The azure-native:machinelearningservices:Job resource, part of the Pulumi Azure Native provider, defines Azure Machine Learning jobs that execute training, tuning, or pipeline workflows on managed compute. This guide focuses on four capabilities: command jobs for custom scripts, AutoML for automated model selection, sweep jobs for hyperparameter optimization, and pipeline jobs for multi-step workflows.
Jobs run within an Azure ML workspace and reference compute clusters, environments, code assets, and datasets that must exist separately. The examples are intentionally small. Combine them with your own workspace infrastructure, data registrations, and deployment pipelines.
Run a command job with distributed training
Machine learning teams often start by running training scripts on managed compute, passing parameters and environment variables to control behavior.
import * as pulumi from "@pulumi/pulumi";
import * as azure_native from "@pulumi/azure-native";
const job = new azure_native.machinelearningservices.Job("job", {
id: "string",
properties: {
codeId: "string",
command: "string",
computeId: "string",
description: "string",
displayName: "string",
distribution: {
distributionType: "TensorFlow",
parameterServerCount: 1,
workerCount: 1,
},
environmentId: "string",
environmentVariables: {
string: "string",
},
experimentName: "string",
identity: {
identityType: "AMLToken",
},
inputs: {
string: {
description: "string",
jobInputType: "literal",
value: "string",
},
},
jobType: "Command",
limits: {
jobLimitsType: "Command",
timeout: "PT5M",
},
outputs: {
string: {
description: "string",
jobOutputType: "uri_file",
mode: azure_native.machinelearningservices.OutputDeliveryMode.ReadWriteMount,
uri: "string",
},
},
properties: {
string: "string",
},
resources: {
instanceCount: 1,
instanceType: "string",
properties: {
string: {
"e6b6493e-7d5e-4db3-be1e-306ec641327e": null,
},
},
},
services: {
string: {
endpoint: "string",
jobServiceType: "string",
port: 1,
properties: {
string: "string",
},
},
},
tags: {
string: "string",
},
},
resourceGroupName: "test-rg",
workspaceName: "my-aml-workspace",
});
import pulumi
import pulumi_azure_native as azure_native
job = azure_native.machinelearningservices.Job("job",
id="string",
properties={
"code_id": "string",
"command": "string",
"compute_id": "string",
"description": "string",
"display_name": "string",
"distribution": {
"distribution_type": "TensorFlow",
"parameter_server_count": 1,
"worker_count": 1,
},
"environment_id": "string",
"environment_variables": {
"string": "string",
},
"experiment_name": "string",
"identity": {
"identity_type": "AMLToken",
},
"inputs": {
"string": {
"description": "string",
"job_input_type": "literal",
"value": "string",
},
},
"job_type": "Command",
"limits": {
"job_limits_type": "Command",
"timeout": "PT5M",
},
"outputs": {
"string": {
"description": "string",
"job_output_type": "uri_file",
"mode": azure_native.machinelearningservices.OutputDeliveryMode.READ_WRITE_MOUNT,
"uri": "string",
},
},
"properties": {
"string": "string",
},
"resources": {
"instance_count": 1,
"instance_type": "string",
"properties": {
"string": {
"e6b6493e-7d5e-4db3-be1e-306ec641327e": None,
},
},
},
"services": {
"string": {
"endpoint": "string",
"job_service_type": "string",
"port": 1,
"properties": {
"string": "string",
},
},
},
"tags": {
"string": "string",
},
},
resource_group_name="test-rg",
workspace_name="my-aml-workspace")
package main
import (
machinelearningservices "github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v3"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
_, err := machinelearningservices.NewJob(ctx, "job", &machinelearningservices.JobArgs{
Id: pulumi.String("string"),
Properties: &machinelearningservices.CommandJobArgs{
CodeId: pulumi.String("string"),
Command: pulumi.String("string"),
ComputeId: pulumi.String("string"),
Description: pulumi.String("string"),
DisplayName: pulumi.String("string"),
Distribution: machinelearningservices.TensorFlow{
DistributionType: "TensorFlow",
ParameterServerCount: 1,
WorkerCount: 1,
},
EnvironmentId: pulumi.String("string"),
EnvironmentVariables: pulumi.StringMap{
"string": pulumi.String("string"),
},
ExperimentName: pulumi.String("string"),
Identity: machinelearningservices.AmlToken{
IdentityType: "AMLToken",
},
Inputs: pulumi.Map{
"string": machinelearningservices.LiteralJobInput{
Description: "string",
JobInputType: "literal",
Value: "string",
},
},
JobType: pulumi.String("Command"),
Limits: &machinelearningservices.CommandJobLimitsArgs{
JobLimitsType: pulumi.String("Command"),
Timeout: pulumi.String("PT5M"),
},
Outputs: pulumi.Map{
"string": machinelearningservices.UriFileJobOutput{
Description: "string",
JobOutputType: "uri_file",
Mode: machinelearningservices.OutputDeliveryModeReadWriteMount,
Uri: "string",
},
},
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
Resources: &machinelearningservices.JobResourceConfigurationArgs{
InstanceCount: pulumi.Int(1),
InstanceType: pulumi.String("string"),
Properties: pulumi.Map{
"string": pulumi.Any(map[string]interface{}{
"e6b6493e-7d5e-4db3-be1e-306ec641327e": nil,
}),
},
},
