When I add workers to neutral network I get an error, pytorch - neural-network

Question

I have checked a lot of post and none of them seem to work for me. But when I try to add workers to the dataloader in pytorch it just feeds me an error back. I have tired reading it and figuring it out but I can't seem to find a solution. I assume there is something I'm supposed to add to make the workers able to do their job.
I have 64GB of ram, i9-9900k, and a 3080ti. So I don't think its a memory error is it?
I included the error code with 1 worker and 4 workers because they seem to be different.
Also it works with zero workers.
here is the error with 4 workers:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\multiprocessing\spawn.py", line 114, in _main
prepare(preparation_data)
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\multiprocessing\spawn.py", line 225, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
run_name="__mp_main__")
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
Traceback (most recent call last):
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 990, in _try_get_data
data = self._data_queue.get(timeout=timeout)
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\queue.py", line 172, in get raise Empty
queue.Empty
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "c:/Users/14055/Desktop/Class 1 Project/Chegg.py", line 202, in <module>
training()
File "c:/Users/14055/Desktop/Class 1 Project/Chegg.py", line 122, in training
for data, target in load_data.train_loader:
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 521, in __next__ data = self._next_data()
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 1186, in _next_data idx, data = self._get_data()
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 1142, in _get_data success, data = self._try_get_data()
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 1003, in _try_get_data
raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) from e
RuntimeError: DataLoader worker (pid(s) 23204, 7668, 13636, 6132) exited unexpectedly
Error with 1 worker:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\multiprocessing\spawn.py", line 114, in _main
prepare(preparation_data)
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\multiprocessing\spawn.py", line 225, in prepare
_fixup_main_from_path(data['init_main_from_path'])
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
run_name="__mp_main__")
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\runpy.py", line 263, in run_path
pkg_name=pkg_name, script_name=fname)
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\runpy.py", line 96, in _run_module_code
mod_name, mod_spec, pkg_name, script_name)
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "c:\Users\14055\Desktop\Class 1 Project\Chegg.py", line 202, in <module>
training()
File "c:\Users\14055\Desktop\Class 1 Project\Chegg.py", line 122, in training
for data, target in load_data.train_loader:
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 359, in __iter__
return self._get_iterator()
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 305, in _get_iterator
return _MultiProcessingDataLoaderIter(self)
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 918, in __init__
w.start()
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\multiprocessing\process.py", line 105, in start
self._popen = self._Popen(self)
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\multiprocessing\context.py", line 223, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\multiprocessing\context.py", line 322, in _Popen
return Popen(process_obj)
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\multiprocessing\popen_spawn_win32.py", line 33, in __init__
prep_data = spawn.get_preparation_data(process_obj._name)
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\multiprocessing\spawn.py", line 143, in get_preparation_data
_check_not_importing_main()
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\multiprocessing\spawn.py", line 136, in _check_not_importing_main
This probably means that you are not using fork to start your
child processes and you have forgotten to use the proper idiom
in the main module:
if __name__ == '__main__':
freeze_support()
...
The "freeze_support()" line can be omitted if the program
is not going to be frozen to produce an executable.
Traceback (most recent call last):
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 990, in _try_get_data
data = self._data_queue.get(timeout=timeout)
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\queue.py", line 172, in get
raise Empty
queue.Empty
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "c:/Users/14055/Desktop/Class 1 Project/Chegg.py", line 202, in <module>
training()
File "c:/Users/14055/Desktop/Class 1 Project/Chegg.py", line 122, in training
for data, target in load_data.train_loader:
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 521, in __next__
data = self._next_data()
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 1186, in _next_data
idx, data = self._get_data()
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 1142, in _get_data
success, data = self._try_get_data()
File "C:\Users\14055\AppData\Local\Programs\Python\Python36\lib\site-packages\torch\utils\data\dataloader.py", line 1003, in _try_get_data
raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str)) from e
RuntimeError: DataLoader worker (pid(s) 3372) exited unexpectedly
Code:
from numpy import testing
import torch.cuda
import numpy as np
import time
import array as arr
import os
from datetime import date, datetime
from torchvision import datasets
import torchvision.transforms as transforms
import matplotlib.pyplot as plt
import torch.nn as nn
import torch.nn.functional as F
from torchsummary import summary
torch.cuda.set_device(0)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
def load_data():
