mlflow done.

.gitignore

@@ -5,3 +5,4 @@ metrics.tsv
 *.pt
 plot.png
 my_runs
+mlruns

Dockerfile

@@ -14,6 +14,7 @@ RUN pip3 install -r requirments.txt
 RUN pip3 install torch==1.8.1+cpu torchvision==0.9.1+cpu torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
 RUN pip3 install sacred
 RUN pip3 install pymongo
+RUN pip3 install mlflow
 # Stwórzmy w kontenerze (jeśli nie istnieje) katalog /app i przejdźmy do niego (wszystkie kolejne polecenia RUN, CMD, ENTRYPOINT, COPY i ADD będą w nim wykonywane)
 WORKDIR /app
 
@@ -30,3 +31,5 @@ COPY ./evaluation.py ./
 RUN chmod +x evaluation.py
 COPY ./plot.py ./
 RUN chmod +x plot.py
+COPY ./train_mlflow.py ./
+RUN chmod +x train_mlflow.py

Jenkinsfile_train

@@ -33,6 +33,8 @@ pipeline {
                     img.inside {
                         sh 'chmod +x dlgssdpytorch.py'
                         sh 'python3 ./dlgssdpytorch.py $PARAMETRY'
+                        sh 'chmod +x train_mlflow.py'
+                        sh 'python3 ./train_mlflow.py'
                     }
                 }
 			}

dlgssdpytorch.py

@@ -8,6 +8,7 @@ import argparse
 from sacred import Experiment
 from sacred.observers import MongoObserver, FileStorageObserver
 
+
 ex = Experiment("426206", interactive=False, save_git_info=False)
 ex.observers.append(FileStorageObserver('my_runs'))
 ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017', db_name='sacred'))
@@ -116,11 +117,11 @@ def train(lr, n_epochs, _run):
         # 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')
+    torch.save({
+                'model_state_dict': model.state_dict(),
+                'optimizer_state_dict': optimizer.state_dict(),
+                    'loss': lr,
+                }, 'model.pt')
 
 @ex.automain
 def my_main(lr, n_epochs, _run):

train_mlflow.py

@@ -0,0 +1,133 @@
+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
+
+
+class LayerLinearRegression(nn.Module):
+    def __init__(self):
+        super().__init__()
+        # Instead of our custom parameters, we use a Linear layer with single input and single output
+        self.linear = nn.Linear(1, 1)
+                
+    def forward(self, x):
+        # Now it only takes a call to the layer to make predictions
+        return self.linear(x)
+
+parser = argparse.ArgumentParser(description='Program do uczenia modelu')
+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()
+
+if __name__ == "__main__":
+    lr = args.lr
+    n_epochs = args.epochs
+    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')
+
+        tracking_url_type_store = urlparse(mlflow.get_tracking_uri()).scheme
+
+        # Model registry does not work with file store
+        if tracking_url_type_store != "file":
+
+            # Register the model
+            # There are other ways to use the Model Registry, which depends on the use case,
+            # please refer to the doc for more information:
+            # https://mlflow.org/docs/latest/model-registry.html#api-workflow
+            mlflow.sklearn.log_model(model, "model", registered_model_name="ElasticnetWineModel")
+        else:
+            mlflow.sklearn.log_model(model, "model")

train_mlflow/MLproject

@@ -0,0 +1,13 @@
+name: 426206mlflow
+
+#conda_env: conda.yaml #ścieżka do pliku conda.yaml z definicją środowiska
+    
+#docker_env:
+#  image: mlflow-docker-example-environment
+
+entry_points:
+  main:
+    parameters:
+      epochs: {type: int, default: 100}
+      lr: {type: float, default: 0.001}
+    command: "python3 train_mlflow.py -e {epochs} -l {lr}"