/AWS1/IF_SGM=>UPDATEINFERENCEEXPERIMENT()

About UpdateInferenceExperiment

Updates an inference experiment that you created. The status of the inference experiment has to be either Created, Running. For more information on the status of an inference experiment, see DescribeInferenceExperiment.

Method Signature

METHODS /AWS1/IF_SGM~UPDATEINFERENCEEXPERIMENT
  IMPORTING
    !IV_NAME TYPE /AWS1/SGMINFERENCEEXPERIMENT00 OPTIONAL
    !IO_SCHEDULE TYPE REF TO /AWS1/CL_SGMINFERENCEEXPERIM00 OPTIONAL
    !IV_DESCRIPTION TYPE /AWS1/SGMINFERENCEEXPERIMENT02 OPTIONAL
    !IT_MODELVARIANTS TYPE /AWS1/CL_SGMMODELVARIANTCONFIG=>TT_MODELVARIANTCONFIGLIST OPTIONAL
    !IO_DATASTORAGECONFIG TYPE REF TO /AWS1/CL_SGMINFERENCEEXPERIM01 OPTIONAL
    !IO_SHADOWMODECONFIG TYPE REF TO /AWS1/CL_SGMSHADOWMODECONFIG OPTIONAL
  RETURNING
    VALUE(OO_OUTPUT) TYPE REF TO /aws1/cl_sgmupinferenceexper01
  RAISING
    /AWS1/CX_SGMCONFLICTEXCEPTION
    /AWS1/CX_SGMRESOURCENOTFOUND
    /AWS1/CX_SGMCLIENTEXC
    /AWS1/CX_SGMSERVEREXC
    /AWS1/CX_RT_TECHNICAL_GENERIC
    /AWS1/CX_RT_SERVICE_GENERIC.

IMPORTING

Required arguments:

iv_name TYPE /AWS1/SGMINFERENCEEXPERIMENT00 /AWS1/SGMINFERENCEEXPERIMENT00

The name of the inference experiment to be updated.

Optional arguments:

io_schedule TYPE REF TO /AWS1/CL_SGMINFERENCEEXPERIM00 /AWS1/CL_SGMINFERENCEEXPERIM00

The duration for which the inference experiment will run. If the status of the inference experiment is Created, then you can update both the start and end dates. If the status of the inference experiment is Running, then you can update only the end date.

iv_description TYPE /AWS1/SGMINFERENCEEXPERIMENT02 /AWS1/SGMINFERENCEEXPERIMENT02

The description of the inference experiment.

it_modelvariants TYPE /AWS1/CL_SGMMODELVARIANTCONFIG=>TT_MODELVARIANTCONFIGLIST TT_MODELVARIANTCONFIGLIST

An array of ModelVariantConfig objects. There is one for each variant, whose infrastructure configuration you want to update.

io_datastorageconfig TYPE REF TO /AWS1/CL_SGMINFERENCEEXPERIM01 /AWS1/CL_SGMINFERENCEEXPERIM01

The Amazon S3 location and configuration for storing inference request and response data.

io_shadowmodeconfig TYPE REF TO /AWS1/CL_SGMSHADOWMODECONFIG /AWS1/CL_SGMSHADOWMODECONFIG

The configuration of ShadowMode inference experiment type. Use this field to specify a production variant which takes all the inference requests, and a shadow variant to which Amazon SageMaker replicates a percentage of the inference requests. For the shadow variant also specify the percentage of requests that Amazon SageMaker replicates.

RETURNING

oo_output TYPE REF TO /aws1/cl_sgmupinferenceexper01 /AWS1/CL_SGMUPINFERENCEEXPER01

Domain	/AWS1/RT_ACCOUNT_ID
Primitive Type	NUMC

Examples

Syntax Example

This is an example of the syntax for calling the method. It includes every possible argument and initializes every possible value. The data provided is not necessarily semantically accurate (for example the value "string" may be provided for something that is intended to be an instance ID, or in some cases two arguments may be mutually exclusive). The syntax shows the ABAP syntax for creating the various data structures.

DATA(lo_result) = lo_client->updateinferenceexperiment(
  io_datastorageconfig = new /aws1/cl_sgminferenceexperim01(
    io_contenttype = new /aws1/cl_sgmcaptureconttypeh00(
      it_csvcontenttypes = VALUE /aws1/cl_sgmcsvcontenttypes_w=>tt_csvcontenttypes(
        ( new /aws1/cl_sgmcsvcontenttypes_w( |string| ) )
      )
      it_jsoncontenttypes = VALUE /aws1/cl_sgmjsoncontenttypes_w=>tt_jsoncontenttypes(
        ( new /aws1/cl_sgmjsoncontenttypes_w( |string| ) )
      )
    )
    iv_destination = |string|
    iv_kmskey = |string|
  )
  io_schedule = new /aws1/cl_sgminferenceexperim00(
    iv_endtime = '20150101000000.0000000'
    iv_starttime = '20150101000000.0000000'
  )
  io_shadowmodeconfig = new /aws1/cl_sgmshadowmodeconfig(
    it_shadowmodelvariants = VALUE /aws1/cl_sgmshadowmdelvarian00=>tt_shadowmodelvariantcfglist(
      (
        new /aws1/cl_sgmshadowmdelvarian00(
          iv_samplingpercentage = 123
          iv_shadowmodelvariantname = |string|
        )
      )
    )
    iv_sourcemodelvariantname = |string|
  )
  it_modelvariants = VALUE /aws1/cl_sgmmodelvariantconfig=>tt_modelvariantconfiglist(
    (
      new /aws1/cl_sgmmodelvariantconfig(
        io_infrastructureconfig = new /aws1/cl_sgmmdelinfrastructu00(
          io_realtimeinferenceconfig = new /aws1/cl_sgmrealtimeinferenc01(
            iv_instancecount = 123
            iv_instancetype = |string|
          )
          iv_infrastructuretype = |string|
        )
        iv_modelname = |string|
        iv_variantname = |string|
      )
    )
  )
  iv_description = |string|
  iv_name = |string|
).

This is an example of reading all possible response values

lo_result = lo_result.
IF lo_result IS NOT INITIAL.
  lv_inferenceexperimentarn = lo_result->get_inferenceexperimentarn( ).
ENDIF.