEarlyTermination = bool
exitScore = int
maxConcurrentTrials = int
maxCoresPerTrial = int
maxTrials = int
timeout = "string"
trialTimeout = "string"
}
nCrossValidations = {
mode = "string"
// For remaining properties, see NCrossValidations objects
}
primaryMetric = "string"
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"
}
}
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 = ?
}
]
}
}
limitSettings = {
enableEarlyTermination = bool
exitScore = int
maxConcurrentTrials = int
maxCoresPerTrial = int
maxTrials = int
timeout = "string"
trialTimeout = "string"
}
nCrossValidations = {
mode = "string"
// For remaining properties, see NCrossValidations objects
}
primaryMetric = "string"
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"
}
}
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
validationDataSize = int
weightColumnName = "string"
}
For TextClassification, use:
{
featurizationSettings = {
datasetLanguage = "string"
}
limitSettings = {
maxConcurrentTrials = int
maxTrials = int
timeout = "string"
}
primaryMetric = "string"
taskType = "TextClassification"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
}
For TextClassificationMultilabel, use:
{
featurizationSettings = {
datasetLanguage = "string"
}
limitSettings = {
maxConcurrentTrials = int
maxTrials = int
timeout = "string"
}
taskType = "TextClassificationMultilabel"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
}
For TextNER, use:
{
featurizationSettings = {
datasetLanguage = "string"
}
limitSettings = {
maxConcurrentTrials = int
maxTrials = int
timeout = "string"
}
taskType = "TextNER"
validationData = {
description = "string"
jobInputType = "string"
mode = "string"
uri = "string"
}
}
Nodes objects
Set the nodesValueType property to specify the type of object.
For All, use:
{
nodesValueType = "All"
}
ForecastHorizon objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode = "Auto"
}
For Custom, use:
{
mode = "Custom"
value = int
}
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"
}
FineTuningVertical objects
Set the modelProvider property to specify the type of object.
For AzureOpenAI, use:
{
hyperParameters = {
batchSize = int
learningRateMultiplier = int
nEpochs = int
}
modelProvider = "AzureOpenAI"
}
For Custom, use:
{
hyperParameters = {
{customized property} = "string"
}
modelProvider = "Custom"
}
TargetLags objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode = "Auto"
}
For Custom, use:
{
mode = "Custom"
values = [
int
]
}
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:
{
rule = "string"
samplingAlgorithmType = "Random"
seed = int
}
Seasonality objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode = "Auto"
}
For Custom, use:
{
mode = "Custom"
value = int
}
JobOutput objects
Set the jobOutputType property to specify the type of object.
For custom_model, use:
{
assetName = "string"
jobOutputType = "custom_model"
mode = "string"
uri = "string"
}
For mlflow_model, use:
{
assetName = "string"
jobOutputType = "mlflow_model"
mode = "string"
uri = "string"
}
For mltable, use:
{
assetName = "string"
jobOutputType = "mltable"
mode = "string"
uri = "string"
}
For triton_model, use:
{
assetName = "string"
jobOutputType = "triton_model"
mode = "string"
uri = "string"
}
For uri_file, use:
{
assetName = "string"
jobOutputType = "uri_file"
mode = "string"
uri = "string"
}
For uri_folder, use:
{
assetName = "string"
jobOutputType = "uri_folder"
mode = "string"
uri = "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"
}
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"
}
NCrossValidations objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode = "Auto"
}
For Custom, use:
{
mode = "Custom"
value = int
}
TargetRollingWindowSize objects
Set the mode property to specify the type of object.
