Web
data class WebServiceArgs(val location: Output<String>? = null, val properties: Output<WebServicePropertiesForGraphArgs>? = null, val resourceGroupName: Output<String>? = null, val tags: Output<Map<String, String>>? = null, val webServiceName: Output<String>? = null) : ConvertibleToJava<WebServiceArgs>
Instance of an Azure ML web service resource. Azure REST API version: 2017-01-01. Prior API version in Azure Native 1.x: 2017-01-01. Other available API versions: 2016-05-01-preview.
Example Usage
PUT WebService
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
var webService = new AzureNative.MachineLearning.WebService("webService", new()
{
Location = "West US",
Properties = new AzureNative.MachineLearning.Inputs.WebServicePropertiesForGraphArgs
{
Assets =
{
{ "asset1", new AzureNative.MachineLearning.Inputs.AssetItemArgs
{
LocationInfo = new AzureNative.MachineLearning.Inputs.BlobLocationArgs
{
Credentials = "",
Uri = "aml://module/moduleId-1",
},
Name = "Execute R Script",
Type = "Module",
} },
{ "asset2", new AzureNative.MachineLearning.Inputs.AssetItemArgs
{
LocationInfo = new AzureNative.MachineLearning.Inputs.BlobLocationArgs
{
Credentials = "",
Uri = "aml://module/moduleId-2",
},
Name = "Import Data",
Type = "Module",
} },
},
CommitmentPlan = new AzureNative.MachineLearning.Inputs.CommitmentPlanArgs
{
Id = "/subscriptions/subscriptionId/resourceGroups/resourceGroupName/providers/Microsoft.MachineLearning/commitmentPlans/commitmentPlanName",
},
Description = "Web Service Description",
Diagnostics = new AzureNative.MachineLearning.Inputs.DiagnosticsConfigurationArgs
{
Level = "None",
},
ExampleRequest = new AzureNative.MachineLearning.Inputs.ExampleRequestArgs
{
Inputs =
{
{ "input1", new[]
{
new[]
{
"age",
},
new[]
{
"workclass",
},
new[]
{
"fnlwgt",
},
new[]
{
"education",
},
new[]
{
"education-num",
},
} },
},
},
ExposeSampleData = true,
Input = new AzureNative.MachineLearning.Inputs.ServiceInputOutputSpecificationArgs
{
Description = "",
Properties =
{
{ "input1", new AzureNative.MachineLearning.Inputs.TableSpecificationArgs
{
Description = "",
Properties =
{
{ "column_name", new AzureNative.MachineLearning.Inputs.ColumnSpecificationArgs
{
Type = "String",
XMsIsnullable = false,
} },
},
Title = "",
Type = "object",
} },
},
Title = "",
Type = "object",
},
MachineLearningWorkspace = new AzureNative.MachineLearning.Inputs.MachineLearningWorkspaceArgs
{
Id = "workspaceId",
},
Output = new AzureNative.MachineLearning.Inputs.ServiceInputOutputSpecificationArgs
{
Description = "",
Properties =
{
{ "output1", new AzureNative.MachineLearning.Inputs.TableSpecificationArgs
{
Description = "",
Properties =
{
{ "age", new AzureNative.MachineLearning.Inputs.ColumnSpecificationArgs
{
Format = "Int32",
Type = "Integer",
XMsIsnullable = true,
} },
{ "workclass", new AzureNative.MachineLearning.Inputs.ColumnSpecificationArgs
{
Type = "String",
XMsIsnullable = false,
} },
},
Title = "",
Type = "object",
} },
},
Title = "",
Type = "object",
},
Package = new AzureNative.MachineLearning.Inputs.GraphPackageArgs
{
Edges = new[]
{
new AzureNative.MachineLearning.Inputs.GraphEdgeArgs
{
SourceNodeId = "node2",
