DefaultModelMonitor: DefaultModelMonitor Class
In DyfanJones/sagemaker-r-mlcore: sagemaker machine learning core classes and methods
DefaultModelMonitor R Documentation
DefaultModelMonitor Class
Description
Sets up Amazon SageMaker Monitoring Schedules and baseline suggestions. Use this class when
you want to utilize Amazon SageMaker Monitoring's plug-and-play solution that only requires
your dataset and optional pre/postprocessing scripts.
For a more customized experience, consider using the ModelMonitor class instead.
Super class
sagemaker.mlcore::ModelMonitor -> DefaultModelMonitor
Public fields
JOB_DEFINITION_BASE_NAMEModel definition base name
Methods
Public methods
Inherited methods
Method new()
Initializes a “Monitor“ instance. The Monitor handles baselining datasets and
creating Amazon SageMaker Monitoring Schedules to monitor SageMaker endpoints.
Usage
DefaultModelMonitor$new(
role,
instance_count = 1,
instance_type = "ml.m5.xlarge",
volume_size_in_gb = 30,
volume_kms_key = NULL,
output_kms_key = NULL,
max_runtime_in_seconds = NULL,
base_job_name = NULL,
sagemaker_session = NULL,
env = NULL,
tags = NULL,
network_config = NULL
)
Arguments
role(str): An AWS IAM role name or ARN. The Amazon SageMaker jobs use this role.
instance_count(int): The number of instances to run the jobs with.
instance_type(str): Type of EC2 instance to use for the job, for example,
'ml.m5.xlarge'.
volume_size_in_gb(int): Size in GB of the EBS volume
to use for storing data during processing (default: 30).
volume_kms_key(str): A KMS key for the processing volume.
output_kms_key(str): The KMS key id for the job's outputs.
max_runtime_in_seconds(int): Timeout in seconds. After this amount of
time, Amazon SageMaker terminates the job regardless of its current status.
Default: 3600
base_job_name(str): Prefix for the job name. If not specified,
a default name is generated based on the training image name and
current timestamp.
sagemaker_session(sagemaker.session.Session): Session object which
manages interactions with Amazon SageMaker APIs and any other
AWS services needed. If not specified, one is created using
the default AWS configuration chain.
env(dict): Environment variables to be passed to the job.
tags([dict]): List of tags to be passed to the job.
network_config(sagemaker.network.NetworkConfig): A NetworkConfig
object that configures network isolation, encryption of
inter-container traffic, security group IDs, and subnets.
Method monitoring_type()
Type of the monitoring job.
Usage
DefaultModelMonitor$monitoring_type()
Method suggest_baseline()
Suggest baselines for use with Amazon SageMaker Model Monitoring Schedules.
Usage
DefaultModelMonitor$suggest_baseline(
baseline_dataset,
dataset_format,
record_preprocessor_script = NULL,
post_analytics_processor_script = NULL,
output_s3_uri = NULL,
wait = TRUE,
logs = TRUE,
job_name = NULL
)
Arguments
baseline_dataset(str): The path to the baseline_dataset file. This can be a local path
or an S3 uri.
dataset_format(dict): The format of the baseline_dataset.
record_preprocessor_script(str): The path to the record preprocessor script. This can
be a local path or an S3 uri.
post_analytics_processor_script(str): The path to the record post-analytics processor
script. This can be a local path or an S3 uri.
output_s3_uri(str): Desired S3 destination Destination of the constraint_violations
and statistics json files.
Default: "s3://<default_session_bucket>/<job_name>/output"
wait(bool): Whether the call should wait until the job completes (default: True).
logs(bool): Whether to show the logs produced by the job.
Only meaningful when wait is True (default: True).
job_name(str): Processing job name. If not specified, the processor generates
a default job name, based on the image name and current timestamp.
Returns
sagemaker.processing.ProcessingJob: The ProcessingJob object representing the
baselining job.
Method create_monitoring_schedule()
Creates a monitoring schedule to monitor an Amazon SageMaker Endpoint.
