/AWS1/CL_SGMTRAININGJOBDEFN¶
Defines the input needed to run a training job using the algorithm.
CONSTRUCTOR¶
IMPORTING¶
Required arguments:¶
iv_traininginputmode TYPE /AWS1/SGMTRAININGINPUTMODE /AWS1/SGMTRAININGINPUTMODE¶
TrainingInputMode
it_inputdataconfig TYPE /AWS1/CL_SGMCHANNEL=>TT_INPUTDATACONFIG TT_INPUTDATACONFIG¶
An array of
Channelobjects, each of which specifies an input source.
io_outputdataconfig TYPE REF TO /AWS1/CL_SGMOUTPUTDATACONFIG /AWS1/CL_SGMOUTPUTDATACONFIG¶
the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
io_resourceconfig TYPE REF TO /AWS1/CL_SGMRESOURCECONFIG /AWS1/CL_SGMRESOURCECONFIG¶
The resources, including the ML compute instances and ML storage volumes, to use for model training.
io_stoppingcondition TYPE REF TO /AWS1/CL_SGMSTOPPINGCONDITION /AWS1/CL_SGMSTOPPINGCONDITION¶
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
Optional arguments:¶
it_hyperparameters TYPE /AWS1/CL_SGMHYPERPARAMETERS_W=>TT_HYPERPARAMETERS TT_HYPERPARAMETERS¶
The hyperparameters used for the training job.
Queryable Attributes¶
TrainingInputMode¶
TrainingInputMode
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_TRAININGINPUTMODE() |
Getter for TRAININGINPUTMODE, with configurable default |
ASK_TRAININGINPUTMODE() |
Getter for TRAININGINPUTMODE w/ exceptions if field has no v |
HAS_TRAININGINPUTMODE() |
Determine if TRAININGINPUTMODE has a value |
HyperParameters¶
The hyperparameters used for the training job.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_HYPERPARAMETERS() |
Getter for HYPERPARAMETERS, with configurable default |
ASK_HYPERPARAMETERS() |
Getter for HYPERPARAMETERS w/ exceptions if field has no val |
HAS_HYPERPARAMETERS() |
Determine if HYPERPARAMETERS has a value |
InputDataConfig¶
An array of
Channelobjects, each of which specifies an input source.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_INPUTDATACONFIG() |
Getter for INPUTDATACONFIG, with configurable default |
ASK_INPUTDATACONFIG() |
Getter for INPUTDATACONFIG w/ exceptions if field has no val |
HAS_INPUTDATACONFIG() |
Determine if INPUTDATACONFIG has a value |
OutputDataConfig¶
the path to the S3 bucket where you want to store model artifacts. SageMaker creates subfolders for the artifacts.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_OUTPUTDATACONFIG() |
Getter for OUTPUTDATACONFIG |
ResourceConfig¶
The resources, including the ML compute instances and ML storage volumes, to use for model training.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_RESOURCECONFIG() |
Getter for RESOURCECONFIG |
StoppingCondition¶
Specifies a limit to how long a model training job can run. It also specifies how long a managed Spot training job has to complete. When the job reaches the time limit, SageMaker ends the training job. Use this API to cap model training costs.
To stop a job, SageMaker sends the algorithm the SIGTERM signal, which delays job termination for 120 seconds. Algorithms can use this 120-second window to save the model artifacts.
Accessible with the following methods¶
| Method | Description |
|---|---|
GET_STOPPINGCONDITION() |
Getter for STOPPINGCONDITION |