SourcePortId = "Results dataset",
TargetNodeId = "node1",
TargetPortId = "Dataset2",
},
new AzureNative.MachineLearning.Inputs.GraphEdgeArgs
{
SourceNodeId = "node3",
TargetNodeId = "node1",
TargetPortId = "Dataset1",
},
new AzureNative.MachineLearning.Inputs.GraphEdgeArgs
{
SourceNodeId = "node1",
SourcePortId = "Result Dataset",
TargetNodeId = "node4",
},
},
GraphParameters = null,
Nodes =
{
{ "node1", new AzureNative.MachineLearning.Inputs.GraphNodeArgs
{
AssetId = "asset1",
Parameters =
{
{ "R Script", new AzureNative.MachineLearning.Inputs.WebServiceParameterArgs
{
CertificateThumbprint = "",
Value = "The R Script",
} },
{ "R Version", new AzureNative.MachineLearning.Inputs.WebServiceParameterArgs
{
CertificateThumbprint = "",
Value = "CRAN R 3.1.0",
} },
},
} },
{ "node2", new AzureNative.MachineLearning.Inputs.GraphNodeArgs
{
AssetId = "asset2",
Parameters =
{
{ "Account Key", new AzureNative.MachineLearning.Inputs.WebServiceParameterArgs
{
CertificateThumbprint = "TheThumbprint",
Value = "Encrypted Key",
} },
{ "Account Name", new AzureNative.MachineLearning.Inputs.WebServiceParameterArgs
{
CertificateThumbprint = "",
Value = "accountName",
} },
{ "Please Specify Authentication Type", new AzureNative.MachineLearning.Inputs.WebServiceParameterArgs
{
CertificateThumbprint = "",
Value = "Account",
} },
{ "Please Specify Data Source", new AzureNative.MachineLearning.Inputs.WebServiceParameterArgs
{
CertificateThumbprint = "",
Value = "AzureBlobStorage",
} },
},
} },
{ "node3", new AzureNative.MachineLearning.Inputs.GraphNodeArgs
{
InputId = "input1",
} },
{ "node4", new AzureNative.MachineLearning.Inputs.GraphNodeArgs
{
OutputId = "output1",
} },
},
},
PackageType = "Graph",
Parameters = null,
PayloadsInBlobStorage = false,
ReadOnly = false,
RealtimeConfiguration = new AzureNative.MachineLearning.Inputs.RealtimeConfigurationArgs
{
MaxConcurrentCalls = 4,
},
StorageAccount = new AzureNative.MachineLearning.Inputs.StorageAccountArgs
{
Key = "Storage_Key",
Name = "Storage_Name",
},
Title = "Web Service Title",
},
ResourceGroupName = "OneResourceGroupName",
Tags =
{
{ "tag1", "value1" },
{ "tag2", "value2" },
},
WebServiceName = "TargetWebServiceName",
});
});Content copied to clipboard
package main
import (
"github.com/pulumi/pulumi-azure-native-sdk/machinelearning/v2"
"github.com/pulumi/pulumi/sdk/v3/go/pulumi"
)
func main() {
pulumi.Run(func(ctx *pulumi.Context) error {
_, err := machinelearning.NewWebService(ctx, "webService", &machinelearning.WebServiceArgs{
Location: pulumi.String("West US"),
Properties: interface{}{
Assets: interface{}{
Asset1: &machinelearning.AssetItemArgs{
LocationInfo: &machinelearning.BlobLocationArgs{
Credentials: pulumi.String(""),
Uri: pulumi.String("aml://module/moduleId-1"),
},
Name: pulumi.String("Execute R Script"),
Type: pulumi.String("Module"),
},
Asset2: &machinelearning.AssetItemArgs{
LocationInfo: &machinelearning.BlobLocationArgs{
Credentials: pulumi.String(""),
Uri: pulumi.String("aml://module/moduleId-2"),
},
Name: pulumi.String("Import Data"),
Type: pulumi.String("Module"),
},
},
CommitmentPlan: &machinelearning.CommitmentPlanTypeArgs{
