ClarifyModelMonitor: Base class of Amazon SageMaker Explainability API model...

In DyfanJones/sagemaker-r-mlcore: sagemaker machine learning core classes and methods

Base class of Amazon SageMaker Explainability API model monitors.

Description

This class is an “abstract base class“, please instantiate its subclasses if you want to monitor bias metrics or feature attribution of an endpoint.

Super class

sagemaker.mlcore::ModelMonitor -> ClarifyModelMonitor

Methods

Public methods

• ClarifyModelMonitor$new()

• ClarifyModelMonitor$run_baseline()

• ClarifyModelMonitor$latest_monitorying_statistics()

• ClarifyModelMonitor$list_executions()

• ClarifyModelMonitor$clone()

Inherited methods

• sagemaker.mlcore::ModelMonitor$attach()

• sagemaker.mlcore::ModelMonitor$baseline_statistics()

• sagemaker.mlcore::ModelMonitor$create_monitoring_schedule()

• sagemaker.mlcore::ModelMonitor$delete_monitoring_schedule()

• sagemaker.mlcore::ModelMonitor$describe_latest_baselining_job()

• sagemaker.mlcore::ModelMonitor$describe_schedule()

• sagemaker.mlcore::ModelMonitor$format()

• sagemaker.mlcore::ModelMonitor$latest_monitoring_constraint_violations()

• sagemaker.mlcore::ModelMonitor$latest_monitoring_statistics()

• sagemaker.mlcore::ModelMonitor$monitoring_type()

• sagemaker.mlcore::ModelMonitor$start_monitoring_schedule()

• sagemaker.mlcore::ModelMonitor$stop_monitoring_schedule()

• sagemaker.mlcore::ModelMonitor$suggested_constraints()

• sagemaker.mlcore::ModelMonitor$update_monitoring_schedule()

---

Method new()

Initializes a monitor instance. The monitor handles baselining datasets and creating Amazon SageMaker Monitoring Schedules to monitor SageMaker endpoints.

Usage

ClarifyModelMonitor$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. 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 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

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 run_baseline()

Not implemented. .run_baseline()' is only allowed for ModelMonitor objects. Please use 'suggest_baseline' instead.

Usage

ClarifyModelMonitor$run_baseline(...)

Arguments

...

: Unused argument

---

Method latest_monitorying_statistics()

Not implemented. The class doesn't support statistics.

Usage

ClarifyModelMonitor$latest_monitorying_statistics(...)

Arguments

...

: Unused argument

---

Method list_executions()

Get the list of the latest monitoring executions in descending order of "ScheduledTime".

Usage

ClarifyModelMonitor$list_executions()

Returns

[sagemaker.model_monitor.ClarifyMonitoringExecution]: List of ClarifyMonitoringExecution in descending order of "ScheduledTime".

---

Method clone()

The objects of this class are cloneable with this method.

Usage

ClarifyModelMonitor$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

DyfanJones/sagemaker-r-mlcore documentation built on May 3, 2022, 10:08 a.m.