There are more AWS SDK examples available in the [AWS Doc SDK Examples](https://github.com/awsdocs/aws-doc-sdk-examples) GitHub repo.
# Actions for SageMaker AI using AWS SDKs
The following code examples demonstrate how to perform individual SageMaker AI actions with AWS SDKs. Each example includes a link to GitHub, where you can find instructions for setting up and running the code.
These excerpts call the SageMaker AI API and are code excerpts from larger programs that must be run in context. You can see actions in context in [Scenarios for SageMaker AI using AWS SDKs](sagemaker_code_examples_scenarios.md).
The following examples include only the most commonly used actions. For a complete list, see the [Amazon SageMaker AI API Reference](https://docs.aws.amazon.com/sagemaker/latest/APIReference/Welcome.html).
**Topics**
+ [`CreateEndpoint`](sagemaker_example_sagemaker_CreateEndpoint_section.md)
+ [`CreateModel`](sagemaker_example_sagemaker_CreateModel_section.md)
+ [`CreatePipeline`](sagemaker_example_sagemaker_CreatePipeline_section.md)
+ [`CreateTrainingJob`](sagemaker_example_sagemaker_CreateTrainingJob_section.md)
+ [`CreateTransformJob`](sagemaker_example_sagemaker_CreateTransformJob_section.md)
+ [`DeleteEndpoint`](sagemaker_example_sagemaker_DeleteEndpoint_section.md)
+ [`DeleteModel`](sagemaker_example_sagemaker_DeleteModel_section.md)
+ [`DeletePipeline`](sagemaker_example_sagemaker_DeletePipeline_section.md)
+ [`DescribePipelineExecution`](sagemaker_example_sagemaker_DescribePipelineExecution_section.md)
+ [`DescribeTrainingJob`](sagemaker_example_sagemaker_DescribeTrainingJob_section.md)
+ [`ListAlgorithms`](sagemaker_example_sagemaker_ListAlgorithms_section.md)
+ [`ListModels`](sagemaker_example_sagemaker_ListModels_section.md)
+ [`ListNotebookInstances`](sagemaker_example_sagemaker_ListNotebookInstances_section.md)
+ [`ListTrainingJobs`](sagemaker_example_sagemaker_ListTrainingJobs_section.md)
+ [`StartPipelineExecution`](sagemaker_example_sagemaker_StartPipelineExecution_section.md)
+ [`UpdatePipeline`](sagemaker_example_sagemaker_UpdatePipeline_section.md)
# Use `CreateEndpoint` with an AWS SDK
The following code example shows how to use `CreateEndpoint`.
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 models and endpoints](sagemaker_example_sagemaker_Scenario_GettingStarted_section.md)
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
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/sap-abap/services/sgm#code-examples).
```
DATA lt_production_variants TYPE /aws1/cl_sgmproductionvariant=>tt_productionvariantlist.
DATA lo_production_variants TYPE REF TO /aws1/cl_sgmproductionvariant.
DATA oo_ep_config_result TYPE REF TO /aws1/cl_sgmcreateendptcfgout.
"Create a production variant as an ABAP object."
"Identifies a model that you want to host and the resources chosen to deploy for hosting it."
lo_production_variants = NEW #( iv_variantname = iv_variant_name
iv_modelname = iv_model_name
iv_initialinstancecount = iv_initial_instance_count
iv_instancetype = iv_instance_type ).
INSERT lo_production_variants INTO TABLE lt_production_variants.
"Create an endpoint configuration."
TRY.
oo_ep_config_result = lo_sgm->createendpointconfig(
iv_endpointconfigname = iv_endpoint_config_name
it_productionvariants = lt_production_variants ).
MESSAGE 'Endpoint configuration created.' TYPE 'I'.
CATCH /aws1/cx_sgmresourcelimitexcd.
MESSAGE 'You have reached the limit on the number of resources.' TYPE 'E'.
ENDTRY.
"Create an endpoint."
TRY.
oo_result = lo_sgm->createendpoint( " oo_result is returned for testing purposes. "
iv_endpointconfigname = iv_endpoint_config_name
iv_endpointname = iv_endpoint_name ).
MESSAGE 'Endpoint created.' TYPE 'I'.
CATCH /aws1/cx_sgmresourcelimitexcd.
MESSAGE 'You have reached the limit on the number of resources.' TYPE 'E'.
ENDTRY.
```
+ For API details, see [CreateEndpoint](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `CreateModel` with an AWS SDK
The following code example shows how to use `CreateModel`.
