Machine Learning Dataset Args
data class MachineLearningDatasetArgs(val datasetName: Output<String>? = null, val datasetType: Output<Either<String, DatasetType>>? = null, val parameters: Output<DatasetCreateRequestParametersArgs>? = null, val registration: Output<DatasetCreateRequestRegistrationArgs>? = null, val resourceGroupName: Output<String>? = null, val skipValidation: Output<Boolean>? = null, val timeSeries: Output<DatasetCreateRequestTimeSeriesArgs>? = null, val workspaceName: Output<String>? = null) : ConvertibleToJava<MachineLearningDatasetArgs>
Machine Learning dataset object wrapped into ARM resource envelope. Uses Azure REST API version 2020-05-01-preview. In version 2.x of the Azure Native provider, it used API version 2020-05-01-preview.
Example Usage
Create Dataset
using System.Collections.Generic;
using System.Linq;
using Pulumi;
using AzureNative = Pulumi.AzureNative;
return await Deployment.RunAsync(() =>
{
var machineLearningDataset = new AzureNative.MachineLearningServices.MachineLearningDataset("machineLearningDataset", new()
{
DatasetName = "datasetName123",
DatasetType = AzureNative.MachineLearningServices.DatasetType.File,
Parameters = new AzureNative.MachineLearningServices.Inputs.DatasetCreateRequestParametersArgs
{
Path = new AzureNative.MachineLearningServices.Inputs.DatasetCreateRequestPathArgs
{
DataPath = new AzureNative.MachineLearningServices.Inputs.DatasetCreateRequestDataPathArgs
{
DatastoreName = "testblobfromarm",
RelativePath = "UI/03-26-2020_083359_UTC/latin1encoding.csv",
},
},
},
Registration = new AzureNative.MachineLearningServices.Inputs.DatasetCreateRequestRegistrationArgs
{
Description = "test description",
Name = "datasetName123",
},
ResourceGroupName = "acjain-mleastUS2",
SkipValidation = false,
WorkspaceName = "acjain-mleastUS2",
});
});
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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.NewMachineLearningDataset(ctx, "machineLearningDataset", &machinelearningservices.MachineLearningDatasetArgs{
DatasetName: pulumi.String("datasetName123"),
DatasetType: pulumi.String(machinelearningservices.DatasetTypeFile),
Parameters: &machinelearningservices.DatasetCreateRequestParametersArgs{
Path: &machinelearningservices.DatasetCreateRequestPathArgs{
DataPath: &machinelearningservices.DatasetCreateRequestDataPathArgs{
DatastoreName: pulumi.String("testblobfromarm"),
RelativePath: pulumi.String("UI/03-26-2020_083359_UTC/latin1encoding.csv"),
},
},
},
Registration: &machinelearningservices.DatasetCreateRequestRegistrationArgs{
Description: pulumi.String("test description"),
Name: pulumi.String("datasetName123"),
},
ResourceGroupName: pulumi.String("acjain-mleastUS2"),
SkipValidation: pulumi.Bool(false),
WorkspaceName: pulumi.String("acjain-mleastUS2"),
})
if err != nil {
return err
}
return nil
})
}
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package generated_program;
import com.pulumi.Context;
import com.pulumi.Pulumi;
import com.pulumi.core.Output;
import com.pulumi.azurenative.machinelearningservices.MachineLearningDataset;
import com.pulumi.azurenative.machinelearningservices.MachineLearningDatasetArgs;
import com.pulumi.azurenative.machinelearningservices.inputs.DatasetCreateRequestParametersArgs;
import com.pulumi.azurenative.machinelearningservices.inputs.DatasetCreateRequestPathArgs;
import com.pulumi.azurenative.machinelearningservices.inputs.DatasetCreateRequestDataPathArgs;
import com.pulumi.azurenative.machinelearningservices.inputs.DatasetCreateRequestRegistrationArgs;
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 machineLearningDataset = new MachineLearningDataset("machineLearningDataset", MachineLearningDatasetArgs.builder()
.datasetName("datasetName123")
.datasetType("file")
.parameters(DatasetCreateRequestParametersArgs.builder()
.path(DatasetCreateRequestPathArgs.builder()
.dataPath(DatasetCreateRequestDataPathArgs.builder()
.datastoreName("testblobfromarm")
.relativePath("UI/03-26-2020_083359_UTC/latin1encoding.csv")
.build())
.build())
.build())
.registration(DatasetCreateRequestRegistrationArgs.builder()
.description("test description")
.name("datasetName123")
.build())
.resourceGroupName("acjain-mleastUS2")
.skipValidation(false)
.workspaceName("acjain-mleastUS2")
.build());
}
}
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Import
An existing resource can be imported using its type token, name, and identifier, e.g.
$ pulumi import azure-native:machinelearningservices:MachineLearningDataset datasetName123 /subscriptions/{subscriptionId}/resourceGroups/{resourceGroupName}/providers/Microsoft.MachineLearningServices/workspaces/{workspaceName}/datasets/{datasetName}
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Constructors
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constructor(datasetName: Output<String>? = null, datasetType: Output<Either<String, DatasetType>>? = null, parameters: Output<DatasetCreateRequestParametersArgs>? = null, registration: Output<DatasetCreateRequestRegistrationArgs>? = null, resourceGroupName: Output<String>? = null, skipValidation: Output<Boolean>? = null, timeSeries: Output<DatasetCreateRequestTimeSeriesArgs>? = null, workspaceName: Output<String>? = null)
Properties
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The Dataset name.
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Specifies dataset type.
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Name of the resource group in which workspace is located.
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Skip validation that ensures data can be loaded from the dataset before registration.
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Name of Azure Machine Learning workspace.