If constraints and statistics are provided, or if they are able to be retrieved from a
previous baselining job associated with this monitor, those will be used.
If constraints and statistics cannot be automatically retrieved, baseline_inputs will be
required in order to kick off a baselining job.
Usage
DefaultModelMonitor$create_monitoring_schedule(
endpoint_input,
record_preprocessor_script = NULL,
post_analytics_processor_script = NULL,
output_s3_uri = NULL,
constraints = NULL,
statistics = NULL,
monitor_schedule_name = NULL,
schedule_cron_expression = NULL,
enable_cloudwatch_metrics = TRUE
)
Arguments
endpoint_input(str or sagemaker.model_monitor.EndpointInput): The endpoint to monitor.
This can either be the endpoint name or an EndpointInput.
record_preprocessor_script(str): The path to the record preprocessor script. This can
be a local path or an S3 uri.
post_analytics_processor_script(str): The path to the record post-analytics processor
script. This can be a local path or an S3 uri.
output_s3_uri(str): Desired S3 destination of the constraint_violations and
statistics json files.
Default: "s3://<default_session_bucket>/<job_name>/output"
constraints(sagemaker.model_monitor.Constraints or str): If provided alongside
statistics, these will be used for monitoring the endpoint. This can be a
sagemaker.model_monitor.Constraints object or an s3_uri pointing to a constraints
JSON file.
statistics(sagemaker.model_monitor.Statistic or str): If provided alongside
constraints, these will be used for monitoring the endpoint. This can be a
sagemaker.model_monitor.Constraints object or an s3_uri pointing to a constraints
JSON file.
monitor_schedule_name(str): Schedule name. If not specified, the processor generates
a default job name, based on the image name and current timestamp.
schedule_cron_expression(str): The cron expression that dictates the frequency that
this job run. See sagemaker.model_monitor.CronExpressionGenerator for valid
expressions. Default: Daily.
enable_cloudwatch_metrics(bool): Whether to publish cloudwatch metrics as part of
the baselining or monitoring jobs.
Method update_monitoring_schedule()
Updates the existing monitoring schedule.
Usage
DefaultModelMonitor$update_monitoring_schedule(
endpoint_input = NULL,
record_preprocessor_script = NULL,
post_analytics_processor_script = NULL,
output_s3_uri = NULL,
statistics = NULL,
constraints = NULL,
schedule_cron_expression = NULL,
instance_count = NULL,
instance_type = NULL,
volume_size_in_gb = NULL,
volume_kms_key = NULL,
output_kms_key = NULL,
max_runtime_in_seconds = NULL,
env = NULL,
network_config = NULL,
enable_cloudwatch_metrics = NULL,
role = NULL
)
Arguments
endpoint_input(str or sagemaker.model_monitor.EndpointInput): The endpoint to monitor.
This can either be the endpoint name or an EndpointInput.
record_preprocessor_script(str): The path to the record preprocessor script. This can
be a local path or an S3 uri.
post_analytics_processor_script(str): The path to the record post-analytics processor
script. This can be a local path or an S3 uri.
output_s3_uri(str): Desired S3 destination of the constraint_violations and
statistics json files.
statistics(sagemaker.model_monitor.Statistic or str): If provided alongside
constraints, these will be used for monitoring the endpoint. This can be a
sagemaker.model_monitor.Constraints object or an S3 uri pointing to a constraints
JSON file.
constraints(sagemaker.model_monitor.Constraints or str): If provided alongside
statistics, these will be used for monitoring the endpoint. This can be a
sagemaker.model_monitor.Constraints object or an S3 uri pointing to a constraints
JSON file.
schedule_cron_expression(str): The cron expression that dictates the frequency that
this job runs at. See sagemaker.model_monitor.CronExpressionGenerator for valid
expressions.
instance_count(int): The number of instances to run
the jobs with.
instance_type(str): Type of EC2 instance to use for
the job, for example, 'ml.m5.xlarge'.
volume_size_in_gb(int): Size in GB of the EBS volume
to use for storing data during processing (default: 30).
volume_kms_key(str): A KMS key for the job's volume.
output_kms_key(str): The KMS key id for the job's outputs.
max_runtime_in_seconds(int): Timeout in seconds. After this amount of
time, Amazon SageMaker terminates the job regardless of its current status.
