diff --git a/gpu/pretrain.py b/gpu/pretrain.py
new file mode 100755
index 0000000..191da3c
--- /dev/null
+++ b/gpu/pretrain.py
@@ -0,0 +1,128 @@
+#!/usr/bin/python
+#coding=utf-8
+'''
+If there are Chinese comments in the code，please add at the beginning：
+#!/usr/bin/python
+#coding=utf-8   
+
+1，The dataset structure of the single-dataset in this example
+ MnistDataset_torch.zip
+  ├── test
+  └── train  
+
+2，Due to the adaptability of a100, before using the training environment, please use the recommended image of the 
+platform with cuda 11.Then adjust the code and submit the image.
+The image of this example is: dockerhub.pcl.ac.cn:5000/user-images/openi:cuda111_python37_pytorch191
+In the training environment, the uploaded dataset will be automatically placed in the /dataset directory. 
+Note: the paths are different when selecting a single dataset and multiple datasets.
+(1)If it is a single dataset: if MnistDataset_torch.zip is selected, 
+   the dataset directory is /dataset/train, /dataset/test;
+   If it is a multiple dataset: if MnistDataset_torch.zip is selected, 
+   the dataset directory is /dataset/MnistDataset_torch/train, /dataset/MnistDataset_torch/test;
+
+(2)If the pre-training model file is selected, the selected pre-training model will be 
+automatically placed in the /pretrainmodel directory.
+for example:
+    If the model file is selected, the calling method is: '/pretrainmodel/' + args.pretrainmodelname
+
+The model download path is under /model by default. Please specify the model output location to /model, 
+and the Qizhi platform will provide file downloads under the /model directory.
+'''
+
+
+from model import Model
+import numpy as np
+import torch
+from torchvision.datasets import mnist
+from torch.nn import CrossEntropyLoss
+from torch.optim import SGD
+from torch.utils.data import DataLoader
+from torchvision.transforms import ToTensor
+import argparse
+import os
+
+# Training settings
+parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
+#The dataset location is placed under /dataset
+parser.add_argument('--traindata', default="/dataset/train" ,help='path to train dataset')
+parser.add_argument('--testdata', default="/dataset/test" ,help='path to test dataset')
+parser.add_argument('--epoch_size', type=int, default=10, help='how much epoch to train')
+parser.add_argument('--batch_size', type=int, default=256, help='how much batch_size in epoch')
+#获取模型文件名称
+parser.add_argument('--ckpt_url', default="", help='pretrain model path')
+
+# 参数声明
+WORKERS = 0   # dataloder线程数
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model = Model().to(device)
+optimizer = SGD(model.parameters(), lr=1e-1)
+cost = CrossEntropyLoss()
+
+# 模型训练
+def train(model, train_loader, epoch):
+    model.train()
+    train_loss = 0
+    for i, data in enumerate(train_loader, 0):
+        x, y = data
+        x = x.to(device)
+        y = y.to(device)
+        optimizer.zero_grad()
+        y_hat = model(x)
+        loss = cost(y_hat, y)
+        loss.backward()
+        optimizer.step()
+        train_loss += loss
+    loss_mean = train_loss / (i+1)
+    print('Train Epoch: {}\t Loss: {:.6f}'.format(epoch, loss_mean.item()))
+ 
+# 模型测试
+def test(model, test_loader, test_data):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for i, data in enumerate(test_loader, 0):
+            x, y = data
+            x = x.to(device)
+            y = y.to(device)
+            optimizer.zero_grad()
+            y_hat = model(x)
+            test_loss += cost(y_hat, y).item()
+            pred = y_hat.max(1, keepdim=True)[1]
+            correct += pred.eq(y.view_as(pred)).sum().item()
+        test_loss /= (i+1)
+        print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+            test_loss, correct, len(test_data), 100. * correct / len(test_data)))
+def main():
+    # 如果有保存的模型，则加载模型，并在其基础上继续训练
+    if os.path.exists(args.ckpt_url):
+        checkpoint = torch.load(args.ckpt_url)
+        model.load_state_dict(checkpoint['model'])
+        optimizer.load_state_dict(checkpoint['optimizer'])
+        start_epoch = checkpoint['epoch']
+        print('加载 epoch {} 权重成功！'.format(start_epoch))