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 models and endpoints](sagemaker_example_sagemaker_Scenario_GettingStarted_section.md)
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
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/sap-abap/services/sgm#code-examples).
```
DATA lo_primarycontainer TYPE REF TO /aws1/cl_sgmcontainerdefn.
"Create an ABAP object for the container image based on input variables."
lo_primarycontainer = NEW #( iv_image = iv_container_image
iv_modeldataurl = iv_model_data_url ).
"Create an Amazon SageMaker model."
TRY.
oo_result = lo_sgm->createmodel( " oo_result is returned for testing purposes. "
iv_executionrolearn = iv_execution_role_arn
iv_modelname = iv_model_name
io_primarycontainer = lo_primarycontainer ).
MESSAGE 'Model created.' TYPE 'I'.
CATCH /aws1/cx_sgmresourcelimitexcd.
MESSAGE 'You have reached the limit on the number of resources.' TYPE 'E'.
ENDTRY.
```
+ For API details, see [CreateModel](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `CreatePipeline` with an AWS SDK
The following code examples show how to use `CreatePipeline`.
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).
```
///
/// Create a pipeline from a JSON definition, or update it if the pipeline already exists.
///
/// The Amazon Resource Name (ARN) of the pipeline.
public async Task SetupPipeline(string pipelineJson, string roleArn, string name, string description, string displayName)
{
try
{
var updateResponse = await _amazonSageMaker.UpdatePipelineAsync(
new UpdatePipelineRequest()
{
PipelineDefinition = pipelineJson,
PipelineDescription = description,
PipelineDisplayName = displayName,
PipelineName = name,
RoleArn = roleArn
});
return updateResponse.PipelineArn;
}
catch (Amazon.SageMaker.Model.ResourceNotFoundException)
{
var createResponse = await _amazonSageMaker.CreatePipelineAsync(
new CreatePipelineRequest()
{
PipelineDefinition = pipelineJson,
PipelineDescription = description,
PipelineDisplayName = displayName,
PipelineName = name,
RoleArn = roleArn
});
return createResponse.PipelineArn;
}
}
```
+ For API details, see [CreatePipeline](https://docs.aws.amazon.com/goto/DotNetSDKV3/sagemaker-2017-07-24/CreatePipeline) 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).
```
// Create a pipeline from the example pipeline JSON.
public static void setupPipeline(SageMakerClient sageMakerClient, String filePath, String roleArn,
String functionArn, String pipelineName) {
System.out.println("Setting up the pipeline.");
JSONParser parser = new JSONParser();
// Read JSON and get pipeline definition.
try (FileReader reader = new FileReader(filePath)) {
Object obj = parser.parse(reader);
JSONObject jsonObject = (JSONObject) obj;
JSONArray stepsArray = (JSONArray) jsonObject.get("Steps");
for (Object stepObj : stepsArray) {
JSONObject step = (JSONObject) stepObj;
if (step.containsKey("FunctionArn")) {
step.put("FunctionArn", functionArn);
}
}
System.out.println(jsonObject);
// Create the pipeline.
CreatePipelineRequest pipelineRequest = CreatePipelineRequest.builder()
.pipelineDescription("Java SDK example pipeline")
.roleArn(roleArn)
.pipelineName(pipelineName)
.pipelineDefinition(jsonObject.toString())
.build();
sageMakerClient.createPipeline(pipelineRequest);
} catch (IamException e) {
System.err.println(e.awsErrorDetails().errorMessage());
System.exit(1);
} catch (IOException | ParseException e) {
throw new RuntimeException(e);
}
}
```
+ For API details, see [CreatePipeline](https://docs.aws.amazon.com/goto/SdkForJavaV2/sagemaker-2017-07-24/CreatePipeline) 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).
A function that creates a SageMaker AI pipeline using a locally provided JSON definition.
```
/**
* Create the Amazon SageMaker pipeline using a JSON pipeline definition. The definition
* can also be provided as an Amazon S3 object using PipelineDefinitionS3Location.
* @param {{roleArn: string, name: string, sagemakerClient: import('@aws-sdk/client-sagemaker').SageMakerClient}} props
*/
export async function createSagemakerPipeline({
// Assumes an AWS IAM role has been created for this pipeline.
roleArn,
name,
// Assumes an AWS Lambda function has been created for this pipeline.
functionArn,
sagemakerClient,
}) {
const pipelineDefinition = readFileSync(
// dirnameFromMetaUrl is a local utility function. You can find its implementation
// on GitHub.