Default: 3600
env(dict): Environment variables to be passed to the job.
network_config(sagemaker.network.NetworkConfig): A NetworkConfig
object that configures network isolation, encryption of
inter-container traffic, security group IDs, and subnets.
enable_cloudwatch_metrics(bool): Whether to publish cloudwatch metrics as part of
the baselining or monitoring jobs.
role(str): An AWS IAM role name or ARN. The Amazon SageMaker jobs use this role.
Method delete_monitoring_schedule()
Deletes the monitoring schedule and its job definition.
Usage
DefaultModelMonitor$delete_monitoring_schedule()
Method run_baseline()
'run_baseline()' is only allowed for ModelMonitor objects. Please use suggest_baseline
for DefaultModelMonitor objects, instead.
Usage
DefaultModelMonitor$run_baseline()
Method attach()
Sets this object's schedule name to point to the Amazon Sagemaker Monitoring Schedule
name provided. This allows subsequent describe_schedule or list_executions calls to point
to the given schedule.
Usage
DefaultModelMonitor$attach(monitor_schedule_name, sagemaker_session = NULL)
Arguments
monitor_schedule_name(str): The name of the schedule to attach to.
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 latest_monitoring_statistics()
Returns the sagemaker.model_monitor.Statistics generated by the latest monitoring
execution.
Usage
DefaultModelMonitor$latest_monitoring_statistics()
Returns
sagemaker.model_monitoring.Statistics: The Statistics object representing the file
generated by the latest monitoring execution.
Method latest_monitoring_constraint_violations()
Returns the sagemaker.model_monitor.ConstraintViolations generated by the latest
monitoring execution.
Usage
DefaultModelMonitor$latest_monitoring_constraint_violations()
Returns
sagemaker.model_monitoring.ConstraintViolations: The ConstraintViolations object
representing the file generated by the latest monitoring execution.
Method clone()
The objects of this class are cloneable with this method.
Usage
DefaultModelMonitor$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
DyfanJones/sagemaker-r-mlcore documentation built on May 3, 2022, 10:08 a.m.
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| DefaultModelMonitor | R Documentation |
DefaultModelMonitor Class
Description
Sets up Amazon SageMaker Monitoring Schedules and baseline suggestions. Use this class when you want to utilize Amazon SageMaker Monitoring's plug-and-play solution that only requires your dataset and optional pre/postprocessing scripts. For a more customized experience, consider using the ModelMonitor class instead.
Super class
sagemaker.mlcore::ModelMonitor -> DefaultModelMonitor
Public fields
JOB_DEFINITION_BASE_NAMEModel definition base name
Methods
Public methods
Inherited methods
Method new()
Initializes a “Monitor“ instance. The Monitor handles baselining datasets and creating Amazon SageMaker Monitoring Schedules to monitor SageMaker endpoints.
Usage
DefaultModelMonitor$new( role, instance_count = 1, instance_type = "ml.m5.xlarge", volume_size_in_gb = 30, volume_kms_key = NULL, output_kms_key = NULL, max_runtime_in_seconds = NULL, base_job_name = NULL, sagemaker_session = NULL, env = NULL, tags = NULL, network_config = NULL )
Arguments
role(str): An AWS IAM role name or ARN. The Amazon SageMaker jobs use this role.
instance_count(int): The number of instances to run the jobs with.
instance_type(str): Type of EC2 instance to use for the job, for example, 'ml.m5.xlarge'.
volume_size_in_gb(int): Size in GB of the EBS volume to use for storing data during processing (default: 30).
volume_kms_key(str): A KMS key for the processing volume.
output_kms_key(str): The KMS key id for the job's outputs.
max_runtime_in_seconds(int): Timeout in seconds. After this amount of time, Amazon SageMaker terminates the job regardless of its current status. Default: 3600
base_job_name(str): Prefix for the job name. If not specified, a default name is generated based on the training image name and current timestamp.