Services: machinelearningservices.JobServiceMap{
"string": &machinelearningservices.JobServiceArgs{
Endpoint: pulumi.String("string"),
JobServiceType: pulumi.String("string"),
Port: pulumi.Int(1),
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
},
Tags: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
ResourceGroupName: pulumi.String("test-rg"),
WorkspaceName: pulumi.String("my-aml-workspace"),
})
if err != nil {
return err
}
return nil
})
}
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
var job = new AzureNative.MachineLearningServices.Job("job", new()
{
Id = "string",
Properties = new AzureNative.MachineLearningServices.Inputs.CommandJobArgs
{
CodeId = "string",
Command = "string",
ComputeId = "string",
Description = "string",
DisplayName = "string",
Distribution = new AzureNative.MachineLearningServices.Inputs.TensorFlowArgs
{
DistributionType = "TensorFlow",
ParameterServerCount = 1,
WorkerCount = 1,
},
EnvironmentId = "string",
EnvironmentVariables =
{
{ "string", "string" },
},
ExperimentName = "string",
Identity = new AzureNative.MachineLearningServices.Inputs.AmlTokenArgs
{
IdentityType = "AMLToken",
},
Inputs =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.LiteralJobInputArgs
{
Description = "string",
JobInputType = "literal",
Value = "string",
} },
},
JobType = "Command",
Limits = new AzureNative.MachineLearningServices.Inputs.CommandJobLimitsArgs
{
JobLimitsType = "Command",
Timeout = "PT5M",
},
Outputs =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.UriFileJobOutputArgs
{
Description = "string",
JobOutputType = "uri_file",
Mode = AzureNative.MachineLearningServices.OutputDeliveryMode.ReadWriteMount,
Uri = "string",
} },
},
Properties =
{
{ "string", "string" },
},
Resources = new AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationArgs
{
InstanceCount = 1,
InstanceType = "string",
Properties =
{
{ "string", new Dictionary<string, object?>
{
["e6b6493e-7d5e-4db3-be1e-306ec641327e"] = null,
} },
},
},
Services =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.JobServiceArgs
{
Endpoint = "string",
JobServiceType = "string",
Port = 1,
Properties =
{
{ "string", "string" },
},
} },
},
Tags =
{
{ "string", "string" },
},
},
ResourceGroupName = "test-rg",
WorkspaceName = "my-aml-workspace",
});
});
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Job;
import com.pulumi.azurenative.machinelearningservices.JobArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
var job = new Job("job", JobArgs.builder()
.id("string")
.properties(CommandJobArgs.builder()
.codeId("string")
.command("string")
.computeId("string")
.description("string")
.displayName("string")
.distribution(TensorFlowArgs.builder()
.distributionType("TensorFlow")
.parameterServerCount(1)
.workerCount(1)
.build())
.environmentId("string")
.environmentVariables(Map.of("string", "string"))
.experimentName("string")
.identity(AmlTokenArgs.builder()
.identityType("AMLToken")
.build())
.inputs(Map.of("string", LiteralJobInputArgs.builder()
.description("string")
.jobInputType("literal")
.value("string")
.build()))
.jobType("Command")
.limits(Map.ofEntries(
Map.entry("jobLimitsType", "Command"),
Map.entry("timeout", "PT5M")
))
.outputs(Map.of("string", UriFileJobOutputArgs.builder()
.description("string")
.jobOutputType("uri_file")
.mode("ReadWriteMount")
.uri("string")
.build()))
.properties(Map.of("string", "string"))
.resources(JobResourceConfigurationArgs.builder()
.instanceCount(1)
.instanceType("string")
.properties(Map.of("string", Map.of("e6b6493e-7d5e-4db3-be1e-306ec641327e", null)))
.build())
.services(Map.of("string", JobServiceArgs.builder()
.endpoint("string")
.jobServiceType("string")
.port(1)
.properties(Map.of("string", "string"))
.build()))
.tags(Map.of("string", "string"))
.build())
.resourceGroupName("test-rg")
.workspaceName("my-aml-workspace")
.build());
}
}
resources:
job:
type: azure-native:machinelearningservices:Job
properties:
id: string
properties:
codeId: string
command: string
computeId: string
description: string
displayName: string
distribution:
distributionType: TensorFlow
parameterServerCount: 1
workerCount: 1
environmentId: string
environmentVariables:
string: string
experimentName: string
identity:
identityType: AMLToken
inputs:
string:
description: string
jobInputType: literal
value: string
jobType: Command
limits:
jobLimitsType: Command
timeout: PT5M
outputs:
string:
description: string
jobOutputType: uri_file
mode: ReadWriteMount
uri: string
properties:
string: string
resources:
instanceCount: 1
instanceType: string
properties:
string:
e6b6493e-7d5e-4db3-be1e-306ec641327e: null
services:
string:
endpoint: string
jobServiceType: string
port: 1
properties:
string: string
tags:
string: string
resourceGroupName: test-rg
workspaceName: my-aml-workspace
The command property specifies the script to execute. The distribution block configures TensorFlow’s parameter server strategy, splitting work across workerCount nodes and parameterServerCount coordinators. The limits block sets a timeout (PT5M = 5 minutes) to prevent runaway jobs. The codeId and environmentId reference pre-registered assets in your workspace.
