如何修改代码使用gpu的cuda来运行
#使用dnn模型（k折交叉验证）import torch
import torch.nn as nn
from torch.utils import data
from torch.utils.data import Dataset,DataLoader
from torch import optim
#定义神经网络模型
class SimpleNN(nn.Module):
def __init__(self):
super(SimpleNN,self).__init__()
self.hidden_layer1 = nn.Linear(154,1024)
self.hidden_layer2 = nn.Linear(1024,1024)
self.hidden_layer3 = nn.Linear(1024,1024)
self.hidden_layer4 = nn.Linear(1024,1024)
self.output_layer = nn.Linear(1024,1)
self.dropout = nn.Dropout(p=0.5)
nn.init.xavier_uniform_(self.hidden_layer1.weight)
nn.init.xavier_uniform_(self.hidden_layer2.weight)
nn.init.xavier_uniform_(self.hidden_layer3.weight)
nn.init.xavier_uniform_(self.hidden_layer4.weight)
nn.init.xavier_uniform_(self.output_layer.weight)
def forward(self,x):
inputs = x
layer1_out = torch.nn.functional.gelu(self.hidden_layer1(inputs))
layer1_out = self.dropout(layer1_out)
layer2_out = torch.nn.functional.gelu(self.hidden_layer2(layer1_out))
layer2_out = self.dropout(layer2_out)
layer3_out = torch.nn.functional.gelu(self.hidden_layer3(layer2_out))
layer3_out = self.dropout(layer3_out)
layer4_out = torch.nn.functional.gelu(self.hidden_layer4(layer3_out))
layer4_out = self.dropout(layer4_out)
output = torch.relu(self.output_layer(layer4_out))
return output
# 设置超参数
k = 5
batch_size = 128
num_epochs = 1000
weight_decay = 0
#初始化模型和优化器
dnn_model = SimpleNN()
optimizer = optim.AdamW(dnn_model.parameters(),lr=0.0001,weight_decay=weight_decay) #定义优化器
#k折交叉验证选取训练集与验证集
def get_k_fold_data(k, i, X, y):
assert k > 1
fold_size = len(X) // k
X_train, y_train = None, None
for j in range(k):
start = j * fold_size
end = (j + 1) * fold_size
if j == i:
X_valid, y_valid = X.iloc, y.iloc
elif X_train is None:
X_train, y_train = X.iloc, y.iloc
else:
X_train = pd.concat(], ignore_index=True)
y_train = pd.concat(], ignore_index=True)
return X_train, y_train, X_valid, y_valid
#初始化列表
train_ls, valid_ls = [], []
for i in range(k):
X_train, y_train, X_valid, y_valid = get_k_fold_data(k, i, X, y)
print(f'FOLD {i}')
print('--------------------------------')
#将DataFrame数据转换为NumPy数组，然后再转换为PyTorch张量
X_train = torch.tensor(X_train.astype(np.float32).values, dtype=torch.float32)
y_train = torch.tensor(y_train.astype(np.float32).values, dtype=torch.float32)
X_valid = torch.tensor(X_valid.astype(np.float32).values, dtype=torch.float32)
y_valid = torch.tensor(y_valid.astype(np.float32).values, dtype=torch.float32)
#创建数据集
train_ds = data.TensorDataset(X_train, y_train)
valid_ds = data.TensorDataset(X_valid, y_valid)
# 获取一个数据迭代器
train_iter = DataLoader(dataset=train_ds,batch_size=batch_size,shuffle=True,num_workers=2)#shuffle=True相当于sampler=RandomSampler(dataset)
valid_iter = DataLoader(dataset=valid_ds,batch_size=batch_size,shuffle=True,num_workers=2)
#开始迭代
for epoch in range(num_epochs):
train_loss = 0
for tensor_x, tensor_y in train_iter:#训练集执行梯度更新
tensor_x = tensor_x.float()
tensor_y = tensor_y.float().reshape(-1, 1)
optimizer.zero_grad() #梯度清零
pre_train = dnn_model(tensor_x)
train_l = MSLE_loss(pre_train, tensor_y) #损失应避免与全局变量loss重名
train_l.backward()#前向传播
optimizer.step()#梯度下降
train_loss += train_l.item() * len(tensor_x)
train_loss /= len(train_ds) #每次迭代平均损失
if epoch % 200 == 0:
