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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 "". 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:***" Any idea how I get around this error? Full code below. Thank you in advance!!!! ``` %%writefile 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='', 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) ``` ```'s3://{}/{}/source/'.format(bucket, prefix), inputs=[], outputs=[ProcessingOutput(output_name='output', source='/opt/ml/processing/output', destination='s3://intl-euro-uk-datascientist-prod/Mark/IODataProfiles/')]) ```
asked a month ago