Id: pulumi.String("/subscriptions/subscriptionId/resourceGroups/resourceGroupName/providers/Microsoft.MachineLearning/commitmentPlans/commitmentPlanName"),
},
Description: pulumi.String("Web Service Description"),
Diagnostics: &machinelearning.DiagnosticsConfigurationArgs{
Level: pulumi.String("None"),
},
ExampleRequest: &machinelearning.ExampleRequestArgs{
Inputs: pulumi.ArrayArrayMap{
"input1": pulumi.ArrayArray{
pulumi.Array{
pulumi.Any("age"),
},
pulumi.Array{
pulumi.Any("workclass"),
},
pulumi.Array{
pulumi.Any("fnlwgt"),
},
pulumi.Array{
pulumi.Any("education"),
},
pulumi.Array{
pulumi.Any("education-num"),
},
},
},
},
ExposeSampleData: pulumi.Bool(true),
Input: interface{}{
Description: pulumi.String(""),
Properties: machinelearning.TableSpecificationMap{
"input1": &machinelearning.TableSpecificationArgs{
Description: pulumi.String(""),
Properties: machinelearning.ColumnSpecificationMap{
"column_name": &machinelearning.ColumnSpecificationArgs{
Type: pulumi.String("String"),
XMsIsnullable: pulumi.Bool(false),
},
},
Title: pulumi.String(""),
Type: pulumi.String("object"),
},
},
Title: pulumi.String(""),
Type: pulumi.String("object"),
},
MachineLearningWorkspace: &machinelearning.MachineLearningWorkspaceArgs{
Id: pulumi.String("workspaceId"),
},
Output: interface{}{
Description: pulumi.String(""),
Properties: machinelearning.TableSpecificationMap{
"output1": &machinelearning.TableSpecificationArgs{
Description: pulumi.String(""),
Properties: machinelearning.ColumnSpecificationMap{
"age": &machinelearning.ColumnSpecificationArgs{
Format: pulumi.String("Int32"),
Type: pulumi.String("Integer"),
XMsIsnullable: pulumi.Bool(true),
},
"workclass": &machinelearning.ColumnSpecificationArgs{
Type: pulumi.String("String"),
XMsIsnullable: pulumi.Bool(false),
},
},
Title: pulumi.String(""),
Type: pulumi.String("object"),
},
},
Title: pulumi.String(""),
Type: pulumi.String("object"),
},
Package: interface{}{
Edges: machinelearning.GraphEdgeArray{
&machinelearning.GraphEdgeArgs{
SourceNodeId: pulumi.String("node2"),
SourcePortId: pulumi.String("Results dataset"),
TargetNodeId: pulumi.String("node1"),
TargetPortId: pulumi.String("Dataset2"),
},
&machinelearning.GraphEdgeArgs{
SourceNodeId: pulumi.String("node3"),
TargetNodeId: pulumi.String("node1"),
TargetPortId: pulumi.String("Dataset1"),
},
&machinelearning.GraphEdgeArgs{
SourceNodeId: pulumi.String("node1"),
SourcePortId: pulumi.String("Result Dataset"),
TargetNodeId: pulumi.String("node4"),
},
},
GraphParameters: nil,
Nodes: machinelearning.GraphNodeMap{
"node1": &machinelearning.GraphNodeArgs{
AssetId: pulumi.String("asset1"),
Parameters: machinelearning.WebServiceParameterMap{
"R Script": &machinelearning.WebServiceParameterArgs{
CertificateThumbprint: pulumi.String(""),
Value: pulumi.Any("The R Script"),
},
"R Version": &machinelearning.WebServiceParameterArgs{
CertificateThumbprint: pulumi.String(""),
Value: pulumi.Any("CRAN R 3.1.0"),
},
},
},
"node2": &machinelearning.GraphNodeArgs{
AssetId: pulumi.String("asset2"),
Parameters: machinelearning.WebServiceParameterMap{
"Account Key": &machinelearning.WebServiceParameterArgs{
CertificateThumbprint: pulumi.String("TheThumbprint"),
Value: pulumi.Any("Encrypted Key"),
