Add sacred

Dockerfile

@@ -16,7 +16,8 @@ RUN pip3 install pandas
 RUN pip3 install seaborn
 RUN pip3 install matplotlib
 RUN pip3 install --no-cache-dir tensorflow
+RUN pip3 install sacred
+RUN pip3 install pymongo
 
 CMD ./run.sh
-CMD ./run_training.sh
 

Jenkinsfile_training

@@ -27,7 +27,7 @@ pipeline {
 		}
 		stage('Archive artifacts') {
 			steps{
-				archiveArtifacts artifacts: 'model_movies.h5'
+				archiveArtifacts artifacts: 'model_movies.h5,my_runs/**'
 			}
 		}
     }

ium_zadanie6_training.py

@@ -6,28 +6,56 @@ from tensorflow import convert_to_tensor
 import numpy as np
 import pandas as pd
 from sklearn.metrics import mean_squared_error
+from sacred.observers import FileStorageObserver, MongoObserver
+from sacred import Experiment
+from sacred.observers import MongoObserver
+from datetime import datetime
 
-movies_train = pd.read_csv('movies_train.csv')
 
-x_train = movies_train.copy()
+ex = Experiment("434684", interactive=False, save_git_info=False)
-y_train = x_train.pop('rottentomatoes_audience_score')
+ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
-x_train.pop('Unnamed: 0')
+ex.observers.append(FileStorageObserver('my_runs'))
 
-learning_rate = float(sys.argv[1])
+@ex.config
-model = Sequential()
+def my_config():
-model.add(layers.Input(shape=(22,)))
+    learning_rate = float(sys.argv[1])
-model.add(layers.Dense(64))
-model.add(layers.Dense(64))
-model.add(layers.Dense(32))
-model.add(layers.Dense(1))
 
-model.compile(loss='mean_absolute_error', optimizer=Adam(learning_rate))
 
-model.fit(
+def prepare_train_model(learning_rate, _run):
-    x = convert_to_tensor(x_train, np.float32),
+	_run.info["prepare_model"] = str(datetime.now())
-    y = y_train,
+
-    verbose=0, epochs=99)
+	movies_train = pd.read_csv('movies_train.csv')
+
+	x_train = movies_train.copy()
+	y_train = x_train.pop('rottentomatoes_audience_score')
+	x_train.pop('Unnamed: 0')
+
+	learning_rate = float(sys.argv[1])
+	model = Sequential()
+	model.add(layers.Input(shape=(22,)))
+	model.add(layers.Dense(64))
+	model.add(layers.Dense(64))
+	model.add(layers.Dense(32))
+	model.add(layers.Dense(1))
+
+	model.compile(loss='mean_absolute_error', optimizer=Adam(learning_rate))
 	
-model.save('model_movies.h5')
+	_run.info["train model"] = str(datetime.now())
 	
+	history = model.fit(
+		x = convert_to_tensor(x_train, np.float32),
+		y = y_train,
+		verbose=0, epochs=99)
+		
+	loss = history.history['loss'][-1]
+	_run.info["Loss"] = str(loss)
+	model.save('model_movies.h5')
+		
 
+@ex.main
+def my_main(learning_rate):
+    print(prepare_train_model())
+
+
+r = ex.run()
+ex.add_artifact("model_movies.h5")