+    else:
+        start_epoch = 0
+        print('无保存模型，将从头开始训练！')
+ 
+    for epoch in range(start_epoch+1, epochs):
+        train(model, train_loader, epoch)
+        test(model, test_loader, test_dataset)
+        # 保存模型
+        state = {'model':model.state_dict(), 'optimizer':optimizer.state_dict(), 'epoch':epoch}
+        torch.save(state, '/model/mnist_epoch{}.pkl'.format(epoch))
+
+if __name__ == '__main__':
+    args, unknown = parser.parse_known_args()
+    #log output
+    print('cuda is available:{}'.format(torch.cuda.is_available()))  
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    batch_size = args.batch_size
+    epochs = args.epoch_size
+    train_dataset = mnist.MNIST(root=args.traindata, train=True, transform=ToTensor(),download=False)
+    test_dataset = mnist.MNIST(root=args.testdata, train=False, transform=ToTensor(),download=False)
+    train_loader = DataLoader(train_dataset, batch_size=batch_size)
+    test_loader = DataLoader(test_dataset, batch_size=batch_size)
+    main()
+
+
diff --git a/gpu/pretrain_for_c2net.py b/gpu/pretrain_for_c2net.py
new file mode 100755
index 0000000..fba79d3
--- /dev/null
+++ b/gpu/pretrain_for_c2net.py
@@ -0,0 +1,144 @@
+#!/usr/bin/python
+#coding=utf-8
+'''
+If there are Chinese comments in the code，please add at the beginning：
+#!/usr/bin/python
+#coding=utf-8   
+
+In the training environment, 
+(1)the code will be automatically placed in the /tmp/code directory, 
+(2)the uploaded dataset will be automatically placed in the /tmp/dataset directory
+Note: the paths are different when selecting a single dataset and multiple datasets.
+(1)If it is a single dataset: if MnistDataset_torch.zip is selected, 
+   the dataset directory is /tmp/dataset/train, /dataset/test;
+
+The dataset structure of the single dataset in the training image in this example:
+  tmp
+    ├──dataset 
+         ├── test
+         └── train 
+
+If multiple datasets are selected, such as MnistDataset_torch.zip and checkpoint_epoch1_0.73.zip, 
+the dataset directory is /tmp/dataset/MnistDataset_torch/train, /tmp/dataset/MnistDataset_torch/test
+and /tmp/dataset/checkpoint_epoch1_0.73/mnist_epoch1_0.73.pkl
+The dataset structure in the training image for multiple datasets in this example:
+tmp
+  ├──dataset
+     ├── MnistDataset_torch
+     |     ├── test
+     |     └── train 
+     └── checkpoint_epoch1_0.73 
+           ├── mnist_epoch1_0.73.pkl
+(3)the model download path is under /tmp/output by default, please specify the model output location to /tmp/output, 
+qizhi platform will provide file downloads under the /tmp/output directory.
+(4)If the pre-training model file is selected, the selected pre-training model will be 
+automatically placed in the /tmp/pretrainmodel directory.
+for example:
+    If the model file is selected, the calling method is: '/pretrainmodel/' + args.pretrainmodelname
+
+In addition, if you want to get the model file after each training, you can call the uploader_for_gpu tool, 
+which is written as: 
+import os
+os.system("cd /tmp/script_for_grampus/ &&./uploader_for_gpu " + "/tmp/output/")
+'''
+
+
+from model import Model
+import numpy as np
+import torch
+from torchvision.datasets import mnist
+from torch.nn import CrossEntropyLoss
+from torch.optim import SGD
+from torch.utils.data import DataLoader
+from torchvision.transforms import ToTensor
+import argparse
+import os
+
+# Training settings
+parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
+#The dataset location is placed under /dataset
+parser.add_argument('--traindata', default="/tmp/dataset/train" ,help='path to train dataset')
+parser.add_argument('--testdata', default="/tmp/dataset/test" ,help='path to test dataset')
+parser.add_argument('--epoch_size', type=int, default=10, help='how much epoch to train')
+parser.add_argument('--batch_size', type=int, default=256, help='how much batch_size in epoch')
+#获取模型文件名称
+parser.add_argument('--ckpt_url', default="", help='pretrain model path')
+
+# 参数声明
+WORKERS = 0   # dataloder线程数
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model = Model().to(device)
+optimizer = SGD(model.parameters(), lr=1e-1)