`${dirnameFromMetaUrl(
import.meta.url,
)}../../../../../scenarios/features/sagemaker_pipelines/resources/GeoSpatialPipeline.json`,
)
.toString()
.replace(/\*FUNCTION_ARN\*/g, functionArn);
let arn = null;
const createPipeline = () =>
sagemakerClient.send(
new CreatePipelineCommand({
PipelineName: name,
PipelineDefinition: pipelineDefinition,
RoleArn: roleArn,
}),
);
try {
const { PipelineArn } = await createPipeline();
arn = PipelineArn;
} catch (caught) {
if (
caught instanceof Error &&
caught.name === "ValidationException" &&
caught.message.includes(
"Pipeline names must be unique within an AWS account and region",
)
) {
const { PipelineArn } = await sagemakerClient.send(
new DescribePipelineCommand({ PipelineName: name }),
);
arn = PipelineArn;
} else {
throw caught;
}
}
return {
arn,
cleanUp: async () => {
await sagemakerClient.send(
new DeletePipelineCommand({ PipelineName: name }),
);
},
};
}
```
+ For API details, see [CreatePipeline](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/sagemaker/command/CreatePipelineCommand) 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).
```
// Create a pipeline from the example pipeline JSON.
suspend fun setupPipeline(filePath: String?, roleArnVal: String?, functionArnVal: String?, pipelineNameVal: String?) {
println("Setting up the pipeline.")
val parser = JSONParser()
// Read JSON and get pipeline definition.
FileReader(filePath).use { reader ->
val obj: Any = parser.parse(reader)
val jsonObject: JSONObject = obj as JSONObject
val stepsArray: JSONArray = jsonObject.get("Steps") as JSONArray
for (stepObj in stepsArray) {
val step: JSONObject = stepObj as JSONObject
if (step.containsKey("FunctionArn")) {
step.put("FunctionArn", functionArnVal)
}
}
println(jsonObject)
// Create the pipeline.
val pipelineRequest = CreatePipelineRequest {
pipelineDescription = "Kotlin SDK example pipeline"
roleArn = roleArnVal
pipelineName = pipelineNameVal
pipelineDefinition = jsonObject.toString()
}
SageMakerClient { region = "us-west-2" }.use { sageMakerClient ->
sageMakerClient.createPipeline(pipelineRequest)
}
}
}
```
+ For API details, see [CreatePipeline](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*.
------
# Use `CreateTrainingJob` with an AWS SDK
The following code example shows how to use `CreateTrainingJob`.
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 models and endpoints](sagemaker_example_sagemaker_Scenario_GettingStarted_section.md)
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
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/sap-abap/services/sgm#code-examples).
```
DATA lo_hyperparameters_w TYPE REF TO /aws1/cl_sgmhyperparameters_w.
DATA lt_hyperparameters TYPE /aws1/cl_sgmhyperparameters_w=>tt_hyperparameters.
DATA lt_input_data_config TYPE /aws1/cl_sgmchannel=>tt_inputdataconfig.
DATA lo_trn_channel TYPE REF TO /aws1/cl_sgmchannel.
DATA lo_trn_datasource TYPE REF TO /aws1/cl_sgmdatasource.
DATA lo_trn_s3datasource TYPE REF TO /aws1/cl_sgms3datasource.
DATA lo_val_channel TYPE REF TO /aws1/cl_sgmchannel.
DATA lo_val_datasource TYPE REF TO /aws1/cl_sgmdatasource.
DATA lo_val_s3datasource TYPE REF TO /aws1/cl_sgms3datasource.
DATA lo_algorithm_specification TYPE REF TO /aws1/cl_sgmalgorithmspec.
DATA lo_resource_config TYPE REF TO /aws1/cl_sgmresourceconfig.
DATA lo_output_data_config TYPE REF TO /aws1/cl_sgmoutputdataconfig.
DATA lo_stopping_condition TYPE REF TO /aws1/cl_sgmstoppingcondition.
"Create ABAP internal table for hyperparameters based on input variables."
"These hyperparameters are based on the Amazon SageMaker built-in algorithm, XGBoost."
lo_hyperparameters_w = NEW #( iv_value = iv_hp_max_depth ).
INSERT VALUE #( key = 'max_depth' value = lo_hyperparameters_w ) INTO TABLE lt_hyperparameters.
lo_hyperparameters_w = NEW #( iv_value = iv_hp_eta ).
INSERT VALUE #( key = 'eta' value = lo_hyperparameters_w ) INTO TABLE lt_hyperparameters.
lo_hyperparameters_w = NEW #( iv_value = iv_hp_eval_metric ).