For Auto, use:
{
mode = "Auto"
}
For Custom, use:
{
mode = "Custom"
value = int
}
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
}
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) |
AutoMLJob
Name | Description | Value |
---|---|---|
environmentId | The ARM resource ID of the Environment specification for the job. This is optional value to provide, if not provided, AutoML will default this to Production AutoML curated environment version when running the job. |
string |
environmentVariables | Environment variables included in the job. | AutoMLJobEnvironmentVariables |
jobType | [Required] Specifies the type of job. | 'AutoML' (required) |
outputs | Mapping of output data bindings used in the job. | AutoMLJobOutputs |
queueSettings | Queue settings for the job | QueueSettings |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
taskDetails | [Required] This represents scenario which can be one of Tables/NLP/Image | AutoMLVertical (required) |
AutoMLJobEnvironmentVariables
Name | Description | Value |
---|
AutoMLJobOutputs
Name | Description | Value |
---|
AutoMLVertical
Name | Description | Value |
---|---|---|
logVerbosity | Log verbosity for the job. | 'Critical' 'Debug' 'Error' 'Info' 'NotSet' 'Warning' |
targetColumnName | Target column name: This is prediction values column. Also known as label column name in context of classification tasks. |
string |
taskType | Set to 'Classification' for type Classification. Set to 'Forecasting' for type Forecasting. Set to 'ImageClassification' for type ImageClassification. Set to 'ImageClassificationMultilabel' for type ImageClassificationMultilabel. Set to 'ImageInstanceSegmentation' for type ImageInstanceSegmentation. Set to 'ImageObjectDetection' for type ImageObjectDetection. Set to 'Regression' for type Regression. Set to 'TextClassification' for type TextClassification. Set to 'TextClassificationMultilabel' for type TextClassificationMultilabel. Set to 'TextNER' for type TextNer. | 'Classification' 'Forecasting' 'ImageClassification' 'ImageClassificationMultilabel' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'Regression' 'TextClassification' 'TextClassificationMultilabel' 'TextNER' (required) |
trainingData | [Required] Training data input. | MLTableJobInput (required) |
AutoNCrossValidations
Name | Description | Value |
---|---|---|
mode | [Required] Mode for determining N-Cross validations. | 'Auto' (required) |
AutoSeasonality
Name | Description | Value |
---|---|---|
mode | [Required] Seasonality mode. | 'Auto' (required) |
AutoTargetLags
Name | Description | Value |
---|---|---|
mode | [Required] Set target lags mode - Auto/Custom | 'Auto' (required) |
AutoTargetRollingWindowSize
Name | Description | Value |
---|---|---|
mode | [Required] TargetRollingWindowSiz detection mode. | 'Auto' (required) |
AzureDevOpsWebhook
Name | Description | Value |
---|---|---|
webhookType | [Required] Specifies the type of service to send a callback | 'AzureDevOps' (required) |
AzureOpenAiFineTuning
Name | Description | Value |
---|---|---|
hyperParameters | HyperParameters for fine tuning Azure Open AI model. | AzureOpenAiHyperParameters |
modelProvider | [Required] Enum to determine the type of fine tuning. | 'AzureOpenAI' (required) |
AzureOpenAiHyperParameters
Name | Description | Value |
---|---|---|
batchSize | Number of examples in each batch. A larger batch size means that model parameters are updated less frequently, but with lower variance. | int |
learningRateMultiplier | Scaling factor for the learning rate. A smaller learning rate may be useful to avoid over fitting. | int |
nEpochs | The number of epochs to train the model for. An epoch refers to one full cycle through the training dataset. | int |
BanditPolicy
Name | Description | Value |
---|---|---|
policyType | [Required] Name of policy configuration | 'Bandit' (required) |
slackAmount | Absolute distance allowed from the best performing run. | int |
slackFactor | Ratio of the allowed distance from the best performing run. | int |
BayesianSamplingAlgorithm
Name | Description | Value |
---|---|---|
samplingAlgorithmType | [Required] The algorithm used for generating hyperparameter values, along with configuration properties | 'Bayesian' (required) |
Classification
Name | Description | Value |
---|---|---|
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
positiveLabel | Positive label for binary metrics calculation. | string |
primaryMetric | Primary metric for the task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
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 |
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 |
---|---|---|
codeId | ARM resource ID of the code asset. | string |
command | [Required] The command to execute on startup of the job. eg. "python train.py" | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
