add sacred

lab5/eval/Dockerfile

@@ -11,6 +11,7 @@ WORKDIR /app
 
 COPY ./eval.py ./
 COPY ./script.sh ./
+COPY ./test.csv ./
 RUN chmod +x script.sh
 
 COPY ./requirements.txt ./

lab5/train/Dockerfile

@@ -8,7 +8,6 @@ RUN apt install -y unzip >>/dev/null
 
 WORKDIR /app
 
-COPY ./train.py ./
 COPY ./script.sh ./
 RUN chmod +x script.sh
 

lab5/train/requirements.txt

@@ -2,3 +2,4 @@ numpy~=1.19.2
 pandas
 tensorflow
 keras==2.3.1
+sacred

lab5/train/train.py

@@ -1,29 +1,55 @@
+from datetime import datetime
 import pandas as pd
+from sacred import Experiment
+from sacred.observers import MongoObserver
 import sys
 import tensorflow
 from tensorflow.keras import layers
 
-X_train = pd.read_csv('train.csv')
-X_valid = pd.read_csv('valid.csv')
+ex = Experiment("470607", interactive=False, save_git_info=False)
+ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
+ex.observers.append(FileStorageObserver('my_runs'))
 
-Y_train = X_train.pop('stabf')
-Y_train = pd.get_dummies(Y_train)
+@ex.config
+def my_config():
+    learning_rate = float(sys.argv[1])
 
-Y_valid = X_valid.pop('stabf')
-Y_valid = pd.get_dummies(Y_valid)
+@ex.capture
+def prepare_train_model(learning_rate, _run):
+    _run.info["prepare_model"] = str(datetime.now())
 
-model = tensorflow.keras.Sequential([
-                          layers.Input(shape=(12,)),
-                          layers.Dense(32),
-                          layers.Dense(16),
-                          layers.Dense(2, activation='softmax')
-])
+    X_train = pd.read_csv('train.csv')
+    X_valid = pd.read_csv('valid.csv')
 
-model.compile(
-    loss=tensorflow.keras.losses.BinaryCrossentropy(),
-    optimizer=tensorflow.keras.optimizers.Adam(lr=float(sys.argv[1])),
-    metrics=[tensorflow.keras.metrics.BinaryAccuracy()])
+    Y_train = X_train.pop('stabf')
+    Y_train = pd.get_dummies(Y_train)
 
-history = model.fit(X_train, Y_train, epochs=2, validation_data=(X_valid, Y_valid))
+    Y_valid = X_valid.pop('stabf')
+    Y_valid = pd.get_dummies(Y_valid)
+
+    model = tensorflow.keras.Sequential([
+                              layers.Input(shape=(12,)),
+                              layers.Dense(32),
+                              layers.Dense(16),
+                              layers.Dense(2, activation='softmax')
+    ])
+
+    model.compile(
+        loss=tensorflow.keras.losses.BinaryCrossentropy(),
+        optimizer=tensorflow.keras.optimizers.Adam(lr=learning_rate),
+        metrics=[tensorflow.keras.metrics.BinaryAccuracy()])
+
+    history = model.fit(X_train, Y_train, epochs=2, validation_data=(X_valid, Y_valid))
+
+    model.save('grid-stability-dense.h5')
+
+    _run['history'] = history
+
+@ex.main
+def my_main(learning_rate):
+    print(prepare_train_model())
+
+
+r = ex.run()
+ex.add_artifact('grid-stability-dense.h5')
 
-model.save('grid-stability-dense.h5')