diff --git a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/README.tpl.md b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/README.tpl.md
index 60aa98d..882eefe 100644
--- a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/README.tpl.md
+++ b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/README.tpl.md
@@ -116,9 +116,9 @@ The bash command will access the Dockerfile in the folder, create the image and
 
 To test the images in ECR, execute the following notebooks:
   
-- project-name/src/ml/notebooks/Sagemaker_Processor.ipynb
-- project-name/src/ml/notebooks/Sagemaker_Train.ipynb
-- project-name/src/ml/notebooks/Sagemaker_Inference.ipynb
+- project-name/src/ml/notebooks/1_Sagemaker_Processor.ipynb
+- project-name/src/ml/notebooks/2_Sagemaker_Train.ipynb
+- project-name/src/ml/notebooks/3_Sagemaker_Inference.ipynb
 
 ## Stepfunctions
 
@@ -240,5 +240,5 @@ Next, create and attach another new policy to the role you created:
 
 To create and test the Step Functions state machines, execute the following notebooks:
 
-- project-name/src/ml/notebooks/Sagemaker_StepFunctions_Train.ipynb
-- project-name/src/ml/notebooks/Sagemaker_StepFunctions_Inference.ipynb
\ No newline at end of file
+- project-name/src/ml/notebooks/4_Sagemaker_StepFunctions_Train.ipynb
+- project-name/src/ml/notebooks/5_Sagemaker_StepFunctions_Inference.ipynb
\ No newline at end of file
diff --git a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/inference/handler.py b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/inference/handler.py
index b6bdc50..b0a0ce5 100644
--- a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/inference/handler.py
+++ b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/inference/handler.py
@@ -3,9 +3,9 @@
 
 import os
 import logging
-import pandas as pd
 from joblib import load
 from six import StringIO
+import pandas as pd
 
 from ml.model.wrapper import Wrapper
 from sagemaker_inference.default_inference_handler import DefaultInferenceHandler
@@ -17,49 +17,106 @@
 # Path to access the model
 MODEL_DIR = '/opt/ml/model'
 
-def _csv_to_pandas(string_like):  # type: (str) -> pd.DataFrame
-    """Convert a CSV object to a pandas DataFrame.
-    Args:
-        string_like (str): CSV string.
-        
-    Returns:
-        (pd.DataFrame): pandas DataFrame
-    """    
+
+def _csv_to_pandas(string_like):
+    """
+    Convert a CSV object to a pandas DataFrame.
+
+    Parameters
+    ----------
+    string_like : String
+                  CSV string.
+
+    Returns
+    -------
+    pd.DataFrame : pandas DataFrame
+    """
     stream = StringIO(string_like)
     res = pd.read_csv(stream)
     return res
 
+
 class HandlerService(DefaultHandlerService, DefaultInferenceHandler):
     """
-        Execute the inference step in the virtual environment
-        
+    Execute the inference step in the virtual environment
+
     """
     def __init__(self):
         op = transformer.Transformer(default_inference_handler=self)
         super(HandlerService, self).__init__(transformer=op)
-    
-    # Loads the model from the disk
+
     def default_model_fn(self, model_dir):
-        logging.info('Loading the model')   
+        """
+        Loads the model from the disk
+
+        Parameters
+        ----------            
+        model_dir   : string
+                      Path of the model
+
+        Returns
+        -------
+        pkl : model
+        """
+        logging.info('Loading the model')
         return load(os.path.join(MODEL_DIR, "model.pkl"))
-    
-    # Parse and check the format of the input data
+
     def default_input_fn(self, input_data, content_type):
+        """
+        Parse and check the format of the input data
+
+        Parameters
+        ----------            
+        input_data   : string
+                       CSV string
+        content_type : string
+                       Type of the file
+
+        Returns
+        -------
+        pd.DataFrame : pandas DataFrame
+        """
         global colunas
         if content_type != "text/csv":
             raise Exception("Invalid content-type: %s" % content_type)
-        return _csv_to_pandas(input_data)                           
-    
-    # Run our model and do the prediction
+        return _csv_to_pandas(input_data)
+
     def default_predict_fn(self, df, model):
-        logging.info('Predicting...')        
-        resultados = model.predict(df,included_input=True)
-        logging.info('Prediction Complete')     
+        """
+        Run our model and do the prediction
+
+        Parameters
+        ----------            
+        df    : pd.DataFrame
+                Data to be predicted
+        model : pkl
+                Model to predict the data
+
+        Returns
+        -------
+        pd.DataFrame : pandas DataFrame
+        """
+        logging.info('Predicting...')
+        resultados = model.predict(df, included_input=True)
+        logging.info('Prediction Complete')
         return resultados.reset_index(drop=True).T.reset_index().T
-    
-    # Gets the prediction output and format it to be returned to the user
+
     def default_output_fn(self, prediction, accept):
-        logging.info('Saving') 
+        """
+        Gets the prediction output and format it to be returned to the user
+
+        Parameters
+        ----------            
+        prediction    : pd.DataFrame
+                        Predicted dataset
+        accept        : string
+                        Output type
+
+        Returns
+        -------
+        CSV : CSV file
+        """
+        logging.info('Saving')
         if accept != "text/csv":
             raise Exception("Invalid accept: %s" % accept)
-        return encoder.encode(prediction, accept)
\ No newline at end of file
+        return encoder.encode(prediction, accept)
diff --git a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/inference/main.py b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/inference/main.py
index 9ff9b2a..803a9e9 100644
--- a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/inference/main.py
+++ b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/inference/main.py
@@ -1,12 +1,10 @@
-import argparse
 import sys
 import os
+import argparse
 import logging
 from sagemaker_inference import model_server
 
 logging.getLogger().setLevel(logging.INFO)
 
-
 if __name__ == "__main__":
-
-    model_server.start_model_server(handler_service="serving.handler")
\ No newline at end of file
+    model_server.start_model_server(handler_service="serving.handler")
diff --git a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/processor/preprocessor.py b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/processor/preprocessor.py
index c78c24b..bb269eb 100644
--- a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/processor/preprocessor.py
+++ b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/processor/preprocessor.py
@@ -1,71 +1,101 @@
-from ml.preprocessing.preprocessing import Preprocessing
-from ml.preprocessing.dataquality import DataQuality
-from ml.data_source.spreadsheet import Spreadsheet
-import great_expectations as ge
-from datetime import date
-import pandas as pd
 import argparse
 import logging
+from datetime import date
+
+import pandas as pd
 import glob
 import json
 from joblib import dump, load
+import great_expectations as ge
+
+from ml.preprocessing.preprocessing import Preprocessing
+from ml.preprocessing.dataquality import DataQuality
+from ml.data_source.spreadsheet import Spreadsheet
 
 logging.getLogger().setLevel('INFO')
 
-if __name__=='__main__':
+path_input = '/opt/ml/processing/input/'
+path_output = '/opt/ml/processing/output/'
+date = date.today().strftime('%Y%m%d')
+
+def data_quality(df, step_train):
+    """
+    If True, it creates the DataQuality object,
+    otherwise it loads an existing one
+
+    Parameters
+    ----------            
+    df          : pd.Dataframe
+                  Train or test dataset
+    step_train  : boolean
+                  Train or test
+
     """
-        Execute the processor step in the virtual environment
-        
+    if step_train:
+        dq = DataQuality(discrete_cat_cols=['Sex', 'Pclass', 'Survived'])
+        df_ge = dq.perform(df)
+        df_ge.save_expectation_suite(path_output +
+                                     'expectations/expectations.json')
+    else:
+        df_ge = ge.dataset.PandasDataset(df)
+        ge_val = df_ge.validate(expectation_suite=path_input +
+                                'expectations/expectations.json',
+                                only_return_failures=False)
+        with open(f'{path_output}validations/{date}.json', 'w') as f:
+            json.dump(ge_val.to_json_dict(), f)
+
+
+def preprocessing(df, step_train):
+    """
+    If True, it creates the Preprocessing object,
+    otherwise it loads an existing one
+
+    Parameters
+    ----------            
+    df          : pd.Dataframe
+                  Train or test dataset
+    step_train  : boolean
+                  Train or test
+
+    """
+    if step_train:
+        norm_cols = {'min-max': ['Age']}
+        oneHot_cols = ['Pclass', 'Sex']
+        p = Preprocessing(norm_cols, oneHot_cols)
+        train, test_train = p.execute(df, step_train=True, val_size=0.2)
+        logging.info("Saving")
+        dump(p, path_output+'preprocessing/preprocessing.pkl')
+        train.to_csv(path_output+'processed/train/train.csv', index=False)
+        test_train.to_csv(path_output+'processed/val/val.csv', index=False)
+    else:
+        p = load(path_input+'preprocessing/preprocessing.pkl')
+        test = p.execute(df, step_train=False)
+        logging.info("Saving")
+        test.to_csv(path_output+'processed/inference/inference.csv',
+                    index=False)
+
+
+if __name__ == '__main__':
+    """
+    Execute the processor step in the virtual environment
+
     """
     logging.info('Starting the preprocessing')
-    
+
     # Read the step argument (train or test)
     parser = argparse.ArgumentParser()
     parser.add_argument('--step', type=str, default='train')
-    args = parser.parse_args()    
+    args = parser.parse_args()
     step_train = True if args.step == "train" else False
     logging.info(f'step_train: {step_train}')
-    
+
     logging.info('Reading the inputs')
-    file = glob.glob("/opt/ml/processing/input/raw_data/*.csv")[0]
+    file = glob.glob(path_input+"raw_data/*.csv")[0]
     logging.info(f'Reading file: {file}')
     df = Spreadsheet().get_data(file)
-      
+
     logging.info("Data Quality")
-    # If True, it creates the DataQuality object, otherwise it loads an existing one
-    if step_train:
-        dq = DataQuality(discrete_cat_cols=['Sex', 'Pclass']) 
-        df_ge = dq.perform(df, target='Survived')
-        df_ge.save_expectation_suite('/opt/ml/processing/output/expectations/expectations.json')
-    else:
-        date = date.today().strftime('%Y%m%d')
-        df_without_target = df.copy()
-        if 'Survived' in df_without_target.columns:
-            df_without_target.drop(columns=['Survived'], inplace=True)
-        df_ge = ge.dataset.PandasDataset(df_without_target)
-        ge_val = df_ge.validate(expectation_suite='/opt/ml/processing/input/expectations/expectations.json', only_return_failures=False)
-        with open(f'/opt/ml/processing/output/validations/{date}.json', 'w') as f: 
-            json.dump(ge_val.to_json_dict(), f)
+    data_quality(df, step_train)
 
