Skip to content

AutoMLJobConfig

Used by
Source code in docs_src/capo_sagemaker/types/auto_ml_job_config.py 
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
class AutoMLJobConfig(TypedDict, closed=True):
    completion_criteria: NotRequired[
        "capo_sagemaker.types.auto_ml_job_completion_criteria.AutoMLJobCompletionCriteria"
    ]
    """<p>How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.</p>"""
    security_config: NotRequired[
        "capo_sagemaker.types.auto_ml_security_config.AutoMLSecurityConfig"
    ]
    """<p>The security configuration for traffic encryption or Amazon VPC settings.</p>"""
    candidate_generation_config: NotRequired[
        "capo_sagemaker.types.auto_ml_candidate_generation_config.AutoMLCandidateGenerationConfig"
    ]
    """<p>The configuration for generating a candidate for an AutoML job (optional). </p>"""
    data_split_config: NotRequired[
        "capo_sagemaker.types.auto_ml_data_split_config.AutoMLDataSplitConfig"
    ]
    """<p>The configuration for splitting the input training dataset.</p> <p>Type: AutoMLDataSplitConfig</p>"""
    mode: NotRequired["capo_sagemaker.types.auto_ml_mode.AutoMLMode"]
    """<p>The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting <code>AUTO</code>. In <code>AUTO</code> mode, Autopilot chooses <code>ENSEMBLING</code> for datasets smaller than 100 MB, and <code>HYPERPARAMETER_TUNING</code> for larger ones.</p> <p>The <code>ENSEMBLING</code> mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support">Autopilot algorithm support</a> for a list of algorithms supported by <code>ENSEMBLING</code> mode.</p> <p>The <code>HYPERPARAMETER_TUNING</code> (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See <a href="https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support">Autopilot algorithm support</a> for a list of algorithms supported by <code>HYPERPARAMETER_TUNING</code> mode.</p>"""

candidate_generation_config instance-attribute 

candidate_generation_config: NotRequired[AutoMLCandidateGenerationConfig]

The configuration for generating a candidate for an AutoML job (optional).

completion_criteria instance-attribute 

completion_criteria: NotRequired[AutoMLJobCompletionCriteria]

How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

data_split_config instance-attribute 

data_split_config: NotRequired[AutoMLDataSplitConfig]

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

mode instance-attribute 

mode: NotRequired[AutoMLMode]

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

security_config instance-attribute 

security_config: NotRequired[AutoMLSecurityConfig]

The security configuration for traffic encryption or Amazon VPC settings.