Step 4: Remove the Old Virtual Environment

Your existing environment was created using the previous Python version.

Go to:

E:
cd \sagemaker

Remove:

rmdir /s /q sm-env
Step 5: Create New Python 3.11 Virtual Environment

Create using Python 3.11 explicitly:

py -3.11 -m venv sm-env

Activate:

sm-env\Scripts\activate

Your prompt should show:

(sm-env) E:\sagemaker>
Step 6: Verify Virtual Environment Python

Run:

python --version

Expected:

Python 3.11.x

Check location:

where python

Expected first entry:

E:\sagemaker\sm-env\Scripts\python.exe
Step 7: Upgrade Build Tools

Inside (sm-env):

python -m pip install --upgrade pip setuptools wheel
Step 8: Install SageMaker SDK

Now install:

pip install sagemaker==2.243.0

Verify:

pip show sagemaker

Expected:

Name: sagemaker
Version: 2.243.0
Location: E:\sagemaker\sm-env\Lib\site-packages

Test:

python

Then:

import sagemaker

print(sagemaker.__version__)

Expected:

2.243.0
Step 9: Install Your EC2 Patch Compliance ML Packages

After SageMaker works:

pip install pandas numpy scikit-learn xgboost boto3 openpyxl jupyter

Your final environment:

E:\sagemaker

|
+-- sm-env                 (Python 3.11)
|
+-- ec2-patch-ml
       |
       +-- pipeline.py
       +-- processing.py
       +-- train.py
       +-- evaluate.py

This setup will be suitable for building your EC2 Patch Compliance Prediction SageMaker Pipeline.