save model pb

pytorch/pytorch.py

@@ -130,8 +130,9 @@ model=WineQuality()
 def evaluate(model, val_loader):
     outputs = [model.validation_step(batch) for batch in val_loader]
     return model.validation_epoch_end(outputs)
-
-def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):
+    
+@ex.capture
+def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD, _run):
     history = []
     optimizer = opt_func(model.parameters(), lr)
     for epoch in range(epochs):
@@ -143,15 +144,25 @@ def fit(epochs, lr, model, train_loader, val_loader, opt_func=torch.optim.SGD):
         result = evaluate(model, val_loader)
         model.epoch_end(epoch, result, epochs)
         history.append(result)
+    ex.add_artifact("saved_model.pb")
     return history
 
 
 # In[12]:
 
+try: 
+  numberOfEpochParam = int(sys.argv[1])
+except:
+  # dafault val
+  numberOfEpochParam = 1500
+
+
+@ex.config
+def my_config():
+  epochs = numberOfEpochParam
+
+
 
-#epochs = int(sys.argv[1])
-lr = 1e-6
-history5 = fit(epochs, lr, model, train_loader, val_loader)
 
 
 # In[27]:
@@ -184,4 +195,9 @@ with open("result.txt", "w+") as file:
         file.write(str(predict_single(input_, target, model)))
 
 
+@ex.main
+def main():
+    lr = 1e-6
+    history5 = fit(epochs, lr, model, train_loader, val_loader)
 
+ex.run()