HyperparameterTuningJobAnalytics: HyperparameterTuningJobAnalytics Class

In DyfanJones/sagemaker-r-common: R6sagemaker lower level api calls

HyperparameterTuningJobAnalytics Class

Description

Fetch results about a hyperparameter tuning job and make them accessible for analytics.

Super class

sagemaker.common::AnalyticsMetricsBase -> HyperparameterTuningJobAnalytics

Active bindings

name

Name of the HyperparameterTuningJob being analyzed

tuning_ranges

A dictionary describing the ranges of all tuned hyperparameters. The keys are the names of the hyperparameter, and the values are the ranges. The output can take one of two forms: * If the 'TrainingJobDefinition' field is present in the job description, the output is a dictionary constructed from 'ParameterRanges' in 'HyperParameterTuningJobConfig' of the job description. The keys are the parameter names, while the values are the parameter ranges. Example: >>> >>> "eta": "MaxValue": "1", "MinValue": "0", "Name": "eta", >>> "gamma": "MaxValue": "10", "MinValue": "0", "Name": "gamma", >>> "iterations": "MaxValue": "100", "MinValue": "50", "Name": "iterations", >>> "num_layers": "MaxValue": "30", "MinValue": "5", "Name": "num_layers", >>> * If the 'TrainingJobDefinitions' field (list) is present in the job description, the output is a dictionary with keys as the 'DefinitionName' values from all items in 'TrainingJobDefinitions', and each value would be a dictionary constructed from 'HyperParameterRanges' in each item in 'TrainingJobDefinitions' in the same format as above Example: >>> >>> "estimator_1": >>> "eta": "MaxValue": "1", "MinValue": "0", "Name": "eta", >>> "gamma": "MaxValue": "10", "MinValue": "0", "Name": "gamma", >>> , >>> "estimator_2": >>> "framework": "Values": ["TF", "MXNet"], "Name": "framework", >>> "gamma": "MaxValue": "1.0", "MinValue": "0.2", "Name": "gamma" >>> >>> For more details about the 'TrainingJobDefinition' and 'TrainingJobDefinitions' fields in job description, see https://botocore.readthedocs.io/en/latest/reference/services/sagemaker.html#SageMaker.Client.create_hyper_parameter_tuning_job

Methods

Public methods

• HyperparameterTuningJobAnalytics$new()

• HyperparameterTuningJobAnalytics$description()

• HyperparameterTuningJobAnalytics$training_job_summaries()

• HyperparameterTuningJobAnalytics$clear_cache()

• HyperparameterTuningJobAnalytics$clone()

Inherited methods

• sagemaker.common::AnalyticsMetricsBase$dataframe()
• sagemaker.common::AnalyticsMetricsBase$export_csv()
• sagemaker.common::AnalyticsMetricsBase$format()

---

Method new()

Initialize a “HyperparameterTuningJobAnalytics“ instance.

Usage

HyperparameterTuningJobAnalytics$new(
  hyperparameter_tuning_job_name,
  sagemaker_session = NULL
)

Arguments

hyperparameter_tuning_job_name

(str): name of the HyperparameterTuningJob to analyze.

sagemaker_session

(sagemaker.session.Session): Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, one is created using the default AWS configuration chain.

---

Method description()

Call “DescribeHyperParameterTuningJob“ for the hyperparameter tuning job.

Usage

HyperparameterTuningJobAnalytics$description(force_refresh = FALSE)

Arguments

force_refresh

(bool): Set to True to fetch the latest data from SageMaker API.

Returns

dict: The Amazon SageMaker response for “DescribeHyperParameterTuningJob“.

---

Method training_job_summaries()

A (paginated) list of everything from “ListTrainingJobsForTuningJob“.

Usage

HyperparameterTuningJobAnalytics$training_job_summaries(force_refresh = FALSE)

Arguments

force_refresh

(bool): Set to True to fetch the latest data from SageMaker API.

Returns

dict: The Amazon SageMaker response for “ListTrainingJobsForTuningJob“.

---

Method clear_cache()

Clear the object of all local caches of API methods.

Usage

HyperparameterTuningJobAnalytics$clear_cache()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

HyperparameterTuningJobAnalytics$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

DyfanJones/sagemaker-r-common documentation built on June 14, 2022, 10:31 p.m.