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diff --git a/gpu-job.yml b/gpu-job.yml
index a19453d29e5a2e8fd2a61a1e31d2753a83fb01b3..b604db3a4cb617aa8d95441e1ead69fcdf70da2b 100644
--- a/gpu-job.yml
+++ b/gpu-job.yml
@@ -20,9 +20,11 @@ spec:
         # the name of the container
         - name: traffic-sign-recognition-system
           # the image: can be from any pubic facing registry
-          image: gitlab-registry.nrp-nautilus.io/peicongcheng/traffic-sign-recognition-system
+          #image: bitnami/pytorch:latest
+          image: gitlab-registry.nrp-nautilus.io/zihaozhou/nautilus_tutorial:jupyterhub
+
           # the working dir when the container starts
-          workingDir: /path/to/mydir
+          workingDir: /work
           # should Kube pull it
           imagePullPolicy: IfNotPresent
           # we need to expose the port
@@ -30,38 +32,38 @@ spec:
           ports:
             - containerPort: 8880
           # setting of env variables
-          env:
+          #env:
             # which interface to use
-            - name: NCCL_SOCKET_IFNAME
-              value: eth0
+           # - name: NCCL_SOCKET_IFNAME
+           #   value: eth0
             # prints some INFO level
             # NCCL logs
-            - name: NCCL_DEBUG
-              value: INFO
+           # - name: NCCL_DEBUG
+           #   value: INFO
           # the command to run when the container starts
-          command: ["python", "-m", "train.py", "./train_cfg.py"]
+          command: ["python3", "/work/main.py"]
           # define the resources for this container
           resources:
             # limits - the max given to the container
             limits:
               # RAM
-              memory: 64Gi
+              memory: 16Gi
               # cores
-              cpu: 32
-              # NVIDIA GPUs
-              nvidia.com/gpu: 4
+              cpu: 8
+              # NVIDA GPUs
+              nvidia.com/gpu: 1
             # requests - what we'd like
             requests:
               # RAM
-              memory: 64Gi
+              memory: 8Gi
               # CPU Cores
-              cpu: 32
+              cpu: 4
               # GPUs
-              nvidia.com/gpu: 4
+              #nvidia.com/gpu: 1
           # what volumes should we mount
           volumeMounts:
             # my datasets PVC should mount to /data
-            - mountPath: /data
+            - mountPath: /work
               name: peicong
             # IMPORTANT: we need SHM for DDP
             - mountPath: /dev/shm
diff --git a/gpu-pod.yml b/gpu-pod.yml
index 8c06f70df4a01d843671f6c2dd293242248987f6..58a4d350af2b45c6bc120e671b89ff0e5347313e 100644
--- a/gpu-pod.yml
+++ b/gpu-pod.yml
@@ -7,7 +7,7 @@ metadata:
 spec:
   containers:
     - name: traffic-sign-recognition-system
-      image: gitlab-registry.nrp-nautilus.io/peicongcheng/traffic-sign-recognition-system
+      image: gitlab-registry.nrp-nautilus.io/zihaozhou/nautilus_tutorial:jupyterhub
       imagePullPolicy: IfNotPresent
       command: ["sleep", "infinity"]
       resources:
@@ -20,7 +20,7 @@ spec:
           cpu: 2
           nvidia.com/gpu: 1
       volumeMounts:
-        - mountPath: /data
+        - mountPath: /tutorial
           name: peicong
         # IMPORTANT: we need SHM for PyTorch
         - mountPath: /dev/shm
diff --git a/main.py b/main.py
index 52c3514a10673c8fe9bf1dba95a4badb0aaf15af..13e17f2ad099d9e4e37fbd2295465894ca59fa4d 100644
--- a/main.py
+++ b/main.py
@@ -1,7 +1,7 @@
 import pathlib
 import os
 import time
-import matplotlib.pyplot as plt
+#import matplotlib.pyplot as plt
 import numpy as np
 import torch.optim as optim
 from torchvision.transforms import transforms
@@ -16,7 +16,7 @@ import random
 
 device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
 
-num_epochs = 200
+num_epochs = 500
 batch_size = 8
 learning_rate = 0.001
 
@@ -34,7 +34,7 @@ transforms = transforms.Compose([transforms.RandomResizedCrop(224),
 #
 # train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
 
