SKLearn Processing Container - Error: "WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager."

Hey all,

I am trying to run the script below in the writefile titled "vw_aws_a_bijlageprofile.py". This code has worked for me using other data sources, but now I am getting the following error message from the CloudWatch Logs:

"***2022-08-24T20:09:19.708-05:00

WARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv***"

Any idea how I get around this error?

Full code below.

Thank you in advance!!!!

%%writefile vw_aws_a_bijlageprofile.py

import os
import sys
import subprocess
def install(package):
    subprocess.check_call([sys.executable, "-q", "-m", "pip", "install", package])
install('awswrangler')
install('tqdm')
install('pandas')
install('botocore')
install('ruamel.yaml')
install('pandas-profiling')
import awswrangler as wr
import pandas as pd
import numpy as np
import datetime as dt
from dateutil.relativedelta import relativedelta
from string import Template
import gc
import boto3

from pandas_profiling import ProfileReport

client = boto3.client('s3')
session = boto3.Session(region_name="eu-west-2")


def run_profile():



    query = """
    SELECT  * FROM "intl-euro-archmcc-database"."vw_aws_a_bijlage"
    ;
    """
                                        #swich table name above
        
    tableforprofile = wr.athena.read_sql_query(query,
                                            database="intl-euro-archmcc-database",
                                            boto3_session=session,
                                            ctas_approach=False,
                                            workgroup='DataScientists')
    print("read in the table queried above")

    print("got rid of missing and added a new index")

    profile_tblforprofile = ProfileReport(tableforprofile, 
                                  title="Pandas Profiling Report", 
                                  minimal=True)

    print("Generated table profile")
                                      
    return profile_tblforprofile


if __name__ == '__main__':

    profile_tblforprofile = run_profile()
    
    print("Generated outputs")

    output_path_tblforprofile = ('/opt/ml/processing/output/profile_vw_aws_a_bijlage.html')
                                    #switch profile name above
    print(output_path_tblforprofile)
    
    profile_tblforprofile.to_file(output_path_tblforprofile)

import sagemaker
from sagemaker.processing import ProcessingInput, ProcessingOutput

session = boto3.Session(region_name="eu-west-2")

bucket = 'intl-euro-uk-datascientist-prod'

prefix = 'Mark'

sm_session = sagemaker.Session(boto_session=session, default_bucket=bucket)
sm_session.upload_data(path='vw_aws_a_bijlageprofile.py',
                                bucket=bucket,
                                key_prefix=f'{prefix}/source')

import boto3
#import sagemaker
from sagemaker import get_execution_role
from sagemaker.sklearn.processing import SKLearnProcessor

region = boto3.session.Session().region_name


S3_ROOT_PATH = "s3://{}/{}".format(bucket, prefix)

role = get_execution_role()
sklearn_processor = SKLearnProcessor(framework_version='0.20.0',
                                     role=role,
                                     sagemaker_session=sm_session,
                                     instance_type='ml.m5.24xlarge',
                                     instance_count=1)

sklearn_processor.run(code='s3://{}/{}/source/vw_aws_a_bijlageprofile.py'.format(bucket, prefix),
                      inputs=[],
                      outputs=[ProcessingOutput(output_name='output',
                                                source='/opt/ml/processing/output',
                                                destination='s3://intl-euro-uk-datascientist-prod/Mark/IODataProfiles/')])