num_workers = 1
load_data.batch_size = 20
transform = transforms.ToTensor()
train_data = datasets.MNIST(root='data', train=True, download=True, transform=transform)
load_data.train_loader = torch.utils.data.DataLoader(train_data,
batch_size=load_data.batch_size, num_workers=num_workers, pin_memory=True,
shuffle=True)
test_data = datasets.MNIST(root='data', train=False, download=True, transform=transform)
load_data.test_loader = torch.utils.data.DataLoader(test_data,
batch_size=load_data.batch_size, num_workers=num_workers, pin_memory=True,
shuffle=True)
def visualize():
dataiter = iter(load_data.train_loader)
visualize.images, labels = dataiter.next()
visualize.images = visualize.images.numpy()
fig = plt.figure(figsize=(25, 4))
for idx in np.arange(load_data.batch_size):
ax = fig.add_subplot(2, load_data.batch_size/2, idx+1, xticks=[], yticks=[])
ax.imshow(np.squeeze(visualize.images[idx]), cmap='gray')
ax.set_title(str(labels[idx].item()))
#plt.show()
def fig_values():
img = np.squeeze(visualize.images[1])
fig = plt.figure(figsize = (12,12))
ax = fig.add_subplot(111)
ax.imshow(img, cmap='gray')
width, height = img.shape
thresh = img.max()/2.5
for x in range(width):
for y in range(height):
val = round(img[x][y],2) if img[x][y] !=0 else 0
ax.annotate(str(val), xy=(y,x),
horizontalalignment='center',
verticalalignment='center',
color='white' if img[x][y]<thresh else 'black')
#plt.show()
load_data()
#visualize()
#fig_values()
class NeuralNet(nn.Module):
def __init__(self, gpu = True):
super(NeuralNet, self ).__init__()
self.conv1 = nn.Conv2d(in_channels=1, out_channels=128, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(num_features=128)
self.tns1 = nn.Conv2d(in_channels=128, out_channels=4, kernel_size=1, padding=1)
self.conv2 = nn.Conv2d(in_channels=4, out_channels=16, kernel_size=3, padding=1)
self.bn2 = nn.BatchNorm2d(num_features=16)
self.pool1 = nn.MaxPool2d(2,2)
self.conv3 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, padding=1)
self.bn3 = nn.BatchNorm2d(num_features=16)
self.conv4 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=1)
self.bn4 = nn.BatchNorm2d(num_features=32)
self.pool2 = nn.MaxPool2d(2,2)
self.tns2 = nn.Conv2d(in_channels=32, out_channels=16, kernel_size=1, padding=1)
self.conv5 = nn.Conv2d(in_channels=16, out_channels=16, kernel_size=3, padding=1)
self.bn5 = nn.BatchNorm2d(num_features=16)
self.conv6 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, padding=1)
self.bn6 = nn.BatchNorm2d(num_features=32)
self.conv7 = nn.Conv2d(in_channels=32, out_channels=10, kernel_size=1, padding=1)
self.gpool = nn.AvgPool2d(kernel_size=7)
self.drop = nn.Dropout2d(0.1)
def forward(self, x):
x = self.tns1(self.drop(self.bn1(F.relu(self.conv1(x)))))
x = self.drop(self.bn2(F.relu(self.conv2(x))))
x = self.pool1(x)
x = self.drop(self.bn3(F.relu(self.conv3(x))))
x = self.drop(self.bn4(F.relu(self.conv4(x))))
x = self.tns2(self.pool2(x))
x = self.drop(self.bn5(F.relu(self.conv5(x))))
x = self.drop(self.bn6(F.relu(self.conv6(x))))
x = self.conv7(x)
x = self.gpool(x)
x = x.view(-1, 10)
return F.log_softmax(x).to(device)
#has antioverfit
def training():
model.to(device)
optimizer= torch.optim.SGD(model.parameters(), lr=0.003, weight_decay= 0.00005, momentum = .9, nesterov = True)
n_epochs = 20000
a = np.float64([9,9,9,9,9]) #antioverfit
testing_loss = 0.0
for epoch in range(n_epochs) :
if(testing_loss <= a[4]): # part of anti overfit
train_loss = 0.0
testing_loss = 0.0
model.train().to(device)
for data, target in load_data.train_loader:
optimizer.zero_grad()
data = data.to(device) #gpu
target = target.to(device) #gpu
output = model(data).to(device)
loss = F.nll_loss(output, target)
loss.backward()
optimizer.step()
train_loss += loss.item()*data.size(0)
train_loss = train_loss/len(load_data.train_loader.dataset)
print('Epoch: {} \tTraining Loss: {:.6f}'.format(epoch+1, train_loss))
model.eval().to(device) # Gets Validation loss
train_loss = 0.0
with torch.no_grad():
for data, target in load_data.test_loader:
data = data.to(device)
target = target.to(device)
output = model(data).to(device)
loss =F.nll_loss(output, target)
testing_loss += loss.item()*data.size(0)
testing_loss = testing_loss / len(load_data.test_loader.dataset)
print('Validation loss = ' , testing_loss)
a = np.insert(a,0,testing_loss) # part of anti overfit
a = np.delete(a,5)
print('Validation loss = ' , testing_loss)
def evalution():
test_loss = 0.0
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
model.eval().to(device)
for data, target in load_data.test_loader:
data = data.to(device)
target = target.to(device)
output = model(data).to(device)
loss =F.nll_loss(output, target)
test_loss += loss.item()*data.size(0)
_, pred = torch.max(output, 1)
correct = np.squeeze(pred.eq(target.data.view_as(pred))).to(device)
for i in range(load_data.batch_size):
try:
label = target.data[i]
class_correct[label] += correct[i].item()
class_total[label] += 1
except IndexError:
break
# calculate and print avg test loss
test_loss = test_loss/len(load_data.test_loader.dataset)
print('Test Loss: {:.6f}\n'.format(test_loss))
for i in range(10):
if class_total[i] > 0:
print('Test Accuracy of %5s: %2d%% (%2d/%2d)' % (
str(i), 100 * class_correct[i] / class_total[i],
np.sum(class_correct[i]), np.sum(class_total[i])))
else:
print('Test Accuracy of %5s: N/A (no training examples)' )
print('\nTest Accuracy (Overall): %2d%% (%2d/%2d)' % (
100. * np.sum(class_correct) / np.sum(class_total),
np.sum(class_correct), np.sum(class_total)))
acc = (
100. * np.sum(class_correct) / np.sum(class_total),
np.sum(class_correct), np.sum(class_total))
name = f"model-{acc}.pt"
name2 = f"model-{acc}.pth"
save_path = os.path.join("models", name)
save_path2 = os.path.join("models", name2)
torch.save(model, save_path)
torch.save(model, save_path2)
model = NeuralNet().to(device)
summary(model, input_size=(1, 28, 28))
training()
evalution()