Added sacred.

.gitignore

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

Dockerfile

@@ -12,6 +12,7 @@ RUN chmod -R 777 /.kaggle
 COPY ./requirments.txt ./
 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
 
 # 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

dlgssdpytorch.py

@@ -5,21 +5,12 @@ import torch.nn as nn
 import torch.optim as optim
 from torch.utils.data import Dataset, TensorDataset, DataLoader
 import argparse
+from sacred import Experiment
+from sacred.observers import MongoObserver, FileStorageObserver
 
-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()
-
-lr = args.lr
-n_epochs = args.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)
-
+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'))
 class LayerLinearRegression(nn.Module):
     def __init__(self):
         super().__init__()
@@ -30,6 +21,25 @@ class LayerLinearRegression(nn.Module):
         # 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()
+#python3 dlgssdpytorch.py with lr=0.01 n_epochs=10
+
+@ex.config
+def my_config():    
+    lr = 1e-3
+    n_epochs = 100
+
+@ex.capture
+def train(lr, n_epochs, _run):
+    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())   
@@ -64,6 +74,8 @@ validation_losses = []
     #print(model.state_dict())   
     # For each epoch...
     for epoch in range(n_epochs):
+
+        _run.log_scalar("Epoch",  str(epoch))
         losses = []
         # Uses loader to fetch one mini-batch for training
         for x_batch, y_batch in train_loader:
@@ -77,6 +89,8 @@ for epoch in range(n_epochs):
         training_loss = np.mean(losses)
         training_losses.append(training_loss)
 
+        _run.log_scalar("MSE", str(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.    
@@ -107,3 +121,10 @@ torch.save({
                     'optimizer_state_dict': optimizer.state_dict(),
                         'loss': lr,
                     }, 'model.pt')
+
+@ex.automain
+def my_main(lr, n_epochs, _run):
+    train(lr, n_epochs, _run)    
+
+ex.run()
+ex.add_artifact('model.pt')