print('Loss: {}Epoch:{}'.format(train_loss, epoch))
#print(train_l)
with torch.no_grad():
valid_loss = 0
for tensor_x, tensor_y in valid_iter:
tensor_x = tensor_x.float()
tensor_y = tensor_y.float().reshape(-1, 1)
pre_valid = dnn_model(tensor_x)
valid_l = MSLE_loss(pre_valid, tensor_y)
valid_loss += valid_l.item() * len(tensor_x)
valid_loss /= len(valid_ds)
if epoch % 200 == 0:
print('Valid Loss: {}Epoch:{}'.format(valid_loss, epoch))
#print(valid_l)
#将每折的损失添加到列表中
train_ls.append(train_loss)
valid_ls.append(valid_loss)
print('Training Ended')
print('Train Average Loss: {} Valid Average Loss: {}'.format(np.mean(train_ls),np.mean(valid_ls)))
迭代次数太多cpu跑不动了， 要怎么修改用gpu跑 为了让你的代码在GPU上运行，主要需要以下几个步骤：
1. **检查是否有可用的GPU**。
2. **将模型移到GPU上**。
3. **将输入数据和目标数据移到GPU上**。
下面是修改后的代码示例：
import torch
import torch.nn as nn
from torch.utils import data
from torch.utils.data import Dataset, DataLoader
from torch import optim
# 检查GPU是否可用
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 定义神经网络模型
class SimpleNN(nn.Module):
def __init__(self):
super(SimpleNN, self).__init__()
self.hidden_layer1 = nn.Linear(154, 1024)
self.hidden_layer2 = nn.Linear(1024, 1024)
self.hidden_layer3 = nn.Linear(1024, 1024)
self.hidden_layer4 = nn.Linear(1024, 1024)
self.output_layer = nn.Linear(1024, 1)
self.dropout = nn.Dropout(p=0.5)
nn.init.xavier_uniform_(self.hidden_layer1.weight)
nn.init.xavier_uniform_(self.hidden_layer2.weight)
nn.init.xavier_uniform_(self.hidden_layer3.weight)
nn.init.xavier_uniform_(self.hidden_layer4.weight)
nn.init.xavier_uniform_(self.output_layer.weight)
def forward(self, x):
inputs = x
layer1_out = torch.nn.functional.gelu(self.hidden_layer1(inputs))
layer1_out = self.dropout(layer1_out)
layer2_out = torch.nn.functional.gelu(self.hidden_layer2(layer1_out))
layer2_out = self.dropout(layer2_out)
layer3_out = torch.nn.functional.gelu(self.hidden_layer3(layer2_out))
layer3_out = self.dropout(layer3_out)
layer4_out = torch.nn.functional.gelu(self.hidden_layer4(layer3_out))
layer4_out = self.dropout(layer4_out)
output = torch.relu(self.output_layer(layer4_out))
return output
# 设置超参数
k = 5
batch_size = 128
num_epochs = 1000
weight_decay = 0
# 初始化模型和优化器
dnn_model = SimpleNN().to(device)# 将模型移到GPU上
optimizer = optim.AdamW(dnn_model.parameters(), lr=0.0001, weight_decay=weight_decay)# 定义优化器
# k折交叉验证选取训练集与验证集
def get_k_fold_data(k, i, X, y):
assert k > 1
fold_size = len(X) // k
X_train, y_train = None, None
for j in range(k):
start = j * fold_size
end = (j + 1) * fold_size
if j == i:
X_valid, y_valid = X.iloc, y.iloc
elif X_train is None:
X_train, y_train = X.iloc, y.iloc
else:
X_train = pd.concat(, ignore_index=True)
y_train = pd.concat(, ignore_index=True)
return X_train, y_train, X_valid, y_valid
# 初始化列表
train_ls, valid_ls = [], []
for i in range(k):
X_train, y_train, X_valid, y_valid = get_k_fold_data(k, i, X, y)
print(f'FOLD {i}')
print('--------------------------------')
# 将DataFrame数据转换为NumPy数组，然后再转换为PyTorch张量，并且移动到GPU上
X_train = torch.tensor(X_train.astype(np.float32).values, dtype=torch.float32).to(device)
y_train = torch.tensor(y_train.astype(np.float32).values, dtype=torch.float32).to(device)
X_valid = torch.tensor(X_valid.astype(np.float32).values, dtype=torch.float32).to(device)
