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StartPipelineExecution
Úselo con un AWS SDK o CLI
En los siguientes ejemplos de código, se muestra cómo utilizar StartPipelineExecution
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Los ejemplos de acciones son extractos de código de programas más grandes y deben ejecutarse en contexto. Puede ver esta acción en contexto en el siguiente ejemplo de código:
- .NET
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- AWS SDK for .NET
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nota
Hay más en marcha GitHub. Busque el ejemplo completo y aprenda a configurar y ejecutar en el Repositorio de ejemplos de código de AWS
. /// <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; }
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Para API obtener más información, consulte StartPipelineExecutionla AWS SDK for .NET APIReferencia.
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- Java
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- SDKpara Java 2.x
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nota
Hay más información. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el Repositorio de ejemplos de código de AWS
. // 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(); }
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Para API obtener más información, consulte StartPipelineExecutionla AWS SDK for Java 2.x APIReferencia.
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- JavaScript
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- SDKpara JavaScript (v3)
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nota
Hay más información. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el Repositorio de ejemplos de código de AWS
. Inicie una ejecución en SageMaker canalización.
/** * 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: `${name}-example-execution`, PipelineParameters: [ { Name: "parameter_execution_role", Value: roleArn }, { Name: "parameter_queue_url", Value: queueUrl }, { Name: "parameter_vej_input_config", Value: JSON.stringify(inputConfig), }, { Name: "parameter_vej_export_config", Value: JSON.stringify(outputConfig), }, { Name: "parameter_step_1_vej_config", Value: JSON.stringify(jobConfig), }, ], }), ); return { arn: PipelineExecutionArn, }; }
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Para API obtener más información, consulte StartPipelineExecutionla AWS SDK for JavaScript APIReferencia.
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- Kotlin
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- SDKpara Kotlin
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nota
Hay más información. GitHub Busque el ejemplo completo y aprenda a configurar y ejecutar en el Repositorio de ejemplos de código de AWS
. // Start a pipeline run with job configurations. suspend fun executePipeline(bucketName: String, queueUrl: String?, roleArn: String?, pipelineNameVal: String): String? { println("Starting pipeline execution.") val inputBucketLocation = "s3://$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 } }
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Para API obtener más información, consulta StartPipelineExecution
la AWS SDKAPIreferencia sobre Kotlin.
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