Train image classification with AutoML
AutoML jobs automate model selection and hyperparameter tuning for common ML tasks, exploring architectures to find optimal settings.
import * as pulumi from "@pulumi/pulumi";
import * as azure_native from "@pulumi/azure-native";
const job = new azure_native.machinelearningservices.Job("job", {
id: "string",
properties: {
computeId: "string",
description: "string",
displayName: "string",
environmentId: "string",
environmentVariables: {
string: "string",
},
experimentName: "string",
identity: {
identityType: "AMLToken",
},
isArchived: false,
jobType: "AutoML",
outputs: {
string: {
description: "string",
jobOutputType: "uri_file",
mode: azure_native.machinelearningservices.OutputDeliveryMode.ReadWriteMount,
uri: "string",
},
},
properties: {
string: "string",
},
resources: {
instanceCount: 1,
instanceType: "string",
properties: {
string: {
"9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad": null,
},
},
},
services: {
string: {
endpoint: "string",
jobServiceType: "string",
port: 1,
properties: {
string: "string",
},
},
},
tags: {
string: "string",
},
taskDetails: {
limitSettings: {
maxTrials: 2,
},
modelSettings: {
validationCropSize: 2,
},
searchSpace: [{
validationCropSize: "choice(2, 360)",
}],
targetColumnName: "string",
taskType: "ImageClassification",
trainingData: {
jobInputType: "mltable",
uri: "string",
},
},
},
resourceGroupName: "test-rg",
workspaceName: "my-aml-workspace",
});
import pulumi
import pulumi_azure_native as azure_native
job = azure_native.machinelearningservices.Job("job",
id="string",
properties={
"compute_id": "string",
"description": "string",
"display_name": "string",
"environment_id": "string",
"environment_variables": {
"string": "string",
},
"experiment_name": "string",
"identity": {
"identity_type": "AMLToken",
},
"is_archived": False,
"job_type": "AutoML",
"outputs": {
"string": {
"description": "string",
"job_output_type": "uri_file",
"mode": azure_native.machinelearningservices.OutputDeliveryMode.READ_WRITE_MOUNT,
"uri": "string",
},
},
"properties": {
"string": "string",
},
"resources": {
"instance_count": 1,
"instance_type": "string",
"properties": {
"string": {
"9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad": None,
},
},
},
"services": {
"string": {
"endpoint": "string",
"job_service_type": "string",
"port": 1,
"properties": {
"string": "string",
},
},
},
"tags": {
"string": "string",
},
"task_details": {
"limit_settings": {
"max_trials": 2,
},
"model_settings": {
"validation_crop_size": 2,
},
"search_space": [{
"validation_crop_size": "choice(2, 360)",
}],
"target_column_name": "string",
"task_type": "ImageClassification",
"training_data": {
"job_input_type": "mltable",
"uri": "string",
},
},
},
resource_group_name="test-rg",
workspace_name="my-aml-workspace")
package main
import (
machinelearningservices "github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v3"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
_, err := machinelearningservices.NewJob(ctx, "job", &machinelearningservices.JobArgs{
Id: pulumi.String("string"),
Properties: &machinelearningservices.AutoMLJobArgs{
ComputeId: pulumi.String("string"),
Description: pulumi.String("string"),
DisplayName: pulumi.String("string"),
EnvironmentId: pulumi.String("string"),
EnvironmentVariables: pulumi.StringMap{
"string": pulumi.String("string"),
},
ExperimentName: pulumi.String("string"),
Identity: machinelearningservices.AmlToken{
IdentityType: "AMLToken",
},
IsArchived: pulumi.Bool(false),
JobType: pulumi.String("AutoML"),
Outputs: pulumi.Map{
"string": machinelearningservices.UriFileJobOutput{
Description: "string",
JobOutputType: "uri_file",
Mode: machinelearningservices.OutputDeliveryModeReadWriteMount,
Uri: "string",
},
},
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
Resources: &machinelearningservices.JobResourceConfigurationArgs{
InstanceCount: pulumi.Int(1),
InstanceType: pulumi.String("string"),
Properties: pulumi.Map{
"string": pulumi.Any(map[string]interface{}{
"9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad": nil,
}),
},
},
Services: machinelearningservices.JobServiceMap{
"string": &machinelearningservices.JobServiceArgs{
Endpoint: pulumi.String("string"),
JobServiceType: pulumi.String("string"),
Port: pulumi.Int(1),
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
},
Tags: pulumi.StringMap{
"string": pulumi.String("string"),
},
TaskDetails: machinelearningservices.ImageClassification{
LimitSettings: machinelearningservices.ImageLimitSettings{
MaxTrials: 2,
},
ModelSettings: machinelearningservices.ImageModelSettingsClassification{
ValidationCropSize: 2,
},
SearchSpace: []machinelearningservices.ImageModelDistributionSettingsClassification{
{
ValidationCropSize: "choice(2, 360)",
},
},
TargetColumnName: "string",
TaskType: "ImageClassification",
TrainingData: machinelearningservices.MLTableJobInput{
JobInputType: "mltable",
Uri: "string",
},
},
},
ResourceGroupName: pulumi.String("test-rg"),
WorkspaceName: pulumi.String("my-aml-workspace"),
})
if err != nil {
return err
}
return nil
})
