微网站制作,张家港企业网站,时事新闻热点事件,美橙互联网站建设好不好01 环境搭建 《PyTorch基础教程》01 搭建环境 基于Docker搭建ubuntu22+Python3.10+Pytorch2+cuda11+jupyter的开发环境 Docker部署PyTorch 拉取cnstark/pytorch镜像 拉取镜像： docker pull cnstark/pytorch:2.0.1-py3.10.11-cuda11.8.0-ubuntu22.04导出镜像： docker sa…01 环境搭建 《PyTorch基础教程》01 搭建环境 基于Docker搭建ubuntu22+Python3.10+Pytorch2+cuda11+jupyter的开发环境 Docker部署PyTorch 拉取cnstark/pytorch镜像 拉取镜像： docker pull cnstark/pytorch:2.0.1-py3.10.11-cuda11.8.0-ubuntu22.04导出镜像： docker save -o pytorch2_python310_cuda11_ubuntu22.tar cnstark/pytorch:2.0.1-py3.10.11-cuda11.8.0-ubuntu22.04导入镜像： docker load -i pytorch2_python310_ubuntu22.tar运行镜像： mkdir -p /docker/pytorch/project mkdir -p /docker/pytorch/dataset docker run --name pytorch -itd -v /docker/pytorch/project:/workspace -v /docker/pytorch/dataset:/workspace/dataset cnstark/pytorch:2.0.1-py3.10.11-cuda11.8.0-ubuntu22.04# 开启GPU docker run --name pytorch --gpus all -itd -v /docker/pytorch/project:/workspace -v /docker/pytorch/dataset:/workspace/dataset cnstark/pytorch:2.0.1-py3.10.11-cuda11.8.0-ubuntu22.04测试PyTorch脚本 main.py # -*- coding: utf-8 -*-import torchdtype = torch.FloatTensor # dtype = torch.cuda.FloatTensor # Uncomment this to run on GPU# N is batch size; D_in is input dimension; # H is hidden dimension; D_out is output dimension. N, D_in, H, D_out = 64, 1000, 100, 10# Create random input and output data x = torch.randn(N, D_in).type(dtype) y = torch.randn(N, D_out).type(dtype)# Randomly initialize weights w1 = torch.randn(D_in, H).type(dtype) w2 = torch.randn(H, D_out).