     logging.info("Preprocessing")
-    # If True, it creates the Preprocessing object, otherwise it loads an existing one
-    if step_train:
-        norm_cols = {'min-max': ['Age']}
-        oneHot_cols = ['Pclass','Sex']
-        p = Preprocessing(norm_cols, oneHot_cols)
-        train, test_train = p.execute(df, step_train = True, val_size = 0.2)
-    else:
-        p = load("/opt/ml/processing/input/preprocessing/preprocessing.pkl")
-        test = p.execute(df, step_train = False)
-
-    logging.info("Saving")
-    # If True, it saves the Preprocessing to be used later in the inference step
-    if step_train:
-        dump(p, '/opt/ml/processing/output/preprocessing/preprocessing.pkl')
-        
-    # If True, it saves the train and val files, otherwise it saves only the inference file    
-    if step_train:
-        train.to_csv('/opt/ml/processing/output/processed/train/train.csv', index=False)
-        test_train.to_csv('/opt/ml/processing/output/processed/val/val.csv', index=False)
-    else:
-        test.to_csv('/opt/ml/processing/output/processed/inference/inference.csv', index=False)
\ No newline at end of file
+    preprocessing(df, step_train)
diff --git a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/model/metrics.py b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/model/metrics.py
index 34cd079..f9ed342 100644
--- a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/model/metrics.py
+++ b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/model/metrics.py
@@ -3,210 +3,220 @@
 from sklearn.metrics import make_scorer
 from sklearn.model_selection import cross_validate
 
+
 class Metrics:
-    
+
     @classmethod
     def smape(cls, A, F):
         """
-    	Calculates the smape value between the real and the predicted
-    
-    	Parameters
-    	----------            
+        Calculates the smape value between the real and the predicted
+
+        Parameters
+        ----------
         A : array
             Target values
         F : array
             Predicted values
-    
-    	Returns
-    	-------
-    	float: smape value
-    	"""
+
+        Returns
+        -------
+        float: smape value
+        """
         return 100/len(A) * np.sum(np.abs(F - A) / (np.abs(A) + np.abs(F)))
-    
+
     @classmethod
     def __custom_score(cls, y_true, y_pred):
         """
-    	Creates a custom metric
-    
-    	Parameters
-    	----------            
+        Creates a custom metric
+
+        Parameters
+        ----------
         y_true : array
                  Target values
         y_pred : array
                  Predicted values
-    
-    	Returns
-    	-------
-    	sklearn.metrics
-    	"""
+
+        Returns
+        -------
+        sklearn.metrics
+        """
         #return sklearn.metrics.fbeta_score(y_true, y_pred, 2)
         pass
-     
+
     @classmethod
     def customized(cls, y_true, y_pred):
         """
-    	Creates a custom metric
-    
-    	Parameters
-    	----------            
+        Creates a custom metric
+
+        Parameters
+        ----------
         y_true : array
                  Target values
         y_pred : array
                  Predicted values
-    
-    	Returns
-    	-------
-    	float
-    	"""
+
+        Returns
+        -------
+        float
+        """
         custom_metric = make_scorer(cls.__custom_score, greater_is_better=True)
         return custom_metric
-        
+
     @classmethod
     def mape(cls, y_true, y_pred):
         """
-    	Calculates the map value between the real and the predicted
-    
-    	Parameters
-    	----------            
+        Calculates the map value between the real and the predicted
+
+        Parameters
+        ----------
         y_true : array
                  Target values
         y_pred : array
                  Predicted values
-    
-    	Returns
-    	-------
-    	float : value of mape
-    	"""
+
+        Returns
+        -------
+        float : value of mape
+        """
         y_true, y_pred = np.array(y_true), np.array(y_pred)
         return np.mean(np.abs(((y_true+1) - (y_pred+1)) / (y_true+1))) * 100
 
-    
     @classmethod
     def regression(cls, y_true, y_pred):
         """
-    	Calculates some metrics for regression problems
-    
-    	Parameters
-    	----------            
+        Calculates some metrics for regression problems
+
+        Parameters
+        ----------
         y_true : array
                  Target values
         y_pred : array
                  Predicted values
-    
-    	Returns
-    	-------
-    	dict : metrics results
-    	"""
-        results =   {'mean_absolute_error': round(mean_absolute_error(y_true, y_pred), 7),
-                      'root_mean_squared_error': round(np.sqrt(mean_squared_error(y_true, y_pred)), 7),
-                      'r2': round(r2_score(y_true, y_pred), 7),
-                      'smape': round(cls.smape(y_true, y_pred), 7),
-                      'mape': round(cls.mape(y_true, y_pred), 7)
-                     }        
+
+        Returns
+        -------
+        dict : metrics results
+        """
+        results = {'mean_absolute_error': round(mean_absolute_error(
+            y_true, y_pred), 7),
+                    'root_mean_squared_error': round(np.sqrt(
+                          mean_squared_error(y_true, y_pred)), 7),
+                    'r2': round(r2_score(y_true, y_pred), 7),
+                    'smape': round(cls.smape(y_true, y_pred), 7),
+                    'mape': round(cls.mape(y_true, y_pred), 7)
+                     }
         return results
-        
+
     @classmethod
-    def crossvalidation(cls, model, X, y, classification: bool, cv=5, agg=np.mean):
+    def crossvalidation(cls, model, X, y, classification: bool,
+                        cv=5, agg=np.mean):
         if classification:
             if len(set(y)) > 2:
-                metrics = ['accuracy','f1_weighted', 'recall_weighted','precision_weighted']
+                metrics = ['accuracy', 'f1_weighted',
+                           'recall_weighted', 'precision_weighted']
             else:
-                metrics = ['accuracy','f1', 'recall','precision', 'roc_auc']
+                metrics = ['accuracy', 'f1', 'recall', 'precision', 'roc_auc']
         else:
-            metrics = ['mean_absolute_error', 'r2', 'root_mean_squared_error', 'smape', 'mape']
-        res_metrics = cross_validate(model, X, y, cv=cv, return_train_score=False, scoring=metrics)
-        results = {metric.replace("test_", ""): round(agg(res_metrics[metric]),7) for metric in res_metrics}
+            metrics = ['mean_absolute_error', 'r2', 'root_mean_squared_error',
+                       'smape', 'mape']
+        res_metrics = cross_validate(model, X, y, cv=cv,
+                                     return_train_score=False,
+                                     scoring=metrics)
+        results = {metric.replace("test_", ""): round(agg(
+            res_metrics[metric]), 7)
+                   for metric in res_metrics}
         return results
 