}
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
var job = new AzureNative.MachineLearningServices.Job("job", new()
{
Id = "string",
Properties = new AzureNative.MachineLearningServices.Inputs.AutoMLJobArgs
{
ComputeId = "string",
Description = "string",
DisplayName = "string",
EnvironmentId = "string",
EnvironmentVariables =
{
{ "string", "string" },
},
ExperimentName = "string",
Identity = new AzureNative.MachineLearningServices.Inputs.AmlTokenArgs
{
IdentityType = "AMLToken",
},
IsArchived = false,
JobType = "AutoML",
Outputs =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.UriFileJobOutputArgs
{
Description = "string",
JobOutputType = "uri_file",
Mode = AzureNative.MachineLearningServices.OutputDeliveryMode.ReadWriteMount,
Uri = "string",
} },
},
Properties =
{
{ "string", "string" },
},
Resources = new AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationArgs
{
InstanceCount = 1,
InstanceType = "string",
Properties =
{
{ "string", new Dictionary<string, object?>
{
["9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad"] = null,
} },
},
},
Services =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.JobServiceArgs
{
Endpoint = "string",
JobServiceType = "string",
Port = 1,
Properties =
{
{ "string", "string" },
},
} },
},
Tags =
{
{ "string", "string" },
},
TaskDetails = new AzureNative.MachineLearningServices.Inputs.ImageClassificationArgs
{
LimitSettings = new AzureNative.MachineLearningServices.Inputs.ImageLimitSettingsArgs
{
MaxTrials = 2,
},
ModelSettings = new AzureNative.MachineLearningServices.Inputs.ImageModelSettingsClassificationArgs
{
ValidationCropSize = 2,
},
SearchSpace = new[]
{
new AzureNative.MachineLearningServices.Inputs.ImageModelDistributionSettingsClassificationArgs
{
ValidationCropSize = "choice(2, 360)",
},
},
TargetColumnName = "string",
TaskType = "ImageClassification",
TrainingData = new AzureNative.MachineLearningServices.Inputs.MLTableJobInputArgs
{
JobInputType = "mltable",
Uri = "string",
},
},
},
ResourceGroupName = "test-rg",
WorkspaceName = "my-aml-workspace",
});
});
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Job;
import com.pulumi.azurenative.machinelearningservices.JobArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
var job = new Job("job", JobArgs.builder()
.id("string")
.properties(AutoMLJobArgs.builder()
.computeId("string")
.description("string")
.displayName("string")
.environmentId("string")
.environmentVariables(Map.of("string", "string"))
.experimentName("string")
.identity(AmlTokenArgs.builder()
.identityType("AMLToken")
.build())
.isArchived(false)
.jobType("AutoML")
.outputs(Map.of("string", UriFileJobOutputArgs.builder()
.description("string")
.jobOutputType("uri_file")
.mode("ReadWriteMount")
.uri("string")
.build()))
.properties(Map.of("string", "string"))
.resources(JobResourceConfigurationArgs.builder()
.instanceCount(1)
.instanceType("string")
.properties(Map.of("string", Map.of("9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad", null)))
.build())
.services(Map.of("string", JobServiceArgs.builder()
.endpoint("string")
.jobServiceType("string")
.port(1)
.properties(Map.of("string", "string"))
.build()))
.tags(Map.of("string", "string"))
.taskDetails(ImageClassificationArgs.builder()
.limitSettings(ImageLimitSettingsArgs.builder()
.maxTrials(2)
.build())
.modelSettings(ImageModelSettingsClassificationArgs.builder()
.validationCropSize(2)
.build())
.searchSpace(ImageModelDistributionSettingsClassificationArgs.builder()
.validationCropSize("choice(2, 360)")
.build())
.targetColumnName("string")
.taskType("ImageClassification")
.trainingData(MLTableJobInputArgs.builder()
.jobInputType("mltable")
.uri("string")
.build())
.build())
.build())
.resourceGroupName("test-rg")
.workspaceName("my-aml-workspace")
.build());
}
}
resources:
job:
type: azure-native:machinelearningservices:Job
properties:
id: string
properties:
computeId: string
description: string
displayName: string
environmentId: string
environmentVariables:
string: string
experimentName: string
identity:
identityType: AMLToken
isArchived: false
jobType: AutoML
outputs:
string:
description: string
jobOutputType: uri_file
mode: ReadWriteMount
uri: string
properties:
string: string
resources:
instanceCount: 1
instanceType: string
properties:
string:
9bec0ab0-c62f-4fa9-a97c-7b24bbcc90ad: null
services:
string:
endpoint: string
jobServiceType: string
port: 1
properties:
string: string
tags:
string: string
taskDetails:
limitSettings:
maxTrials: 2
modelSettings:
validationCropSize: 2
searchSpace:
- validationCropSize: choice(2, 360)
targetColumnName: string
taskType: ImageClassification
trainingData:
jobInputType: mltable
uri: string
resourceGroupName: test-rg
workspaceName: my-aml-workspace
The taskDetails block defines the ML task. The taskType specifies ImageClassification, and trainingData points to an MLTable containing labeled images. The searchSpace array defines hyperparameter ranges to explore (e.g., validationCropSize can be 2 or 360 pixels). The limitSettings caps the search at maxTrials, controlling compute costs.
