sacred3

pytorch-training-eval/Dockerfile

@@ -2,7 +2,6 @@ FROM ubuntu:latest
 
 RUN apt-get update && apt-get install -y python3-pip && pip3 install setuptools && pip3 install numpy && pip3 install pandas && pip3 install wget && pip3 install scikit-learn && pip3 install matplotlib && rm -rf /var/lib/apt/lists/*
 RUN pip3 install torch torchvision torchaudio 
-RUN pip3 install sacred 
 WORKDIR /app 
 
 COPY ./../create.py ./

stroke-pytorch.py

@@ -13,10 +13,10 @@ from sacred import Experiment
 from sacred.observers import FileStorageObserver
 np.set_printoptions(suppress=False)
 
-ex = Experiment("stroke-pytorch", interactive=True) 
-ex.observers.append(FileStorageObserver('ium_s434766O_files'))
-ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@localhost:27017',
-                                  db_name='sacred'))
+# ex = Experiment("stroke-pytorch", interactive=True) 
+# ex.observers.append(FileStorageObserver('ium_s434766O_files'))
+# ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@localhost:27017',
+#                                   db_name='sacred'))
 class LogisticRegressionModel(nn.Module):
     def __init__(self, input_dim, output_dim):
         super(LogisticRegressionModel, self).__init__()
@@ -26,54 +26,54 @@ class LogisticRegressionModel(nn.Module):
         out = self.linear(x)
         return self.sigmoid(out)
 
-@ex.main
-def my_main(_log):
-    data_train = pd.read_csv("data_train.csv")
-    data_test  = pd.read_csv("data_test.csv")
-    data_val   = pd.read_csv("data_val.csv")
-    FEATURES   =  ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
+# @ex.main
+# def my_main(_log):
+data_train = pd.read_csv("data_train.csv")
+data_test  = pd.read_csv("data_test.csv")
+data_val   = pd.read_csv("data_val.csv")
+FEATURES   =  ['age','hypertension','heart_disease','ever_married', 'avg_glucose_level', 'bmi']
 
-    x_train = data_train[FEATURES].astype(np.float32)
-    y_train = data_train['stroke'].astype(np.float32)
+x_train = data_train[FEATURES].astype(np.float32)
+y_train = data_train['stroke'].astype(np.float32)
 
-    x_test = data_test[FEATURES].astype(np.float32)
-    y_test = data_test['stroke'].astype(np.float32)
+x_test = data_test[FEATURES].astype(np.float32)
+y_test = data_test['stroke'].astype(np.float32)
 
-    fTrain = torch.from_numpy(x_train.values)
-    tTrain = torch.from_numpy(y_train.values.reshape(2945,1))
+fTrain = torch.from_numpy(x_train.values)
+tTrain = torch.from_numpy(y_train.values.reshape(2945,1))
 
-    fTest= torch.from_numpy(x_test.values)
-    tTest = torch.from_numpy(y_test.values)
+fTest= torch.from_numpy(x_test.values)
+tTest = torch.from_numpy(y_test.values)
 
-    batch_size = int(sys.argv[1]) if len(sys.argv) > 1 else 16
-    num_epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
-    learning_rate = 0.001
-    input_dim = 6
-    output_dim = 1
-    info_params = "Batch size = " + str(batch_size) + " Epochs = " + str(num_epochs)
-    _log.info(info_params)
-    model = LogisticRegressionModel(input_dim, output_dim)
+batch_size = int(sys.argv[1]) if len(sys.argv) > 1 else 16
+num_epochs = int(sys.argv[2]) if len(sys.argv) > 2 else 5
+learning_rate = 0.001
+input_dim = 6
+output_dim = 1
+info_params = "Batch size = " + str(batch_size) + " Epochs = " + str(num_epochs)
+# _log.info(info_params)
+model = LogisticRegressionModel(input_dim, output_dim)
 
-    criterion = torch.nn.BCELoss(reduction='mean') 
-    optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
+criterion = torch.nn.BCELoss(reduction='mean') 
+optimizer = torch.optim.SGD(model.parameters(), lr = learning_rate)
 
-    for epoch in range(num_epochs):
-        # print ("Epoch #",epoch)
-        model.train()
-        optimizer.zero_grad()
-        # Forward pass
-        y_pred = model(fTrain)
-        # Compute Loss
-        loss = criterion(y_pred, tTrain)
-        # print(loss.item())
-        # Backward pass
-        loss.backward()
-        optimizer.step()
-    info_loss = "Last loss = " + str(loss.item())
-    _log.info(info_loss)
-    y_pred = model(fTest)
-    # print("predicted Y value: ", y_pred.data)
+for epoch in range(num_epochs):
+    # print ("Epoch #",epoch)
+    model.train()
+    optimizer.zero_grad()
+    # Forward pass
+    y_pred = model(fTrain)
+    # Compute Loss
+    loss = criterion(y_pred, tTrain)
+    # print(loss.item())
+    # Backward pass
+    loss.backward()
+    optimizer.step()
+info_loss = "Last loss = " + str(loss.item())
+    # _log.info(info_loss)
+y_pred = model(fTest)
+print("predicted Y value: ", y_pred.data)
 
-    torch.save(model.state_dict(), 'stroke.pth')
+torch.save(model.state_dict(), 'stroke.pth')
 
-ex.run()
+# ex.run()