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.
env(dict): Environment variables to be passed to the job.
tags([dict]): List of tags to be passed to the job.
network_config(sagemaker.network.NetworkConfig): A NetworkConfig object that configures network isolation, encryption of inter-container traffic, security group IDs, and subnets.
Method monitoring_type()
Type of the monitoring job.
Usage
DefaultModelMonitor$monitoring_type()
Method suggest_baseline()
Suggest baselines for use with Amazon SageMaker Model Monitoring Schedules.
Usage
DefaultModelMonitor$suggest_baseline( baseline_dataset, dataset_format, record_preprocessor_script = NULL, post_analytics_processor_script = NULL, output_s3_uri = NULL, wait = TRUE, logs = TRUE, job_name = NULL )
Arguments
baseline_dataset(str): The path to the baseline_dataset file. This can be a local path or an S3 uri.
dataset_format(dict): The format of the baseline_dataset.
record_preprocessor_script(str): The path to the record preprocessor script. This can be a local path or an S3 uri.
post_analytics_processor_script(str): The path to the record post-analytics processor script. This can be a local path or an S3 uri.
output_s3_uri(str): Desired S3 destination Destination of the constraint_violations and statistics json files. Default: "s3://<default_session_bucket>/<job_name>/output"
wait(bool): Whether the call should wait until the job completes (default: True).
logs(bool): Whether to show the logs produced by the job. Only meaningful when wait is True (default: True).
job_name(str): Processing job name. If not specified, the processor generates a default job name, based on the image name and current timestamp.
Returns
sagemaker.processing.ProcessingJob: The ProcessingJob object representing the baselining job.
Method create_monitoring_schedule()
Creates a monitoring schedule to monitor an Amazon SageMaker Endpoint. If constraints and statistics are provided, or if they are able to be retrieved from a previous baselining job associated with this monitor, those will be used. If constraints and statistics cannot be automatically retrieved, baseline_inputs will be required in order to kick off a baselining job.
Usage
DefaultModelMonitor$create_monitoring_schedule( endpoint_input, record_preprocessor_script = NULL, post_analytics_processor_script = NULL, output_s3_uri = NULL, constraints = NULL, statistics = NULL, monitor_schedule_name = NULL, schedule_cron_expression = NULL, enable_cloudwatch_metrics = TRUE )
Arguments
endpoint_input(str or sagemaker.model_monitor.EndpointInput): The endpoint to monitor. This can either be the endpoint name or an EndpointInput.
record_preprocessor_script(str): The path to the record preprocessor script. This can be a local path or an S3 uri.
post_analytics_processor_script(str): The path to the record post-analytics processor script. This can be a local path or an S3 uri.
output_s3_uri(str): Desired S3 destination of the constraint_violations and statistics json files. Default: "s3://<default_session_bucket>/<job_name>/output"
constraints(sagemaker.model_monitor.Constraints or str): If provided alongside statistics, these will be used for monitoring the endpoint. This can be a sagemaker.model_monitor.Constraints object or an s3_uri pointing to a constraints JSON file.
statistics(sagemaker.model_monitor.Statistic or str): If provided alongside constraints, these will be used for monitoring the endpoint. This can be a sagemaker.model_monitor.Constraints object or an s3_uri pointing to a constraints JSON file.
monitor_schedule_name(str): Schedule name. If not specified, the processor generates a default job name, based on the image name and current timestamp.
schedule_cron_expression(str): The cron expression that dictates the frequency that this job run. See sagemaker.model_monitor.CronExpressionGenerator for valid expressions. Default: Daily.
enable_cloudwatch_metrics(bool): Whether to publish cloudwatch metrics as part of the baselining or monitoring jobs.
Method update_monitoring_schedule()
Updates the existing monitoring schedule.