+cost = CrossEntropyLoss()
+
+# 模型训练
+def train(model, train_loader, epoch):
+    model.train()
+    train_loss = 0
+    for i, data in enumerate(train_loader, 0):
+        x, y = data
+        x = x.to(device)
+        y = y.to(device)
+        optimizer.zero_grad()
+        y_hat = model(x)
+        loss = cost(y_hat, y)
+        loss.backward()
+        optimizer.step()
+        train_loss += loss
+    loss_mean = train_loss / (i+1)
+    print('Train Epoch: {}\t Loss: {:.6f}'.format(epoch, loss_mean.item()))
+ 
+# 模型测试
+def test(model, test_loader, test_data):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for i, data in enumerate(test_loader, 0):
+            x, y = data
+            x = x.to(device)
+            y = y.to(device)
+            optimizer.zero_grad()
+            y_hat = model(x)
+            test_loss += cost(y_hat, y).item()
+            pred = y_hat.max(1, keepdim=True)[1]
+            correct += pred.eq(y.view_as(pred)).sum().item()
+        test_loss /= (i+1)
+        print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+            test_loss, correct, len(test_data), 100. * correct / len(test_data)))
+def main():
+    # 如果有保存的模型，则加载模型，并在其基础上继续训练
+    if os.path.exists(args.ckpt_url):
+        checkpoint = torch.load(args.ckpt_url)
+        model.load_state_dict(checkpoint['model'])
+        optimizer.load_state_dict(checkpoint['optimizer'])
+        start_epoch = checkpoint['epoch']
+        print('加载 epoch {} 权重成功！'.format(start_epoch))
+    else:
+        start_epoch = 0
+        print('无保存模型，将从头开始训练！')
+ 
+    for epoch in range(start_epoch+1, epochs):
+        train(model, train_loader, epoch)
+        test(model, test_loader, test_dataset)
+        # 保存模型
+        state = {'model':model.state_dict(), 'optimizer':optimizer.state_dict(), 'epoch':epoch}
+        torch.save(state, '/tmp/output/mnist_epoch{}.pkl'.format(epoch))
+        #After calling uploader_for_gpu, after each epoch training, the result file under /tmp/output will be sent back to Qizhi
+        os.system("cd /tmp/script_for_grampus/ &&./uploader_for_gpu " + "/tmp/output/")
+
+if __name__ == '__main__':
+    args, unknown = parser.parse_known_args()
+    #log output
+    print('cuda is available:{}'.format(torch.cuda.is_available()))  
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    batch_size = args.batch_size
+    epochs = args.epoch_size
+    train_dataset = mnist.MNIST(root=args.traindata, train=True, transform=ToTensor(),download=False)
+    test_dataset = mnist.MNIST(root=args.testdata, train=False, transform=ToTensor(),download=False)
+    train_loader = DataLoader(train_dataset, batch_size=batch_size)
+    test_loader = DataLoader(test_dataset, batch_size=batch_size)
+    main()
+
+
+        
\ No newline at end of file
diff --git a/gpu/pretrain_wjtest.py b/gpu/pretrain_wjtest.py
new file mode 100755
index 0000000..a791b21
--- /dev/null
+++ b/gpu/pretrain_wjtest.py
@@ -0,0 +1,127 @@
+#!/usr/bin/python
+#coding=utf-8
+'''
+If there are Chinese comments in the code，please add at the beginning：
+#!/usr/bin/python
+#coding=utf-8   
+
+1，The dataset structure of the single-dataset in this example
+ MnistDataset_torch.zip
+  ├── test
+  └── train  
+
+2，Due to the adaptability of a100, before using the training environment, please use the recommended image of the 
+platform with cuda 11.Then adjust the code and submit the image.
+The image of this example is: dockerhub.pcl.ac.cn:5000/user-images/openi:cuda111_python37_pytorch191
+In the training environment, the uploaded dataset will be automatically placed in the /dataset directory. 
+Note: the paths are different when selecting a single dataset and multiple datasets.
+(1)If it is a single dataset: if MnistDataset_torch.zip is selected, 
+   the dataset directory is /dataset/train, /dataset/test;
+   If it is a multiple dataset: if MnistDataset_torch.zip is selected, 
+   the dataset directory is /dataset/MnistDataset_torch/train, /dataset/MnistDataset_torch/test;
+
+(2)If the pre-training model file is selected, the selected pre-training model will be 
+automatically placed in the /pretrainmodel directory.
