Ubuntu上安装与使用PyTorch可视化工具
一 常用工具与适用场景
二 安装与环境准备
python3 -m venv ~/venvs/torchvizsource ~/venvs/torchviz/bin/activatepip install -U pippip install torch torchvisionpip install tensorboard matplotlib seaborn pandaspip install torchinfo torchvizpip install visdompip install netronsudo apt update && sudo apt install -y graphvizdot -version(应输出版本号)。三 快速上手示例
from torch.utils.tensorboard import SummaryWriterwriter = SummaryWriter('runs/exp1')for epoch in range(10): loss = 0.9 ** epoch; writer.add_scalar('Loss/train', loss, epoch)writer.close()tensorboard --logdir=runs,浏览器访问 http://localhost:6006。python -m visdom.serverimport visdom; import torchvis = visdom.Visdom(); x = torch.arange(10); y = torch.randn(10)vis.line(X=x, Y=y, opts=dict(title='Line Plot'))from torchinfo import summaryimport torchvision.models as modelsmodel = models.resnet18(); summary(model, input_size=(1, 3, 224, 224))import torch; from torchviz import make_dotfrom torchvision.models import resnet18model = resnet18(); x = torch.randn(1, 3, 224, 224)out = model(x); dot = make_dot(out, params=dict(model.named_parameters()))dot.render("resnet18", format="pdf")torch.save(model.state_dict(), "resnet18.pth")netron resnet18.pth(默认端口),浏览器访问提示地址(常见为 http://localhost:8080)。四 常见问题与排查
tensorboard --logdir=runs --port 6007 或 netron model.pth --port 8081 更换端口;确保进程未占用对应端口。sudo apt install -y graphviz 并确认 dot -version 有输出;Torchviz渲染依赖系统Graphviz。--bind_all 并通过服务器IP访问。tensorboard、visdom、netron 的Python与安装包一致(激活同一虚拟环境)。pip install tensorboardX 并保持与TensorBoard版本兼容。