     @classmethod
     def __multiclass_classification(cls, y_true, y_pred):
         """
-    	Calculates some metrics for multiclass classification problems
-    
-    	Parameters
-    	----------            
+        Calculates some metrics for multiclass classification problems
+
+        Parameters
+        ----------
         y_true    : array
                     Target values
         y_pred    : array
                     Predicted values
-    
-    	Returns
-    	-------
-    	dict : metrics results
-    	"""
-        results =   {'accuracy': accuracy_score(y_true, y_pred),
-                      'f1': f1_score(y_true, y_pred, average='weighted'),
-                      'precision': precision_score(y_true, y_pred, average='weighted'),
-                      'recall': recall_score(y_true, y_pred, average='weighted'),
-                     }
+
+        Returns
+        -------
+        dict : metrics results
+        """
+        results = {'accuracy': accuracy_score(y_true, y_pred),
+                   'f1': f1_score(y_true, y_pred, average='weighted'),
+                   'precision': precision_score(y_true, y_pred,
+                                                average='weighted'),
+                   'recall': recall_score(y_true, y_pred,
+                                          average='weighted')}
         return results
-    
+
     @classmethod
     def __binary_classification(cls, y_true, y_pred, y_probs):
         """
-    	Calculates some metrics for binary classification problems
-    
-    	Parameters
-    	----------            
+        Calculates some metrics for binary classification problems
+
+        Parameters
+        ----------
         y_true    : array
                     Target values
         y_pred    : array
                     Predicted values
-    
-    	Returns
-    	-------
-    	dict : metrics results
-    	"""
-        results =    {'accuracy': accuracy_score(y_true, y_pred),
-                      'f1': f1_score(y_true, y_pred),
-                      'precision': precision_score(y_true, y_pred),
-                      'recall': recall_score(y_true, y_pred),
-                      'roc_auc': roc_auc_score(y_true, y_probs)
-                     }        
+
+        Returns
+        -------
+        dict : metrics results
+        """
+        results = {'accuracy': accuracy_score(y_true, y_pred),
+                   'f1': f1_score(y_true, y_pred),
+                   'precision': precision_score(y_true, y_pred),
+                   'recall': recall_score(y_true, y_pred),
+                   'roc_auc': roc_auc_score(y_true, y_probs)}
         return results
-    
+
     @classmethod
     def classification(cls, y_true, y_pred, y_probs):
         """
-    	Checks which classification method will be applied: binary or multiclass
-    
-    	Parameters
-    	----------            
+        Checks which classification method will be applied:
+        binary or multiclass
+
+        Parameters
+        ----------
         y_true    : array
                     Target values
         y_pred    : array
                     Predicted values
         y_probs   : array
                     Probabilities values
-    
-    	Returns
-    	-------
-    	dict: metrics results
-    	"""
+
+        Returns
+        -------
+        dict: metrics results
+        """
         if len(set(y_true)) > 2:
             results = cls.__multiclass_classification(y_true, y_pred)
         else:
             results = cls.__binary_classification(y_true, y_pred, y_probs)
         return results
-            
-        
+
     @classmethod
     def clusterization(cls, X, labels):
         """
-    	Calculates some metrics on clustering quality
-    
-    	Parameters
-    	----------            
+        Calculates some metrics on clustering quality
+
+        Parameters
+        ----------
         X      : array[array], shape (n_linha, n_colunas)
                  Matrix with the values that were used in the cluster
         labels : array, shape (n_linha, 1)
-                 Vector with labels selected by the clustering method (eg KMeans)
-    
-    	Returns
-    	-------
-    	dict : metrics results
-    	"""
-        results = {'silhouette': silhouette_score(X, labels, metric='euclidean'),
-                   'calinski_harabaz': calinski_harabaz_score(X, labels)
-                  }
-        return results
\ No newline at end of file
+                 Vector with labels selected by the clustering method
+                 (eg KMeans)
+
+        Returns
+        -------
+        dict : metrics results
+        """
+        results = {'silhouette': silhouette_score(X, labels,
+                                                  metric='euclidean'),
+                   'calinski_harabaz': calinski_harabaz_score(X, labels)}
+        return results
diff --git a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/model/trainer.py b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/model/trainer.py
index 1266611..e73706c 100644
--- a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/model/trainer.py
+++ b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/model/trainer.py
@@ -1,104 +1,253 @@
-from abc import ABC, abstractmethod
-from ml.model.wrapper import Wrapper
-from ml.model.metrics import Metrics
-import statsmodels.formula.api as smf
-from sklearn.model_selection import train_test_split
-import numpy as np
-
-class Trainer(ABC):
-    def __init__(self):
-        """
-    	Constructor
-    
-    	Parameters
-    	----------    
+from joblib import dump, load
+from datetime import date
+import mlflow.pyfunc
+from mlflow import pyfunc
+from interpret.ext.blackbox import TabularExplainer, MimicExplainer
+from interpret.ext.glassbox import *
+import pandas as pd
+
+from util import load_yaml, load_json
+
+
+class Wrapper(mlflow.pyfunc.PythonModel):
+    def __init__(self, model=None, metrics=None, columns=None):
+        """
+        Constructor
+
+        Parameters
+        ----------
+        model         :   object
+                          If it's just a model: enter all parameters
+                          if it is more than one model: do not enter parameters
+                          and use the add method to add each of the models
+        metrics       :   dict
+                          Dictionary with the metrics of the result
+                          of the model
+        columns       :   list
+                          list with columns names
+        Returns
+        -------
+        WrapperModel
+        """
+        self.artifacts = dict()
+        self.artifacts["model"] = model
+        self.artifacts["metrics"] = metrics
+        self.artifacts["columns"] = columns
+        self.artifacts["creation_date"] = date.today()
+
+    def predict(self, model_input, included_input=False):
+        """
+        Method that returns the result of the prediction on a dataset
+
+        Parameters
+        ----------
+        df : pd.DataFrame
+             Data to be predicted
+
+        Returns
+        -------
+        list
+        """
+        df_processed = model_input.copy()
+        model = self.artifacts["model"]
+        columns = self.artifacts["columns"]
+        result = model.predict(df_processed[columns])
+        if included_input:
+            model_input['predict'] = result
+            result = model_input
+        return result
+
+    def predict_proba(self, model_input, binary=False):
+        """
+        Method that returns the result of the prediction on a dataset
+
+        Parameters
+        ----------
+        df : pd.DataFrame
+             data to be predicted
+
+        Returns
+        -------
+        list
+        """
+        df_processed = model_input.copy()
+        model = self.artifacts["model"]
+        columns = self.artifacts["columns"]
+        if binary:
+            return model.predict_proba(df_processed[columns])[:, 1]
+        else:
+            return model.predict_proba(df_processed[columns])
+
+    def save_model(self, path):
+        """
+        Saves the model object to a specific path
+
+        Parameters
+        ----------
+        path : str
+               path where the model object will be saved
+
+        Returns
+        -------
         None
-             
-    	Returns
-    	-------
-    	Trainer
-        """
-    
-    @abstractmethod
-    def train(self):
-        """
-        Abstract method that should be implemented in every class that inherits TrainerModel
-    	Parameters
-    	----------    
+        """
+        dump(self, path)
+
+    @staticmethod
+    def load_model(path):
+        """
+        Loads the model object in a specific path
+
+        Parameters
+        ----------
+        path : str
+               path where the model object will be loaded.
+
+        Returns
+        -------
         None
-             
-    	Returns
-    	-------
-    	None
-        """
-        pass
-        
-class TrainerSklearn(Trainer):
-    
-    def train(self, train, val, y_name,
-                classification: bool, 
-                algorithm, 
-                columns = None,
-                **params):
-        """
-    	Method that builds the Sklearn model
-    
-    	Parameters
-    	----------    
-        train             : pd.Dataframe
-                            data to train the model
-        val               : pd.Dataframe
-                            data to validate the model
-        y_name            : str
-                            target name
-        algorithm         : Sklearn algorithm
-                            algorithm to be trained   
-        classification    : bool
-                            if True, classification model training takes place, otherwise Regression
-        columns           : array
-                            columns name to be used in the train
-             
-    	Returns
-    	-------
-    	Wrapper
-        """
-        model = algorithm(**params) #model        
-        y_train = train[y_name]
-        y_val = val[y_name]       
-        X_train = train[columns]
-        X_val = val[columns]                             
-        model.fit(X_train,y_train)
-        y_pred = model.predict(X_val)
-        y_probs = model.predict_proba(X_val)[:,1]
-        if classification:
-            res_metrics = Metrics.classification(y_val.values, y_pred, y_probs)
-        else:
-            res_metrics = Metrics.regression(y_val.values, y_pred)            
-        model = Wrapper(model, res_metrics, X_train.columns)
-        return model
-    
-
-class TrainerSklearnUnsupervised(Trainer):
-        
-    def train(self, X,
-                algorithm, 
-                **params):
-        """
-    	Method that builds the Sklearn model
-    
-    	Parameters
-    	----------    
-        model_name        : str
-                            model name
-             
-    	Returns
-    	-------
-    	Wrapper
-        """
-        model = algorithm(**params) #model
-        columns = list(X.columns)
-        model.fit(X)
-        labels = model.predict(X)
-        res_metrics = Metrics.clusterization(X, labels)        
-        model = Wrapper(model, res_metrics, columns)
+        """
+        model = load(path)
         return model
+
+    def save(self, path):
+        """
+        Save model as a Wrapper class
+
+        Parameters
+        ----------
+        path : str
+               path where the model object will be loaded.
+
+        Returns
+        -------
+        None
+        """
+        path_artifacts = path + "_artifacts.pkl"
+        dump(self.artifacts, path_artifacts)
+        content = load_json("config/arquivos.json")
+        conda_env = load_yaml(content["path_yaml"])
+        mlflow.pyfunc.save_model(
+            path=path,
+            python_model=self,
+            artifacts={"model": path_artifacts},
+            conda_env=conda_env,
+        )
+
+    def get_metrics(self):
+        """
+        Return metrics
+
+        Parameters
+        ----------
+        self : object Wrapper
+
+        Returns
+        -------
+        dict
+        """
+        return self.artifacts["metrics"]
+
+    def get_columns(self):
+        """
+        Return columns
+
+        Parameters
+        ----------
+        self : object Wrapper
+
+        Returns
+        -------
+        list
+        """
+        return self.artifacts["columns"]
+
+    def get_model(self):
+        """
+        Return model
+
+        Parameters
+        ----------
+        self : object Wrapper
+
+        Returns
+        -------
+        dict
+        """
+        return self.artifacts["model"]
+
+    def train_interpret(self, X, model="tabular"):
+        """
+        Train a interpret model
+
+        Parameters
+        ----------
+        self    : object Wrapper
+        X       : pd.DataFrame
+                  Data that were used in the train for interpret
+        model   : string, optional
+                  Model to use for the interpret [tabular,mimic_LGBME,
+                  mimic_Linear,mimic_SGDE,mimic_Dec_Tree]
+        Returns
+        -------
+        None
+        """
+        mimic_models = {
+            "mimic_LGBME": LGBMExplainableModel,
+            "mimic_Linear": LinearExplainableModel,
+            "mimic_SGDE": SGDExplainableModel,
+            "mimic_Dec_Tree": DecisionTreeExplainableModel,
+        }
+        if model == "tabular":
+            explainer = TabularExplainer(
+                self.artifacts["model"], X, features=self.artifacts["columns"]
+            )
+        else:
+            explainer = MimicExplainer(
+                self.artifacts["model"],
+                X,
+                mimic_models[model],
+                augment_data=True,
+                max_num_of_augmentations=10,
+                features=self.artifacts["columns"],
+            )
+        self.artifacts["explainer"] = explainer
+
+    def local_interpret(self, X, n_feat=3, norm=True):
+        """
+        Return a local interpret for each row in data
+
+        Parameters
+        ----------
+        self    : object Wrapper
+        X       : array[array], shape (n_linha, n_colunas)
+                  Matrix with the data that were used to return interpret
+        n_feat  : int, optional
+                  Number of features to return
+        norm    : bool, optional
+                  if True, do normalization in the features importances
+
+        Returns
+        -------
+        pd.DataFrame
+        """
+        local_explanation = self.artifacts["explainer"].explain_local(X)
+        n_obs = X.shape[0]
+        predictions = self.artifacts["model"].predict(X)
+        local_values = local_explanation.get_ranked_local_values()
+        local_values = [local_values[predictions[i]][i] for i in range(n_obs)]
+        local_names = local_explanation.get_ranked_local_names()
+        local_names = [local_names[predictions[i]][i] for i in range(n_obs)]
+        if norm:
+            local_values = [
+                [(i - min(l)) / (max(l) - min(l)) for i in l] for l in local_values
+            ]
+        result = [
+            (local_names[i][:n_feat] + local_values[i][:n_feat]) for i in range(n_obs)
+        ]
+        column_names = [
+            f"Importance_{item}_{str(i)}"
+            for item in ["Name", "Value"]
+            for i in range(n_feat)
+        ]
+        return pd.DataFrame(result, columns=column_names)
diff --git a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/model/wrapper.py b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/model/wrapper.py
index 8f812cf..7aeaf19 100644
--- a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/model/wrapper.py
+++ b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/model/wrapper.py
@@ -249,4 +249,4 @@ def local_interpret(self, X, n_feat=3, norm=True):
             for item in ["Name", "Value"]
             for i in range(n_feat)
         ]
-        return pd.DataFrame(result, columns=column_names)
\ No newline at end of file
+        return pd.DataFrame(result, columns=column_names)
diff --git a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/Sagemaker_Processor.ipynb b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/1_Sagemaker_Processor.ipynb
similarity index 92%
rename from hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/Sagemaker_Processor.ipynb
rename to hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/1_Sagemaker_Processor.ipynb
index ebd6aae..98961ea 100644
--- a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/Sagemaker_Processor.ipynb
+++ b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/1_Sagemaker_Processor.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "d1dd5820",
+   "id": "0080c0d0",
    "metadata": {},
    "source": [
     "# Sagemaker Processor"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "fbaaa9e6",
+   "id": "7d7b0036",
    "metadata": {},
    "source": [
     "This script generates the train, val and inference files with the processor previous uploaded in ECR."
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "864a2e0f",
+   "id": "3f2a0229",
    "metadata": {},
    "source": [
     "## Import modules"
@@ -27,7 +27,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "fa0d1522",
+   "id": "6e679a79",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -40,7 +40,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "43887859",
+   "id": "a9066e74",
