There are more AWS SDK examples available in the [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub repo.

# Use `StartPipelineExecution` with an AWS SDK
<a name="sagemaker_example_sagemaker_StartPipelineExecution_section"></a>

The following code examples show how to use `StartPipelineExecution`.

Action examples are code excerpts from larger programs and must be run in context. You can see this action in context in the following code example: 
+  [Get started with geospatial jobs and pipelines](sagemaker_example_sagemaker_Scenario_Pipelines_section.md) 

------
#### [ .NET ]

**SDK for .NET**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/dotnetv3/SageMaker#code-examples). 

```
    /// <summary>
    /// Run a pipeline with input and output file locations.
    /// </summary>
    /// <param name="queueUrl">The URL for the queue to use for pipeline callbacks.</param>
    /// <param name="inputLocationUrl">The input location in Amazon Simple Storage Service (Amazon S3).</param>
    /// <param name="outputLocationUrl">The output location in Amazon S3.</param>
    /// <param name="pipelineName">The name of the pipeline.</param>
    /// <param name="executionRoleArn">The ARN of the role.</param>
    /// <returns>The ARN of the pipeline run.</returns>
    public async Task<string> ExecutePipeline(
        string queueUrl,
        string inputLocationUrl,
        string outputLocationUrl,
        string pipelineName,
        string executionRoleArn)
    {
        var inputConfig = new VectorEnrichmentJobInputConfig()
        {
            DataSourceConfig = new()
            {
                S3Data = new VectorEnrichmentJobS3Data()
                {
                    S3Uri = inputLocationUrl
                }
            },
            DocumentType = VectorEnrichmentJobDocumentType.CSV
        };

        var exportConfig = new ExportVectorEnrichmentJobOutputConfig()
        {
            S3Data = new VectorEnrichmentJobS3Data()
            {
                S3Uri = outputLocationUrl
            }
        };

        var jobConfig = new VectorEnrichmentJobConfig()
        {
            ReverseGeocodingConfig = new ReverseGeocodingConfig()
            {
                XAttributeName = "Longitude",
                YAttributeName = "Latitude"
            }
        };

#pragma warning disable SageMaker1002 // Property value does not match required pattern is allowed here to match the pipeline definition.
        var startExecutionResponse = await _amazonSageMaker.StartPipelineExecutionAsync(
            new StartPipelineExecutionRequest()
            {
                PipelineName = pipelineName,
                PipelineExecutionDisplayName = pipelineName + "-example-execution",
                PipelineParameters = new List<Parameter>()
                {
                    new Parameter() { Name = "parameter_execution_role", Value = executionRoleArn },
                    new Parameter() { Name = "parameter_queue_url", Value = queueUrl },
                    new Parameter() { Name = "parameter_vej_input_config", Value = JsonSerializer.Serialize(inputConfig) },
                    new Parameter() { Name = "parameter_vej_export_config", Value = JsonSerializer.Serialize(exportConfig) },
                    new Parameter() { Name = "parameter_step_1_vej_config", Value = JsonSerializer.Serialize(jobConfig) }
                }
            });
#pragma warning restore SageMaker1002
        return startExecutionResponse.PipelineExecutionArn;
    }
```
+  For API details, see [StartPipelineExecution](https://docs.aws.amazon.com/goto/DotNetSDKV3/sagemaker-2017-07-24/StartPipelineExecution) in *AWS SDK for .NET API Reference*. 

------
#### [ Java ]

