/AWS1/CL_SGM=>CREATEAUTOMLJOBV2()
¶
About CreateAutoMLJobV2¶
Creates an Autopilot job also referred to as Autopilot experiment or AutoML job V2.
CreateAutoMLJobV2 and DescribeAutoMLJobV2 are new versions of CreateAutoMLJob and DescribeAutoMLJob which offer backward compatibility.
CreateAutoMLJobV2
can manage tabular problem types identical to those of
its previous version CreateAutoMLJob
, as well as time-series forecasting,
non-tabular problem types such as image or text classification, and text generation
(LLMs fine-tuning).
Find guidelines about how to migrate a CreateAutoMLJob
to
CreateAutoMLJobV2
in Migrate a CreateAutoMLJob to CreateAutoMLJobV2.
For the list of available problem types supported by CreateAutoMLJobV2
, see
AutoMLProblemTypeConfig.
You can find the best-performing model after you run an AutoML job V2 by calling DescribeAutoMLJobV2.
Method Signature¶
IMPORTING¶
Required arguments:¶
IV_AUTOMLJOBNAME
TYPE /AWS1/SGMAUTOMLJOBNAME
/AWS1/SGMAUTOMLJOBNAME
¶
Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
IT_AUTOMLJOBINPUTDATACONFIG
TYPE /AWS1/CL_SGMAUTOMLJOBCHANNEL=>TT_AUTOMLJOBINPUTDATACONFIG
TT_AUTOMLJOBINPUTDATACONFIG
¶
An array of channel objects describing the input data and their location. Each channel is a named input source. Similar to the InputDataConfig attribute in the
CreateAutoMLJob
input parameters. The supported formats depend on the problem type:
For tabular problem types:
S3Prefix
,ManifestFile
.For image classification:
S3Prefix
,ManifestFile
,AugmentedManifestFile
.For text classification:
S3Prefix
.For time-series forecasting:
S3Prefix
.For text generation (LLMs fine-tuning):
S3Prefix
.
IO_OUTPUTDATACONFIG
TYPE REF TO /AWS1/CL_SGMAUTOMLOUTDATACFG
/AWS1/CL_SGMAUTOMLOUTDATACFG
¶
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
IO_AUTOMLPROBLEMTYPECONFIG
TYPE REF TO /AWS1/CL_SGMAUTOMLPROBLEMTYP00
/AWS1/CL_SGMAUTOMLPROBLEMTYP00
¶
Defines the configuration settings of one of the supported problem types.
IV_ROLEARN
TYPE /AWS1/SGMROLEARN
/AWS1/SGMROLEARN
¶
The ARN of the role that is used to access the data.
Optional arguments:¶
IT_TAGS
TYPE /AWS1/CL_SGMTAG=>TT_TAGLIST
TT_TAGLIST
¶
An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, such as by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.
IO_SECURITYCONFIG
TYPE REF TO /AWS1/CL_SGMAUTOMLSECCONFIG
/AWS1/CL_SGMAUTOMLSECCONFIG
¶
The security configuration for traffic encryption or Amazon VPC settings.
IO_AUTOMLJOBOBJECTIVE
TYPE REF TO /AWS1/CL_SGMAUTOMLJOBOBJECTIVE
/AWS1/CL_SGMAUTOMLJOBOBJECTIVE
¶
Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. For the list of default values per problem type, see AutoMLJobObjective.
For tabular problem types: You must either provide both the
AutoMLJobObjective
and indicate the type of supervised learning problem inAutoMLProblemTypeConfig
(TabularJobConfig.ProblemType
), or none at all.For text generation problem types (LLMs fine-tuning): Fine-tuning language models in Autopilot does not require setting the
AutoMLJobObjective
field. Autopilot fine-tunes LLMs without requiring multiple candidates to be trained and evaluated.
Instead, using your dataset, Autopilot directly fine-tunes your target model to enhance a default objective metric, the cross-entropy loss. After fine-tuning a language model, you can evaluate the quality of its generated text using different metrics.
For a list of the available metrics, see Metrics for fine-tuning LLMs in Autopilot.
IO_MODELDEPLOYCONFIG
TYPE REF TO /AWS1/CL_SGMMODELDEPLOYCONFIG
/AWS1/CL_SGMMODELDEPLOYCONFIG
¶
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
IO_DATASPLITCONFIG
TYPE REF TO /AWS1/CL_SGMAUTOMLDATASPLITCFG
/AWS1/CL_SGMAUTOMLDATASPLITCFG
¶
This structure specifies how to split the data into train and validation datasets.
The validation and training datasets must contain the same headers. For jobs created by calling
CreateAutoMLJob
, the validation dataset must be less than 2 GB in size.This attribute must not be set for the time-series forecasting problem type, as Autopilot automatically splits the input dataset into training and validation sets.