y_valid = torch.tensor(y_valid.astype(np.float32).values, dtype=torch.float32).to(device)
# 创建数据集
train_ds = data.TensorDataset(X_train, y_train)
valid_ds = data.TensorDataset(X_valid, y_valid)
# 获取一个数据迭代器
train_iter = DataLoader(dataset=train_ds, batch_size=batch_size, shuffle=True, num_workers=2)
valid_iter = DataLoader(dataset=valid_ds, batch_size=batch_size, shuffle=True, num_workers=2)
# 开始迭代
for epoch in range(num_epochs):
train_loss = 0
for tensor_x, tensor_y in train_iter:# 训练集执行梯度更新
tensor_x = tensor_x.float()
tensor_y = tensor_y.float().reshape(-1, 1)
optimizer.zero_grad()# 梯度清零
pre_train = dnn_model(tensor_x)
train_l = MSLE_loss(pre_train, tensor_y)# 损失应避免与全局变量loss重名
train_l.backward()# 前向传播
optimizer.step()# 梯度下降
train_loss += train_l.item() * len(tensor_x)
train_loss /= len(train_ds)# 每次迭代平均损失
if epoch % 200 == 0:
print('Loss: {}Epoch:{}'.format(train_loss, epoch))
with torch.no_grad():
valid_loss = 0
for tensor_x, tensor_y in valid_iter:
tensor_x = tensor_x.float()
tensor_y = tensor_y.float().reshape(-1, 1)
pre_valid = dnn_model(tensor_x)
valid_l = MSLE_loss(pre_valid, tensor_y)
valid_loss += valid_l.item() * len(tensor_x)
valid_loss /= len(valid_ds)
if epoch % 200 == 0:
print('Valid Loss: {}Epoch:{}'.format(valid_loss, epoch))
# 将每折的损失添加到列表中
train_ls.append(train_loss)
valid_ls.append(valid_loss)
print('Training Ended')
print('Train Average Loss: {} Valid Average Loss: {}'.format(np.mean(train_ls), np.mean(valid_ls)))
**注意**：
1. 在初始化模型时，使用 `.to(device)` 将模型移动到指定的设备（GPU）。
2. 在将数据转换为PyTorch张量后，也使用 `.to(device)` 将数据移动到GPU。
3. 确保你的计算环境中已经安装了CUDA驱动和相关的工具包。
以上回复来自 FishC AI 小助理，如未能正确解答您的问题，请继续追问。 FishC 发表于 2024-10-27 16:15
为了让你的代码在GPU上运行，主要需要以下几个步骤：
1. **检查是否有可用的GPU**。
转换成gpu代码报错---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
Cell In, line 96
94 for epoch in range(num_epochs):
95 train_loss = 0
---> 96 for tensor_x, tensor_y in train_iter:#训练集执行梯度更新
97 tensor_x = tensor_x.float()
98 tensor_y = tensor_y.float().reshape(-1, 1)
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:630, in _BaseDataLoaderIter.__next__(self)
627 if self._sampler_iter is None:
628 # TODO(https://github.com/pytorch/pytorch/issues/76750)
629 self._reset()# type: ignore
--> 630 data = self._next_data()
631 self._num_yielded += 1
632 if self._dataset_kind == _DatasetKind.Iterable and \
633 self._IterableDataset_len_called is not None and \
634 self._num_yielded > self._IterableDataset_len_called:
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1344, in _MultiProcessingDataLoaderIter._next_data(self)
1342 else:
1343 del self._task_info
-> 1344 return self._process_data(data)
File /opt/conda/lib/python3.10/site-packages/torch/utils/data/dataloader.py:1370, in _MultiProcessingDataLoaderIter._process_data(self, data)
1368 self._try_put_index()
1369 if isinstance(data, ExceptionWrapper):
-> 1370 data.reraise()
1371 return data
File /opt/conda/lib/python3.10/site-packages/torch/_utils.py:706, in ExceptionWrapper.reraise(self)
702 except TypeError:
703 # If the exception takes multiple arguments, don't try to
704 # instantiate since we don't know how to
705 raise RuntimeError(msg) from None
--> 706 raise exception
RuntimeError: Caught RuntimeError in DataLoader worker process 0.