INSERT VALUE #( key = 'eval_metric' value = lo_hyperparameters_w ) INTO TABLE lt_hyperparameters.
lo_hyperparameters_w = NEW #( iv_value = iv_hp_scale_pos_weight ).
INSERT VALUE #( key = 'scale_pos_weight' value = lo_hyperparameters_w ) INTO TABLE lt_hyperparameters.
lo_hyperparameters_w = NEW #( iv_value = iv_hp_subsample ).
INSERT VALUE #( key = 'subsample' value = lo_hyperparameters_w ) INTO TABLE lt_hyperparameters.
lo_hyperparameters_w = NEW #( iv_value = iv_hp_objective ).
INSERT VALUE #( key = 'objective' value = lo_hyperparameters_w ) INTO TABLE lt_hyperparameters.
lo_hyperparameters_w = NEW #( iv_value = iv_hp_num_round ).
INSERT VALUE #( key = 'num_round' value = lo_hyperparameters_w ) INTO TABLE lt_hyperparameters.
"Create ABAP objects for training data sources."
lo_trn_s3datasource = NEW #( iv_s3datatype = iv_trn_data_s3datatype
iv_s3datadistributiontype = iv_trn_data_s3datadistribution
iv_s3uri = iv_trn_data_s3uri ).
lo_trn_datasource = NEW #( io_s3datasource = lo_trn_s3datasource ).
lo_trn_channel = NEW #( iv_channelname = 'train'
io_datasource = lo_trn_datasource
iv_compressiontype = iv_trn_data_compressiontype
iv_contenttype = iv_trn_data_contenttype ).
INSERT lo_trn_channel INTO TABLE lt_input_data_config.
"Create ABAP objects for validation data sources."
lo_val_s3datasource = NEW #( iv_s3datatype = iv_val_data_s3datatype
iv_s3datadistributiontype = iv_val_data_s3datadistribution
iv_s3uri = iv_val_data_s3uri ).
lo_val_datasource = NEW #( io_s3datasource = lo_val_s3datasource ).
lo_val_channel = NEW #( iv_channelname = 'validation'
io_datasource = lo_val_datasource
iv_compressiontype = iv_val_data_compressiontype
iv_contenttype = iv_val_data_contenttype ).
INSERT lo_val_channel INTO TABLE lt_input_data_config.
"Create an ABAP object for algorithm specification."
lo_algorithm_specification = NEW #( iv_trainingimage = iv_training_image
iv_traininginputmode = iv_training_input_mode ).
"Create an ABAP object for resource configuration."
lo_resource_config = NEW #( iv_instancecount = iv_instance_count
iv_instancetype = iv_instance_type
iv_volumesizeingb = iv_volume_sizeingb ).
"Create an ABAP object for output data configuration."
lo_output_data_config = NEW #( iv_s3outputpath = iv_s3_output_path ).
"Create an ABAP object for stopping condition."
lo_stopping_condition = NEW #( iv_maxruntimeinseconds = iv_max_runtime_in_seconds ).
"Create a training job."
TRY.
oo_result = lo_sgm->createtrainingjob( " oo_result is returned for testing purposes. "
iv_trainingjobname = iv_training_job_name
iv_rolearn = iv_role_arn
it_hyperparameters = lt_hyperparameters
it_inputdataconfig = lt_input_data_config
io_algorithmspecification = lo_algorithm_specification
io_outputdataconfig = lo_output_data_config
io_resourceconfig = lo_resource_config
io_stoppingcondition = lo_stopping_condition ).
MESSAGE 'Training job created.' TYPE 'I'.
CATCH /aws1/cx_sgmresourceinuse.
MESSAGE 'Resource being accessed is in use.' TYPE 'E'.
CATCH /aws1/cx_sgmresourcenotfound.
MESSAGE 'Resource being accessed is not found.' TYPE 'E'.
CATCH /aws1/cx_sgmresourcelimitexcd.
MESSAGE 'You have reached the limit on the number of resources.' TYPE 'E'.
ENDTRY.
```
+ For API details, see [CreateTrainingJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `CreateTransformJob` with an AWS SDK
The following code example shows how to use `CreateTransformJob`.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
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/sap-abap/services/sgm#code-examples).
```
DATA lo_transforminput TYPE REF TO /aws1/cl_sgmtransforminput.
DATA lo_transformoutput TYPE REF TO /aws1/cl_sgmtransformoutput.
