diff --git a/Biblioteka_DL/dllib-sacred.py b/Biblioteka_DL/dllib-sacred.py
index 139c1eb..17d5c12 100644
--- a/Biblioteka_DL/dllib-sacred.py
+++ b/Biblioteka_DL/dllib-sacred.py
@@ -2,7 +2,6 @@ import numpy as np
 import sys
 import os
 import torch
-import mlflow
 import pandas as pd
 from torch import nn
 from torch.autograd import Variable
@@ -17,15 +16,13 @@ from sacred.observers import MongoObserver
 
 # EPOCHS = int(sys.argv[1])
 
-#ex = Experiment()
-#ex.observers.append(FileStorageObserver('my_res'))
-#ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
+ex = Experiment()
+ex.observers.append(FileStorageObserver('my_res'))
+ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@172.17.0.1:27017', db_name='sacred'))
 
-mlflow.set_experiment("s444356")
-
-#@ex.config
-#def my_config():
-#    epochs = 100
+@ex.config
+def my_config():
+    epochs = 100
 
 class Model(nn.Module):
     def __init__(self, input_dim):
@@ -245,11 +242,8 @@ def remove_list(games):
 # features_g = pd.DataFrame(features_g, dtype=np.float64)
 # features_g = features_g.to_numpy()
 
-epochs = int(sys.argv[1]) if len(sys.argv) > 20 else 20
-
-#@ex.automain
-#def my_main(epochs, _run):
-def my_main(epochs):
+@ex.automain
+def my_main(epochs, _run):
     platform = pd.read_csv('all_games.train.csv', sep=',', usecols=[1], header=None).values.tolist()
     release_date = pd.read_csv('all_games.train.csv', sep=',', usecols=[2], header=None).values.tolist()
     meta_score = pd.read_csv('all_games.train.csv', sep=',', usecols=[4], header=None).values.tolist()
@@ -301,8 +295,7 @@ def my_main(epochs):
     loss_fn = nn.CrossEntropyLoss()
     # epochs = 1000
     # epochs = epochs
-    #_run.info['epochs'] = epochs
-    mlflow.log_param("epochs", epochs)
+    _run.info['epochs'] = epochs
 
     def print_(loss):
         print ("The loss calculated: ", loss)
@@ -329,15 +322,14 @@ def my_main(epochs):
     pred = pred.detach().numpy()
 
     print("The accuracy is", accuracy_score(labels_test_g, np.argmax(pred, axis=1)))
-    #_run.info['accuracy'] = accuracy_score(labels_test_g, np.argmax(pred, axis=1))
+    _run.info['accuracy'] = accuracy_score(labels_test_g, np.argmax(pred, axis=1))
     _run.log_scalar("measure.accuracy", accuracy_score(labels_test_g, np.argmax(pred, axis=1)))
-    mlflow.log_metric("measure.accuracy", accuracy_score(labels_test_g, np.argmax(pred, axis=1)))
     pred = pd.DataFrame(pred)
 
     pred.to_csv('result.csv')
 
     # save model
     torch.save(model, "games_model.pkl")
-    #ex.add_artifact("games_model.pkl")
+    ex.add_artifact("games_model.pkl")