/AWS1/IF_LOE=>CREATERETRAININGSCHEDULER()¶
About CreateRetrainingScheduler¶
Creates a retraining scheduler on the specified model.
Method Signature¶
METHODS /AWS1/IF_LOE~CREATERETRAININGSCHEDULER
IMPORTING
!IV_MODELNAME TYPE /AWS1/LOEMODELNAME OPTIONAL
!IV_RETRAININGSTARTDATE TYPE /AWS1/LOETIMESTAMP OPTIONAL
!IV_RETRAININGFREQUENCY TYPE /AWS1/LOERETRAININGFREQUENCY OPTIONAL
!IV_LOOKBACKWINDOW TYPE /AWS1/LOELOOKBACKWINDOW OPTIONAL
!IV_PROMOTEMODE TYPE /AWS1/LOEMODELPROMOTEMODE OPTIONAL
!IV_CLIENTTOKEN TYPE /AWS1/LOEIDEMPOTENCETOKEN OPTIONAL
RETURNING
VALUE(OO_OUTPUT) TYPE REF TO /aws1/cl_loecreretrnschdrrsp
RAISING
/AWS1/CX_LOEACCESSDENIEDEX
/AWS1/CX_LOECONFLICTEXCEPTION
/AWS1/CX_LOEINTERNALSERVEREX
/AWS1/CX_LOERESOURCENOTFOUNDEX
/AWS1/CX_LOETHROTTLINGEX
/AWS1/CX_LOEVALIDATIONEX
/AWS1/CX_LOECLIENTEXC
/AWS1/CX_LOESERVEREXC
/AWS1/CX_RT_TECHNICAL_GENERIC
/AWS1/CX_RT_SERVICE_GENERIC.
IMPORTING¶
Required arguments:¶
iv_modelname TYPE /AWS1/LOEMODELNAME /AWS1/LOEMODELNAME¶
The name of the model to add the retraining scheduler to.
iv_retrainingfrequency TYPE /AWS1/LOERETRAININGFREQUENCY /AWS1/LOERETRAININGFREQUENCY¶
This parameter uses the ISO 8601 standard to set the frequency at which you want retraining to occur in terms of Years, Months, and/or Days (note: other parameters like Time are not currently supported). The minimum value is 30 days (P30D) and the maximum value is 1 year (P1Y). For example, the following values are valid:
P3M15D – Every 3 months and 15 days
P2M – Every 2 months
P150D – Every 150 days
iv_lookbackwindow TYPE /AWS1/LOELOOKBACKWINDOW /AWS1/LOELOOKBACKWINDOW¶
The number of past days of data that will be used for retraining.
iv_clienttoken TYPE /AWS1/LOEIDEMPOTENCETOKEN /AWS1/LOEIDEMPOTENCETOKEN¶
A unique identifier for the request. If you do not set the client request token, Amazon Lookout for Equipment generates one.
Optional arguments:¶
iv_retrainingstartdate TYPE /AWS1/LOETIMESTAMP /AWS1/LOETIMESTAMP¶
The start date for the retraining scheduler. Lookout for Equipment truncates the time you provide to the nearest UTC day.
iv_promotemode TYPE /AWS1/LOEMODELPROMOTEMODE /AWS1/LOEMODELPROMOTEMODE¶
Indicates how the service will use new models. In
MANAGEDmode, new models will automatically be used for inference if they have better performance than the current model. InMANUALmode, the new models will not be used until they are manually activated.
RETURNING¶
oo_output TYPE REF TO /aws1/cl_loecreretrnschdrrsp /AWS1/CL_LOECRERETRNSCHDRRSP¶
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->createretrainingscheduler(
iv_clienttoken = |string|
iv_lookbackwindow = |string|
iv_modelname = |string|
iv_promotemode = |string|
iv_retrainingfrequency = |string|
iv_retrainingstartdate = '20150101000000.0000000'
).
This is an example of reading all possible response values
lo_result = lo_result.
IF lo_result IS NOT INITIAL.
lv_modelname = lo_result->get_modelname( ).
lv_modelarn = lo_result->get_modelarn( ).
lv_retrainingschedulerstat = lo_result->get_status( ).
ENDIF.
Creates a retraining scheduler with a specific start date¶
DATA(lo_result) = lo_client->createretrainingscheduler(
iv_clienttoken = |sample-client-token|
iv_lookbackwindow = |P360D|
iv_modelname = |sample-model|
iv_retrainingfrequency = |P1M|
iv_retrainingstartdate = '20240101000000.0000000'
).
Creates a retraining scheduler with manual promote mode¶
DATA(lo_result) = lo_client->createretrainingscheduler(
iv_clienttoken = |sample-client-token|
iv_lookbackwindow = |P360D|
iv_modelname = |sample-model|
iv_promotemode = |MANUAL|
iv_retrainingfrequency = |P1M|
).