Optimize hyperparameters with sweep jobs
Hyperparameter tuning explores parameter combinations to find optimal model configurations, using sampling algorithms and early termination to reduce costs.
import * as pulumi from "@pulumi/pulumi";
import * as azure_native from "@pulumi/azure-native";
const job = new azure_native.machinelearningservices.Job("job", {
id: "string",
properties: {
computeId: "string",
description: "string",
displayName: "string",
earlyTermination: {
delayEvaluation: 1,
evaluationInterval: 1,
policyType: "MedianStopping",
},
experimentName: "string",
jobType: "Sweep",
limits: {
jobLimitsType: "Sweep",
maxConcurrentTrials: 1,
maxTotalTrials: 1,
trialTimeout: "PT1S",
},
objective: {
goal: azure_native.machinelearningservices.Goal.Minimize,
primaryMetric: "string",
},
properties: {
string: "string",
},
samplingAlgorithm: {
samplingAlgorithmType: "Grid",
},
searchSpace: {
string: {},
},
services: {
string: {
endpoint: "string",
jobServiceType: "string",
port: 1,
properties: {
string: "string",
},
},
},
tags: {
string: "string",
},
trial: {
codeId: "string",
command: "string",
distribution: {
distributionType: "Mpi",
processCountPerInstance: 1,
},
environmentId: "string",
environmentVariables: {
string: "string",
},
resources: {
instanceCount: 1,
instanceType: "string",
properties: {
string: {
"e6b6493e-7d5e-4db3-be1e-306ec641327e": null,
},
},
},
},
},
resourceGroupName: "test-rg",
workspaceName: "my-aml-workspace",
});
import pulumi
import pulumi_azure_native as azure_native
job = azure_native.machinelearningservices.Job("job",
id="string",
properties={
"compute_id": "string",
"description": "string",
"display_name": "string",
"early_termination": {
"delay_evaluation": 1,
"evaluation_interval": 1,
"policy_type": "MedianStopping",
},
"experiment_name": "string",
"job_type": "Sweep",
"limits": {
"job_limits_type": "Sweep",
"max_concurrent_trials": 1,
"max_total_trials": 1,
"trial_timeout": "PT1S",
},
"objective": {
"goal": azure_native.machinelearningservices.Goal.MINIMIZE,
"primary_metric": "string",
},
"properties": {
"string": "string",
},
"sampling_algorithm": {
"sampling_algorithm_type": "Grid",
},
"search_space": {
"string": {},
},
"services": {
"string": {
"endpoint": "string",
"job_service_type": "string",
"port": 1,
"properties": {
"string": "string",
},
},
},
"tags": {
"string": "string",
},
"trial": {
"code_id": "string",
"command": "string",
"distribution": {
"distribution_type": "Mpi",
"process_count_per_instance": 1,
},
"environment_id": "string",
"environment_variables": {
"string": "string",
},
"resources": {
"instance_count": 1,
"instance_type": "string",
"properties": {
"string": {
"e6b6493e-7d5e-4db3-be1e-306ec641327e": None,
},
},
},
},
},
resource_group_name="test-rg",
workspace_name="my-aml-workspace")
package main
import (
machinelearningservices "github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v3"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
_, err := machinelearningservices.NewJob(ctx, "job", &machinelearningservices.JobArgs{
Id: pulumi.String("string"),
Properties: &machinelearningservices.SweepJobArgs{
ComputeId: pulumi.String("string"),
Description: pulumi.String("string"),
DisplayName: pulumi.String("string"),
EarlyTermination: machinelearningservices.MedianStoppingPolicy{
DelayEvaluation: 1,
EvaluationInterval: 1,
PolicyType: "MedianStopping",
},
ExperimentName: pulumi.String("string"),
JobType: pulumi.String("Sweep"),
Limits: &machinelearningservices.SweepJobLimitsArgs{
JobLimitsType: pulumi.String("Sweep"),
MaxConcurrentTrials: pulumi.Int(1),
MaxTotalTrials: pulumi.Int(1),
TrialTimeout: pulumi.String("PT1S"),
},
Objective: &machinelearningservices.ObjectiveArgs{
Goal: pulumi.String(machinelearningservices.GoalMinimize),
PrimaryMetric: pulumi.String("string"),
},
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
SamplingAlgorithm: machinelearningservices.GridSamplingAlgorithm{
SamplingAlgorithmType: "Grid",
},
SearchSpace: pulumi.Any(map[string]interface{}{
"string": map[string]interface{}{},
}),
Services: machinelearningservices.JobServiceMap{
"string": &machinelearningservices.JobServiceArgs{
Endpoint: pulumi.String("string"),
JobServiceType: pulumi.String("string"),
Port: pulumi.Int(1),
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
},
Tags: pulumi.StringMap{
"string": pulumi.String("string"),
},
Trial: &machinelearningservices.TrialComponentArgs{
CodeId: pulumi.String("string"),
Command: pulumi.String("string"),
Distribution: machinelearningservices.Mpi{
DistributionType: "Mpi",
ProcessCountPerInstance: 1,
},
EnvironmentId: pulumi.String("string"),
EnvironmentVariables: pulumi.StringMap{
"string": pulumi.String("string"),
},
Resources: &machinelearningservices.JobResourceConfigurationArgs{
InstanceCount: pulumi.Int(1),
InstanceType: pulumi.String("string"),
Properties: pulumi.Map{
"string": pulumi.Any(map[string]interface{}{
"e6b6493e-7d5e-4db3-be1e-306ec641327e": nil,
}),
},
},
},
},
ResourceGroupName: pulumi.String("test-rg"),
WorkspaceName: pulumi.String("my-aml-workspace"),
})
