sagemaker deploy model code - setup

sagemaker_setup.py

# Install sagemaker (version 1.72)
import sys
!{sys.executable} -m pip install --quiet sagemaker==1.72 -U

# Imports
import io
import os
import sys
import time
import json
from IPython.display import display
from time import strftime, gmtime
import boto3
import re
import sagemaker
from sagemaker import get_execution_role

# Get the boto3 session and sagemaker client, as well as the current execution role
sess = boto3.Session()
sm = sess.client('sagemaker')
role = sagemaker.get_execution_role()

# Name of the docker image containing the model code
docker_image_name = '<Name of docker image in registry on AWS>'

# Name and prefix for the S3 bucket storing the model output
account_id = sess.client('sts', region_name=sess.region_name).get_caller_identity()["Account"]
bucket = 'sagemaker-studio-{}-{}'.format(sess.region_name, account_id)
prefix = 'anomaly-detection'