DATA lo_transformresources TYPE REF TO /aws1/cl_sgmtransformresources.
DATA lo_datasource TYPE REF TO /aws1/cl_sgmtransformdatasrc.
DATA lo_s3datasource TYPE REF TO /aws1/cl_sgmtransforms3datasrc.
"Create an ABAP object for an Amazon Simple Storage Service (Amazon S3) data source."
lo_s3datasource = NEW #( iv_s3uri = iv_tf_data_s3uri
iv_s3datatype = iv_tf_data_s3datatype ).
"Create an ABAP object for data source."
lo_datasource = NEW #( io_s3datasource = lo_s3datasource ).
"Create an ABAP object for transform data source."
lo_transforminput = NEW #( io_datasource = lo_datasource
iv_contenttype = iv_tf_data_contenttype
iv_compressiontype = iv_tf_data_compressiontype ).
"Create an ABAP object for resource configuration."
lo_transformresources = NEW #( iv_instancecount = iv_instance_count
iv_instancetype = iv_instance_type ).
"Create an ABAP object for output data configuration."
lo_transformoutput = NEW #( iv_s3outputpath = iv_s3_output_path ).
"Create a transform job."
TRY.
oo_result = lo_sgm->createtransformjob( " oo_result is returned for testing purposes. "
iv_modelname = iv_tf_model_name
iv_transformjobname = iv_tf_job_name
io_transforminput = lo_transforminput
io_transformoutput = lo_transformoutput
io_transformresources = lo_transformresources ).
MESSAGE 'Transform job created.' TYPE 'I'.
CATCH /aws1/cx_sgmresourceinuse.
MESSAGE 'Resource being accessed is in use.' TYPE 'E'.
CATCH /aws1/cx_sgmresourcenotfound.
MESSAGE 'Resource being accessed is not found.' TYPE 'E'.
CATCH /aws1/cx_sgmresourcelimitexcd.
MESSAGE 'You have reached the limit on the number of resources.' TYPE 'E'.
ENDTRY.
```
+ For API details, see [CreateTransformJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `DeleteEndpoint` with an AWS SDK
The following code example shows how to use `DeleteEndpoint`.
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 models and endpoints](sagemaker_example_sagemaker_Scenario_GettingStarted_section.md)
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
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/sap-abap/services/sgm#code-examples).
```
"Delete an endpoint."
TRY.
lo_sgm->deleteendpoint(
iv_endpointname = iv_endpoint_name ).
MESSAGE 'Endpoint configuration deleted.' TYPE 'I'.
CATCH /aws1/cx_rt_service_generic INTO DATA(lo_endpoint_exception).
DATA(lv_endpoint_error) = |"{ lo_endpoint_exception->av_err_code }" - { lo_endpoint_exception->av_err_msg }|.
MESSAGE lv_endpoint_error TYPE 'E'.
ENDTRY.
"Delete an endpoint configuration."
TRY.
lo_sgm->deleteendpointconfig(
iv_endpointconfigname = iv_endpoint_config_name ).
MESSAGE 'Endpoint deleted.' TYPE 'I'.
CATCH /aws1/cx_rt_service_generic INTO DATA(lo_endpointconfig_exception).
DATA(lv_endpointconfig_error) = |"{ lo_endpointconfig_exception->av_err_code }" - { lo_endpointconfig_exception->av_err_msg }|.
MESSAGE lv_endpointconfig_error TYPE 'E'.
ENDTRY.
```
+ For API details, see [DeleteEndpoint](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `DeleteModel` with an AWS SDK
The following code example shows how to use `DeleteModel`.
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 models and endpoints](sagemaker_example_sagemaker_Scenario_GettingStarted_section.md)
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
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/sap-abap/services/sgm#code-examples).
```
TRY.
lo_sgm->deletemodel(
iv_modelname = iv_model_name ).
MESSAGE 'Model deleted.' TYPE 'I'.
CATCH /aws1/cx_rt_service_generic INTO DATA(lo_exception).
DATA(lv_error) = |"{ lo_exception->av_err_code }" - { lo_exception->av_err_msg }|.
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ For API details, see [DeleteModel](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `DeletePipeline` with an AWS SDK
The following code examples show how to use `DeletePipeline`.
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).
```
///
/// Delete a SageMaker pipeline by name.
///
/// The name of the pipeline to delete.