distribution | Distribution configuration of the job. If set, this should be one of Mpi, Tensorflow, PyTorch, or null. | DistributionConfiguration |
environmentId | [Required] The ARM resource ID of the Environment specification for the job. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
environmentVariables | Environment variables included in the job. | CommandJobEnvironmentVariables |
inputs | Mapping of input data bindings used in the job. | CommandJobInputs |
jobType | [Required] Specifies the type of job. | 'Command' (required) |
limits | Command Job limit. | CommandJobLimits |
outputs | Mapping of output data bindings used in the job. | CommandJobOutputs |
queueSettings | Queue settings for the job | QueueSettings |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
CommandJobEnvironmentVariables
Name | Description | Value |
---|
CommandJobInputs
Name | Description | Value |
---|
CommandJobLimits
Name | Description | Value |
---|---|---|
jobLimitsType | [Required] JobLimit type. | 'Command' 'Sweep' (required) |
timeout | The max run duration in ISO 8601 format, after which the job will be cancelled. Only supports duration with precision as low as Seconds. | string |
CommandJobOutputs
Name | Description | Value |
---|
CustomForecastHorizon
Name | Description | Value |
---|---|---|
mode | [Required] Set forecast horizon value selection mode. | 'Custom' (required) |
value | [Required] Forecast horizon value. | int (required) |
CustomModelFineTuning
Name | Description | Value |
---|---|---|
hyperParameters | HyperParameters for fine tuning custom model. | CustomModelFineTuningHyperParameters |
modelProvider | [Required] Enum to determine the type of fine tuning. | 'Custom' (required) |
CustomModelFineTuningHyperParameters
Name | Description | Value |
---|
CustomModelJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'custom_model' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
CustomModelJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | string |
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) |
FineTuningJob
Name | Description | Value |
---|---|---|
fineTuningDetails | [Required] | FineTuningVertical (required) |
jobType | [Required] Specifies the type of job. | 'FineTuning' (required) |
outputs | [Required] | FineTuningJobOutputs (required) |
queueSettings | Queue settings for the job | QueueSettings |
resources | Instance types and other resources for the job | JobResources |
FineTuningJobOutputs
Name | Description | Value |
---|
FineTuningVertical
Name | Description | Value |
---|---|---|
model | [Required] Input model for fine tuning. | JobInput (required) |
modelProvider | Set to 'AzureOpenAI' for type AzureOpenAiFineTuning. Set to 'Custom' for type CustomModelFineTuning. | 'AzureOpenAI' 'Custom' (required) |
taskType | [Required] Fine tuning task type. | 'ChatCompletion' 'ImageClassification' 'ImageInstanceSegmentation' 'ImageObjectDetection' 'QuestionAnswering' 'TextClassification' 'TextCompletion' 'TextSummarization' 'TextTranslation' 'TokenClassification' 'VideoMultiObjectTracking' (required) |
trainingData | [Required] Training data for fine tuning. | JobInput (required) |
validationData | Validation data for fine tuning. | JobInput |
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 |
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' |
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 |
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 'FineTuning' for type FineTuningJob. Set to 'Pipeline' for type PipelineJob. Set to 'Spark' for type SparkJob. Set to 'Sweep' for type SweepJob. | 'AutoML' 'Command' 'FineTuning' 'Pipeline' 'Spark' 'Sweep' (required) |
notificationSetting | Notification setting for the job | NotificationSetting |
properties | The asset property dictionary. | ResourceBaseProperties |
services | List of JobEndpoints. For local jobs, a job endpoint will have an endpoint value of FileStreamObject. |
JobBaseServices |
tags | Tag dictionary. Tags can be added, removed, and updated. | 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 |
dockerArgsList | Extra arguments to pass to the Docker run command, as a collection. 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] |
JobResources
Name | Description | Value |
---|---|---|
instanceTypes | List of instance types to choose from. | string[] |
JobService
Name | Description | Value |
---|---|---|
endpoint | Url for endpoint. | string |
jobServiceType | Endpoint type. | string |
nodes | Nodes that user would like to start the service on. If Nodes is not set or set to null, the service will only be started on leader node. |
Nodes |
port | Port for endpoint. | int |
properties | Additional properties to set on the endpoint. | JobServiceProperties |
JobServiceProperties