    "metadata": {},
    "source": [
     "## Setup"
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "4422ac46",
+   "id": "60ec8b7b",
    "metadata": {},
    "source": [
     "Modify according to your configurations."
@@ -57,7 +57,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "4d423fcf",
+   "id": "9d9b2d23",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -68,7 +68,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "b503dba8",
+   "id": "1cd1aa77",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -80,7 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "c00d86d1",
+   "id": "464d9cec",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -91,7 +91,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "667c8bb6",
+   "id": "a0649d24",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -102,7 +102,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "3b02cf9e",
+   "id": "f71c6f3c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -114,7 +114,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "32c8ab3d",
+   "id": "db98e9a2",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -133,8 +133,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "id": "eac4ac37",
+   "execution_count": 8,
+   "id": "52ba34ff",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -146,8 +146,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "id": "1b175317",
+   "execution_count": 9,
+   "id": "b1744737",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "a9bcf199",
+   "id": "281216e9",
    "metadata": {},
    "source": [
     "## Processor - Train"
@@ -166,8 +166,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "id": "becf4d16",
+   "execution_count": 10,
+   "id": "3191cd98",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -181,8 +181,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "id": "2ccaf4a1",
+   "execution_count": 11,
+   "id": "9998dd3a",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -214,8 +214,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "id": "e0287211",
+   "execution_count": 12,
+   "id": "a0d4af1b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -228,8 +228,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
-   "id": "854dc0d7",
+   "execution_count": 13,
+   "id": "065f6fca",
    "metadata": {},
    "outputs": [
     {
@@ -237,11 +237,10 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Job Name:  hermione-processor-2021-05-25-21-03-59-873\n",
+      "Job Name:  hermione-processor-2021-07-22-19-53-22-425\n",
       "Inputs:  [{'InputName': 'raw_data', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://hermione-sagemaker/TRAIN_RAW', 'LocalPath': '/opt/ml/processing/input/raw_data', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}]\n",
       "Outputs:  [{'OutputName': 'expectations', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://hermione-sagemaker/PREPROCESSING/EXPECTATIONS', 'LocalPath': '/opt/ml/processing/output/expectations', 'S3UploadMode': 'EndOfJob'}}, {'OutputName': 'preprocessing', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://hermione-sagemaker/PREPROCESSING/PREPROCESSING', 'LocalPath': '/opt/ml/processing/output/preprocessing', 'S3UploadMode': 'EndOfJob'}}, {'OutputName': 'train_data', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://hermione-sagemaker/PREPROCESSING/TRAIN_PROCESSED', 'LocalPath': '/opt/ml/processing/output/processed/train', 'S3UploadMode': 'EndOfJob'}}, {'OutputName': 'val_data', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://hermione-sagemaker/PREPROCESSING/VAL_PROCESSED', 'LocalPath': '/opt/ml/processing/output/processed/val', 'S3UploadMode': 'EndOfJob'}}]\n",
-      "......................................................\n",
-      "\u001b[34mINFO:root:Starting the preprocessing\u001b[0m\n",
+      "......................................................\u001b[34mINFO:root:Starting the preprocessing\u001b[0m\n",
       "\u001b[34mINFO:root:step_train: True\u001b[0m\n",
       "\u001b[34mINFO:root:Reading the inputs\u001b[0m\n",
       "\u001b[34mINFO:root:Reading file: /opt/ml/processing/input/raw_data/raw_train.csv\u001b[0m\n",
@@ -255,7 +254,7 @@
       "\u001b[0m\n",
       "\u001b[34mINFO:root:Divide train and test\u001b[0m\n",
       "\u001b[34mINFO:root:Normalizing\u001b[0m\n",
-      "\u001b[34mWARNING:py.warnings:/usr/local/lib/python3.8/dist-packages/pandas/core/indexing.py:1738: SettingWithCopyWarning: \u001b[0m\n",
+      "\u001b[34mWARNING:py.warnings:/usr/local/lib/python3.8/dist-packages/pandas/core/indexing.py:1835: SettingWithCopyWarning: \u001b[0m\n",
       "\u001b[34mA value is trying to be set on a copy of a slice from a DataFrame.\u001b[0m\n",
       "\u001b[34mTry using .loc[row_indexer,col_indexer] = value instead\n",
       "\u001b[0m\n",
@@ -265,8 +264,9 @@
       "\u001b[34mINFO:root:Normalizing\u001b[0m\n",
       "\u001b[34mINFO:root:shape train (393, 7) val (99, 7)\u001b[0m\n",
       "\u001b[34mINFO:root:Saving\u001b[0m\n",
-      "CPU times: user 1.02 s, sys: 104 ms, total: 1.13 s\n",
-      "Wall time: 9min 14s\n"
+      "\n",
+      "CPU times: user 1.09 s, sys: 71.1 ms, total: 1.16 s\n",
+      "Wall time: 9min 48s\n"
      ]
     }
    ],
@@ -281,7 +281,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "0f54bf21",
+   "id": "5db80626",
    "metadata": {},
    "source": [
     "## Processor - Inference"
@@ -289,8 +289,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "bb2a86dc",
+   "execution_count": 10,
+   "id": "8d08c6c9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -310,8 +310,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "c3e8dd48",
+   "execution_count": 11,
+   "id": "4273ba95",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -332,8 +332,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "62de176e",
+   "execution_count": 12,
+   "id": "b4d816d3",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -346,8 +346,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "e9255f5a",
+   "execution_count": 13,
+   "id": "28aa9b95",
    "metadata": {},
    "outputs": [
     {
@@ -355,11 +355,10 @@
      "output_type": "stream",
      "text": [
       "\n",
-      "Job Name:  hermione-processor-2021-05-25-21-13-13-987\n",
+      "Job Name:  hermione-processor-2021-07-22-19-40-48-848\n",
       "Inputs:  [{'InputName': 'raw_data', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://hermione-sagemaker/TEST_RAW', 'LocalPath': '/opt/ml/processing/input/raw_data', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}, {'InputName': 'preprocessing', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://hermione-sagemaker/PREPROCESSING/PREPROCESSING', 'LocalPath': '/opt/ml/processing/input/preprocessing', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}, {'InputName': 'expectations', 'AppManaged': False, 'S3Input': {'S3Uri': 's3://hermione-sagemaker/PREPROCESSING/EXPECTATIONS', 'LocalPath': '/opt/ml/processing/input/expectations', 'S3DataType': 'S3Prefix', 'S3InputMode': 'File', 'S3DataDistributionType': 'FullyReplicated', 'S3CompressionType': 'None'}}]\n",
       "Outputs:  [{'OutputName': 'inference_data', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://hermione-sagemaker/PREPROCESSING/INFERENCE_PROCESSED', 'LocalPath': '/opt/ml/processing/output/processed/inference', 'S3UploadMode': 'EndOfJob'}}, {'OutputName': 'validations', 'AppManaged': False, 'S3Output': {'S3Uri': 's3://hermione-sagemaker/PREPROCESSING/VALIDATIONS', 'LocalPath': '/opt/ml/processing/output/validations', 'S3UploadMode': 'EndOfJob'}}]\n",
-      "............................................................\n",
-      "\u001b[34mINFO:root:Starting the preprocessing\u001b[0m\n",
+      "...........................................................\u001b[34mINFO:root:Starting the preprocessing\u001b[0m\n",
       "\u001b[34mINFO:root:step_train: False\u001b[0m\n",
       "\u001b[34mINFO:root:Reading the inputs\u001b[0m\n",
       "\u001b[34mINFO:root:Reading file: /opt/ml/processing/input/raw_data/raw_test.csv\u001b[0m\n",
@@ -370,8 +369,9 @@
       "\u001b[34mINFO:root:Normalizing\u001b[0m\n",
       "\u001b[34mINFO:root:shape (222, 7)\u001b[0m\n",
       "\u001b[34mINFO:root:Saving\u001b[0m\n",
-      "CPU times: user 1.19 s, sys: 38.4 ms, total: 1.23 s\n",
-      "Wall time: 10min 14s\n"
+      "\n",
+      "CPU times: user 1.18 s, sys: 39.6 ms, total: 1.22 s\n",
+      "Wall time: 10min 15s\n"
      ]
     }
    ],
diff --git a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/Sagemaker_Train.ipynb b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/2_Sagemaker_Train.ipynb
similarity index 81%
rename from hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/Sagemaker_Train.ipynb
rename to hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/2_Sagemaker_Train.ipynb
index b0a796f..5951690 100644
--- a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/Sagemaker_Train.ipynb
+++ b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/2_Sagemaker_Train.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "577c4f6b",
+   "id": "0481ea58",
    "metadata": {},
    "source": [
     "# Sagemaker Train"
@@ -10,7 +10,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "501ef5b6",
+   "id": "c14f3a6e",
    "metadata": {},
    "source": [
     "This script creates and trains the model with the uploaded image in ECR."
@@ -18,7 +18,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "e66b3975",
+   "id": "737135a7",
    "metadata": {},
    "source": [
     "## Import modules"
@@ -26,8 +26,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "d658fb44",
+   "execution_count": 1,
+   "id": "010b1646",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,7 +39,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "64036230",
+   "id": "ed6ec079",
    "metadata": {},
    "source": [
     "## Setup"
@@ -47,7 +47,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "28411012",
+   "id": "ff8d388c",
    "metadata": {},
    "source": [
     "Modify according to your configurations."
@@ -55,8 +55,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "7e937373",
+   "execution_count": 2,
+   "id": "6278a767",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -66,8 +66,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "id": "16450249",
+   "execution_count": 3,
+   "id": "1fe9ed45",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -78,8 +78,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "2e144eb8",
+   "execution_count": 4,
+   "id": "f6216acf",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -89,8 +89,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "id": "50b4a590",
+   "execution_count": 5,
+   "id": "c9a8d55b",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -100,8 +100,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "8d56e6ca",
+   "execution_count": 6,
+   "id": "f281ac39",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,8 +112,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "id": "e710ea0a",
+   "execution_count": 7,
+   "id": "4eee7169",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -127,8 +127,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "id": "f8a27026",
+   "execution_count": 8,
+   "id": "44002452",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -138,7 +138,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "b6efb8ce",
+   "id": "6aa3f5a8",
    "metadata": {},
    "source": [
     "## Train"
@@ -146,8 +146,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
-   "id": "ed9cb39b",
+   "execution_count": 9,
+   "id": "77e64d0c",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -160,8 +160,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
-   "id": "34f144e0",
+   "execution_count": 10,
+   "id": "33726510",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -174,8 +174,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "id": "a0bbbf7d",
+   "execution_count": 11,
+   "id": "1f0350b8",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,8 +185,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "id": "299813d5",
+   "execution_count": 12,
+   "id": "0832ebb9",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -213,8 +213,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
-   "id": "4ad41d36",
+   "execution_count": 13,
+   "id": "7a2931e1",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -236,26 +236,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "id": "62c1894f",
+   "execution_count": 14,
+   "id": "d12aa777",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2021-05-26 12:41:29 Starting - Starting the training job...\n",
-      "2021-05-26 12:41:52 Starting - Launching requested ML instancesProfilerReport-1622032889: InProgress\n",
-      "......\n",
-      "2021-05-26 12:42:52 Starting - Preparing the instances for training......\n",
-      "2021-05-26 12:43:52 Downloading - Downloading input data\n",
-      "2021-05-26 12:43:52 Training - Downloading the training image.....\u001b[34m2021-05-26 09:44:41,407 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
-      "\n",
-      "2021-05-26 12:45:00 Uploading - Uploading generated training model\n",
-      "2021-05-26 12:45:00 Completed - Training job completed\n",
-      "\u001b[34m2021-05-26 09:44:47,642 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
-      "\u001b[34m2021-05-26 09:44:47,653 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
-      "\u001b[34m2021-05-26 09:44:47,663 sagemaker-training-toolkit INFO     Invoking user script\n",
+      "2021-07-22 20:15:35 Starting - Starting the training job...\n",
+      "2021-07-22 20:15:59 Starting - Launching requested ML instancesProfilerReport-1626984935: InProgress\n",
+      "...\n",
+      "2021-07-22 20:16:35 Starting - Preparing the instances for training.........\n",
+      "2021-07-22 20:18:00 Downloading - Downloading input data...\n",
+      "2021-07-22 20:18:20 Training - Downloading the training image.....\u001b[34m2021-07-22 17:19:11,614 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
+      "\u001b[34m2021-07-22 17:19:11,630 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
+      "\u001b[34m2021-07-22 17:19:11,640 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
+      "\u001b[34m2021-07-22 17:19:11,648 sagemaker-training-toolkit INFO     Invoking user script\n",
       "\u001b[0m\n",
       "\u001b[34mTraining Env:\n",
       "\u001b[0m\n",
@@ -288,7 +285,7 @@
       "    },\n",
       "    \"input_dir\": \"/opt/ml/input\",\n",
       "    \"is_master\": true,\n",
-      "    \"job_name\": \"Hermione-train-2021-05-26-12-41-29-505\",\n",
+      "    \"job_name\": \"Hermione-train-2021-07-22-20-15-35-496\",\n",
       "    \"log_level\": 20,\n",
       "    \"master_hostname\": \"algo-1\",\n",
       "    \"model_dir\": \"/opt/ml/model\",\n",
@@ -332,7 +329,7 @@
       "\u001b[34mSM_NUM_GPUS=0\u001b[0m\n",
       "\u001b[34mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n",
       "\u001b[34mSM_MODULE_DIR=/opt/ml/code\u001b[0m\n",
-      "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"train\":\"/opt/ml/input/data/train\",\"validation\":\"/opt/ml/input/data/validation\"},\"current_host\":\"algo-1\",\"framework_module\":null,\"hosts\":[\"algo-1\"],\"hyperparameters\":{},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"train\":{\"ContentType\":\"text/csv\",\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"validation\":{\"ContentType\":\"text/csv\",\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"is_master\":true,\"job_name\":\"Hermione-train-2021-05-26-12-41-29-505\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"/opt/ml/code\",\"module_name\":\"train\",\"network_interface_name\":\"eth0\",\"num_cpus\":2,\"num_gpus\":0,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"train.py\"}\u001b[0m\n",
+      "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"train\":\"/opt/ml/input/data/train\",\"validation\":\"/opt/ml/input/data/validation\"},\"current_host\":\"algo-1\",\"framework_module\":null,\"hosts\":[\"algo-1\"],\"hyperparameters\":{},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"train\":{\"ContentType\":\"text/csv\",\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"validation\":{\"ContentType\":\"text/csv\",\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"is_master\":true,\"job_name\":\"Hermione-train-2021-07-22-20-15-35-496\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"/opt/ml/code\",\"module_name\":\"train\",\"network_interface_name\":\"eth0\",\"num_cpus\":2,\"num_gpus\":0,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"train.py\"}\u001b[0m\n",
       "\u001b[34mSM_USER_ARGS=[]\u001b[0m\n",
       "\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n",
       "\u001b[34mSM_CHANNEL_VALIDATION=/opt/ml/input/data/validation\u001b[0m\n",
@@ -344,21 +341,21 @@
       "\u001b[34m/usr/bin/python3 train.py\n",
       "\n",
       "\u001b[0m\n",
-      "\u001b[34m/usr/local/lib/python3.8/dist-packages/interpret_community/common/gpu_kmeans.py:30: UserWarning: cuML is required to use GPU explainers. Check https://rapids.ai/start.html         for more information on how to install it.\n",
-      "  warnings.warn(\u001b[0m\n",
-      "\u001b[34mcuML is required to use GPU explainers. Check https://rapids.ai/start.html         for more information on how to install it.\u001b[0m\n",
       "\u001b[34mINFO:root:Starting the training\u001b[0m\n",
       "\u001b[34mINFO:root:Reading the inputs\u001b[0m\n",