Original Traceback (most recent call last):
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py", line 309, in _worker_loop
data = fetcher.fetch(index)# type: ignore
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 52, in fetch
data = for idx in possibly_batched_index]
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py", line 52, in <listcomp>
data = for idx in possibly_batched_index]
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataset.py", line 211, in __getitem__
return tuple(tensor for tensor in self.tensors)
File "/opt/conda/lib/python3.10/site-packages/torch/utils/data/dataset.py", line 211, in <genexpr>
return tuple(tensor for tensor in self.tensors)
RuntimeError: CUDA error: initialization error
CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
----------------------------------以下是源代码
#使用dnn模型（k折交叉验证）
import torch
import torch.nn as nn
from torch.utils import data
from torch.utils.data import Dataset,DataLoader
from torch import optim
#定义神经网络模型
class SimpleNN(nn.Module):
def __init__(self):
super(SimpleNN,self).__init__()
self.hidden_layer1 = nn.Linear(154,1024)
self.hidden_layer2 = nn.Linear(1024,1024)
self.hidden_layer3 = nn.Linear(1024,1024)
self.hidden_layer4 = nn.Linear(1024,1024)
self.output_layer = nn.Linear(1024,1)
self.dropout = nn.Dropout(p=0.5)
nn.init.xavier_uniform_(self.hidden_layer1.weight)
nn.init.xavier_uniform_(self.hidden_layer2.weight)
nn.init.xavier_uniform_(self.hidden_layer3.weight)
nn.init.xavier_uniform_(self.hidden_layer4.weight)
nn.init.xavier_uniform_(self.output_layer.weight)
def forward(self,x):
inputs = x
layer1_out = torch.nn.functional.gelu(self.hidden_layer1(inputs))
layer1_out = self.dropout(layer1_out)
layer2_out = torch.nn.functional.gelu(self.hidden_layer2(layer1_out))
layer2_out = self.dropout(layer2_out)
layer3_out = torch.nn.functional.gelu(self.hidden_layer3(layer2_out))
layer3_out = self.dropout(layer3_out)
layer4_out = torch.nn.functional.gelu(self.hidden_layer4(layer3_out))
layer4_out = self.dropout(layer4_out)
output = torch.relu(self.output_layer(layer4_out))
return output
# 设置超参数
k = 5
batch_size = 128
num_epochs = 1000
weight_decay = 0
#初始化模型和优化器
dnn_model = SimpleNN().to(device)# 将模型移到GPU上
optimizer = optim.AdamW(dnn_model.parameters(),lr=0.0001,weight_decay=weight_decay) #定义优化器
#k折交叉验证选取训练集与验证集
def get_k_fold_data(k, i, X, y):
assert k > 1
fold_size = len(X) // k
X_train, y_train = None, None
for j in range(k):
start = j * fold_size
end = (j + 1) * fold_size
if j == i:
X_valid, y_valid = X.iloc, y.iloc
elif X_train is None:
X_train, y_train = X.iloc, y.iloc
else:
X_train = pd.concat(], ignore_index=True)
y_train = pd.concat(], ignore_index=True)
return X_train, y_train, X_valid, y_valid
#初始化列表
train_ls, valid_ls = [], []
for i in range(k):
X_train, y_train, X_valid, y_valid = get_k_fold_data(k, i, X, y)
print(f'FOLD {i}')
print('--------------------------------')
# 将DataFrame数据转换为NumPy数组，然后再转换为PyTorch张量，并且移动到GPU上
X_train = torch.tensor(X_train.astype(np.float32).values, dtype=torch.float32).to(device)
y_train = torch.tensor(y_train.astype(np.float32).values, dtype=torch.float32).to(device)
