def main():
sagemaker_session = LocalSession()
sagemaker_session.config = {'local': {'local_code': True}}
dummy_model_file = Path("dummy.model")
dummy_model_file.touch()
with tarfile.open("model.tar.gz", "w:gz") as tar:
tar.add(dummy_model_file.as_posix())
# For local training a dummy role will be sufficient
role = DUMMY_IAM_ROLE
model = SKLearnModel(role=role,
model_data='file://./model.tar.gz',
framework_version='0.23-1',
py_version='py3',
source_dir='code',
entry_point='inference.py')
print('Deploying endpoint in local mode')
print(
'Note: if launching for the first time in local mode, container image download might take a few minutes to complete.'
)
predictor = model.deploy(
initial_instance_count=1,
instance_type='local',
)
do_inference_on_local_endpoint(predictor)
print('About to delete the endpoint to stop paying (if in cloud mode).')
predictor.delete_endpoint(predictor.endpoint_name)
# 3. Open terminal and run the following commands:
# docker build -t sagemaker-sklearn-rf-regressor-local container/.
########################################################################################################################
import tarfile
import boto3
import pandas as pd
from sagemaker import Model, LocalSession
from sagemaker.deserializers import CSVDeserializer
from sklearn.datasets import fetch_california_housing
from sklearn.model_selection import train_test_split
from sagemaker.predictor import Predictor
from sagemaker.serializers import CSVSerializer
sagemaker_session = LocalSession()
sagemaker_session.config = {'local': {'local_code': True}}
DUMMY_IAM_ROLE = 'arn:aws:iam::111111111111:role/service-role/AmazonSageMaker-ExecutionRole-20200101T000001'
s3 = boto3.client('s3')
def main():
image_name = "sagemaker-sklearn-rf-regressor-local"
# Prepare data for model inference - we use the Boston housing dataset
print('Preparing data for model inference')
data = fetch_california_housing()
X_train, X_test, y_train, y_test = train_test_split(data.data,
data.target,
test_size=0.25,