**SDK for Java 2.x**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javav2/usecases/workflow_sagemaker_pipes#code-examples). 

```
    // Start a pipeline run with job configurations.
    public static String executePipeline(SageMakerClient sageMakerClient, String bucketName, String queueUrl,
            String roleArn, String pipelineName) {
        System.out.println("Starting pipeline execution.");
        String inputBucketLocation = "s3://" + bucketName + "/samplefiles/latlongtest.csv";
        String output = "s3://" + bucketName + "/outputfiles/";
        Gson gson = new GsonBuilder()
                .setFieldNamingPolicy(FieldNamingPolicy.UPPER_CAMEL_CASE)
                .setPrettyPrinting().create();

        // Set up all parameters required to start the pipeline.
        List<Parameter> parameters = new ArrayList<>();
        Parameter para1 = Parameter.builder()
                .name("parameter_execution_role")
                .value(roleArn)
                .build();

        Parameter para2 = Parameter.builder()
                .name("parameter_queue_url")
                .value(queueUrl)
                .build();

        String inputJSON = "{\n" +
                "  \"DataSourceConfig\": {\n" +
                "    \"S3Data\": {\n" +
                "      \"S3Uri\": \"s3://" + bucketName + "/samplefiles/latlongtest.csv\"\n" +
                "    },\n" +
                "    \"Type\": \"S3_DATA\"\n" +
                "  },\n" +
                "  \"DocumentType\": \"CSV\"\n" +
                "}";

        System.out.println(inputJSON);

        Parameter para3 = Parameter.builder()
                .name("parameter_vej_input_config")
                .value(inputJSON)
                .build();

        // Create an ExportVectorEnrichmentJobOutputConfig object.
        VectorEnrichmentJobS3Data jobS3Data = VectorEnrichmentJobS3Data.builder()
                .s3Uri(output)
                .build();

        ExportVectorEnrichmentJobOutputConfig outputConfig = ExportVectorEnrichmentJobOutputConfig.builder()
                .s3Data(jobS3Data)
                .build();

        String gson4 = gson.toJson(outputConfig);
        Parameter para4 = Parameter.builder()
                .name("parameter_vej_export_config")
                .value(gson4)
                .build();
        System.out.println("parameter_vej_export_config:" + gson.toJson(outputConfig));

        // Create a VectorEnrichmentJobConfig object.
        ReverseGeocodingConfig reverseGeocodingConfig = ReverseGeocodingConfig.builder()
                .xAttributeName("Longitude")
                .yAttributeName("Latitude")
                .build();

        VectorEnrichmentJobConfig jobConfig = VectorEnrichmentJobConfig.builder()
                .reverseGeocodingConfig(reverseGeocodingConfig)
                .build();

        String para5JSON = "{\"MapMatchingConfig\":null,\"ReverseGeocodingConfig\":{\"XAttributeName\":\"Longitude\",\"YAttributeName\":\"Latitude\"}}";
        Parameter para5 = Parameter.builder()
                .name("parameter_step_1_vej_config")
                .value(para5JSON)
                .build();

        System.out.println("parameter_step_1_vej_config:" + gson.toJson(jobConfig));
        parameters.add(para1);
        parameters.add(para2);
        parameters.add(para3);
        parameters.add(para4);
        parameters.add(para5);

        StartPipelineExecutionRequest pipelineExecutionRequest = StartPipelineExecutionRequest.builder()
                .pipelineExecutionDescription("Created using Java SDK")
                .pipelineExecutionDisplayName(pipelineName + "-example-execution")
                .pipelineParameters(parameters)
                .pipelineName(pipelineName)
                .build();

        StartPipelineExecutionResponse response = sageMakerClient.startPipelineExecution(pipelineExecutionRequest);
        return response.pipelineExecutionArn();
    }
```
+  For API details, see [StartPipelineExecution](https://docs.aws.amazon.com/goto/SdkForJavaV2/sagemaker-2017-07-24/StartPipelineExecution) in *AWS SDK for Java 2.x API Reference*. 

------
#### [ JavaScript ]