-all_data_dir = 'dataset/train'
+all_data_dir = 'dataset_copy/train'
 
 all_dataset = datasets.ImageFolder(root=all_data_dir, transform=transforms)
 all_dataset_len = len(all_dataset)
@@ -50,19 +50,19 @@ test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, s
 classes = [str(i) for i in range(10)]
 
 
-def imshow(img, title):
-        img = img.numpy().transpose(1, 2, 0)
-        img = std * img + mean
-        img = np.clip(img, 0, 1)
-        plt.imshow(img)
-        plt.title(title)
-        plt.show()
-
-
-dataiter = iter(train_loader)
-images, labels = next(dataiter)
-
-imshow(torchvision.utils.make_grid(images), title=labels)
+# def imshow(img, title):
+#         img = img.numpy().transpose(1, 2, 0)
+#         img = std * img + mean
+#         img = np.clip(img, 0, 1)
+#         plt.imshow(img)
+#         plt.title(title)
+#         plt.show()
+#
+#
+# dataiter = iter(train_loader)
+# images, labels = next(dataiter)
+#
+# imshow(torchvision.utils.make_grid(images), title=labels)
 
 #print(images.shape)
 
@@ -113,7 +113,7 @@ for epoch in range(num_epochs):
     print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {total_loss.item()/len(train_loader):.4f}')
 
 print('Finished Training')
-PATH = './data_model/cnn.pth'
+PATH = './datacopy_model/cnn.pth'
 torch.save(model.state_dict(), PATH)
 
 # predict traffic sign image from traindata
@@ -127,7 +127,7 @@ torch.save(model.state_dict(), PATH)
 PATH = "./datacopy_model/best_cnn.pth"
 
 best_model = ConvNet().to(device)
-best_model.load_state_dict(torch.load(PATH))
+best_model.load_state_dict(torch.load(PATH, map_location=device))
 best_model.eval()
 
 
@@ -172,6 +172,16 @@ with torch.no_grad():
     acc2 = 100.0 * n_correct2 / n_samples2
     print(f'Best model: Accuracy of the train dataset: {acc2} %')
 
+file = open("outputresult.txt", "w")
+file.write("Trained model acc = {}\n".format(acc1))
+file.write("Loaded best model acc = {}\n".format(acc2))
+
+PATH_output = "./datacopy_model/output.txt"
+with open(PATH, 'w') as file_output:
+    file_output.write(f"Trained model acc = {acc1}\n")
+    file_output.write(f"Best model acc = {acc2}\n")
+
+
 # compare with best_cnn.pth
 if acc1 > acc2:
     PATH = "./datacopy_model/best_cnn.pth"
diff --git a/pod-pvc.yml b/pod-pvc.yml
index f5f0f34e6761525e01b00f8df404f44e2d6748a1..e9ac13a10d2b61ea24f3bf4b9b7f83b8027fd463 100644
--- a/pod-pvc.yml
+++ b/pod-pvc.yml
@@ -5,7 +5,7 @@ metadata:
 spec:
   containers:
     - name: traffic-sign-recognition-system # YOUR CONTAINER NAME HERE
-      image: gitlab-registry.nrp-nautilus.io/peicongcheng/traffic-sign-recognition-system
+      image: bitnami/pytorch:latest
       command: ["sh", "-c", "echo 'Im a new pod' && sleep infinity"]
       resources:
         limits:
@@ -15,10 +15,10 @@ spec:
           memory: 10Gi
           cpu: 2
       volumeMounts:
-        - mountPath: /data
+        - mountPath: /app
           name: peicong # YOUR PVC NAME HERE
   volumes:
     - name: peicong # YOUR PVC NAME HERE
       persistentVolumeClaim:
         claimName: peicong # YOUR PVC NAME HERE
-
+  
diff --git a/requirements.txt b/requirements.txt
index c03c0e5755bdea1128fca5ab3568296ae190af44..b3896585a85c99736b7b26e172fb589d6775e8be 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,4 +1,3 @@
-matplotlib==3.6.3
 numpy==1.24.1
 Pillow==9.4.0
 Pillow==10.2.0