},
"Account Name": &machinelearning.WebServiceParameterArgs{
CertificateThumbprint: pulumi.String(""),
Value: pulumi.Any("accountName"),
},
"Please Specify Authentication Type": &machinelearning.WebServiceParameterArgs{
CertificateThumbprint: pulumi.String(""),
Value: pulumi.Any("Account"),
},
"Please Specify Data Source": &machinelearning.WebServiceParameterArgs{
CertificateThumbprint: pulumi.String(""),
Value: pulumi.Any("AzureBlobStorage"),
},
},
},
"node3": &machinelearning.GraphNodeArgs{
InputId: pulumi.String("input1"),
},
"node4": &machinelearning.GraphNodeArgs{
OutputId: pulumi.String("output1"),
},
},
},
PackageType: pulumi.String("Graph"),
Parameters: nil,
PayloadsInBlobStorage: pulumi.Bool(false),
ReadOnly: pulumi.Bool(false),
RealtimeConfiguration: &machinelearning.RealtimeConfigurationArgs{
MaxConcurrentCalls: pulumi.Int(4),
},
StorageAccount: &machinelearning.StorageAccountArgs{
Key: pulumi.String("Storage_Key"),
Name: pulumi.String("Storage_Name"),
},
Title: pulumi.String("Web Service Title"),
},
ResourceGroupName: pulumi.String("OneResourceGroupName"),
Tags: pulumi.StringMap{
"tag1": pulumi.String("value1"),
"tag2": pulumi.String("value2"),
},
WebServiceName: pulumi.String("TargetWebServiceName"),
})
if err != nil {
return err
}
return nil
})
}Content copied to clipboard
package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearning.WebService;
import com.pulumi.azurenative.machinelearning.WebServiceArgs;
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 webService = new WebService("webService", WebServiceArgs.builder()
.location("West US")
.properties(Map.ofEntries(
Map.entry("assets", Map.ofEntries(
Map.entry("asset1", Map.ofEntries(
Map.entry("locationInfo", Map.ofEntries(
Map.entry("credentials", ""),
Map.entry("uri", "aml://module/moduleId-1")
)),
Map.entry("name", "Execute R Script"),
Map.entry("type", "Module")
)),
Map.entry("asset2", Map.ofEntries(
Map.entry("locationInfo", Map.ofEntries(
Map.entry("credentials", ""),
Map.entry("uri", "aml://module/moduleId-2")
)),
Map.entry("name", "Import Data"),
Map.entry("type", "Module")
))
)),
Map.entry("commitmentPlan", Map.of("id", "/subscriptions/subscriptionId/resourceGroups/resourceGroupName/providers/Microsoft.MachineLearning/commitmentPlans/commitmentPlanName")),
Map.entry("description", "Web Service Description"),
Map.entry("diagnostics", Map.of("level", "None")),
Map.entry("exampleRequest", Map.of("inputs", Map.of("input1",
"age",
"workclass",
"fnlwgt",
"education",
"education-num"))),
Map.entry("exposeSampleData", true),
Map.entry("input", Map.ofEntries(
Map.entry("description", ""),
Map.entry("properties", Map.of("input1", Map.ofEntries(
Map.entry("description", ""),
Map.entry("properties", Map.of("column_name", Map.ofEntries(
Map.entry("type", "String"),
Map.entry("xMsIsnullable", false)
))),
Map.entry("title", ""),
Map.entry("type", "object")
))),
Map.entry("title", ""),
Map.entry("type", "object")
)),
Map.entry("machineLearningWorkspace", Map.of("id", "workspaceId")),
Map.entry("output", Map.ofEntries(
Map.entry("description", ""),
Map.entry("properties", Map.of("output1", Map.ofEntries(
Map.entry("description", ""),
