Rule: Debug Rule Class
In DyfanJones/sagemaker-r-common: R6sagemaker lower level api calls
Rule R Documentation
Debug Rule Class
Description
The SageMaker Debugger Rule class configures *debugging* rules to debug your training job.
The debugging rules analyze tensor outputs from your training job
and monitor conditions that are critical for the success of the training
job.
SageMaker Debugger comes pre-packaged with built-in *debugging* rules.
For example, the debugging rules can detect whether gradients are getting too large or
too small, or if a model is overfitting.
For a full list of built-in rules for debugging, see
'List of Debugger Built-in Rules'
https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-rules.html.
You can also write your own rules using the custom rule classmethod.
Super class
sagemaker.common::RuleBase -> Rule
Public fields
collection_configsA list of :class:'~sagemaker.debugger.CollectionConfig
actionsPlaceholder
Methods
Public methods
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Inherited methods
Method new()
Configure the debugging rules using the following classmethods.
Usage
Rule$new(
name = NULL,
image_uri = NULL,
instance_type = NULL,
container_local_output_path = NULL,
s3_output_path = NULL,
volume_size_in_gb = NULL,
rule_parameters = NULL,
collections_to_save = NULL,
actions = NULL
)
Arguments
name(str): The name of the rule.
image_uri(str): The image URI to use the rule.
instance_type(str): Type of EC2 instance to use. For example, 'ml.c4.xlarge'.
container_local_output_path(str): The local path to store the Rule output.
s3_output_path(str): The location in S3 to store the output.
volume_size_in_gb(int): Size in GB of the EBS volume to use for storing data.
rule_parameters(dict): A dictionary of parameters for the rule.
collections_to_save([sagemaker.debugger.CollectionConfig]): Optional. A list
of :class:'~sagemaker.debugger.CollectionConfig' objects to be saved.
actions:
Method sagemaker()
Initialize a “Rule“ object for a built-in debugging rule.
Usage
Rule$sagemaker(
base_config,
name = NULL,
container_local_output_path = NULL,
s3_output_path = NULL,
other_trials_s3_input_paths = NULL,
rule_parameters = NULL,
collections_to_save = NULL,
actions = NULL
)
Arguments
base_config(dict): Required. This is the base rule config dictionary returned from the
:class:sagemaker.debugger method.
For example, sagemaker.debugger::dead_relu().
For a full list of built-in rules for debugging, see
'List of Debugger Built-in Rules'
https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-rules.html.
name(str): Optional. The name of the debugger rule. If one is not provided,
the name of the base_config will be used.
container_local_output_path(str): Optional. The local path in the rule processing
container.
s3_output_path(str): Optional. The location in Amazon S3 to store the output tensors.
The default Debugger output path for debugging data is created under the
default output path of the :class:'~sagemaker.estimator.Estimator' class.
For example,
s3://sagemaker-<region>-<12digit_account_id>/<training-job-name>/debug-output/.
other_trials_s3_input_paths([str]): Optional. The Amazon S3 input paths
of other trials to use the SimilarAcrossRuns rule.
rule_parameters(dict): Optional. A dictionary of parameters for the rule.
collections_to_save(:class:sagemaker.debugger::CollectionConfig):
Optional. A list
of :class:sagemaker.debugger::CollectionConfig objects to be saved.
actions:
Returns
:class:'~sagemaker.debugger.Rule': An instance of the built-in rule.
Method custom()
Initialize a “Rule“ object for a *custom* debugging rule.
You can create a custom rule that analyzes tensors emitted
during the training of a model
and monitors conditions that are critical for the success of a training
job. For more information, see 'Create Debugger Custom Rules for Training Job Analysis'
https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-custom-rules.html.