/// The ARN of the pipeline.
public async Task DeletePipelineByName(string pipelineName)
{
var deleteResponse = await _amazonSageMaker.DeletePipelineAsync(
new DeletePipelineRequest()
{
PipelineName = pipelineName
});
return deleteResponse.PipelineArn;
}
```
+ For API details, see [DeletePipeline](https://docs.aws.amazon.com/goto/DotNetSDKV3/sagemaker-2017-07-24/DeletePipeline) 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).
```
// Delete a SageMaker pipeline by name.
public static void deletePipeline(SageMakerClient sageMakerClient, String pipelineName) {
DeletePipelineRequest pipelineRequest = DeletePipelineRequest.builder()
.pipelineName(pipelineName)
.build();
sageMakerClient.deletePipeline(pipelineRequest);
System.out.println("*** Successfully deleted " + pipelineName);
}
```
+ For API details, see [DeletePipeline](https://docs.aws.amazon.com/goto/SdkForJavaV2/sagemaker-2017-07-24/DeletePipeline) 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).
The syntax for deleting a SageMaker AI pipeline. This code is part of a larger function. Refer to 'Create a pipeline' or the GitHub repository for more context.
```
await sagemakerClient.send(
new DeletePipelineCommand({ PipelineName: name }),
);
```
+ For API details, see [DeletePipeline](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/sagemaker/command/DeletePipelineCommand) 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).
```
// Delete a SageMaker pipeline by name.
suspend fun deletePipeline(pipelineNameVal: String) {
val pipelineRequest = DeletePipelineRequest {
pipelineName = pipelineNameVal
}
SageMakerClient { region = "us-west-2" }.use { sageMakerClient ->
sageMakerClient.deletePipeline(pipelineRequest)
println("*** Successfully deleted $pipelineNameVal")
}
}
```
+ For API details, see [DeletePipeline](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*.
------
# Use `DescribePipelineExecution` with an AWS SDK
The following code examples show how to use `DescribePipelineExecution`.
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).
```
///
/// Check the status of a run.
///
/// The ARN.
/// The status of the pipeline.
public async Task CheckPipelineExecutionStatus(string pipelineExecutionArn)
{
var describeResponse = await _amazonSageMaker.DescribePipelineExecutionAsync(
new DescribePipelineExecutionRequest()
{
PipelineExecutionArn = pipelineExecutionArn
});
return describeResponse.PipelineExecutionStatus;
}
```
+ For API details, see [DescribePipelineExecution](https://docs.aws.amazon.com/goto/DotNetSDKV3/sagemaker-2017-07-24/DescribePipelineExecution) 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).
```
// Check the status of a pipeline execution.
public static void waitForPipelineExecution(SageMakerClient sageMakerClient, String executionArn)
throws InterruptedException {
String status;
int index = 0;
do {
DescribePipelineExecutionRequest pipelineExecutionRequest = DescribePipelineExecutionRequest.builder()
.pipelineExecutionArn(executionArn)
.build();
DescribePipelineExecutionResponse response = sageMakerClient
.describePipelineExecution(pipelineExecutionRequest);
status = response.pipelineExecutionStatusAsString();
System.out.println(index + ". The Status of the pipeline is " + status);
TimeUnit.SECONDS.sleep(4);
index++;
} while ("Executing".equals(status));
System.out.println("Pipeline finished with status " + status);
}
```
+ For API details, see [DescribePipelineExecution](https://docs.aws.amazon.com/goto/SdkForJavaV2/sagemaker-2017-07-24/DescribePipelineExecution) 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).
Wait for a SageMaker AI pipeline execution to succeed, fail, or stop.
```
/**
* Poll the executing pipeline until the status is 'SUCCEEDED', 'STOPPED', or 'FAILED'.
* @param {{ arn: string, sagemakerClient: import('@aws-sdk/client-sagemaker').SageMakerClient, wait: (ms: number) => Promise}} props
*/
export async function waitForPipelineComplete({ arn, sagemakerClient, wait }) {
const command = new DescribePipelineExecutionCommand({
PipelineExecutionArn: arn,
});
let complete = false;
const intervalInSeconds = 15;
const COMPLETION_STATUSES = [
PipelineExecutionStatus.FAILED,
PipelineExecutionStatus.STOPPED,
PipelineExecutionStatus.SUCCEEDED,
];
do {
const { PipelineExecutionStatus: status, FailureReason } =
await sagemakerClient.send(command);
complete = COMPLETION_STATUSES.includes(status);
if (!complete) {
console.log(
`Pipeline is ${status}. Waiting ${intervalInSeconds} seconds before checking again.`,
);
await wait(intervalInSeconds);
} else if (status === PipelineExecutionStatus.FAILED) {
throw new Error(`Pipeline failed because: ${FailureReason}`);
} else if (status === PipelineExecutionStatus.STOPPED) {
throw new Error("Pipeline was forcefully stopped.");
} else {
console.log(`Pipeline execution ${status}.`);
}
} while (!complete);
}
```
+ For API details, see [DescribePipelineExecution](https://docs.aws.amazon.com/AWSJavaScriptSDK/v3/latest/client/sagemaker/command/DescribePipelineExecutionCommand) in *AWS SDK for JavaScript API Reference*.