+for example:
+    If the model file is selected, the calling method is: '/pretrainmodel/' + args.pretrainmodelname
+
+The model download path is under /model by default. Please specify the model output location to /model, 
+and the Qizhi platform will provide file downloads under the /model directory.
+'''
+
+
+from model import Model
+import numpy as np
+import torch
+from torchvision.datasets import mnist
+from torch.nn import CrossEntropyLoss
+from torch.optim import SGD
+from torch.utils.data import DataLoader
+from torchvision.transforms import ToTensor
+import argparse
+import os
+
+# Training settings
+parser = argparse.ArgumentParser(description='PyTorch MNIST Example')
+#The dataset location is placed under /dataset
+parser.add_argument('--traindata', default="/dataset/train" ,help='path to train dataset')
+parser.add_argument('--testdata', default="/dataset/test" ,help='path to test dataset')
+parser.add_argument('--epoch_size', type=int, default=10, help='how much epoch to train')
+parser.add_argument('--batch_size', type=int, default=256, help='how much batch_size in epoch')
+#获取模型文件名称
+parser.add_argument('--ckpt_url', default="", help='pretrain model path')
+
+# 参数声明
+WORKERS = 0   # dataloder线程数
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+model = Model().to(device)
+optimizer = SGD(model.parameters(), lr=1e-1)
+cost = CrossEntropyLoss()
+
+# 模型训练
+def train(model, train_loader, epoch):
+    model.train()
+    train_loss = 0
+    for i, data in enumerate(train_loader, 0):
+        x, y = data
+        x = x.to(device)
+        y = y.to(device)
+        optimizer.zero_grad()
+        y_hat = model(x)
+        loss = cost(y_hat, y)
+        loss.backward()
+        optimizer.step()
+        train_loss += loss
+    loss_mean = train_loss / (i+1)
+    print('Train Epoch: {}\t Loss: {:.6f}'.format(epoch, loss_mean.item()))
+ 
+# 模型测试
+def test(model, test_loader, test_data):
+    model.eval()
+    test_loss = 0
+    correct = 0
+    with torch.no_grad():
+        for i, data in enumerate(test_loader, 0):
+            x, y = data
+            x = x.to(device)
+            y = y.to(device)
+            optimizer.zero_grad()
+            y_hat = model(x)
+            test_loss += cost(y_hat, y).item()
+            pred = y_hat.max(1, keepdim=True)[1]
+            correct += pred.eq(y.view_as(pred)).sum().item()
+        test_loss /= (i+1)
+        print('Test set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
+            test_loss, correct, len(test_data), 100. * correct / len(test_data)))
+def main():
+    # 如果有保存的模型，则加载模型，并在其基础上继续训练
+    print('------ckpt_url is: ', args.ckpt_url);
+    if os.path.exists(args.ckpt_url):
+        checkpoint = torch.load(args.ckpt_url)
+        model.load_state_dict(checkpoint['model'])
+        optimizer.load_state_dict(checkpoint['optimizer'])
+        start_epoch = checkpoint['epoch']
+        print('加载 epoch {} 权重成功！'.format(start_epoch))
+    else:
+        start_epoch = 0
+        print('无保存模型，将从头开始训练！')
+ 
+    for epoch in range(start_epoch+1, epochs):
+        train(model, train_loader, epoch)
+        test(model, test_loader, test_dataset)
+        # 保存模型
+        state = {'model':model.state_dict(), 'optimizer':optimizer.state_dict(), 'epoch':epoch}
+        torch.save(state, '/model/mnist_epoch{}.pkl'.format(epoch))
+
+if __name__ == '__main__':
+    args, unknown = parser.parse_known_args()
+    #log output
+    print('cuda is available:{}'.format(torch.cuda.is_available()))  
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    batch_size = args.batch_size
+    epochs = args.epoch_size
+    train_dataset = mnist.MNIST(root=args.traindata, train=True, transform=ToTensor(),download=False)
+    test_dataset = mnist.MNIST(root=args.testdata, train=False, transform=ToTensor(),download=False)
+    train_loader = DataLoader(train_dataset, batch_size=batch_size)
+    test_loader = DataLoader(test_dataset, batch_size=batch_size)
+    main()
\ No newline at end of file