       "\u001b[34mINFO:root:Training the model\u001b[0m\n",
       "\u001b[34mINFO:root:Saving\u001b[0m\n",
-      "\u001b[34mINFO:root:accuracy=0.7373737373737373; f1=0.6976744186046512;  precision=0.6382978723404256;  recall=0.7692307692307693;\u001b[0m\n",
+      "\u001b[34mINFO:root:accuracy=0.7373737373737373;                      f1=0.6976744186046512;                      precision=0.6382978723404256;                      recall=0.7692307692307693;\u001b[0m\n",
       "\u001b[34mINFO:root:Training complete.\u001b[0m\n",
-      "\u001b[34m2021-05-26 09:44:51,898 sagemaker-training-toolkit INFO     Reporting training SUCCESS\u001b[0m\n",
-      "Training seconds: 85\n",
-      "Billable seconds: 36\n",
-      "Managed Spot Training savings: 57.6%\n",
-      "CPU times: user 450 ms, sys: 19.9 ms, total: 470 ms\n",
-      "Wall time: 3min 42s\n"
+      "\u001b[34m2021-07-22 17:19:17,315 sagemaker-training-toolkit INFO     Reporting training SUCCESS\u001b[0m\n",
+      "\n",
+      "2021-07-22 20:19:30 Uploading - Uploading generated training model\n",
+      "2021-07-22 20:19:30 Completed - Training job completed\n",
+      "Training seconds: 96\n",
+      "Billable seconds: 39\n",
+      "Managed Spot Training savings: 59.4%\n",
+      "CPU times: user 491 ms, sys: 48.5 ms, total: 539 ms\n",
+      "Wall time: 4min 12s\n"
      ]
     }
    ],
@@ -367,6 +364,14 @@
     "# Train the model and validate\n",
     "est.fit({'train':train_config, 'validation':val_config}, wait=True, logs=True)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bf57258c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
diff --git a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/3_Sagemaker_Inference.ipynb b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/3_Sagemaker_Inference.ipynb
new file mode 100644
index 0000000..525a5a0
--- /dev/null
+++ b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/3_Sagemaker_Inference.ipynb
@@ -0,0 +1,374 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4558d673",
+   "metadata": {},
+   "source": [
+    "# Sagemaker Inference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "733a4c1b",
+   "metadata": {},
+   "source": [
+    "This script predicts new data with the uploaded image in ECR."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73ec63de",
+   "metadata": {},
+   "source": [
+    "## Import modules"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9f4bb4b1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import time\n",
+    "import boto3\n",
+    "import sagemaker\n",
+    "from sagemaker import get_execution_role"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf4f0baf",
+   "metadata": {},
+   "source": [
+    "## Setup"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a36daf9a",
+   "metadata": {},
+   "source": [
+    "Modify according to your configurations."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "ff34a81c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Bucket name in S3\n",
+    "bucket = \"hermione-sagemaker\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "aa6732aa",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set session\n",
+    "region_name=\"us-east-1\"\n",
+    "boto3.setup_default_session(region_name=region_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0515bb41",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get user role\n",
+    "role = get_execution_role()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "ef2ae3ae",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Get AWS Account ID\n",
+    "account_number = boto3.client(\"sts\").get_caller_identity()[\"Account\"]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "31861461",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Image previous uploaded in ECR\n",
+    "image_name = \"hermione-inference\"\n",
+    "image_uri = f\"{account_number}.dkr.ecr.{region_name}.amazonaws.com/{image_name}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "1eec0163",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Input and output paths to execute inference\n",
+    "paths = {\n",
+    "    'inference_processed': f\"s3://{bucket}/PREPROCESSING/INFERENCE_PROCESSED/inference.csv\",\n",
+    "    'model': f\"s3://{bucket}/PREPROCESSING/MODEL/Hermione-train-2021-05-26-12-41-29-505/output/model.tar.gz\",\n",
+    "    'output_path': f\"s3://{bucket}/PREPROCESSING/OUTPUT\"\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "76ce3950",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# instance to run the code\n",
+    "instance_type=\"ml.m5.large\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f44e5b91",
+   "metadata": {},
+   "source": [
+    "## Inference"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "a78cd291",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Receives the processed inference data in S3\n",
+    "input_path = paths['inference_processed']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c8f2a674",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Receives the model created during the training in S3\n",
+    "model_path = paths['model']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "6ec78d16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Saves the prediction in S3\n",
+    "output_path = paths['output_path']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c167eff0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creates the model to access the ECR image\n",
+    "model = sagemaker.model.Model(\n",
+    "    image_uri= image_uri,\n",
+    "    model_data=model_path,\n",
+    "    role=role)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0b2651c1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Creates a transformer object from the trained model\n",
+    "transformer = model.transformer(\n",
+    "                          instance_count=1,\n",
+    "                          instance_type=instance_type,   \n",
+    "                          output_path=output_path,\n",
+    "                          accept = 'text/csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "1c5bd0b8",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "..........................\u001b[34mWarning: MMS is using non-default JVM parameters: -XX:-UseContainerSupport\u001b[0m\n",
+      "\u001b[35mWarning: MMS is using non-default JVM parameters: -XX:-UseContainerSupport\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,272 [INFO ] main com.amazonaws.ml.mms.ModelServer - \u001b[0m\n",
+      "\u001b[34mMMS Home: /usr/local/lib/python3.8/dist-packages\u001b[0m\n",
+      "\u001b[34mCurrent directory: /\u001b[0m\n",
+      "\u001b[34mTemp directory: /tmp\u001b[0m\n",
+      "\u001b[34mNumber of GPUs: 0\u001b[0m\n",
+      "\u001b[34mNumber of CPUs: 2\u001b[0m\n",
+      "\u001b[34mMax heap size: 1726 M\u001b[0m\n",
+      "\u001b[34mPython executable: /usr/bin/python3\u001b[0m\n",
+      "\u001b[34mConfig file: /etc/sagemaker-mms.properties\u001b[0m\n",
+      "\u001b[34mInference address: http://0.0.0.0:8080\u001b[0m\n",
+      "\u001b[34mManagement address: http://0.0.0.0:8080\u001b[0m\n",
+      "\u001b[34mModel Store: /.sagemaker/mms/models\u001b[0m\n",
+      "\u001b[34mInitial Models: ALL\u001b[0m\n",
+      "\u001b[34mLog dir: /logs\u001b[0m\n",
+      "\u001b[34mMetrics dir: /logs\u001b[0m\n",
+      "\u001b[34mNetty threads: 0\u001b[0m\n",
+      "\u001b[34mNetty client threads: 0\u001b[0m\n",
+      "\u001b[34mDefault workers per model: 2\u001b[0m\n",
+      "\u001b[34mBlacklist Regex: N/A\u001b[0m\n",
+      "\u001b[34mMaximum Response Size: 6553500\u001b[0m\n",
+      "\u001b[34mMaximum Request Size: 6553500\u001b[0m\n",
+      "\u001b[34mPreload model: false\u001b[0m\n",
+      "\u001b[34mPrefer direct buffer: false\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,384 [WARN ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerLifeCycle - attachIOStreams() threadName=W-9000-model\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,452 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - model_service_worker started with args: --sock-type unix --sock-name /tmp/.mms.sock.9000 --handler serving.handler --model-path /.sagemaker/mms/models/model --model-name model --preload-model false --tmp-dir /tmp\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,272 [INFO ] main com.amazonaws.ml.mms.ModelServer - \u001b[0m\n",
+      "\u001b[35mMMS Home: /usr/local/lib/python3.8/dist-packages\u001b[0m\n",
+      "\u001b[35mCurrent directory: /\u001b[0m\n",
+      "\u001b[35mTemp directory: /tmp\u001b[0m\n",
+      "\u001b[35mNumber of GPUs: 0\u001b[0m\n",
+      "\u001b[35mNumber of CPUs: 2\u001b[0m\n",
+      "\u001b[35mMax heap size: 1726 M\u001b[0m\n",
+      "\u001b[35mPython executable: /usr/bin/python3\u001b[0m\n",
+      "\u001b[35mConfig file: /etc/sagemaker-mms.properties\u001b[0m\n",
+      "\u001b[35mInference address: http://0.0.0.0:8080\u001b[0m\n",
+      "\u001b[35mManagement address: http://0.0.0.0:8080\u001b[0m\n",
+      "\u001b[35mModel Store: /.sagemaker/mms/models\u001b[0m\n",
+      "\u001b[35mInitial Models: ALL\u001b[0m\n",
+      "\u001b[35mLog dir: /logs\u001b[0m\n",
+      "\u001b[35mMetrics dir: /logs\u001b[0m\n",
+      "\u001b[35mNetty threads: 0\u001b[0m\n",
+      "\u001b[35mNetty client threads: 0\u001b[0m\n",
+      "\u001b[35mDefault workers per model: 2\u001b[0m\n",
+      "\u001b[35mBlacklist Regex: N/A\u001b[0m\n",
+      "\u001b[35mMaximum Response Size: 6553500\u001b[0m\n",
+      "\u001b[35mMaximum Request Size: 6553500\u001b[0m\n",
+      "\u001b[35mPreload model: false\u001b[0m\n",
+      "\u001b[35mPrefer direct buffer: false\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,384 [WARN ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerLifeCycle - attachIOStreams() threadName=W-9000-model\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,452 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - model_service_worker started with args: --sock-type unix --sock-name /tmp/.mms.sock.9000 --handler serving.handler --model-path /.sagemaker/mms/models/model --model-name model --preload-model false --tmp-dir /tmp\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,454 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Listening on port: /tmp/.mms.sock.9000\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,454 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - [PID] 24\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,455 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - MMS worker started.\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,455 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Python runtime: 3.8.10\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,456 [INFO ] main com.amazonaws.ml.mms.wlm.ModelManager - Model model loaded.\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,460 [INFO ] main com.amazonaws.ml.mms.ModelServer - Initialize Inference server with: EpollServerSocketChannel.\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,472 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Connecting to: /tmp/.mms.sock.9000\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,476 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Connecting to: /tmp/.mms.sock.9000\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,536 [INFO ] main com.amazonaws.ml.mms.ModelServer - Inference API bind to: http://0.0.0.0:8080\u001b[0m\n",
+      "\u001b[34mModel server started.\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,555 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Connection accepted: /tmp/.mms.sock.9000.\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,555 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Connection accepted: /tmp/.mms.sock.9000.\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:24,567 [WARN ] pool-2-thread-1 com.amazonaws.ml.mms.metrics.MetricCollector - worker pid is not available yet.\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,454 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Listening on port: /tmp/.mms.sock.9000\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,454 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - [PID] 24\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,455 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - MMS worker started.\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,455 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Python runtime: 3.8.10\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,456 [INFO ] main com.amazonaws.ml.mms.wlm.ModelManager - Model model loaded.\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,460 [INFO ] main com.amazonaws.ml.mms.ModelServer - Initialize Inference server with: EpollServerSocketChannel.\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,472 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Connecting to: /tmp/.mms.sock.9000\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,476 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Connecting to: /tmp/.mms.sock.9000\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,536 [INFO ] main com.amazonaws.ml.mms.ModelServer - Inference API bind to: http://0.0.0.0:8080\u001b[0m\n",
+      "\u001b[35mModel server started.\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,555 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Connection accepted: /tmp/.mms.sock.9000.\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,555 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Connection accepted: /tmp/.mms.sock.9000.\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:24,567 [WARN ] pool-2-thread-1 com.amazonaws.ml.mms.metrics.MetricCollector - worker pid is not available yet.\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:27,441 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - generated new fontManager\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:27,450 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - generated new fontManager\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:27,839 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Loading the model\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:27,854 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Loading the model\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:27,441 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - generated new fontManager\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:27,450 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - generated new fontManager\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:27,839 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Loading the model\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:27,854 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Loading the model\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:27,886 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Model model loaded io_fd=0242a9fffefeff83-0000000a-00000000-2860f330bbe7ac20-d219266e\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:27,898 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Backend response time: 3268\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:27,900 [WARN ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerLifeCycle - attachIOStreams() threadName=W-model-1\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:27,916 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Model model loaded io_fd=0242a9fffefeff83-0000000a-00000001-9aea1030bbe7ac23-7076a78a\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:27,916 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Backend response time: 3285\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:27,916 [WARN ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerLifeCycle - attachIOStreams() threadName=W-model-2\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:27,886 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Model model loaded io_fd=0242a9fffefeff83-0000000a-00000000-2860f330bbe7ac20-d219266e\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:27,898 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Backend response time: 3268\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:27,900 [WARN ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerLifeCycle - attachIOStreams() threadName=W-model-1\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:27,916 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Model model loaded io_fd=0242a9fffefeff83-0000000a-00000001-9aea1030bbe7ac23-7076a78a\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:27,916 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Backend response time: 3285\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:27,916 [WARN ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerLifeCycle - attachIOStreams() threadName=W-model-2\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:31,830 [INFO ] pool-1-thread-4 ACCESS_LOG - /169.254.255.130:60460 \"GET /ping HTTP/1.1\" 200 15\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:31,840 [INFO ] epollEventLoopGroup-3-2 ACCESS_LOG - /169.254.255.130:60464 \"GET /execution-parameters HTTP/1.1\" 404 1\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:31,965 [INFO ] W-model-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Predicting...\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:31,981 [INFO ] W-model-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Prediction Complete\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:31,983 [INFO ] W-model-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Saving\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:31,985 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Backend response time: 26\u001b[0m\n",
