4-Deployment/Batch/config/solution_lab2.py [21:44]:
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sess = boto3.Session()
sm = sess.client('sagemaker')
role = sagemaker.get_execution_role()

#Supress default INFO loggingd
import logging
logger = logging.getLogger()
logger.setLevel(logging.CRITICAL)

PATH = os.path.dirname(__file__)

def get_estimator_from_lab2(docker_image_name, framework_version):
    print("Getting solution from Lab 2...")
    print("Please wait 5 minutes for the training job to run.")
    
    # Set the paths for the datasets saved locally
    path_to_lab2 = "/root/sagemaker-end-to-end-workshop/2-Modeling/"
    
    local_train_path = path_to_lab2 + 'config/train.csv'
    train_df = pd.read_csv(local_train_path, header=None)

    # Let's check the validation dataset
    local_validation_path = path_to_lab2 + 'config/validation.csv'
    validation_df = pd.read_csv(local_validation_path, header=None)
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4-Deployment/RealTime/config/solution_lab2.py [21:44]:
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sess = boto3.Session()
sm = sess.client('sagemaker')
role = sagemaker.get_execution_role()

#Supress default INFO loggingd
import logging
logger = logging.getLogger()
logger.setLevel(logging.CRITICAL)

PATH = os.path.dirname(__file__)

def get_estimator_from_lab2(docker_image_name, framework_version):
    print("Getting solution from Lab 2...")
    print("Please wait 5 minutes for the training job to run.")
    
    # Set the paths for the datasets saved locally
    path_to_lab2 = "/root/sagemaker-end-to-end-workshop/2-Modeling/"
    
    local_train_path = path_to_lab2 + 'config/train.csv'
    train_df = pd.read_csv(local_train_path, header=None)

    # Let's check the validation dataset
    local_validation_path = path_to_lab2 + 'config/validation.csv'
    validation_df = pd.read_csv(local_validation_path, header=None)
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