Usage
Rule$custom(
name,
image_uri,
instance_type,
volume_size_in_gb,
source = NULL,
rule_to_invoke = NULL,
container_local_output_path = NULL,
s3_output_path = NULL,
other_trials_s3_input_paths = NULL,
rule_parameters = NULL,
collections_to_save = NULL,
actions = NULL
)
Arguments
name(str): Required. The name of the debugger rule.
image_uri(str): Required. The URI of the image to be used by the debugger rule.
instance_type(str): Required. Type of EC2 instance to use, for example,
'ml.c4.xlarge'.
volume_size_in_gb(int): Required. Size in GB of the EBS volume
to use for storing data.
source(str): Optional. A source file containing a rule to invoke. If provided,
you must also provide rule_to_invoke. This can either be an S3 uri or
a local path.
rule_to_invoke(str): Optional. The name of the rule to invoke within the source.
If provided, you must also provide source.
container_local_output_path(str): Optional. The local path in the container.
s3_output_path(str): Optional. The location in Amazon S3 to store the output tensors.
The default Debugger output path for debugging data is created under the
default output path of the :class:'~sagemaker.estimator.Estimator' class.
For example,
s3://sagemaker-<region>-<12digit_account_id>/<training-job-name>/debug-output/.
other_trials_s3_input_paths([str]): Optional. The Amazon S3 input paths
of other trials to use the SimilarAcrossRuns rule.
rule_parameters(dict): Optional. A dictionary of parameters for the rule.
collections_to_save([sagemaker.debugger.CollectionConfig]): Optional. A list
of :class:'~sagemaker.debugger.CollectionConfig' objects to be saved.
actions:
Returns
:class:'~sagemaker.debugger.Rule': The instance of the custom rule.
Method prepare_actions()
Prepare actions for Debugger Rule.
Usage
Rule$prepare_actions(training_job_name)
Arguments
training_job_name(str): The training job name. To be set as the default training job
prefix for the StopTraining action if it is specified.
Method to_debugger_rule_config_list()
Generates a request dictionary using the parameters provided when initializing object.
Usage
Rule$to_debugger_rule_config_list()
Returns
dict: An portion of an API request as a dictionary.
Method clone()
The objects of this class are cloneable with this method.
Usage
Rule$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
DyfanJones/sagemaker-r-common documentation built on June 14, 2022, 10:31 p.m.
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| Rule | R Documentation |
Debug Rule Class
Description
The SageMaker Debugger Rule class configures *debugging* rules to debug your training job. The debugging rules analyze tensor outputs from your training job and monitor conditions that are critical for the success of the training job. SageMaker Debugger comes pre-packaged with built-in *debugging* rules. For example, the debugging rules can detect whether gradients are getting too large or too small, or if a model is overfitting. For a full list of built-in rules for debugging, see 'List of Debugger Built-in Rules' https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-rules.html. You can also write your own rules using the custom rule classmethod.
Super class
sagemaker.common::RuleBase -> Rule
Public fields
collection_configsA list of :class:'~sagemaker.debugger.CollectionConfig
actionsPlaceholder
Methods
Public methods
Inherited methods
Method new()
Configure the debugging rules using the following classmethods.
Usage
Rule$new( name = NULL, image_uri = NULL, instance_type = NULL, container_local_output_path = NULL, s3_output_path = NULL, volume_size_in_gb = NULL, rule_parameters = NULL, collections_to_save = NULL, actions = NULL )
Arguments
name(str): The name of the rule.
image_uri(str): The image URI to use the rule.
instance_type(str): Type of EC2 instance to use. For example, 'ml.c4.xlarge'.
container_local_output_path(str): The local path to store the Rule output.
s3_output_path(str): The location in S3 to store the output.
volume_size_in_gb(int): Size in GB of the EBS volume to use for storing data.
rule_parameters(dict): A dictionary of parameters for the rule.
collections_to_save([sagemaker.debugger.CollectionConfig]): Optional. A list of :class:'~sagemaker.debugger.CollectionConfig' objects to be saved.
actions:
Method sagemaker()
Initialize a “Rule“ object for a built-in debugging rule.
