Analytics¶
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class
sagemaker.analytics.AnalyticsMetricsBase¶ Bases:
objectBase class for tuning job or training job analytics classes. Understands common functionality like persistence and caching.
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export_csv(filename)¶ Persists the analytics dataframe to a file.
Parameters: filename (str) – The name of the file to save to.
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dataframe(force_refresh=False)¶ A pandas dataframe with lots of interesting results about this object. Created by calling SageMaker List and Describe APIs and converting them into a convenient tabular summary.
Parameters: force_refresh (bool) – Set to True to fetch the latest data from SageMaker API.
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clear_cache()¶ Clear the object of all local caches of API methods, so that the next time any properties are accessed they will be refreshed from the service.
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class
sagemaker.analytics.HyperparameterTuningJobAnalytics(hyperparameter_tuning_job_name, sagemaker_session=None)¶ Bases:
sagemaker.analytics.AnalyticsMetricsBaseFetch results about a hyperparameter tuning job and make them accessible for analytics.
Initialize a
HyperparameterTuningJobAnalyticsinstance.Parameters: - 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.
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name¶ Name of the HyperparameterTuningJob being analyzed
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clear_cache()¶ Clear the object of all local caches of API methods.
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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
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class
sagemaker.analytics.TrainingJobAnalytics(training_job_name, metric_names=None, sagemaker_session=None, start_time=None, end_time=None, period=None)¶ Bases:
sagemaker.analytics.AnalyticsMetricsBaseFetch training curve data from CloudWatch Metrics for a specific training job.
Initialize a
TrainingJobAnalyticsinstance.Parameters: - training_job_name (str) – name of the TrainingJob to analyze.
- metric_names (list, optional) – string names of all the metrics to collect for this training job. If not specified, then it will use all metric names configured for this job.
- 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 specified using the default AWS configuration chain.
- start_time –
- end_time –
- period –
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CLOUDWATCH_NAMESPACE= '/aws/sagemaker/TrainingJobs'¶
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name¶ Name of the TrainingJob being analyzed
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clear_cache()¶ Clear the object of all local caches of API methods, so that the next time any properties are accessed they will be refreshed from the service.
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class
sagemaker.analytics.ExperimentAnalytics(experiment_name=None, search_expression=None, sort_by=None, sort_order=None, metric_names=None, parameter_names=None, sagemaker_session=None)¶ Bases:
sagemaker.analytics.AnalyticsMetricsBaseFetch trial component data and make them accessible for analytics.
Initialize a
ExperimentAnalyticsinstance.Parameters: - experiment_name (str, optional) – Name of the experiment if you want to constrain the search to only trial components belonging to an experiment.
- search_expression (dict, optional) – The search query to find the set of trial components to use to populate the data frame.
- sort_by (str, optional) – The name of the resource property used to sort the set of trial components.
- sort_order (str optional) – How trial components are ordered, valid values are Ascending and Descending. The default is Descending.
- metric_names (list, optional) – string names of all the metrics to be shown in the data frame. If not specified, all metrics will be shown of all trials.
- parameter_names (list, optional) – string names of the parameters to be shown in the data frame. If not specified, all parameters will be shown of all trials.
- 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.
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MAX_TRIAL_COMPONENTS= 10000¶
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name¶ Name of the Experiment being analyzed
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clear_cache()¶ Clear the object of all local caches of API methods.