Registred model.

Jenkinsfile_train

@@ -35,10 +35,10 @@ pipeline {
                         sh 'chmod +x dlgssdpytorch.py'
                         sh 'python3 ./dlgssdpytorch.py $PARAMETRY'
                         sh 'chmod +x train_mlflow.py'
-						//sh 'chmod +x generate_MLmodel.py'
-						//sh 'python3 ./generate_MLmodel.py'
 						sh 'python3 ./train_mlflow.py -e 5'
 						//sh 'mlflow run --experiment-name s426206 .' //Uruchamiany projekt nie moze znajdowac sie w katalogach z wielkimi literami.
+						sh 'chmod +x generate_MLmodel.py'
+						sh 'python3 ./generate_MLmodel.py -e 5'
                     }
                 }
 			}

MLproject

@@ -4,7 +4,6 @@ name: 426206mlflow
     
 docker_env:
   image: rokoch/ium:01
-  volumes: ["/etc/passwd:/etc/passwd"]
 
 entry_points:
   main:

generate_MLmodel.py

@@ -1,12 +1,13 @@
 import torch
 import numpy as np
+
 import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import Dataset, TensorDataset, DataLoader
+import argparse
 import mlflow
 import mlflow.pytorch
 from urllib.parse import urlparse
-
 from mlflow.models.signature import infer_signature
 
 class LayerLinearRegression(nn.Module):
@@ -19,25 +20,125 @@ class LayerLinearRegression(nn.Module):
         # Now it only takes a call to the layer to make predictions
         return self.linear(x)
 
-checkpoint = torch.load('model.pt')
+if __name__ == "__main__":
 
-model = LayerLinearRegression()
+    parser = argparse.ArgumentParser(description='Program do uczenia modelu')
-#optimizer = optim.SGD(model.parameters(), lr=checkpoint['loss'])
+    parser.add_argument('-l', '--lr', type=float, default=1e-3, help="Współczynik uczenia (lr)", required=False)
+    parser.add_argument('-e', '--epochs', type=int, default=100, help="Liczba epok", required=False)
+    args = parser.parse_args()
 
-model.load_state_dict(checkpoint['model_state_dict'])
+    lr = args.lr
+    n_epochs = args.epochs
+    mlflow.set_experiment("s426206")
+    with mlflow.start_run():
+        mlflow.log_param("lr", lr)
+        mlflow.log_param("epochs", n_epochs)
 
         train_dataset = torch.load('train_dataset.pt')
+        #val_dataset = torch.load('val_dataset.pt')
+
+        train_loader = DataLoader(dataset=train_dataset)
+        #val_loader = DataLoader(dataset=val_dataset)
+        
+        model = LayerLinearRegression()
+        # Checks model's parameters
+        #print(model.state_dict())   
+
+        loss_fn = nn.MSELoss(reduction='mean')
+        optimizer = optim.SGD(model.parameters(), lr=lr)
+
+        def make_train_step(model, loss_fn, optimizer):
+            # Builds function that performs a step in the train loop
+            def train_step(x, y):
+                # Sets model to TRAIN mode
+                model.train()
+                # Makes predictions
+                yhat = model(x)
+                # Computes loss
+                loss = loss_fn(y, yhat)
+                # Computes gradients
+                loss.backward()
+                # Updates parameters and zeroes gradients
+                optimizer.step()
+                optimizer.zero_grad()
+                # Returns the loss
+                return loss.item()
+            
+            # Returns the function that will be called inside the train loop
+            return train_step
+
+        # Creates the train_step function for our model, loss function and optimizer
+        train_step = make_train_step(model, loss_fn, optimizer)
+        training_losses = []
+        validation_losses = []
+        #print(model.state_dict())   
+        # For each epoch...
+        for epoch in range(n_epochs):
+
+            losses = []
+            # Uses loader to fetch one mini-batch for training
+            for x_batch, y_batch in train_loader:
+                # NOW, sends the mini-batch data to the device
+                # so it matches location of the MODEL
+                # x_batch = x_batch.to(device)
+                # y_batch = y_batch.to(device)
+                # One stpe of training
+                loss = train_step(x_batch, y_batch)
+                losses.append(loss)
+            training_loss = np.mean(losses)
+            training_losses.append(training_loss)
+
+            mlflow.log_metric("MSE", training_loss)
+                
+            # After finishing training steps for all mini-batches,
+            # it is time for evaluation!
+            # Ewaluacja jest już tutaj nie potrzebna bo odbywa sie w evaluation.py. Można jednak włączyć podgląd ewaluacji dla poszczególnych epok.    
+            # # We tell PyTorch to NOT use autograd...
+            # # Do you remember why?
+            # with torch.no_grad():
+            #     val_losses = []
+            #     # Uses loader to fetch one mini-batch for validation
+            #     for x_val, y_val in val_loader:
+            #         # Again, sends data to same device as model
+            #         # x_val = x_val.to(device)
+            #         # y_val = y_val.to(device)
+                    
+            #         model.eval()
+            #         # Makes predictions
+            #         yhat = model(x_val)
+            #         # Computes validation loss
+            #         val_loss = loss_fn(y_val, yhat)
+            #         val_losses.append(val_loss.item())
+            #     validation_loss = np.mean(val_losses)
+            #     validation_losses.append(validation_loss)
+
+            # print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t Validation loss: {validation_loss:.3f}")
+            print(f"[{epoch+1}] Training loss: {training_loss:.3f}\t")
+
+        torch.save({
+                    'model_state_dict': model.state_dict(),
+                    'optimizer_state_dict': optimizer.state_dict(),
+                        'loss': lr,
+                    }, 'model.pt')
+
         x_train = np.array(train_dataset)[:,0] #(Sales Sum row)
         input_example = np.reshape(x_train, (-1,1))
 
         with torch.no_grad():        
             model.eval()
             siganture = infer_signature(x_train, model(torch.tensor(np.reshape(x_train, (-1,1))).float()).numpy())
+
+        #mlflow.set_experiment("s426206")
         mlflow.set_tracking_uri("http://172.17.0.1:5000")
         tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
         # print(tracking_url_type_store)
         # Model registry does not work with file store
+
         if tracking_url_type_store != "file":
-            mlflow.sklearn.log_model(model, "model", registered_model_name="s426206", signature=siganture, input_example=input_example)
+            mlflow.pytorch.log_model(model, "model", registered_model_name="s426206", signature=siganture, input_example=input_example)
         else:
-            mlflow.sklearn.log_model(model, "model", signature=siganture, input_example=input_example)
+            mlflow.pytorch.log_model(model, "model", signature=siganture, input_example=input_example)
+            mlflow.pytorch.save_model(model, "my_model", signature=siganture, input_example=input_example)
+
+        #export MLFLOW_CONDA_HOME=/home/jan/miniconda3/
+        #mlflow models serve -m my_model/

train_mlflow.py

@@ -139,3 +139,6 @@ if __name__ == "__main__":
         else:
             mlflow.pytorch.log_model(model, "model", signature=siganture, input_example=input_example)
             mlflow.pytorch.save_model(model, "my_model", signature=siganture, input_example=input_example)
+
+        #export MLFLOW_CONDA_HOME=/home/jan/miniconda3/
+        #mlflow models serve -m my_model/