Name | Description | Value |
---|
LiteralJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'literal' (required) |
value | [Required] Literal value for the input. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
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/jobs
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. | JobBaseProperties (required) |
type | The resource type | "Microsoft.MachineLearningServices/workspaces/jobs@2024-10-01-preview" |
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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobInput
Name | Description | Value |
---|---|---|
description | Description for the input. | string |
jobInputType | [Required] Specifies the type of job. | 'custom_model' 'literal' 'mlflow_model' 'mltable' 'triton_model' 'uri_file' 'uri_folder' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLFlowModelJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | string |
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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLTableJobInput
Name | Description | Value |
---|---|---|
jobInputType | [Required] Specifies the type of job. | 'mltable' (required) |
mode | Input Asset Delivery Mode. | 'Direct' 'Download' 'EvalDownload' 'EvalMount' 'ReadOnlyMount' 'ReadWriteMount' |
uri | [Required] Input Asset URI. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
MLTableJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | string |
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) |
NlpVerticalFeaturizationSettings
Name | Description | Value |
---|---|---|
datasetLanguage | Dataset language, useful for the text data. | string |
NlpVerticalLimitSettings
Name | Description | Value |
---|---|---|
maxConcurrentTrials | Maximum Concurrent AutoML iterations. | int |
maxTrials | Number of AutoML iterations. | int |
timeout | AutoML job timeout. | string |
Nodes
Name | Description | Value |
---|---|---|
nodesValueType | Set to 'All' for type AllNodes. | 'All' (required) |
NotificationSetting
Name | Description | Value |
---|---|---|
emailOn | Send email notification to user on specified notification type | String array containing any of: 'JobCancelled' 'JobCompleted' 'JobFailed' |
emails | This is the email recipient list which has a limitation of 499 characters in total concat with comma separator | string[] |
webhooks | Send webhook callback to a service. Key is a user-provided name for the webhook. | NotificationSettingWebhooks |
NotificationSettingWebhooks
Name | Description | Value |
---|
Objective
Name | Description | Value |
---|---|---|
goal | [Required] Defines supported metric goals for hyperparameter tuning | 'Maximize' 'Minimize' (required) |
primaryMetric | [Required] Name of the metric to optimize. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
PipelineJob
Name | Description | Value |
---|---|---|
inputs | Inputs for the pipeline job. | PipelineJobInputs |
jobs | Jobs construct the Pipeline Job. | PipelineJobJobs |
jobType | [Required] Specifies the type of job. | 'Pipeline' (required) |
outputs | Outputs for the pipeline job | PipelineJobOutputs |
settings | Pipeline settings, for things like ContinueRunOnStepFailure etc. | any |
sourceJobId | ARM resource ID of source job. | string |
PipelineJobInputs
Name | Description | Value |
---|
PipelineJobJobs
Name | Description | Value |
---|
PipelineJobOutputs
Name | Description | Value |
---|
PyTorch
Name | Description | Value |
---|---|---|
distributionType | [Required] Specifies the type of distribution framework. | 'PyTorch' (required) |
processCountPerInstance | Number of processes per node. | int |
QueueSettings
Name | Description | Value |
---|---|---|
jobTier | Controls the compute job tier | 'Basic' 'Null' 'Premium' 'Spot' 'Standard' |
RandomSamplingAlgorithm
Name | Description | Value |
---|---|---|
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 |
Regression
Name | Description | Value |
---|---|---|
cvSplitColumnNames | Columns to use for CVSplit data. | string[] |
featurizationSettings | Featurization inputs needed for AutoML job. | TableVerticalFeaturizationSettings |
limitSettings | Execution constraints for AutoMLJob. | TableVerticalLimitSettings |
nCrossValidations | Number of cross validation folds to be applied on training dataset when validation dataset is not provided. |
NCrossValidations |
primaryMetric | Primary metric for regression task. | 'NormalizedMeanAbsoluteError' 'NormalizedRootMeanSquaredError' 'R2Score' 'SpearmanCorrelation' |
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 |
ResourceBaseProperties
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) |
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-id of the code asset. | string (required) |
conf | Spark configured properties. | SparkJobConf |