+      "\u001b[34m2021-07-22 20:28:31,986 [INFO ] W-9000-model ACCESS_LOG - /169.254.255.130:60468 \"POST /invocations HTTP/1.1\" 200 30\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:31,830 [INFO ] pool-1-thread-4 ACCESS_LOG - /169.254.255.130:60460 \"GET /ping HTTP/1.1\" 200 15\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:31,840 [INFO ] epollEventLoopGroup-3-2 ACCESS_LOG - /169.254.255.130:60464 \"GET /execution-parameters HTTP/1.1\" 404 1\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:31,965 [INFO ] W-model-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Predicting...\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:31,981 [INFO ] W-model-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Prediction Complete\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:31,983 [INFO ] W-model-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Saving\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:31,985 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Backend response time: 26\u001b[0m\n",
+      "\u001b[35m2021-07-22 20:28:31,986 [INFO ] W-9000-model ACCESS_LOG - /169.254.255.130:60468 \"POST /invocations HTTP/1.1\" 200 30\u001b[0m\n",
+      "\u001b[32m2021-07-22T20:28:31.846:[sagemaker logs]: MaxConcurrentTransforms=1, MaxPayloadInMB=6, BatchStrategy=MULTI_RECORD\u001b[0m\n",
+      "\n",
+      "CPU times: user 602 ms, sys: 31.4 ms, total: 634 ms\n",
+      "Wall time: 4min 43s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "# Predicts the data\n",
+    "transformer.transform(data=input_path, data_type='S3Prefix', content_type='text/csv', split_type='Line')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "79b282ec",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda_python3",
+   "language": "python",
+   "name": "conda_python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/Sagemaker_StepFunctions_Train.ipynb b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/4_Sagemaker_StepFunctions_Train.ipynb
similarity index 100%
rename from hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/Sagemaker_StepFunctions_Train.ipynb
rename to hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/4_Sagemaker_StepFunctions_Train.ipynb
diff --git a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/Sagemaker_StepFunctions_Inference.ipynb b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/5_Sagemaker_StepFunctions_Inference.ipynb
similarity index 100%
rename from hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/Sagemaker_StepFunctions_Inference.ipynb
rename to hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/5_Sagemaker_StepFunctions_Inference.ipynb
diff --git a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/Sagemaker_Inference.ipynb b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/Sagemaker_Inference.ipynb
deleted file mode 100644
index aa21796..0000000
--- a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/src/ml/notebooks/Sagemaker_Inference.ipynb
+++ /dev/null
@@ -1,322 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "616d65aa",
-   "metadata": {},
-   "source": [
-    "# Sagemaker Inference"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "aee7320a",
-   "metadata": {},
-   "source": [
-    "This script predicts new data with the uploaded image in ECR."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ea32612e",
-   "metadata": {},
-   "source": [
-    "## Import modules"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "3f188c9f",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import time\n",
-    "import boto3\n",
-    "import sagemaker\n",
-    "from sagemaker import get_execution_role"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "430e1eb4",
-   "metadata": {},
-   "source": [
-    "## Setup"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ebe50488",
-   "metadata": {},
-   "source": [
-    "Modify according to your configurations."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "8893b148",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Bucket name in S3\n",
-    "bucket = \"hermione-sagemaker\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "a6ba2451",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Set session\n",
-    "region_name=\"us-east-1\"\n",
-    "boto3.setup_default_session(region_name=region_name)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "797c5fa6",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get user role\n",
-    "role = get_execution_role()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d8148140",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Get AWS Account ID\n",
-    "account_number = boto3.client(\"sts\").get_caller_identity()[\"Account\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "1b1fba48",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Image previous uploaded in ECR\n",
-    "image_name = \"hermione-inference\"\n",
-    "image_uri = f\"{account_number}.dkr.ecr.{region_name}.amazonaws.com/{image_name}\""
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "f907e610",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Input and output paths to execute inference\n",
-    "paths = {\n",
-    "    'inference_processed': f\"s3://{bucket}/PREPROCESSING/INFERENCE_PROCESSED/inference.csv\",\n",
-    "    'model': f\"s3://{bucket}/PREPROCESSING/MODEL/Hermione-train-2021-05-26-12-41-29-505/output/model.tar.gz\",\n",
-    "    'output_path': f\"s3://{bucket}/PREPROCESSING/OUTPUT\"\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "id": "f5fdfdd8",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# instance to run the code\n",
-    "instance_type=\"ml.m5.large\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "55fe64d7",
-   "metadata": {},
-   "source": [
-    "## Inference"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "id": "60b7dc56",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Receives the processed inference data in S3\n",
-    "input_path = paths['inference_processed']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "id": "e3dc913c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Receives the model created during the training in S3\n",
-    "model_path = paths['model']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "id": "5b69f31c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Saves the prediction in S3\n",
-    "output_path = paths['output_path']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "id": "29f7ce88",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Creates the model to access the ECR image\n",
-    "model = sagemaker.model.Model(\n",
-    "    image_uri= image_uri,\n",
-    "    model_data=model_path,\n",
-    "    role=role)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "id": "aacdf22a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Creates a transformer object from the trained model\n",
-    "transformer = model.transformer(\n",
-    "                          instance_count=1,\n",
-    "                          instance_type=instance_type,   \n",
-    "                          output_path=output_path,\n",
-    "                          accept = 'text/csv')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "id": "6452e276",
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      ".........................\u001b[34m2021-05-26 12:57:00,312 [INFO ] main com.amazonaws.ml.mms.ModelServer - \u001b[0m\n",
-      "\u001b[34mMMS Home: /usr/local/lib/python3.8/dist-packages\u001b[0m\n",
-      "\u001b[34mCurrent directory: /\u001b[0m\n",
-      "\u001b[34mTemp directory: /tmp\u001b[0m\n",
-      "\u001b[34mNumber of GPUs: 0\u001b[0m\n",
-      "\u001b[34mNumber of CPUs: 2\u001b[0m\n",
-      "\u001b[34mMax heap size: 857 M\u001b[0m\n",
-      "\u001b[34mPython executable: /usr/bin/python3\u001b[0m\n",
-      "\u001b[34mConfig file: /etc/sagemaker-mms.properties\u001b[0m\n",
-      "\u001b[34mInference address: http://0.0.0.0:8080\u001b[0m\n",
-      "\u001b[34mManagement address: http://0.0.0.0:8080\u001b[0m\n",
-      "\u001b[34mModel Store: /.sagemaker/mms/models\u001b[0m\n",
-      "\u001b[34mInitial Models: ALL\u001b[0m\n",
-      "\u001b[34mLog dir: /logs\u001b[0m\n",
-      "\u001b[34mMetrics dir: /logs\u001b[0m\n",
-      "\u001b[34mNetty threads: 0\u001b[0m\n",
-      "\u001b[34mNetty client threads: 0\u001b[0m\n",
-      "\u001b[34mDefault workers per model: 2\u001b[0m\n",
-      "\u001b[34mBlacklist Regex: N/A\u001b[0m\n",
-      "\u001b[34mMaximum Response Size: 6553500\u001b[0m\n",
-      "\u001b[34mMaximum Request Size: 6553500\u001b[0m\n",
-      "\u001b[34mPreload model: false\u001b[0m\n",
-      "\u001b[34mPrefer direct buffer: false\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:00,419 [WARN ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerLifeCycle - attachIOStreams() threadName=W-9000-model\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:00,506 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - model_service_worker started with args: --sock-type unix --sock-name /tmp/.mms.sock.9000 --handler serving.handler --model-path /.sagemaker/mms/models/model --model-name model --preload-model false --tmp-dir /tmp\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:00,508 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Listening on port: /tmp/.mms.sock.9000\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:00,509 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - [PID] 23\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:00,509 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - MMS worker started.\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:00,509 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Python runtime: 3.8.5\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:00,512 [INFO ] main com.amazonaws.ml.mms.wlm.ModelManager - Model model loaded.\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:00,517 [INFO ] main com.amazonaws.ml.mms.ModelServer - Initialize Inference server with: EpollServerSocketChannel.\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:00,536 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Connecting to: /tmp/.mms.sock.9000\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:00,536 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Connecting to: /tmp/.mms.sock.9000\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:00,607 [INFO ] main com.amazonaws.ml.mms.ModelServer - Inference API bind to: http://0.0.0.0:8080\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:00,613 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Connection accepted: /tmp/.mms.sock.9000.\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:00,614 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Connection accepted: /tmp/.mms.sock.9000.\u001b[0m\n",
-      "\u001b[34mModel server started.\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:00,636 [WARN ] pool-2-thread-1 com.amazonaws.ml.mms.metrics.MetricCollector - worker pid is not available yet.\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:02,508 [WARN ] W-9000-model-stderr com.amazonaws.ml.mms.wlm.WorkerLifeCycle - /usr/local/lib/python3.8/dist-packages/interpret_community/common/gpu_kmeans.py:30: UserWarning: cuML is required to use GPU explainers. Check https://rapids.ai/start.html         for more information on how to install it.\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:02,510 [WARN ] W-9000-model-stderr com.amazonaws.ml.mms.wlm.WorkerLifeCycle -   warnings.warn(\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:02,510 [WARN ] W-9000-model-stderr com.amazonaws.ml.mms.wlm.WorkerLifeCycle - /usr/local/lib/python3.8/dist-packages/interpret_community/common/gpu_kmeans.py:30: UserWarning: cuML is required to use GPU explainers. Check https://rapids.ai/start.html         for more information on how to install it.\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:02,510 [WARN ] W-9000-model-stderr com.amazonaws.ml.mms.wlm.WorkerLifeCycle -   warnings.warn(\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:03,375 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - generated new fontManager\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:03,393 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - generated new fontManager\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:03,635 [WARN ] W-9000-model-stderr com.amazonaws.ml.mms.wlm.WorkerLifeCycle - cuML is required to use GPU explainers. Check https://rapids.ai/start.html         for more information on how to install it.\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:03,658 [WARN ] W-9000-model-stderr com.amazonaws.ml.mms.wlm.WorkerLifeCycle - cuML is required to use GPU explainers. Check https://rapids.ai/start.html         for more information on how to install it.\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:03,690 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Loading the model\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:03,715 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Loading the model\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:03,741 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Model model loaded io_fd=0242a9fffefeff83-00000009-00000002-e6c9db643cbfeb7b-a47635f7\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:03,750 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Backend response time: 3046\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:03,752 [WARN ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerLifeCycle - attachIOStreams() threadName=W-model-1\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:03,768 [INFO ] W-9000-model-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Model model loaded io_fd=0242a9fffefeff83-00000009-00000001-f549db643cbfeb7b-e2a66100\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:03,768 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Backend response time: 3065\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:03,769 [WARN ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerLifeCycle - attachIOStreams() threadName=W-model-2\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:09,272 [INFO ] pool-1-thread-4 ACCESS_LOG - /169.254.255.130:59054 \"GET /ping HTTP/1.1\" 200 11\u001b[0m\n",
-      "\u001b[35m2021-05-26 12:57:09,272 [INFO ] pool-1-thread-4 ACCESS_LOG - /169.254.255.130:59054 \"GET /ping HTTP/1.1\" 200 11\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:09,353 [INFO ] epollEventLoopGroup-3-2 ACCESS_LOG - /169.254.255.130:59058 \"GET /execution-parameters HTTP/1.1\" 404 2\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:09,462 [INFO ] W-model-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Predicting...\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:09,486 [INFO ] W-model-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Prediction Complete\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:09,491 [INFO ] W-model-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Saving\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:09,494 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Backend response time: 37\u001b[0m\n",
-      "\u001b[34m2021-05-26 12:57:09,494 [INFO ] W-9000-model ACCESS_LOG - /169.254.255.130:59068 \"POST /invocations HTTP/1.1\" 200 42\u001b[0m\n",
-      "\u001b[35m2021-05-26 12:57:09,353 [INFO ] epollEventLoopGroup-3-2 ACCESS_LOG - /169.254.255.130:59058 \"GET /execution-parameters HTTP/1.1\" 404 2\u001b[0m\n",
-      "\u001b[35m2021-05-26 12:57:09,462 [INFO ] W-model-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Predicting...\u001b[0m\n",
-      "\u001b[35m2021-05-26 12:57:09,486 [INFO ] W-model-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Prediction Complete\u001b[0m\n",
-      "\u001b[35m2021-05-26 12:57:09,491 [INFO ] W-model-1-stdout com.amazonaws.ml.mms.wlm.WorkerLifeCycle - Saving\u001b[0m\n",
-      "\u001b[35m2021-05-26 12:57:09,494 [INFO ] W-9000-model com.amazonaws.ml.mms.wlm.WorkerThread - Backend response time: 37\u001b[0m\n",
-      "\u001b[35m2021-05-26 12:57:09,494 [INFO ] W-9000-model ACCESS_LOG - /169.254.255.130:59068 \"POST /invocations HTTP/1.1\" 200 42\u001b[0m\n",
-      "\u001b[32m2021-05-26T12:57:09.364:[sagemaker logs]: MaxConcurrentTransforms=1, MaxPayloadInMB=6, BatchStrategy=MULTI_RECORD\u001b[0m\n",
-      "\n",
-      "CPU times: user 547 ms, sys: 59 ms, total: 606 ms\n",
-      "Wall time: 4min 43s\n"
-     ]
-    }
-   ],
-   "source": [
-    "%%time\n",
-    "# Predicts the data\n",
-    "transformer.transform(data=input_path, data_type='S3Prefix', content_type='text/csv', split_type='Line')"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "conda_python3",
-   "language": "python",
-   "name": "conda_python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/train/train.py b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/train/train.py
index bc7b4cd..183ee0a 100644
--- a/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/train/train.py
+++ b/hermione/module_templates/__IMPLEMENTED_SAGEMAKER__/train/train.py
@@ -2,13 +2,15 @@
 sys.path.append("src/")
 