Map.entry("properties", Map.ofEntries(
Map.entry("age", Map.ofEntries(
Map.entry("format", "Int32"),
Map.entry("type", "Integer"),
Map.entry("xMsIsnullable", true)
)),
Map.entry("workclass", Map.ofEntries(
Map.entry("type", "String"),
Map.entry("xMsIsnullable", false)
))
)),
Map.entry("title", ""),
Map.entry("type", "object")
))),
Map.entry("title", ""),
Map.entry("type", "object")
)),
Map.entry("package", Map.ofEntries(
Map.entry("edges",
Map.ofEntries(
Map.entry("sourceNodeId", "node2"),
Map.entry("sourcePortId", "Results dataset"),
Map.entry("targetNodeId", "node1"),
Map.entry("targetPortId", "Dataset2")
),
Map.ofEntries(
Map.entry("sourceNodeId", "node3"),
Map.entry("targetNodeId", "node1"),
Map.entry("targetPortId", "Dataset1")
),
Map.ofEntries(
Map.entry("sourceNodeId", "node1"),
Map.entry("sourcePortId", "Result Dataset"),
Map.entry("targetNodeId", "node4")
)),
Map.entry("graphParameters", ),
Map.entry("nodes", Map.ofEntries(
Map.entry("node1", Map.ofEntries(
Map.entry("assetId", "asset1"),
Map.entry("parameters", Map.ofEntries(
Map.entry("R Script", Map.ofEntries(
Map.entry("certificateThumbprint", ""),
Map.entry("value", "The R Script")
)),
Map.entry("R Version", Map.ofEntries(
Map.entry("certificateThumbprint", ""),
Map.entry("value", "CRAN R 3.1.0")
))
))
)),
Map.entry("node2", Map.ofEntries(
Map.entry("assetId", "asset2"),
Map.entry("parameters", Map.ofEntries(
Map.entry("Account Key", Map.ofEntries(
Map.entry("certificateThumbprint", "TheThumbprint"),
Map.entry("value", "Encrypted Key")
)),
Map.entry("Account Name", Map.ofEntries(
Map.entry("certificateThumbprint", ""),
Map.entry("value", "accountName")
)),
Map.entry("Please Specify Authentication Type", Map.ofEntries(
Map.entry("certificateThumbprint", ""),
Map.entry("value", "Account")
)),
Map.entry("Please Specify Data Source", Map.ofEntries(
Map.entry("certificateThumbprint", ""),
Map.entry("value", "AzureBlobStorage")
))
))
)),
Map.entry("node3", Map.of("inputId", "input1")),
Map.entry("node4", Map.of("outputId", "output1"))
))
)),
Map.entry("packageType", "Graph"),
Map.entry("parameters", ),
Map.entry("payloadsInBlobStorage", false),
Map.entry("readOnly", false),
Map.entry("realtimeConfiguration", Map.of("maxConcurrentCalls", 4)),
Map.entry("storageAccount", Map.ofEntries(
Map.entry("key", "Storage_Key"),
Map.entry("name", "Storage_Name")
)),
Map.entry("title", "Web Service Title")
))
.resourceGroupName("OneResourceGroupName")
.tags(Map.ofEntries(
Map.entry("tag1", "value1"),
Map.entry("tag2", "value2")
))
.webServiceName("TargetWebServiceName")
.build());
}
}Content copied to clipboard
Import
An existing resource can be imported using its type token, name, and identifier, e.g.
$ pulumi import azure-native:machinelearning:WebService myresource1 /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearning/webServices/{webServiceName}Content copied to clipboard
Constructors
Link copied to clipboard
fun WebServiceArgs(location: Output<String>? = null, properties: Output<WebServicePropertiesForGraphArgs>? = null, resourceGroupName: Output<String>? = null, tags: Output<Map<String, String>>? = null, webServiceName: Output<String>? = null)