Usage
DefaultModelMonitor$update_monitoring_schedule( endpoint_input = NULL, record_preprocessor_script = NULL, post_analytics_processor_script = NULL, output_s3_uri = NULL, statistics = NULL, constraints = NULL, schedule_cron_expression = NULL, instance_count = NULL, instance_type = NULL, volume_size_in_gb = NULL, volume_kms_key = NULL, output_kms_key = NULL, max_runtime_in_seconds = NULL, env = NULL, network_config = NULL, enable_cloudwatch_metrics = NULL, role = NULL )
Arguments
endpoint_input(str or sagemaker.model_monitor.EndpointInput): The endpoint to monitor. This can either be the endpoint name or an EndpointInput.
record_preprocessor_script(str): The path to the record preprocessor script. This can be a local path or an S3 uri.
post_analytics_processor_script(str): The path to the record post-analytics processor script. This can be a local path or an S3 uri.
output_s3_uri(str): Desired S3 destination of the constraint_violations and statistics json files.
statistics(sagemaker.model_monitor.Statistic or str): If provided alongside constraints, these will be used for monitoring the endpoint. This can be a sagemaker.model_monitor.Constraints object or an S3 uri pointing to a constraints JSON file.
constraints(sagemaker.model_monitor.Constraints or str): If provided alongside statistics, these will be used for monitoring the endpoint. This can be a sagemaker.model_monitor.Constraints object or an S3 uri pointing to a constraints JSON file.
schedule_cron_expression(str): The cron expression that dictates the frequency that this job runs at. See sagemaker.model_monitor.CronExpressionGenerator for valid expressions.
instance_count(int): The number of instances to run the jobs with.
instance_type(str): Type of EC2 instance to use for the job, for example, 'ml.m5.xlarge'.
volume_size_in_gb(int): Size in GB of the EBS volume to use for storing data during processing (default: 30).
volume_kms_key(str): A KMS key for the job's volume.
output_kms_key(str): The KMS key id for the job's outputs.
max_runtime_in_seconds(int): Timeout in seconds. After this amount of time, Amazon SageMaker terminates the job regardless of its current status. Default: 3600
env(dict): Environment variables to be passed to the job.
network_config(sagemaker.network.NetworkConfig): A NetworkConfig object that configures network isolation, encryption of inter-container traffic, security group IDs, and subnets.
enable_cloudwatch_metrics(bool): Whether to publish cloudwatch metrics as part of the baselining or monitoring jobs.
role(str): An AWS IAM role name or ARN. The Amazon SageMaker jobs use this role.
Method delete_monitoring_schedule()
Deletes the monitoring schedule and its job definition.
Usage
DefaultModelMonitor$delete_monitoring_schedule()
Method run_baseline()
'run_baseline()' is only allowed for ModelMonitor objects. Please use suggest_baseline for DefaultModelMonitor objects, instead.
Usage
DefaultModelMonitor$run_baseline()
Method attach()
Sets this object's schedule name to point to the Amazon Sagemaker Monitoring Schedule name provided. This allows subsequent describe_schedule or list_executions calls to point to the given schedule.
Usage
DefaultModelMonitor$attach(monitor_schedule_name, sagemaker_session = NULL)
Arguments
monitor_schedule_name(str): The name of the schedule to attach to.
sagemaker_session(sagemaker.session.Session): Session object which manages interactions with Amazon SageMaker APIs and any other AWS services needed. If not specified, one is created using the default AWS configuration chain.
Method latest_monitoring_statistics()
Returns the sagemaker.model_monitor.Statistics generated by the latest monitoring execution.
Usage
DefaultModelMonitor$latest_monitoring_statistics()
Returns
sagemaker.model_monitoring.Statistics: The Statistics object representing the file generated by the latest monitoring execution.
Method latest_monitoring_constraint_violations()
Returns the sagemaker.model_monitor.ConstraintViolations generated by the latest monitoring execution.
Usage
DefaultModelMonitor$latest_monitoring_constraint_violations()
Returns
sagemaker.model_monitoring.ConstraintViolations: The ConstraintViolations object representing the file generated by the latest monitoring execution.
Method clone()
The objects of this class are cloneable with this method.
Usage
DefaultModelMonitor$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
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