 import os
+from util import *
 import traceback
-import pandas as pd
+
 import logging
+import pandas as pd
+
 from sklearn.metrics import *
 from ml.model.trainer import TrainerSklearn
 from sklearn.ensemble import RandomForestClassifier
-from util import *
 
 logging.getLogger().setLevel('INFO')
 
@@ -21,64 +23,80 @@
 error_path = os.path.join(prefix, 'output')
 model_path = os.environ['SM_MODEL_DIR']
 
+
+def read_input(file_path):
+    """
+    Take the set of train files and read them all
+    into a single pandas dataframe
+
+    Parameters
+    ----------            
+    file_path   : string
+                  Path of the file
+
+    Returns
+    -------
+    pd.Dataframe : pandas DataFrame
+    """
+    input_files = [os.path.join(file_path, file)
+                   for file in os.listdir(file_path)]
+    if len(input_files) == 0:
+        raise ValueError(('There are no files in {}.\n' +
+                          'This usually indicates that the channel ({}) was \
+                          incorrectly specified,\n' +
+                          'the data specification in S3 was incorrectly \
+                          specified or the role specified\n' +
+                          'does not have permission to access \
+                          the data.').format(file_path, channel_name))
+    raw_data = [pd.read_csv(file) for file in input_files]
+    return pd.concat(raw_data)
+
+
 def train():
     """
-        Execute the train step in the virtual environment
-        
+    Execute the train step in the virtual environment
+
     """
     logging.info('Starting the training')
     try:
         logging.info('Reading the inputs')
-        # Take the set of train files and read them all into a single pandas dataframe
-        input_files = [ os.path.join(training_path, file) for file in os.listdir(training_path) ]
-        if len(input_files) == 0:
-            raise ValueError(('There are no files in {}.\n' +
-                              'This usually indicates that the channel ({}) was incorrectly specified,\n' +
-                              'the data specification in S3 was incorrectly specified or the role specified\n' +
-                              'does not have permission to access the data.').format(training_path, channel_name))
-        raw_data = [ pd.read_csv(file) for file in input_files ]
-        train = pd.concat(raw_data)
-
-        # Take the set of val files and read them all into a single pandas dataframe
-        input_files = [ os.path.join(val_path, file) for file in os.listdir(val_path) ]
-        if len(input_files) == 0:
-            raise ValueError(('There are no files in {}.\n' +
-                              'This usually indicates that the channel ({}) was incorrectly specified,\n' +
-                              'the data specification in S3 was incorrectly specified or the role specified\n' +
-                              'does not have permission to access the data.').format(val_path, channel_name))
-        raw_data = [ pd.read_csv(file) for file in input_files ]
-        val = pd.concat(raw_data)
-        
+        train = read_input(training_path)
+        val = read_input(val_path)
+
         # Define the target and columns to be used in the train
         target = "Survived"
         columns = train.columns.drop(target)
 