**SDK for JavaScript (v3)**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/javascriptv3/example_code/sagemaker#code-examples). 
Start a SageMaker AI pipeline execution.  

```
/**
 * Start the execution of the Amazon SageMaker pipeline. Parameters that are
 * passed in are used in the AWS Lambda function.
 * @param {{
 *   name: string,
 *   sagemakerClient: import('@aws-sdk/client-sagemaker').SageMakerClient,
 *   roleArn: string,
 *   queueUrl: string,
 *   s3InputBucketName: string,
 * }} props
 */
export async function startPipelineExecution({
  sagemakerClient,
  name,
  bucketName,
  roleArn,
  queueUrl,
}) {
  /**
   * The Vector Enrichment Job requests CSV data. This configuration points to a CSV
   * file in an Amazon S3 bucket.
   * @type {import("@aws-sdk/client-sagemaker-geospatial").VectorEnrichmentJobInputConfig}
   */
  const inputConfig = {
    DataSourceConfig: {
      S3Data: {
        S3Uri: `s3://${bucketName}/input/sample_data.csv`,
      },
    },
    DocumentType: VectorEnrichmentJobDocumentType.CSV,
  };

  /**
   * The Vector Enrichment Job adds additional data to the source CSV. This configuration points
   * to an Amazon S3 prefix where the output will be stored.
   * @type {import("@aws-sdk/client-sagemaker-geospatial").ExportVectorEnrichmentJobOutputConfig}
   */
  const outputConfig = {
    S3Data: {
      S3Uri: `s3://${bucketName}/output/`,
    },
  };

  /**
   * This job will be a Reverse Geocoding Vector Enrichment Job. Reverse Geocoding requires
   * latitude and longitude values.
   * @type {import("@aws-sdk/client-sagemaker-geospatial").VectorEnrichmentJobConfig}
   */
  const jobConfig = {
    ReverseGeocodingConfig: {
      XAttributeName: "Longitude",
      YAttributeName: "Latitude",
    },
  };

  const { PipelineExecutionArn } = await sagemakerClient.send(
    new StartPipelineExecutionCommand({
      PipelineName: name,
      PipelineExecutionDisplayName: `${name}-example-execution`,
      PipelineParameters: [
        { Name: "parameter_execution_role", Value: roleArn },
        { Name: "parameter_queue_url", Value: queueUrl },
        {
          Name: "parameter_vej_input_config",
          Value: JSON.stringify(inputConfig),
        },
        {
          Name: "parameter_vej_export_config",
          Value: JSON.stringify(outputConfig),
        },
        {
          Name: "parameter_step_1_vej_config",
          Value: JSON.stringify(jobConfig),
        },
      ],
    }),
  );

  return {
    arn: PipelineExecutionArn,
  };
}
```
+  For API details, see [StartPipelineExecution](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/sagemaker/command/StartPipelineExecutionCommand) in *AWS SDK for JavaScript API Reference*. 

------
#### [ Kotlin ]

**SDK for Kotlin**  
 There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/workflow_sagemaker_pipes#code-examples). 

```
// Start a pipeline run with job configurations.
suspend fun executePipeline(bucketName: String, queueUrl: String?, roleArn: String?, pipelineNameVal: String): String? {
    println("Starting pipeline execution.")
    val inputBucketLocation = "s3://$bucketName/samplefiles/latlongtest.csv"
    val output = "s3://$bucketName/outputfiles/"

    val gson = GsonBuilder()
        .setFieldNamingPolicy(FieldNamingPolicy.UPPER_CAMEL_CASE)
        .setPrettyPrinting()
        .create()

    // Set up all parameters required to start the pipeline.
    val parameters: MutableList<Parameter> = java.util.ArrayList<Parameter>()

    val para1 = Parameter {
        name = "parameter_execution_role"
        value = roleArn
    }
    val para2 = Parameter {
        name = "parameter_queue_url"
        value = queueUrl
    }

    val inputJSON = """{
        "DataSourceConfig": {
        "S3Data": {
            "S3Uri": "s3://$bucketName/samplefiles/latlongtest.csv"
        },
        "Type": "S3_DATA"
        },
        "DocumentType": "CSV"
    }"""
    println(inputJSON)
    val para3 = Parameter {
        name = "parameter_vej_input_config"
        value = inputJSON
    }

    // Create an ExportVectorEnrichmentJobOutputConfig object.
    val jobS3Data = VectorEnrichmentJobS3Data {
        s3Uri = output
    }

    val outputConfig = ExportVectorEnrichmentJobOutputConfig {
        s3Data = jobS3Data
    }

    val gson4: String = gson.toJson(outputConfig)
    val para4: Parameter = Parameter {
        name = "parameter_vej_export_config"
        value = gson4
    }
    println("parameter_vej_export_config:" + gson.toJson(outputConfig))

    val para5JSON =
        "{\"MapMatchingConfig\":null,\"ReverseGeocodingConfig\":{\"XAttributeName\":\"Longitude\",\"YAttributeName\":\"Latitude\"}}"

    val para5: Parameter = Parameter {
        name = "parameter_step_1_vej_config"
        value = para5JSON
    }

    parameters.add(para1)
    parameters.add(para2)
    parameters.add(para3)
    parameters.add(para4)
    parameters.add(para5)

    val pipelineExecutionRequest = StartPipelineExecutionRequest {
        pipelineExecutionDescription = "Created using Kotlin SDK"
        pipelineExecutionDisplayName = "$pipelineName-example-execution"
        pipelineParameters = parameters
        pipelineName = pipelineNameVal
    }

    SageMakerClient { region = "us-west-2" }.use { sageMakerClient ->
        val response = sageMakerClient.startPipelineExecution(pipelineExecutionRequest)
        return response.pipelineExecutionArn
    }
}
```
+  For API details, see [StartPipelineExecution](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*. 

------