entry | [Required] The entry to execute on startup of the job. | SparkJobEntry (required) |
environmentId | The ARM resource ID of the Environment specification for the job. | string |
environmentVariables | Environment variables included in the job. | SparkJobEnvironmentVariables |
files | Files used in the job. | string[] |
inputs | Mapping of input data bindings used in the job. | SparkJobInputs |
jars | Jar files used in the job. | string[] |
jobType | [Required] Specifies the type of job. | 'Spark' (required) |
outputs | Mapping of output data bindings used in the job. | SparkJobOutputs |
pyFiles | Python files used in the job. | string[] |
queueSettings | Queue settings for the job | QueueSettings |
resources | Compute Resource configuration for the job. | SparkResourceConfiguration |
SparkJobConf
Name | Description | Value |
---|
SparkJobEntry
Name | Description | Value |
---|---|---|
sparkJobEntryType | Set to 'SparkJobPythonEntry' for type SparkJobPythonEntry. Set to 'SparkJobScalaEntry' for type SparkJobScalaEntry. | 'SparkJobPythonEntry' 'SparkJobScalaEntry' (required) |
SparkJobEnvironmentVariables
Name | Description | Value |
---|
SparkJobInputs
Name | Description | Value |
---|
SparkJobOutputs
Name | Description | Value |
---|
SparkJobPythonEntry
Name | Description | Value |
---|---|---|
file | [Required] Relative python file path for job entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobPythonEntry' (required) |
SparkJobScalaEntry
Name | Description | Value |
---|---|---|
className | [Required] Scala class name used as entry point. | string Constraints: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
sparkJobEntryType | [Required] Type of the job's entry point. | 'SparkJobScalaEntry' (required) |
SparkResourceConfiguration
Name | Description | Value |
---|---|---|
instanceType | Optional type of VM used as supported by the compute target. | string |
runtimeVersion | Version of spark runtime used for the job. | string |
StackEnsembleSettings
Name | Description | Value |
---|---|---|
stackMetaLearnerKWargs | Optional parameters to pass to the initializer of the meta-learner. | any |
stackMetaLearnerTrainPercentage | Specifies the proportion of the training set (when choosing train and validation type of training) to be reserved for training the meta-learner. Default value is 0.2. | int |
stackMetaLearnerType | The meta-learner is a model trained on the output of the individual heterogeneous models. | 'ElasticNet' 'ElasticNetCV' 'LightGBMClassifier' 'LightGBMRegressor' 'LinearRegression' 'LogisticRegression' 'LogisticRegressionCV' 'None' |
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 |
queueSettings | Queue settings for the job | QueueSettings |
samplingAlgorithm | [Required] The hyperparameter sampling algorithm | SamplingAlgorithm (required) |
searchSpace | [Required] A dictionary containing each parameter and its distribution. The dictionary key is the name of the parameter | 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 |
---|
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 |
maxTrials | Number of iterations. | 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 |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
primaryMetric | Primary metric for Text-Classification task. | 'Accuracy' 'AUCWeighted' 'AveragePrecisionScoreWeighted' 'NormMacroRecall' 'PrecisionScoreWeighted' |
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 |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
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 |
limitSettings | Execution constraints for AutoMLJob. | NlpVerticalLimitSettings |
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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
environmentVariables | Environment variables included in the job. | TrialComponentEnvironmentVariables |
resources | Compute Resource configuration for the job. | JobResourceConfiguration |
TrialComponentEnvironmentVariables
Name | Description | Value |
---|
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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
TritonModelJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | 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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
UriFileJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | 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: Min length = 1 Pattern = [a-zA-Z0-9_] (required) |
UriFolderJobOutput
Name | Description | Value |
---|---|---|
assetName | Output Asset Name. | 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) |
Webhook
Name | Description | Value |
---|---|---|
eventType | Send callback on a specified notification event | string |
webhookType | Set to 'AzureDevOps' for type AzureDevOpsWebhook. | 'AzureDevOps' (required) |