X_valid = torch.tensor(X_valid.astype(np.float32).values, dtype=torch.float32).to(device)
y_valid = torch.tensor(y_valid.astype(np.float32).values, dtype=torch.float32).to(device)
#创建数据集
train_ds = data.TensorDataset(X_train, y_train)
valid_ds = data.TensorDataset(X_valid, y_valid)
# 获取一个数据迭代器
train_iter = DataLoader(dataset=train_ds,batch_size=batch_size,shuffle=True,num_workers=2)#shuffle=True相当于sampler=RandomSampler(dataset)
valid_iter = DataLoader(dataset=valid_ds,batch_size=batch_size,shuffle=True,num_workers=2)
#开始迭代
for epoch in range(num_epochs):
train_loss = 0
for tensor_x, tensor_y in train_iter:#训练集执行梯度更新
tensor_x = tensor_x.float()
tensor_y = tensor_y.float().reshape(-1, 1)
optimizer.zero_grad() #梯度清零
pre_train = dnn_model(tensor_x)
train_l = MSLE_loss(pre_train, tensor_y) #损失应避免与全局变量loss重名
train_l.backward()#前向传播
optimizer.step()#梯度下降
train_loss += train_l.item() * len(tensor_x)
train_loss /= len(train_ds) #每次迭代平均损失
if epoch % 200 == 0:
print('Loss: {}Epoch:{}'.format(train_loss, epoch))
#print(train_l)
with torch.no_grad():
valid_loss = 0
for tensor_x, tensor_y in valid_iter:
tensor_x = tensor_x.float()
tensor_y = tensor_y.float().reshape(-1, 1)
pre_valid = dnn_model(tensor_x)
valid_l = MSLE_loss(pre_valid, tensor_y)
valid_loss += valid_l.item() * len(tensor_x)
valid_loss /= len(valid_ds)
if epoch % 200 == 0:
print('Valid Loss: {}Epoch:{}'.format(valid_loss, epoch))
#print(valid_l)
#将每折的损失添加到列表中
train_ls.append(train_loss)
valid_ls.append(valid_loss)
print('Training Ended')
print('Train Average Loss: {} Valid Average Loss: {}'.format(np.mean(train_ls),np.mean(valid_ls))) Terence888 发表于 2024-10-27 16:37
转换成gpu代码报错--------------------------------- ...
好的，让我们一步一步来分析和解决这个问题。
### 问题分析
错误信息显示一个 `CUDA error: initialization error`。这通常不是代码本身的问题，而是由于环境或并行处理引起的。在你的代码里，我们使用 `num_workers` 参数，并行处理数据加载，可能导致CUDA初始化错误。
### 解决方案
1. 确保你的代码中正确处理了设备选择和数据移动。
2. 在数据加载器中设置 `num_workers=0` 以避免多进程数据加载的问题，特别是在调试时。
### 修改后的代码
下面是修改后的代码，你可以按照这个示例来运行：
import torch
import torch.nn as nn
from torch.utils import data
from torch.utils.data import Dataset, DataLoader
from torch import optim
import numpy as np
import pandas as pd
# 检查GPU是否可用
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# 定义神经网络模型
class SimpleNN(nn.Module):
def __init__(self):
super(SimpleNN, self).__init__()
self.hidden_layer1 = nn.Linear(154, 1024)
self.hidden_layer2 = nn.Linear(1024, 1024)
self.hidden_layer3 = nn.Linear(1024, 1024)
self.hidden_layer4 = nn.Linear(1024, 1024)
self.output_layer = nn.Linear(1024, 1)
self.dropout = nn.Dropout(p=0.5)
nn.init.xavier_uniform_(self.hidden_layer1.weight)
nn.init.xavier_uniform_(self.hidden_layer2.weight)
nn.init.xavier_uniform_(self.hidden_layer3.weight)
nn.init.xavier_uniform_(self.hidden_layer4.weight)
nn.init.xavier_uniform_(self.output_layer.weight)
def forward(self, x):
inputs = x
layer1_out = torch.nn.functional.gelu(self.hidden_layer1(inputs))
layer1_out = self.dropout(layer1_out)
layer2_out = torch.nn.functional.gelu(self.hidden_layer2(layer1_out))
layer2_out = self.dropout(layer2_out)
layer3_out = torch.nn.functional.gelu(self.hidden_layer3(layer2_out))
layer3_out = self.dropout(layer3_out)
