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/AWS1/IF_CRL=>CREATETRAINEDMODEL()

About CreateTrainedModel

Creates a trained model from an associated configured model algorithm using data from any member of the collaboration.

Method Signature

METHODS /AWS1/IF_CRL~CREATETRAINEDMODEL
  IMPORTING
    !IV_MEMBERSHIPIDENTIFIER TYPE /AWS1/CRLUUID OPTIONAL
    !IV_NAME TYPE /AWS1/CRLNAMESTRING OPTIONAL
    !IV_CFGUREDMDELALGASSOCIATI00 TYPE /AWS1/CRLCFGUREDMDELALGASSOC00 OPTIONAL
    !IT_HYPERPARAMETERS TYPE /AWS1/CL_CRLHYPERPARAMETERS_W=>TT_HYPERPARAMETERS OPTIONAL
    !IT_ENVIRONMENT TYPE /AWS1/CL_CRLENVIRONMENT_W=>TT_ENVIRONMENT OPTIONAL
    !IO_RESOURCECONFIG TYPE REF TO /AWS1/CL_CRLRESOURCECONFIG OPTIONAL
    !IO_STOPPINGCONDITION TYPE REF TO /AWS1/CL_CRLSTOPPINGCONDITION OPTIONAL
    !IT_INCREMENTALTRNDATACHNLS TYPE /AWS1/CL_CRLINCREMENTALTRNDA01=>TT_INCREMENTALTRNDATACHANNELS OPTIONAL
    !IT_DATACHANNELS TYPE /AWS1/CL_CRLMDELTRNDATACHANNEL=>TT_MODELTRAININGDATACHANNELS OPTIONAL
    !IV_TRAININGINPUTMODE TYPE /AWS1/CRLTRAININGINPUTMODE OPTIONAL
    !IV_DESCRIPTION TYPE /AWS1/CRLRESOURCEDESCRIPTION OPTIONAL
    !IV_KMSKEYARN TYPE /AWS1/CRLKMSKEYARN OPTIONAL
    !IT_TAGS TYPE /AWS1/CL_CRLTAGMAP_W=>TT_TAGMAP OPTIONAL
  RETURNING
    VALUE(OO_OUTPUT) TYPE REF TO /aws1/cl_crlcretrainedmodelrsp
  RAISING
    /AWS1/CX_CRLACCESSDENIEDEX
    /AWS1/CX_CRLCONFLICTEXCEPTION
    /AWS1/CX_CRLINTERNALSERVICEEX
    /AWS1/CX_CRLRESOURCENOTFOUNDEX
    /AWS1/CX_CRLSERVICEQUOTAEXCDEX
    /AWS1/CX_CRLTHROTTLINGEX
    /AWS1/CX_CRLVALIDATIONEX
    /AWS1/CX_CRLCLIENTEXC
    /AWS1/CX_CRLSERVEREXC
    /AWS1/CX_RT_TECHNICAL_GENERIC
    /AWS1/CX_RT_SERVICE_GENERIC.

IMPORTING

Required arguments:

iv_membershipidentifier TYPE /AWS1/CRLUUID /AWS1/CRLUUID

The membership ID of the member that is creating the trained model.

iv_name TYPE /AWS1/CRLNAMESTRING /AWS1/CRLNAMESTRING

The name of the trained model.

iv_cfguredmdelalgassociati00 TYPE /AWS1/CRLCFGUREDMDELALGASSOC00 /AWS1/CRLCFGUREDMDELALGASSOC00

The associated configured model algorithm used to train this model.

io_resourceconfig TYPE REF TO /AWS1/CL_CRLRESOURCECONFIG /AWS1/CL_CRLRESOURCECONFIG

Information about the EC2 resources that are used to train this model.

it_datachannels TYPE /AWS1/CL_CRLMDELTRNDATACHANNEL=>TT_MODELTRAININGDATACHANNELS TT_MODELTRAININGDATACHANNELS

Defines the data channels that are used as input for the trained model request.

Limit: Maximum of 20 channels total (including both dataChannels and incrementalTrainingDataChannels).

Optional arguments:

it_hyperparameters TYPE /AWS1/CL_CRLHYPERPARAMETERS_W=>TT_HYPERPARAMETERS TT_HYPERPARAMETERS

Algorithm-specific parameters that influence the quality of the model. You set hyperparameters before you start the learning process.

it_environment TYPE /AWS1/CL_CRLENVIRONMENT_W=>TT_ENVIRONMENT TT_ENVIRONMENT

The environment variables to set in the Docker container.

io_stoppingcondition TYPE REF TO /AWS1/CL_CRLSTOPPINGCONDITION /AWS1/CL_CRLSTOPPINGCONDITION

The criteria that is used to stop model training.

it_incrementaltrndatachnls TYPE /AWS1/CL_CRLINCREMENTALTRNDA01=>TT_INCREMENTALTRNDATACHANNELS TT_INCREMENTALTRNDATACHANNELS

Specifies the incremental training data channels for the trained model.

