I am trying to run DDP or DP with two A100 gpus on server, but whenever the machine accesses the computed loss, the process freezes infinitely. I have to exit the process with ctrl+z and manually kill the process.
I made a simple script which trains resnet18 on CIFAR-10, with or without DP.
The script runs smoothly when DP is disabled. (on both A100 and non-A100 GPUs)
Additionally, the same script runs fine on other servers with 3090 GPUs.
The problem happens when DP is enabled and trained on A100 GPUs.
Therefore, I think that pytorch’s DP or DDP does not work with A100 GPU.
Here is my server spec:
OS: Ubuntu 20.04 Server (updated to the latest)
CPU: Intel Xeon Gold 6226R x2
RAM: 256GB
GPUs: A100 x2
And this is software spec:
anaconda: 4.12.0
pytorch: 1.12 + cuda 11.6
cuda: 11.6
cudnn: 8.4.1
# https://github.com/pytorch/tutorials/blob/master/beginner_source/blitz/cifar10_tutorial.py
import torch
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
from torchvision.models import resnet18, ResNet18_Weights
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
import matplotlib.pyplot as plt
import numpy as np
from tqdm import tqdm
DP = True
#DP = False
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
batch_size = 4
num_workers = 4
cudnn.benchmark = True
torch.backends.cudnn.enabled = True
trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=batch_size,
shuffle=True, num_workers=num_workers,
pin_memory=True)
testset = torchvision.datasets.CIFAR10(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=batch_size,
shuffle=False, num_workers=num_workers)
classes = ('plane', 'car', 'bird', 'cat',
'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
class ResNetWrapper(nn.Module):
def __init__(self):
super().__init__()
self.resnet = resnet18(weights=ResNet18_Weights.IMAGENET1K_V1)
self.loss = nn.CrossEntropyLoss()
def forward(self, x, gt=None):
output = self.resnet(x)
if gt is not None:
return self.loss(output, gt)
return output
print('Initializing Model')
#net = resnet18(weights=ResNet18_Weights.IMAGENET1K_V1)
net = ResNetWrapper()
if DP:
net = torch.nn.DataParallel(net).cuda()
else:
net = net.cuda()
#criterion = nn.CrossEntropyLoss()
#optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
optimizer = optim.Adam(net.parameters(), lr=0.001)
print("Start Training")
first_iteration = True
for epoch in range(2): # loop over the dataset multiple times
running_loss = 0.0
pbar = tqdm(total=len(trainloader))
for i, data in enumerate(trainloader, 0):
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data
if not DP:
inputs = inputs.cuda()
labels = labels.cuda()
if first_iteration:
print("passing data to device")
# zero the parameter gradients
optimizer.zero_grad()
if first_iteration:
print("forwarding input")
# forward + backward + optimize
loss = net(inputs, labels)
loss = loss.sum()
if first_iteration:
print("backwarding loss")
loss.backward()
if first_iteration:
print("updating with optimizer")
first_iteration = False
optimizer.step()
"""
# print statistics
loss_val = loss.detach().cpu().item()
running_loss += loss_val
if i % 2000 == 1999: # print every 2000 mini-batches
print(f'[{epoch + 1}, {i + 1:5d}] loss: {running_loss / 2000:.3f}')
running_loss = 0.0
"""
pbar.update(1)
pbar.close()
print('Finished Training')
########################################################################
# Let's quickly save our trained model:
PATH = './cifar_net.pth'
torch.save(net.state_dict(), PATH)
net = ResNetWrapper()
net.load_state_dict(torch.load(PATH))
correct = 0
total = 0
# prepare to count predictions for each class
correct_pred = {classname: 0 for classname in classes}
total_pred = {classname: 0 for classname in classes}
# since we're not training, we don't need to calculate the gradients for our outputs
with torch.no_grad():
for data in testloader:
images, labels = data
# calculate outputs by running images through the network
outputs = net(images)
# the class with the highest energy is what we choose as prediction
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
for label, prediction in zip(labels, predictions):
if label == prediction:
correct_pred[classes[label]] += 1
total_pred[classes[label]] += 1
print(f'Accuracy of the network on the 10000 test images: {100 * correct // total} %')
# print accuracy for each class
for classname, correct_count in correct_pred.items():
accuracy = 100 * float(correct_count) / total_pred[classname]
print(f'Accuracy for class: {classname:5s} is {accuracy:.1f} %')
# %%%%%%INVISIBLE_CODE_BLOCK%%%%%%
del dataiter