layer4_out = torch.nn.functional.gelu(self.hidden_layer4(layer3_out))
layer4_out = self.dropout(layer4_out)
output = torch.relu(self.output_layer(layer4_out))
return output
# 设置超参数
k = 5
batch_size = 128
num_epochs = 1000
weight_decay = 0
learning_rate = 0.0001
# 初始化模型和优化器
dnn_model = SimpleNN().to(device)
optimizer = optim.AdamW(dnn_model.parameters(), lr=learning_rate, weight_decay=weight_decay)
# k折交叉验证选取训练集与验证集
def get_k_fold_data(k, i, X, y):
assert k > 1
fold_size = len(X) // k
X_train, y_train = None, None
for j in range(k):
start = j * fold_size
end = (j + 1) * fold_size
X_part, y_part = X, y
if j == i:
X_valid, y_valid = X_part, y_part
elif X_train is None:
X_train, y_train = X_part, y_part
else:
X_train = pd.concat(, ignore_index=True)
y_train = pd.concat(, ignore_index=True)
return X_train, y_train, X_valid, y_valid
# 初始化列表
train_ls, valid_ls = [], []
for i in range(k):
X_train, y_train, X_valid, y_valid = get_k_fold_data(k, i, X, y)
print(f'FOLD {i}')
print('--------------------------------')
# 将DataFrame数据转换为NumPy数组，然后再转换为PyTorch张量，并且移动到GPU上
X_train = torch.tensor(X_train.astype(np.float32).values, dtype=torch.float32).to(device)
y_train = torch.tensor(y_train.astype(np.float32).values, dtype=torch.float32).to(device)
X_valid = torch.tensor(X_valid.astype(np.float32).values, dtype=torch.float32).to(device)
y_valid = torch.tensor(y_valid.astype(np.float32).values, dtype=torch.float32).to(device)
# 创建数据集
train_ds = data.TensorDataset(X_train, y_train)
valid_ds = data.TensorDataset(X_valid, y_valid)
# 获取一个数据迭代器
train_iter = DataLoader(dataset=train_ds, batch_size=batch_size, shuffle=True, num_workers=0)
valid_iter = DataLoader(dataset=valid_ds, batch_size=batch_size, shuffle=False, num_workers=0)
# 开始迭代
for epoch in range(num_epochs):
train_loss = 0
for tensor_x, tensor_y in train_iter:# 训练集执行梯度更新
tensor_x = tensor_x.float()
tensor_y = tensor_y.float().reshape(-1, 1)
optimizer.zero_grad()# 梯度清零
pre_train = dnn_model(tensor_x)
train_l = MSLE_loss(pre_train, tensor_y)# 损失应避免与全局变量loss重名
train_l.backward()# 前向传播
optimizer.step()# 梯度下降
train_loss += train_l.item() * len(tensor_x)
train_loss /= len(train_ds)# 每次迭代平均损失
if epoch % 200 == 0:
print('Loss: {}Epoch: {}'.format(train_loss, epoch))
with torch.no_grad():
valid_loss = 0
for tensor_x, tensor_y in valid_iter:
tensor_x = tensor_x.float()
tensor_y = tensor_y.float().reshape(-1, 1)
pre_valid = dnn_model(tensor_x)
valid_l = MSLE_loss(pre_valid, tensor_y)
valid_loss += valid_l.item() * len(tensor_x)
valid_loss /= len(valid_ds)
if epoch % 200 == 0:
print('Valid Loss: {}Epoch: {}'.format(valid_loss, epoch))
# 将每折的损失添加到列表中
train_ls.append(train_loss)
valid_ls.append(valid_loss)
print('Training Ended')
print('Train Average Loss: {} Valid Average Loss: {}'.format(np.mean(train_ls), np.mean(valid_ls)))
### 关键点
1. 将数据加载器的 `num_workers` 参数设置为 `0` 以避免 CUDA 初始化错误。
2. 确保所有的张量和模型都正确地移动到 GPU 上： `tensor.to(device)` 和 `model.to(device)`。
3. 确保所有的计算都在 GPU 上进行，包含损失计算和优化器步骤。
### 注意
- `MSLE_loss` 需要在代码中定义，这个示例假设你已经定义了这个函数。
- 确保你的计算环境中有足够的 GPU 资源，否则可能仍然会遇到运行时错误。
通过上述修改，你应该能够成功地在 GPU 上运行你的深度神经网络模型。
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