Cant generate XGBoost training report in sagemaker, only profiler_report

I am trying to generate the XGBoost trainingreport to see feature importances however the following code only generates the profiler report.

from sagemaker import get_execution_role
import numpy as np
import pandas as pd
from sagemaker.predictor import csv_serializer
from sagemaker.debugger import Rule, rule_configs

# Define IAM role
rules=[
    Rule.sagemaker(rule_configs.create_xgboost_report())
]
role = get_execution_role()
prefix = 'sagemaker/models'
my_region = boto3.session.Session().region_name 

# this line automatically looks for the XGBoost image URI and builds an XGBoost container.
xgboost_container = sagemaker.image_uris.retrieve("xgboost", my_region, "latest")



bucket_name = 'binary-base' 
s3 = boto3.resource('s3')
try:
    if  my_region == 'us-east-1':
      s3.create_bucket(Bucket=bucket_name)
    else: 
      s3.create_bucket(Bucket=bucket_name, CreateBucketConfiguration={ 'LocationConstraint': my_region })
    print('S3 bucket created successfully')
except Exception as e:
    print('S3 error: ',e)

boto3.Session().resource('s3').Bucket(bucket_name).Object(os.path.join(prefix, 'train/train.csv')).upload_file('../Data/Base_Model_Data_No_Labels/train.csv')
boto3.Session().resource('s3').Bucket(bucket_name).Object(os.path.join(prefix, 'validation/val.csv')).upload_file('../Data/Base_Model_Data_No_Labels/val.csv')
boto3.Session().resource('s3').Bucket(bucket_name).Object(os.path.join(prefix, 'test/test.csv')).upload_file('../Data/Base_Model_Data/test.csv'


sess = sagemaker.Session()
xgb = sagemaker.estimator.Estimator(xgboost_container,
                                    role, 
                                    volume_size =5,
                                    instance_count=1, 
                                    instance_type='ml.m4.xlarge',
                                    output_path='s3://{}/{}/output'.format(bucket_name, prefix, 'xgboost_model'),
                                    sagemaker_session=sess, 
                                    rules=rules)

xgb.set_hyperparameters(objective='binary:logistic',
                        num_round=100, 
                        scale_pos_weight=8.5)

xgb.fit({'train': s3_input_train, "validation": s3_input_val}, wait=True)

When Checking the output path via:

rule_output_path = xgb.output_path + "/" + xgb.latest_training_job.job_name + "/rule-output"
! aws s3 ls {rule_output_path} --recursive

We only see the profiler report generated

What am I doing wrong/missing? I wish to generate the XGboost Training report to see its feature importances.