------
#### [ Kotlin ]
**SDK for Kotlin**
There's more on GitHub. Find the complete example and learn how to set up and run in the [AWS Code Examples Repository](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/kotlin/usecases/workflow_sagemaker_pipes#code-examples).
```
suspend fun waitForPipelineExecution(executionArn: String?) {
var status: String
var index = 0
do {
val pipelineExecutionRequest = DescribePipelineExecutionRequest {
pipelineExecutionArn = executionArn
}
SageMakerClient { region = "us-west-2" }.use { sageMakerClient ->
val response = sageMakerClient.describePipelineExecution(pipelineExecutionRequest)
status = response.pipelineExecutionStatus.toString()
println("$index. The status of the pipeline is $status")
TimeUnit.SECONDS.sleep(4)
index++
}
} while ("Executing" == status)
println("Pipeline finished with status $status")
}
```
+ For API details, see [DescribePipelineExecution](https://sdk.amazonaws.com/kotlin/api/latest/index.html) in *AWS SDK for Kotlin API reference*.
------
# Use `DescribeTrainingJob` with an AWS SDK
The following code example shows how to use `DescribeTrainingJob`.
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 models and endpoints](sagemaker_example_sagemaker_Scenario_GettingStarted_section.md)
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
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/sap-abap/services/sgm#code-examples).
```
TRY.
oo_result = lo_sgm->describetrainingjob( " oo_result is returned for testing purposes. "
iv_trainingjobname = iv_training_job_name ).
MESSAGE 'Retrieved description of training job.' TYPE 'I'.
CATCH /aws1/cx_rt_service_generic INTO DATA(lo_exception).
DATA(lv_error) = |"{ lo_exception->av_err_code }" - { lo_exception->av_err_msg }|.
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ For API details, see [DescribeTrainingJob](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `ListAlgorithms` with an AWS SDK
The following code example shows how to use `ListAlgorithms`.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
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/sap-abap/services/sgm#code-examples).
```
TRY.
oo_result = lo_sgm->listalgorithms( " oo_result is returned for testing purposes. "
iv_namecontains = iv_name_contains ).
MESSAGE 'Retrieved list of algorithms.' TYPE 'I'.
CATCH /aws1/cx_rt_service_generic INTO DATA(lo_exception).
DATA(lv_error) = |"{ lo_exception->av_err_code }" - { lo_exception->av_err_msg }|.
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ For API details, see [ListAlgorithms](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `ListModels` with an AWS SDK
The following code example shows how to use `ListModels`.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
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/sap-abap/services/sgm#code-examples).
```
TRY.
oo_result = lo_sgm->listmodels( " oo_result is returned for testing purposes. "
iv_namecontains = iv_name_contains ).
MESSAGE 'Retrieved list of models.' TYPE 'I'.
CATCH /aws1/cx_rt_service_generic INTO DATA(lo_exception).
DATA(lv_error) = |"{ lo_exception->av_err_code }" - { lo_exception->av_err_msg }|.
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ For API details, see [ListModels](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `ListNotebookInstances` with an AWS SDK
The following code examples show how to use `ListNotebookInstances`.
------
#### [ Rust ]
**SDK for Rust**
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/rustv1/examples/sagemaker#code-examples).
```
async fn show_instances(client: &Client) -> Result<(), Error> {
let notebooks = client.list_notebook_instances().send().await?;
println!("Notebooks:");
for n in notebooks.notebook_instances() {
let n_instance_type = n.instance_type().unwrap();
let n_status = n.notebook_instance_status().unwrap();
let n_name = n.notebook_instance_name();
println!(" Name : {}", n_name.unwrap_or("Unknown"));
println!(" Status : {}", n_status.as_ref());
println!(" Instance Type : {}", n_instance_type.as_ref());
println!();
}
Ok(())
}
```
+ For API details, see [ListNotebookInstances](https://docs.rs/aws-sdk-sagemaker/latest/aws_sdk_sagemaker/client/struct.Client.html#method.list_notebook_instances) in *AWS SDK for Rust API reference*.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
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/sap-abap/services/sgm#code-examples).