Usage
Rule$sagemaker( base_config, name = NULL, container_local_output_path = NULL, s3_output_path = NULL, other_trials_s3_input_paths = NULL, rule_parameters = NULL, collections_to_save = NULL, actions = NULL )
Arguments
base_config(dict): Required. This is the base rule config dictionary returned from the :class:
sagemaker.debuggermethod. For example,sagemaker.debugger::dead_relu(). For a full list of built-in rules for debugging, see 'List of Debugger Built-in Rules' https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-built-in-rules.html.name(str): Optional. The name of the debugger rule. If one is not provided, the name of the base_config will be used.
container_local_output_path(str): Optional. The local path in the rule processing container.
s3_output_path(str): Optional. The location in Amazon S3 to store the output tensors. The default Debugger output path for debugging data is created under the default output path of the :class:'~sagemaker.estimator.Estimator' class. For example, s3://sagemaker-<region>-<12digit_account_id>/<training-job-name>/debug-output/.
other_trials_s3_input_paths([str]): Optional. The Amazon S3 input paths of other trials to use the SimilarAcrossRuns rule.
rule_parameters(dict): Optional. A dictionary of parameters for the rule.
collections_to_save(:class:
sagemaker.debugger::CollectionConfig): Optional. A list of :class:sagemaker.debugger::CollectionConfigobjects to be saved.actions:
Returns
:class:'~sagemaker.debugger.Rule': An instance of the built-in rule.
Method custom()
Initialize a “Rule“ object for a *custom* debugging rule. You can create a custom rule that analyzes tensors emitted during the training of a model and monitors conditions that are critical for the success of a training job. For more information, see 'Create Debugger Custom Rules for Training Job Analysis' https://docs.aws.amazon.com/sagemaker/latest/dg/debugger-custom-rules.html.
Usage
Rule$custom( name, image_uri, instance_type, volume_size_in_gb, source = NULL, rule_to_invoke = NULL, container_local_output_path = NULL, s3_output_path = NULL, other_trials_s3_input_paths = NULL, rule_parameters = NULL, collections_to_save = NULL, actions = NULL )
Arguments
name(str): Required. The name of the debugger rule.
image_uri(str): Required. The URI of the image to be used by the debugger rule.
instance_type(str): Required. Type of EC2 instance to use, for example, 'ml.c4.xlarge'.
volume_size_in_gb(int): Required. Size in GB of the EBS volume to use for storing data.
source(str): Optional. A source file containing a rule to invoke. If provided, you must also provide rule_to_invoke. This can either be an S3 uri or a local path.
rule_to_invoke(str): Optional. The name of the rule to invoke within the source. If provided, you must also provide source.
container_local_output_path(str): Optional. The local path in the container.
s3_output_path(str): Optional. The location in Amazon S3 to store the output tensors. The default Debugger output path for debugging data is created under the default output path of the :class:'~sagemaker.estimator.Estimator' class. For example, s3://sagemaker-<region>-<12digit_account_id>/<training-job-name>/debug-output/.
other_trials_s3_input_paths([str]): Optional. The Amazon S3 input paths of other trials to use the SimilarAcrossRuns rule.
rule_parameters(dict): Optional. A dictionary of parameters for the rule.
collections_to_save([sagemaker.debugger.CollectionConfig]): Optional. A list of :class:'~sagemaker.debugger.CollectionConfig' objects to be saved.
actions:
Returns
:class:'~sagemaker.debugger.Rule': The instance of the custom rule.
Method prepare_actions()
Prepare actions for Debugger Rule.
Usage
Rule$prepare_actions(training_job_name)
Arguments
training_job_name(str): The training job name. To be set as the default training job prefix for the StopTraining action if it is specified.
Method to_debugger_rule_config_list()
Generates a request dictionary using the parameters provided when initializing object.
Usage
Rule$to_debugger_rule_config_list()
Returns
dict: An portion of an API request as a dictionary.
Method clone()
The objects of this class are cloneable with this method.
Usage
Rule$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
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