ProfilerRule: The SageMaker Debugger ProfilerRule class configures...
| ProfilerRule | R Documentation |
The SageMaker Debugger ProfilerRule class configures *profiling* rules.
Description
SageMaker Debugger profiling rules automatically analyze hardware system resource utilization and framework metrics of a training job to identify performance bottlenecks. SageMaker Debugger comes pre-packaged with built-in *profiling* rules. For example, the profiling rules can detect if GPUs are underutilized due to CPU bottlenecks or IO bottlenecks. 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 profiling rules using the Amazon SageMaker Debugger APIs.
Super class
sagemaker.common::RuleBase -> ProfilerRule
Methods
Public methods
Inherited methods
Method sagemaker()
Initialize a “ProfilerRule“ object for a *built-in* profiling rule. The rule analyzes system and framework metrics of a given training job to identify performance bottlenecks.
Usage
ProfilerRule$sagemaker( base_config, name = NULL, container_local_output_path = NULL, s3_output_path = NULL )
Arguments
base_config(
sagemaker.debugger::ProfilerReport): The base rule configuration object returned from thesagemaker.debuggermethod. For example,sagemaker.debugger::ProfilerReport$new(). 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): The name of the profiler rule. If one is not provided, the name of the base_config will be used.
container_local_output_path(str): The path in the container.
s3_output_path(str): The location in Amazon S3 to store the profiling output data. The default Debugger output path for profiling 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>/profiler-output/.
Returns
:class:'~sagemaker.debugger.ProfilerRule': The instance of the built-in ProfilerRule.
Method custom()
Initialize a “ProfilerRule“ object for a *custom* profiling rule. You can create a rule that analyzes system and framework metrics emitted during the training of a model and monitors conditions that are critical for the success of a training job.
Usage
ProfilerRule$custom( name, image_uri, instance_type, volume_size_in_gb, source = NULL, rule_to_invoke = NULL, container_local_output_path = NULL, s3_output_path = NULL, rule_parameters = NULL )
Arguments
name(str): The name of the profiler rule.
image_uri(str): The URI of the image to be used by the proflier rule.
instance_type(str): Type of EC2 instance to use, for example, 'ml.c4.xlarge'.
volume_size_in_gb(int): Size in GB of the EBS volume to use for storing data.
source(str): A source file containing a rule to invoke. If provided, you must also provide rule_to_invoke. This can either be an S3 uri or a local path.
rule_to_invoke(str): The name of the rule to invoke within the source. If provided, you must also provide the source.
container_local_output_path(str): The path in the container.
s3_output_path(str): The location in Amazon S3 to store the output. The default Debugger output path for profiling data is created under the default output path of the :class:'~sagemaker.estimator.Estimator' class. For example, s3://sagemaker-<region>-<12digit_account_id>/<training-job-name>/profiler-output/.
rule_parameters(dict): A dictionary of parameters for the rule.
Returns
:class:'~sagemaker.debugger.ProfilerRule': The instance of the custom ProfilerRule.
Method to_profiler_rule_config_list()
Generates a request dictionary using the parameters provided when initializing object.
Usage
ProfilerRule$to_profiler_rule_config_list()
Returns
lict: An portion of an API request as a dictionary.
Method clone()
The objects of this class are cloneable with this method.
Usage
ProfilerRule$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