```
TRY.
oo_result = lo_sgm->listnotebookinstances( " oo_result is returned for testing purposes. "
iv_namecontains = iv_name_contains ).
MESSAGE 'Retrieved list of notebook instances.' TYPE 'I'.
CATCH /aws1/cx_rt_service_generic INTO DATA(lo_exception).
DATA(lv_error) = |"{ lo_exception->av_err_code }" - { lo_exception->av_err_msg }|.
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ For API details, see [ListNotebookInstances](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `ListTrainingJobs` with an AWS SDK
The following code examples show how to use `ListTrainingJobs`.
------
#### [ Rust ]
**SDK for Rust**
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/rustv1/examples/sagemaker#code-examples).
```
async fn show_jobs(client: &Client) -> Result<(), Error> {
let job_details = client.list_training_jobs().send().await?;
println!("Jobs:");
for j in job_details.training_job_summaries() {
let name = j.training_job_name().unwrap_or("Unknown");
let creation_time = j.creation_time().expect("creation time").to_chrono_utc()?;
let training_end_time = j
.training_end_time()
.expect("Training end time")
.to_chrono_utc()?;
let status = j.training_job_status().expect("training status");
let duration = training_end_time - creation_time;
println!(" Name: {}", name);
println!(
" Creation date/time: {}",
creation_time.format("%Y-%m-%d@%H:%M:%S")
);
println!(" Duration (seconds): {}", duration.num_seconds());
println!(" Status: {:?}", status);
println!();
}
Ok(())
}
```
+ For API details, see [ListTrainingJobs](https://docs.rs/aws-sdk-sagemaker/latest/aws_sdk_sagemaker/client/struct.Client.html#method.list_training_jobs) in *AWS SDK for Rust API reference*.
------
#### [ SAP ABAP ]
**SDK for SAP ABAP**
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/sap-abap/services/sgm#code-examples).
```
TRY.
oo_result = lo_sgm->listtrainingjobs( " oo_result is returned for testing purposes. "
iv_namecontains = iv_name_contains
iv_maxresults = iv_max_results ).
MESSAGE 'Retrieved list of training jobs.' TYPE 'I'.
CATCH /aws1/cx_rt_service_generic INTO DATA(lo_exception).
DATA(lv_error) = |"{ lo_exception->av_err_code }" - { lo_exception->av_err_msg }|.
MESSAGE lv_error TYPE 'E'.
ENDTRY.
```
+ For API details, see [ListTrainingJobs](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html) in *AWS SDK for SAP ABAP API reference*.
------
# Use `StartPipelineExecution` with an AWS SDK
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).
```
///
/// Run a pipeline with input and output file locations.
///
/// The URL for the queue to use for pipeline callbacks.
/// The input location in Amazon Simple Storage Service (Amazon S3).
/// The output location in Amazon S3.
/// The name of the pipeline.
/// The ARN of the role.
/// The ARN of the pipeline run.
public async Task 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()
{
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 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 = java.util.ArrayList()
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*.
------
# Use `UpdatePipeline` with an AWS SDK
The following code example shows how to use `UpdatePipeline`.
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).
```
///
/// Create a pipeline from a JSON definition, or update it if the pipeline already exists.
///
/// The Amazon Resource Name (ARN) of the pipeline.
public async Task SetupPipeline(string pipelineJson, string roleArn, string name, string description, string displayName)
{
try
{
var updateResponse = await _amazonSageMaker.UpdatePipelineAsync(
new UpdatePipelineRequest()
{
PipelineDefinition = pipelineJson,
PipelineDescription = description,
PipelineDisplayName = displayName,
PipelineName = name,
RoleArn = roleArn
});
return updateResponse.PipelineArn;
}
catch (Amazon.SageMaker.Model.ResourceNotFoundException)
{
var createResponse = await _amazonSageMaker.CreatePipelineAsync(
new CreatePipelineRequest()
{
PipelineDefinition = pipelineJson,
PipelineDescription = description,
PipelineDisplayName = displayName,
PipelineName = name,
RoleArn = roleArn
});
return createResponse.PipelineArn;
}
}
```
+ For API details, see [UpdatePipeline](https://docs.aws.amazon.com/goto/DotNetSDKV3/sagemaker-2017-07-24/UpdatePipeline) in *AWS SDK for .NET API Reference*.
------