sacred

Jenkinsfile

@@ -48,5 +48,16 @@ pipeline {
               }
             }
         }
+        stage('Experiments') {
+            steps {
+              script {
+               def customImage = docker.build("custom-image")
+                    customImage.inside {
+                        sh 'python3 ./sacred_model.py'
+                        archiveArtifacts artifacts: 'experiments', onlyIfSuccessful: true
+                    }
+              }
+            }
+        }
      }
 }

sacred_model.py

@@ -0,0 +1,126 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torch.utils.data import DataLoader, Dataset
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import LabelEncoder
+import torch.nn.functional as F
+from sacred import Experiment
+from sacred.observers import FileStorageObserver, MongoObserver
+ 
+
+device = (
+    "cuda"
+    if torch.cuda.is_available()
+    else "cpu"
+)
+
+ex = Experiment("464914", interactive=True, save_git_info=False)
+ex.observers.append(FileStorageObserver('experiments'))
+ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@tzietkiewicz.vm.wmi.amu.edu.pl:27017',
+                                  db_name='sacred')) 
+
+class Model(nn.Module):
+    def __init__(self, input_features=54, hidden_layer1=25, hidden_layer2=30, output_features=8):
+        super().__init__()
+        self.fc1 = nn.Linear(input_features,output_features)
+        self.bn1 = nn.BatchNorm1d(hidden_layer1)  # Add batch normalization
+        self.fc2 = nn.Linear(hidden_layer1, hidden_layer2)
+        self.bn2 = nn.BatchNorm1d(hidden_layer2)  # Add batch normalization
+        self.out = nn.Linear(hidden_layer2, output_features)
+        
+    def forward(self, x):
+        x = F.relu(self.fc1(x))  # Apply batch normalization after first linear layer
+        #x = F.relu(self.bn2(self.fc2(x)))  # Apply batch normalization after second linear layer
+        #x = self.out(x)
+        return x
+    
+@ex.capture
+def capture_params(epochs):
+    print(f"epochs: {epochs}")
+
+@ex.main
+def main(_run):
+    forest_train_ex = ex.open_resource('forest_train.csv')
+    forest_val_ex = ex.open_resource('forest_val.csv')
+
+    forest_val = pd.read_csv('forest_val.csv')
+    forest_train = pd.read_csv('forest_train.csv')
+
+    X_train = forest_train.drop(columns=['Cover_Type']).values
+    y_train = forest_train['Cover_Type'].values
+
+    X_val = forest_val.drop(columns=['Cover_Type']).values
+    y_val = forest_val['Cover_Type'].values
+
+
+    # Initialize model, loss function, and optimizer
+    model = Model().to(device)
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.Adam(model.parameters(), lr=0.001)
+
+    # Convert to PyTorch tensors
+    X_train = torch.tensor(X_train, dtype=torch.float32).to(device)
+    y_train = torch.tensor(y_train, dtype=torch.long).to(device)
+    X_val = torch.tensor(X_val, dtype=torch.float32).to(device)
+    y_val = torch.tensor(y_val, dtype=torch.long).to(device)
+
+    # Create DataLoader
+    train_loader = DataLoader(list(zip(X_train, y_train)), batch_size=64, shuffle=True)
+    val_loader = DataLoader(list(zip(X_val, y_val)), batch_size=64)
+
+    # Training loop
+    epochs = 10
+    for epoch in range(epochs):
+        model.train()  # Set model to training mode
+        running_loss = 0.0
+        for inputs, labels in train_loader:
+            inputs, labels = inputs.to(device), labels.to(device)
+
+            optimizer.zero_grad()
+
+            outputs = model(inputs)
+            loss = criterion(outputs, labels)
+            loss.backward()
+            optimizer.step()
+
+            running_loss += loss.item() * inputs.size(0)
+
+        # Calculate training loss
+        epoch_loss = running_loss / len(train_loader.dataset)
+
+        # Validation
+        model.eval()  # Set model to evaluation mode
+        val_running_loss = 0.0
+        correct = 0
+        total = 0
+        with torch.no_grad():
+            for inputs, labels in val_loader:
+                inputs, labels = inputs.to(device), labels.to(device)
+
+                outputs = model(inputs)
+                val_loss = criterion(outputs, labels)
+                val_running_loss += val_loss.item() * inputs.size(0)
+
+                _, predicted = torch.max(outputs, 1)
+                total += labels.size(0)
+                correct += (predicted == labels).sum().item()
+
+        # Calculate validation loss and accuracy
+        val_epoch_loss = val_running_loss / len(val_loader.dataset)
+        val_accuracy = correct / total
+
+        print(f"Epoch {epoch+1}/{epochs}, "
+              f"Train Loss: {epoch_loss:.4f}, "
+              f"Val Loss: {val_epoch_loss:.4f}, "
+              f"Val Accuracy: {val_accuracy:.4f}")
+        _run.log_scalar("train loss", epoch_loss)
+        _run.log_scalar("val loss", val_epoch_loss)
+        
+
+    capture_params(epochs)
+    torch.save(model.state_dict(), 'model.pth')
+    ex.add_artifact("model.pth")
+
+ex.run()