if err != nil {
return err
}
return nil
})
}
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
var job = new AzureNative.MachineLearningServices.Job("job", new()
{
Id = "string",
Properties = new AzureNative.MachineLearningServices.Inputs.SweepJobArgs
{
ComputeId = "string",
Description = "string",
DisplayName = "string",
EarlyTermination = new AzureNative.MachineLearningServices.Inputs.MedianStoppingPolicyArgs
{
DelayEvaluation = 1,
EvaluationInterval = 1,
PolicyType = "MedianStopping",
},
ExperimentName = "string",
JobType = "Sweep",
Limits = new AzureNative.MachineLearningServices.Inputs.SweepJobLimitsArgs
{
JobLimitsType = "Sweep",
MaxConcurrentTrials = 1,
MaxTotalTrials = 1,
TrialTimeout = "PT1S",
},
Objective = new AzureNative.MachineLearningServices.Inputs.ObjectiveArgs
{
Goal = AzureNative.MachineLearningServices.Goal.Minimize,
PrimaryMetric = "string",
},
Properties =
{
{ "string", "string" },
},
SamplingAlgorithm = new AzureNative.MachineLearningServices.Inputs.GridSamplingAlgorithmArgs
{
SamplingAlgorithmType = "Grid",
},
SearchSpace = new Dictionary<string, object?>
{
["string"] = new Dictionary<string, object?>
{
},
},
Services =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.JobServiceArgs
{
Endpoint = "string",
JobServiceType = "string",
Port = 1,
Properties =
{
{ "string", "string" },
},
} },
},
Tags =
{
{ "string", "string" },
},
Trial = new AzureNative.MachineLearningServices.Inputs.TrialComponentArgs
{
CodeId = "string",
Command = "string",
Distribution = new AzureNative.MachineLearningServices.Inputs.MpiArgs
{
DistributionType = "Mpi",
ProcessCountPerInstance = 1,
},
EnvironmentId = "string",
EnvironmentVariables =
{
{ "string", "string" },
},
Resources = new AzureNative.MachineLearningServices.Inputs.JobResourceConfigurationArgs
{
InstanceCount = 1,
InstanceType = "string",
Properties =
{
{ "string", new Dictionary<string, object?>
{
["e6b6493e-7d5e-4db3-be1e-306ec641327e"] = null,
} },
},
},
},
},
ResourceGroupName = "test-rg",
WorkspaceName = "my-aml-workspace",
});
});
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Job;
import com.pulumi.azurenative.machinelearningservices.JobArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
var job = new Job("job", JobArgs.builder()
.id("string")
.properties(SweepJobArgs.builder()
.computeId("string")
.description("string")
.displayName("string")
.earlyTermination(MedianStoppingPolicyArgs.builder()
.delayEvaluation(1)
.evaluationInterval(1)
.policyType("MedianStopping")
.build())
.experimentName("string")
.jobType("Sweep")
.limits(Map.ofEntries(
Map.entry("jobLimitsType", "Sweep"),
Map.entry("maxConcurrentTrials", 1),
Map.entry("maxTotalTrials", 1),
Map.entry("trialTimeout", "PT1S")
))
.objective(ObjectiveArgs.builder()
.goal("Minimize")
.primaryMetric("string")
.build())
.properties(Map.of("string", "string"))
.samplingAlgorithm(GridSamplingAlgorithmArgs.builder()
.samplingAlgorithmType("Grid")
.build())
.searchSpace(Map.of("string", Map.ofEntries(
)))
.services(Map.of("string", JobServiceArgs.builder()
.endpoint("string")
.jobServiceType("string")
.port(1)
.properties(Map.of("string", "string"))
.build()))
.tags(Map.of("string", "string"))
.trial(TrialComponentArgs.builder()
.codeId("string")
.command("string")
.distribution(MpiArgs.builder()
.distributionType("Mpi")
.processCountPerInstance(1)
.build())
.environmentId("string")
.environmentVariables(Map.of("string", "string"))
.resources(JobResourceConfigurationArgs.builder()
.instanceCount(1)
.instanceType("string")
.properties(Map.of("string", Map.of("e6b6493e-7d5e-4db3-be1e-306ec641327e", null)))
.build())
.build())
.build())
.resourceGroupName("test-rg")
.workspaceName("my-aml-workspace")
.build());
}
}
resources:
job:
type: azure-native:machinelearningservices:Job
properties:
id: string
properties:
computeId: string
description: string
displayName: string
earlyTermination:
delayEvaluation: 1
evaluationInterval: 1
policyType: MedianStopping
experimentName: string
jobType: Sweep
limits:
jobLimitsType: Sweep
maxConcurrentTrials: 1
maxTotalTrials: 1
trialTimeout: PT1S
objective:
goal: Minimize
primaryMetric: string
properties:
string: string
samplingAlgorithm:
samplingAlgorithmType: Grid
searchSpace:
string: {}
services:
string:
endpoint: string
jobServiceType: string
port: 1
properties:
string: string
tags:
string: string
trial:
codeId: string
command: string
distribution:
distributionType: Mpi
processCountPerInstance: 1
environmentId: string
environmentVariables:
string: string
resources:
instanceCount: 1
instanceType: string
properties:
string:
e6b6493e-7d5e-4db3-be1e-306ec641327e: null
resourceGroupName: test-rg
workspaceName: my-aml-workspace
The objective block defines what to optimize: minimize or maximize a primaryMetric. The samplingAlgorithm determines how to explore the searchSpace (Grid tries all combinations). The earlyTermination policy stops underperforming trials early, using MedianStopping to compare against the median of all trials. The trial block defines the training code to run for each parameter combination.
