diff --git a/wresnet-pytorch/src/distillation_train.py b/wresnet-pytorch/src/distillation_train.py
index 5be5453..18b28b0 100644
--- a/wresnet-pytorch/src/distillation_train.py
+++ b/wresnet-pytorch/src/distillation_train.py
@@ -1,6 +1,6 @@
 from datetime import datetime
 import time
-
+import argparse
 from utils import json_file_to_pyobj, get_loaders
 from WideResNet import WideResNet
 from opacus.validators import ModuleValidator
@@ -13,7 +13,7 @@ import os
 import torch
 import torch.nn as nn
 from torchvision import models, transforms
-import student_model 
+import student_model
 import torch.optim as optim
 import torch.nn.functional as F
 import opacus
@@ -86,6 +86,14 @@ def test(model, device, test_dl, teacher=False):
     return accuracy
 
 def main():
+    parser = argparse.ArgumentParser(description='Student trainer')
+    parser.add_argument('--teacher', type=Path, help='path to saved teacher .pt', required=True)
+    parser.add_argument('--norm', type=float, help='dpsgd norm clip factor', required=True)
+    parser.add_argument('--cuda', type=int, help='gpu index', required=False)
+    parser.add_argument('--epsilon', type=float, help='dp epsilon', required=False, default=None)
+    parser.add_argument('--epochs', type=int, help='student epochs', required=True)
+    args = parser.parse_args()
+
     json_options = json_file_to_pyobj("wresnet16-audit-cifar10.json")
     training_configurations = json_options.training
 
@@ -93,7 +101,9 @@ def main():
     wrn_width = training_configurations.wrn_width
     dataset = training_configurations.dataset.lower()
 
-    if torch.cuda.is_available():
+    if args.cuda is not None:
+        device = torch.device(f'cuda:{args.cuda}')
+    elif torch.cuda.is_available():
         device = torch.device('cuda:0')
     else:
         device = torch.device('cpu')
@@ -101,19 +111,21 @@ def main():
 
     print("Load the teacher model")
     # instantiate teacher model
-    strides = [1, 1, 2, 2]    
+    strides = [1, 1, 2, 2]
     teacher = WideResNet(d=wrn_depth, k=wrn_width, n_classes=10, input_features=3, output_features=16, strides=strides)
-    teacher = ModuleValidator.fix(teacher)    
+    teacher = ModuleValidator.fix(teacher)
     criterion = nn.CrossEntropyLoss()
     optimizer = optim.SGD(teacher.parameters(), lr=0.1, momentum=0.9, nesterov=True, weight_decay=5e-4)
     scheduler = MultiStepLR(optimizer, milestones=[int(elem*epochs) for elem in [0.3, 0.6, 0.8]], gamma=0.2)
     train_loader, test_loader = get_loaders(dataset, training_configurations.batch_size)
     best_test_set_accuracy = 0
-    dp_epsilon = 8
-    dp_delta = 1e-5
-    norm = 1.0 
-    privacy_engine = opacus.PrivacyEngine()
-    teacher, optimizer, train_loader = privacy_engine.make_private_with_epsilon(
+
+    if args.epsilon is not None:
+        dp_epsilon = args.epsilon
+        dp_delta = 1e-5
+        norm = args.norm
+        privacy_engine = opacus.PrivacyEngine()
+        teacher, optimizer, train_loader = privacy_engine.make_private_with_epsilon(
             module=teacher,
             optimizer=optimizer,
             data_loader=train_loader,
@@ -123,19 +135,28 @@ def main():
             max_grad_norm=norm,
         )
 
-    teacher.load_state_dict(torch.load(os.path.join("wrn-1733078278-8e-1e-05d-12.0n-dict.pt"), weights_only=True))
+    teacher.load_state_dict(torch.load(args.teacher, weights_only=True))
     teacher.to(device)
     teacher.eval()
     #instantiate istudent
     student = student_model.Model(num_classes=10).to(device)
 
-
     print("Training student")
-    train_knowledge_distillation(teacher=teacher, student=student, train_dl=train_loader, epochs=100, learning_rate=0.001, T=2, soft_target_loss_weight=0.25, ce_loss_weight=0.75, device=device)
-    print("Saving student")
-    current_datetime = datetime.now()
-    filename = f"students/studentmodel{current_datetime.strftime('%Y%m%d_%H%M%S')}.pt"
-    torch.save(student.state_dict(), filename)
+    train_knowledge_distillation(
+        teacher=teacher,
+        student=student,
+        train_dl=train_loader,
+        epochs=args.epochs,
+        learning_rate=0.001,
+        T=2,
+        soft_target_loss_weight=0.25,
+        ce_loss_weight=0.75,
+        device=device
+    )
+    print(f"Saving student model for time {int(time.time())}")
+    Path('students').mkdir(exist_ok=True)
+    torch.save(student.state_dict(), f"students/studentmodel-{int(time.time())}.pt")
+
     print("Testing student and teacher")
     test_student = test(student, device, test_loader,)
     test_teacher = test(teacher, device, test_loader, True)
@@ -144,4 +165,5 @@ def main():
 
 
 if __name__ == "__main__":
+
     main()