# Construct a SageMaker AI XGBoost estimator with the Debugger XGBoost Report rule
<a name="debugger-training-xgboost-report-estimator"></a>

The [CreateXgboostReport](debugger-built-in-rules.md#create-xgboost-report) 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.

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#### [ 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)
```

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