Incremental training allows you to create a new trained model with updates without retraining from scratch. You can specify up to one incremental training data channel that references a previously trained model and its version.

Limit: Maximum of 20 channels total (including both incrementalTrainingDataChannels and dataChannels).

iv_traininginputmode TYPE /AWS1/CRLTRAININGINPUTMODE /AWS1/CRLTRAININGINPUTMODE

The input mode for accessing the training data. This parameter determines how the training data is made available to the training algorithm. Valid values are:

  • File - The training data is downloaded to the training instance and made available as files.

  • FastFile - The training data is streamed directly from Amazon S3 to the training algorithm, providing faster access for large datasets.

  • Pipe - The training data is streamed to the training algorithm using named pipes, which can improve performance for certain algorithms.

iv_description TYPE /AWS1/CRLRESOURCEDESCRIPTION /AWS1/CRLRESOURCEDESCRIPTION

The description of the trained model.

iv_kmskeyarn TYPE /AWS1/CRLKMSKEYARN /AWS1/CRLKMSKEYARN

The Amazon Resource Name (ARN) of the KMS key. This key is used to encrypt and decrypt customer-owned data in the trained ML model and the associated data.

it_tags TYPE /AWS1/CL_CRLTAGMAP_W=>TT_TAGMAP TT_TAGMAP

The optional metadata that you apply to the resource to help you categorize and organize them. Each tag consists of a key and an optional value, both of which you define.

The following basic restrictions apply to tags:

  • Maximum number of tags per resource - 50.

  • For each resource, each tag key must be unique, and each tag key can have only one value.

  • Maximum key length - 128 Unicode characters in UTF-8.

  • Maximum value length - 256 Unicode characters in UTF-8.

  • If your tagging schema is used across multiple services and resources, remember that other services may have restrictions on allowed characters. Generally allowed characters are: letters, numbers, and spaces representable in UTF-8, and the following characters: + - = . _ : / @.

  • Tag keys and values are case sensitive.

  • Do not use aws:, AWS:, or any upper or lowercase combination of such as a prefix for keys as it is reserved for AWS use. You cannot edit or delete tag keys with this prefix. Values can have this prefix. If a tag value has aws as its prefix but the key does not, then Clean Rooms ML considers it to be a user tag and will count against the limit of 50 tags. Tags with only the key prefix of aws do not count against your tags per resource limit.

RETURNING

oo_output TYPE REF TO /aws1/cl_crlcretrainedmodelrsp /AWS1/CL_CRLCRETRAINEDMODELRSP

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->createtrainedmodel(
  io_resourceconfig = new /aws1/cl_crlresourceconfig(
    iv_instancecount = 123
    iv_instancetype = |string|
    iv_volumesizeingb = 123
  )
  io_stoppingcondition = new /aws1/cl_crlstoppingcondition( 123 )
  it_datachannels = VALUE /aws1/cl_crlmdeltrndatachannel=>tt_modeltrainingdatachannels(
    (
      new /aws1/cl_crlmdeltrndatachannel(
        iv_channelname = |string|
        iv_mlinputchannelarn = |string|
        iv_s3datadistributiontype = |string|
      )
    )
  )
  it_environment = VALUE /aws1/cl_crlenvironment_w=>tt_environment(
    (
      VALUE /aws1/cl_crlenvironment_w=>ts_environment_maprow(
        key = |string|
        value = new /aws1/cl_crlenvironment_w( |string| )
      )
    )
  )
  it_hyperparameters = VALUE /aws1/cl_crlhyperparameters_w=>tt_hyperparameters(
    (
      VALUE /aws1/cl_crlhyperparameters_w=>ts_hyperparameters_maprow(
        key = |string|
        value = new /aws1/cl_crlhyperparameters_w( |string| )
      )
    )
  )
  it_incrementaltrndatachnls = VALUE /aws1/cl_crlincrementaltrnda01=>tt_incrementaltrndatachannels(
    (
      new /aws1/cl_crlincrementaltrnda01(
        iv_channelname = |string|
        iv_trainedmodelarn = |string|
        iv_versionidentifier = |string|
      )
    )
  )
  it_tags = VALUE /aws1/cl_crltagmap_w=>tt_tagmap(
    (
      VALUE /aws1/cl_crltagmap_w=>ts_tagmap_maprow(
        key = |string|
        value = new /aws1/cl_crltagmap_w( |string| )
      )
    )
  )
  iv_cfguredmdelalgassociati00 = |string|
  iv_description = |string|
  iv_kmskeyarn = |string|
  iv_membershipidentifier = |string|
  iv_name = |string|
  iv_traininginputmode = |string|
).

This is an example of reading all possible response values

lo_result = lo_result.
IF lo_result IS NOT INITIAL.
  lv_trainedmodelarn = lo_result->get_trainedmodelarn( ).
  lv_uuid = lo_result->get_versionidentifier( ).
ENDIF.