Orchestrate multi-step workflows with pipelines
Complex ML workflows often require multiple steps: data preparation, training, evaluation, and deployment. Pipeline jobs coordinate these steps.
import * as pulumi from "@pulumi/pulumi";
import * as azure_native from "@pulumi/azure-native";
const job = new azure_native.machinelearningservices.Job("job", {
id: "string",
properties: {
computeId: "string",
description: "string",
displayName: "string",
experimentName: "string",
inputs: {
string: {
description: "string",
jobInputType: "literal",
value: "string",
},
},
jobType: "Pipeline",
outputs: {
string: {
description: "string",
jobOutputType: "uri_file",
mode: azure_native.machinelearningservices.OutputDeliveryMode.Upload,
uri: "string",
},
},
properties: {
string: "string",
},
services: {
string: {
endpoint: "string",
jobServiceType: "string",
port: 1,
properties: {
string: "string",
},
},
},
settings: {},
tags: {
string: "string",
},
},
resourceGroupName: "test-rg",
workspaceName: "my-aml-workspace",
});
import pulumi
import pulumi_azure_native as azure_native
job = azure_native.machinelearningservices.Job("job",
id="string",
properties={
"compute_id": "string",
"description": "string",
"display_name": "string",
"experiment_name": "string",
"inputs": {
"string": {
"description": "string",
"job_input_type": "literal",
"value": "string",
},
},
"job_type": "Pipeline",
"outputs": {
"string": {
"description": "string",
"job_output_type": "uri_file",
"mode": azure_native.machinelearningservices.OutputDeliveryMode.UPLOAD,
"uri": "string",
},
},
"properties": {
"string": "string",
},
"services": {
"string": {
"endpoint": "string",
"job_service_type": "string",
"port": 1,
"properties": {
"string": "string",
},
},
},
"settings": {},
"tags": {
"string": "string",
},
},
resource_group_name="test-rg",
workspace_name="my-aml-workspace")
package main
import (
machinelearningservices "github.com/pulumi/pulumi-azure-native-sdk/machinelearningservices/v3"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
_, err := machinelearningservices.NewJob(ctx, "job", &machinelearningservices.JobArgs{
Id: pulumi.String("string"),
Properties: &machinelearningservices.PipelineJobArgs{
ComputeId: pulumi.String("string"),
Description: pulumi.String("string"),
DisplayName: pulumi.String("string"),
ExperimentName: pulumi.String("string"),
Inputs: pulumi.Map{
"string": machinelearningservices.LiteralJobInput{
Description: "string",
JobInputType: "literal",
Value: "string",
},
},
JobType: pulumi.String("Pipeline"),
Outputs: pulumi.Map{
"string": machinelearningservices.UriFileJobOutput{
Description: "string",
JobOutputType: "uri_file",
Mode: machinelearningservices.OutputDeliveryModeUpload,
Uri: "string",
},
},
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
Services: machinelearningservices.JobServiceMap{
"string": &machinelearningservices.JobServiceArgs{
Endpoint: pulumi.String("string"),
JobServiceType: pulumi.String("string"),
Port: pulumi.Int(1),
Properties: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
},
Settings: pulumi.Any(map[string]interface{}{}),
Tags: pulumi.StringMap{
"string": pulumi.String("string"),
},
},
ResourceGroupName: pulumi.String("test-rg"),
WorkspaceName: pulumi.String("my-aml-workspace"),
})
if err != nil {
return err
}
return nil
})
}
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
var job = new AzureNative.MachineLearningServices.Job("job", new()
{
Id = "string",
Properties = new AzureNative.MachineLearningServices.Inputs.PipelineJobArgs
{
ComputeId = "string",
Description = "string",
DisplayName = "string",
ExperimentName = "string",
Inputs =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.LiteralJobInputArgs
{
Description = "string",
JobInputType = "literal",
Value = "string",
} },
},
JobType = "Pipeline",
Outputs =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.UriFileJobOutputArgs
{
Description = "string",
JobOutputType = "uri_file",
Mode = AzureNative.MachineLearningServices.OutputDeliveryMode.Upload,
Uri = "string",
} },
},
Properties =
{
{ "string", "string" },
},
Services =
{
{ "string", new AzureNative.MachineLearningServices.Inputs.JobServiceArgs
{
Endpoint = "string",
JobServiceType = "string",
Port = 1,
Properties =
{
{ "string", "string" },
},
} },
},
Settings = null,
Tags =
{
{ "string", "string" },
},
},
ResourceGroupName = "test-rg",
WorkspaceName = "my-aml-workspace",
});
});
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.Job;
import com.pulumi.azurenative.machinelearningservices.JobArgs;
import java.util.List;
import java.util.ArrayList;
import java.util.Map;
import java.io.File;
import java.nio.file.Files;
import java.nio.file.Paths;
public class App {
public static void main(String[] args) {
Pulumi.run(App::stack);
}
public static void stack(Context ctx) {
var job = new Job("job", JobArgs.builder()
.id("string")
.properties(PipelineJobArgs.builder()
.computeId("string")
.description("string")
.displayName("string")
.experimentName("string")
