在Public_cluster使用Conda安装pytorch最新版本

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环境要求

已安装conda环境，如未安装，可参照Public_cluster Conda使用指南

申请GPU资源

在Public_cluster申请一个GPU计算节点，指定gpu队列，2个CPU核、1块GPU卡，指定最大占用时间为1小时。

[wzq@workstation ~]$salloc -N 1 -n 2 -p gpu --gres=gpu:1 -t 1:00:00
salloc: Pending job allocation 3452  ###生成任务ID为3452
salloc: Waiting for resource configuration
salloc: Nodes gpu2 are ready for job  ###分配gpu1节点供任务使用

SSH登录到分配到的GPU计算节点上。

(base) [wzq@workstation ~]$ ssh gpu2  ###登录gpu1节点
Warning: Permanently added 'gpu1,192.168.0.3' (ECDSA) to the list of known hosts.
Last login: Fri Dec 31 03:19:41 2021 from 192.168.0.1

安装pytorch最新版本

创建一个conda环境，命名pytorch2.2，安装python3.8版本

(base) [wzq@gpu2 ~]$ conda create -n pytorch2.2 python=3.8

安装完成

#
# To activate this environment, use
#
#     $ conda activate pytorch2.2
#
# To deactivate an active environment, use
#
#     $ conda deactivate

激活conda环境

(base) [wzq@gpu2 ~]$ conda activate pytorch2.2
(pytorch2.2) [wzq@gpu2 ~]$

安装pytorch，使用清华源

pip3 install torch torchvision torchaudio -i https://pypi.tuna.tsinghua.edu.cn/simple

安装完成

Installing collected packages: mpmath, typing-extensions, sympy, pillow, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, numpy, networkx, MarkupSafe, fsspec, filelock, triton, nvidia-cusparse-cu12, nvidia-cudnn-cu12, jinja2, nvidia-cusolver-cu12, torch, torchvision, torchaudio
Successfully installed MarkupSafe-2.1.5 filelock-3.13.3 fsspec-2024.3.1 jinja2-3.1.3 mpmath-1.3.0 networkx-3.1 numpy-1.24.4 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 pillow-10.3.0 sympy-1.12 torch-2.2.2 torchaudio-2.2.2 torchvision-0.17.2 triton-2.2.0 typing-extensions-4.10.0

运行测试环境，上传测试程序cifar10_tutorial.py

安装测试程序依赖：

(pytorch2.2) [wzq@gpu2 ~]$ pip3 install matplotlib -i https://pypi.tuna.tsinghua.edu.cn/simple

运行测试程序：

(pytorch2.2) [wzq@gpu2 ~]$ python cifar10_tutorial.py

显示运行结果：

truck  ship  deer  bird
[1,  2000] loss: 2.235
[1,  4000] loss: 1.885
[1,  6000] loss: 1.716
[1,  8000] loss: 1.583
[1, 10000] loss: 1.500
[1, 12000] loss: 1.469
[2,  2000] loss: 1.387
[2,  4000] loss: 1.361
[2,  6000] loss: 1.379
[2,  8000] loss: 1.306
[2, 10000] loss: 1.322
[2, 12000] loss: 1.290
Finished Training
GroundTruth:    cat  ship  ship plane
Predicted:    cat plane plane plane
Accuracy of the network on the 10000 test images: 54 %
Accuracy of plane : 77 %
Accuracy of   car : 64 %
Accuracy of  bird : 45 %
Accuracy of   cat : 22 %
Accuracy of  deer : 40 %
Accuracy of   dog : 40 %
Accuracy of  frog : 82 %
Accuracy of horse : 62 %
Accuracy of  ship : 51 %
Accuracy of truck : 56 %
cuda:0

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测试代码：

import torch
import torchvision
import torchvision.transforms as transforms
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

trainset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                        download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=4,
                                          shuffle=True, num_workers=2)

testset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                       download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=4,
                                         shuffle=False, num_workers=2)

classes = ('plane', 'car', 'bird', 'cat',
           'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
import matplotlib.pyplot as plt
import numpy as np
def imshow(img):
    img = img / 2 + 0.5     # unnormalize
    npimg = img.numpy()
    plt.imshow(np.transpose(npimg, (1, 2, 0)))


# get some random training images
dataiter = iter(trainloader)
images, labels = next(dataiter)
next
# show images
imshow(torchvision.utils.make_grid(images))
# print labels
print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool(F.relu(self.conv1(x)))
        x = self.pool(F.relu(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()

import torch.optim as optim

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

for epoch in range(2):  # loop over the dataset multiple times

    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        # get the inputs
        inputs, labels = data

        # zero the parameter gradients
        optimizer.zero_grad()

        # forward + backward + optimize
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

        # print statistics
        running_loss += loss.item()
        if i % 2000 == 1999:    # print every 2000 mini-batches
            print('[%d, %5d] loss: %.3f' %
                  (epoch + 1, i + 1, running_loss / 2000))
            running_loss = 0.0

print('Finished Training')

dataiter = iter(testloader)
images, labels = next(dataiter)

# print images
imshow(torchvision.utils.make_grid(images))
print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
outputs = net(images)

_, predicted = torch.max(outputs, 1)

print('Predicted: ', ' '.join('%5s' % classes[predicted[j]]
                              for j in range(4)))

correct = 0
total = 0
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
    100 * correct / total))
class_correct = list(0. for i in range(10))
class_total = list(0. for i in range(10))
with torch.no_grad():
    for data in testloader:
        images, labels = data
        outputs = net(images)
        _, predicted = torch.max(outputs, 1)
        c = (predicted == labels).squeeze()
        for i in range(4):
            label = labels[i]
            class_correct[label] += c[i].item()
            class_total[label] += 1


for i in range(10):
    print('Accuracy of %5s : %2d %%' % (
        classes[i], 100 * class_correct[i] / class_total[i]))

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# Assume that we are on a CUDA machine, then this should print a CUDA device:

print(device)