mlflow

MLProject

@@ -0,0 +1,8 @@
+name: Skin cancer classification
+
+conda_env: conda.yaml
+entry_points:
+  train:
+    command: "python train.py"
+  test:
+    command: "python test.py"

conda.yaml

@@ -0,0 +1,12 @@
+name: mlflow-env
+
+channels:
+  - defaults
+
+dependencies:
+  - python=3.11
+  - pip
+  - pip:
+    - mlflow
+    - torch
+    - torchvision

test.py

@@ -1,5 +1,6 @@
 import csv
 import torch
+import mlflow
 
 from torchvision import transforms, datasets
 from torch.utils.data import DataLoader
@@ -24,6 +25,9 @@ if __name__ == '__main__':
     BATCH_SIZE = 32
     MODEL_PATH = 'model.pth'
 
+    mlflow.set_experiment('Skin Cancer Detection - Test set')
+    mlflow.start_run()
+
     names = {0: 'Benign', 1: 'Malignant'}
     predictions = []
     
@@ -47,6 +51,8 @@ if __name__ == '__main__':
 
     test_accuracy = test_correct / test_total
 
+    mlflow.log_metric('test_accuracy', test_accuracy)
+
     predictions = [names[pred] for pred in predictions]
 
     print(f'Accuracy test {test_accuracy:.2%}')

train.py

@@ -1,4 +1,5 @@
 import torch
+import mlflow
 import torch.nn.functional as F
 
 from torch import nn
@@ -132,6 +133,9 @@ def train(EPOCHS: int, BATCH_SIZE: int,
         train_accuracy = train_correct / train_total
         val_accuracy = val_correct / val_total
 
+        mlflow.log_metric("train_loss", avg_loss, step=epoch)
+        mlflow.log_metric("val_loss", avg_vloss, step=epoch)
+
         print(f'Loss train {avg_loss:.3f}, loss valid {avg_vloss:.3f}')
         print(f'Accuracy train {train_accuracy:.2%}, accuracy valid {val_accuracy:.2%}')
     
@@ -152,6 +156,14 @@ if __name__ == "__main__":
     EPOCHS = 3
     LEARNING_RATE = 0.001
 
+    mlflow.set_experiment("Skin cancer classification, custom CNN model")
+    mlflow.start_run()
+
+    mlflow.log_param("IMG_SIZE", IMG_SIZE)
+    mlflow.log_param("BATCH_SIZE", BATCH_SIZE)
+    mlflow.log_param("EPOCHS", EPOCHS)
+    mlflow.log_param("LEARNING_RATE", LEARNING_RATE)
+
     train_loader, val_loader, train_size = get_data(IMG_SIZE, BATCH_SIZE, device)
 
     model = Model()
@@ -164,3 +176,4 @@ if __name__ == "__main__":
           val_loader, criterion, optimizer, train_size)
 
     torch.save(model.state_dict(), "model.pth")
+    mlflow.log_artifact("model.pth")