Construct a SageMaker AI XGBoost estimator with the Debugger XGBoost Report rule - Amazon SageMaker AI

Construct a SageMaker AI XGBoost estimator with the Debugger XGBoost Report rule

The CreateXgboostReport rule collects the following output tensors from your training job:

  • hyperparameters – Saves at the first step.

  • metrics – Saves loss and accuracy every 5 steps.

  • feature_importance – Saves every 5 steps.

  • predictions – Saves every 5 steps.

  • labels – Saves every 5 steps.

The output tensors are saved at a default S3 bucket. For example, s3://sagemaker-<region>-<12digit_account_id>/<base-job-name>/debug-output/.

When you construct a SageMaker AI estimator for an XGBoost training job, specify the rule as shown in the following example code.

Using the SageMaker AI generic estimator
import boto3
import sagemaker
from sagemaker.estimator import Estimator
from sagemaker import image_uris
from sagemaker.debugger import Rule, rule_configs

rules=[
    Rule.sagemaker(rule_configs.create_xgboost_report())
]

region = boto3.Session().region_name
xgboost_container=sagemaker.image_uris.retrieve("xgboost", region, "1.2-1")

estimator=Estimator(
    role=sagemaker.get_execution_role()
    image_uri=xgboost_container,
    base_job_name="debugger-xgboost-report-demo",
    instance_count=1,
    instance_type="ml.m5.2xlarge",
    
    # Add the Debugger XGBoost report rule
    rules=rules
)

estimator.fit(wait=False)