         logging.info("Training the model")
-        model = TrainerSklearn().train(train, val, target, classification=True, 
+        model = TrainerSklearn().train(train, val, target, classification=True,
                                        algorithm=RandomForestClassifier,
                                        columns=columns)
-        
+
         # Salve the model and metrics
         logging.info("Saving")
         model.save_model(os.path.join(model_path, 'model.pkl'))
         metrics = model.artifacts["metrics"]
-        logging.info(f"accuracy={metrics['accuracy']}; f1={metrics['f1']};  precision={metrics['precision']};  recall={metrics['recall']};")
-        pd.DataFrame(model.artifacts["metrics"].items(), columns=['Metric', 'Value']).to_csv(os.path.join(model_path, 'metrics.csv'), index=False)
+        logging.info(f"accuracy={metrics['accuracy']}; \
+                     f1={metrics['f1']}; \
+                     precision={metrics['precision']}; \
+                     recall={metrics['recall']};")
+        pd.DataFrame(model.artifacts["metrics"].items(),
+                     columns=['Metric', 'Value']).to_csv(
+            os.path.join(model_path, 'metrics.csv'), index=False)
         logging.info('Training complete.')
-        
+
     except Exception as e:
-        # Write out an error file. This will be returned as the failureReason in the
-        # DescribeTrainingJob result.
+        # Write out an error file
         trc = traceback.format_exc()
         with open(os.path.join(error_path, 'failure'), 'w') as s:
             s.write('Exception during training: ' + str(e) + '\n' + trc)
-        # Printing this causes the exception to be in the training job logs, as well.
-        logging.info('Exception during training: ' + str(e) + '\n' + trc, file=sys.stderr)
-        # A non-zero exit code causes the training job to be marked as Failed.
+        logging.info('Exception during training: ' + str(e) + '\n' + trc,
+                     file=sys.stderr)
+        # A non-zero exit code causes the training job to be marked as Failed
         sys.exit(255)
 
+
 if __name__ == '__main__':
     train()
-
     # A zero exit code causes the job to be marked a Succeeded.
     sys.exit(0)