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流程可参考:https://www.bilibili.com/video/BV1mX4y177dJ
注意:中间可能需要使用命令wsl --update
更新一下wsl。
按照下面流程:开始菜单->设置->应用->安装的应用->搜索“ubuntu”->点选3个点->移动->移动到"非C盘的其他盘"
参考:https://www.bilibili.com/video/BV1mX4y177dJ
启动图形化界面的命令是sudo startxfce4
参考:https://zhuanlan.zhihu.com/p/683058297
nvidia-smi
命令的解释参考:https://blog.csdn.net/daydayup858/article/details/131633445
注意:一定要先查看nvidia-smi
命令中的显示的支持的cuda的最高版本,然后再去下载对应版本的cuda。
如果装错了cuda版本,可以参考这篇文章卸载已安装的cuda:https://blog.csdn.net/ziqibit/article/details/129935737
如果显示nvcc not found
,请参考:https://blog.csdn.net/Maggie_JK/article/details/132666245
参考:https://zhuanlan.zhihu.com/p/683058297
卸载cuDNN的方式是和卸载cuda类似的。
完成安装后可以使用参考链接的内容进行验证,cuDNN是否正确安装。
参考:https://zhuanlan.zhihu.com/p/683058297
安装完conda之后,创建新的环境,并切换到新环境
参考:https://zhuanlan.zhihu.com/p/683058297
去vscode官网安装vscode
vscode上安装wsl扩展包
之后参考https://zhuanlan.zhihu.com/p/683058297
https://learn.microsoft.com/zh-cn/windows/wsl/tutorials/wsl-vscode
参考:https://www.bilibili.com/video/BV1B14y1W7z3
我的测试结果:
如何优雅的使用WSL:https://www.bilibili.com/video/BV1Ku4y1f7nq
参考资料:https://www.bilibili.com/video/BV1gk4y1M76B
参考视频:https://www.bilibili.com/video/BV1j14y1o7k7
如何修改
python console
对应的python默认环境。
一般默认环境是/usr/bin/python3
要修改默认环境,就需要修改这个project的环境
可以通过setting进入修改
如何继续修改pycharm中的python console的默认环境。
参考:PyCharm中如何设置切换Python Console终端的Python版本
参考资料:https://www.bilibili.com/video/BV1t14y1o7vb
from __future__ import print_function import argparse import time import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout(0.25) self.dropout2 = nn.Dropout(0.5) self.fc1 = nn.Linear(9216, 128) self.fc2 = nn.Linear(128, 10) def forward(self, x): x = self.conv1(x) x = F.relu(x) x = self.conv2(x) x = F.relu(x) x = F.max_pool2d(x, 2) x = self.dropout1(x) x = torch.flatten(x, 1) x = self.fc1(x) x = F.relu(x) x = self.dropout2(x) x = self.fc2(x) output = F.log_softmax(x, dim=1) return output def train(args, model, device, train_loader, optimizer, epoch): model.train() for batch_idx, (data, target) in enumerate(train_loader): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) if args.dry_run: break def test(model, device, test_loader): model.eval() for batch_idx, (data, target) in enumerate(test_loader): data, target = data.to(device), target.to(device) output = model(data) loss = F.nll_loss(output, target) print('Test: [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( batch_idx * len(data), len(test_loader.dataset), 0.1 * batch_idx * len(data) / len(test_loader), loss.item())) def main(): # Training settings parser = argparse.ArgumentParser(description='PyTorch MNIST Example') parser.add_argument('--batch-size', type=int, default=64, metavar='N', help='input batch size for training (default: 64)') parser.add_argument('--test-batch-size', type=int, default=1000, metavar='N', help='input batch size for testing (default: 1000)') parser.add_argument('--epochs', type=int, default=1, metavar='N', help='number of epochs to train (default: 14)') parser.add_argument('--lr', type=float, default=1.0, metavar='LR', help='learning rate (default: 1.0)') parser.add_argument('--gamma', type=float, default=0.7, metavar='M', help='Learning rate step gamma (default: 0.7)') parser.add_argument('--no-cuda', action='store_true', default=False, help='disables CUDA training') parser.add_argument('--use_gpu', action='store_true', default=False, help='enable MPS') parser.add_argument('--dry-run', action='store_true', default=False, help='quickly check a single pass') parser.add_argument('--seed', type=int, default=1, metavar='S', help='random seed (default: 1)') parser.add_argument('--log-interval', type=int, default=10, metavar='N', help='how many batches to wait before logging training status') parser.add_argument('--save-model', action='store_true', default=False, help='For Saving the current Model') args = parser.parse_args() use_gpu = args.use_gpu torch.manual_seed(args.seed) device = torch.device("mps" if args.use_gpu else "cpu") train_kwargs = {'batch_size': args.batch_size} test_kwargs = {'batch_size': args.test_batch_size} if use_gpu: cuda_kwargs = {'num_workers': 1, 'pin_memory': True, 'shuffle': True} train_kwargs.update(cuda_kwargs) test_kwargs.update(cuda_kwargs) transform=transforms.Compose([ transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)) ]) dataset1 = datasets.MNIST('./data', train=True, download=True, transform=transform) dataset2 = datasets.MNIST('./data', train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1,**train_kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs) model = Net().to(device) optimizer = optim.Adadelta(model.parameters(), lr=args.lr) scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma) for epoch in range(1, args.epochs + 1): train(args, model, device, train_loader, optimizer, epoch) test(model, device, test_loader) scheduler.step() if __name__ == '__main__': t0 = time.time() main() t1 = time.time() print('time_cost:', t1 - t0)
类似windows上的xshell和fstp
官网:https://royalapps.com/ts/mac/features
基本命令
# 创建tmux会话
tmux new -s test
# 退出会话
tmux detach
# 进入会话
tmux at -t name # at实际上是attach的简写
一定要进tmux会话里面再运行实验代码
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