.inputs(Map.of("string", LiteralJobInputArgs.builder()
.description("string")
.jobInputType("literal")
.value("string")
.build()))
.jobType("Pipeline")
.outputs(Map.of("string", UriFileJobOutputArgs.builder()
.description("string")
.jobOutputType("uri_file")
.mode("Upload")
.uri("string")
.build()))
.properties(Map.of("string", "string"))
.services(Map.of("string", JobServiceArgs.builder()
.endpoint("string")
.jobServiceType("string")
.port(1)
.properties(Map.of("string", "string"))
.build()))
.settings(Map.ofEntries(
))
.tags(Map.of("string", "string"))
.build())
.resourceGroupName("test-rg")
.workspaceName("my-aml-workspace")
.build());
}
}
resources:
job:
type: azure-native:machinelearningservices:Job
properties:
id: string
properties:
computeId: string
description: string
displayName: string
experimentName: string
inputs:
string:
description: string
jobInputType: literal
value: string
jobType: Pipeline
outputs:
string:
description: string
jobOutputType: uri_file
mode: Upload
uri: string
properties:
string: string
services:
string:
endpoint: string
jobServiceType: string
port: 1
properties:
string: string
settings: {}
tags:
string: string
resourceGroupName: test-rg
workspaceName: my-aml-workspace
The jobType identifies this as a Pipeline. The inputs and outputs define data flow between pipeline steps. The settings block (shown empty here) can configure default compute, datastore paths, and other pipeline-wide options. Pipeline jobs manage dependencies between components, ensuring steps execute in the correct order.
Beyond these examples
These snippets focus on specific job-level features: Command, AutoML, Sweep, and Pipeline job types, distributed training and resource allocation, and hyperparameter tuning and early termination. They’re intentionally minimal rather than full ML workflows.
The examples reference pre-existing infrastructure such as Azure ML workspace and compute clusters, registered environments, code assets, and datasets, and resource group with appropriate permissions. They focus on configuring the job rather than provisioning the surrounding workspace infrastructure.
To keep things focused, common job patterns are omitted, including:
- Job scheduling and queue settings (queueSettings)
- Notification configuration (notificationSetting)
- Data validation and test splits
- Feature engineering settings (featurizationSettings)
- Pipeline component definitions and step dependencies
- Model registration and deployment integration
These omissions are intentional: the goal is to illustrate how each job type is wired, not provide drop-in ML pipelines. See the Azure ML Job resource reference for all available configuration options.
Let's configure Azure Machine Learning Jobs
Get started with Pulumi Cloud, then follow our quick setup guide to deploy this infrastructure.
Try Pulumi Cloud for FREEFrequently Asked Questions
Job Configuration & Types
What job types are available in Azure Machine Learning?
Four job types are supported: AutoML (automated machine learning), Command (single command execution), Pipeline (workflow of multiple steps), and Sweep (hyperparameter tuning). The
jobType property determines which configuration options are available.How do I specify a timeout for my job?
Set the
timeout property using ISO 8601 duration format. For example, PT5M represents 5 minutes.What's the difference between ReadWriteMount and Upload output modes?
ReadWriteMount mounts the output location for read/write access during job execution, while Upload uploads outputs after job completion. AutoML and Command job examples use ReadWriteMount, while Pipeline jobs use Upload.Resource Identification & Immutability
What properties can't I change after creating a job?
The
id, resourceGroupName, and workspaceName are immutable. Changing any of these requires replacing the resource.Why is my job ID case-sensitive but resource group name isn't?
The
id property is explicitly case-sensitive for job identification, while resourceGroupName is case-insensitive following Azure resource group naming conventions.Distributed Training & Compute
How do I configure distributed training for my job?
Set the
distribution property with a distributionType such as TensorFlow or Mpi. For TensorFlow, specify parameterServerCount and workerCount. For MPI, specify processCountPerInstance.How do I control the number of compute instances used?
Configure the
resources property with instanceCount and instanceType. Examples show instanceCount: 1 for single-instance jobs.API Versions & Migration
How do I use a different API version for this resource?
Generate a local SDK package using
pulumi package add azure-native machinelearningservices [ApiVersion]. Multiple API versions are available from 2021-03-01-preview through 2025-10-01-preview.How do I import an existing Azure ML job into Pulumi?
Use the import command:
pulumi import azure-native:machinelearningservices:Job <name> /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/jobs/{id}