做自己的程序设计在线测评网站张北网站seo

conda

退出 conda 环境

conda deactivate

进入都d2l环境

conda activate d2l

启动jupyter notebook:

jupyter notebook

python

列出所有安装的包

pip lsit

环境安装指令

安装miniconda

mkdir -p ~/miniconda3
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O ~/miniconda3/miniconda.sh
bash ~/miniconda3/miniconda.sh -b -u -p ~/miniconda3
rm -rf ~/miniconda3/miniconda.sh

新建d2L环境

conda create --name d2l python=3.9 -y

激活d2l环境

conda activate d2l

安装cpu版本torch

pip install torch==1.12.0
pip install torchvision==0.13.0

查看cuda版本

nvidia-smi

安装GPU版本，我的cuda版本是11.4，装了11.3的
下面连接可以下载不同版本的pytorch
https://pytorch.org/get-started/previous-versions/

 conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch

安装jupyter

conda install jupyter

pip install d2l==0.17.6

测试GPU是否可以使用

import torch
flag = torch.cuda.is_available()
print(flag)ngpu= 1
# Decide which device we want to run on
device = torch.device("cuda:0" if (torch.cuda.is_available() and ngpu > 0) else "cpu")
print(device)
print(torch.cuda.get_device_name(0))
print(torch.rand(3,3).cuda()) 

True
cuda:0
GeForce GTX 1080
tensor([[0.9530, 0.4746, 0.9819],
[0.7192, 0.9427, 0.6768],
[0.8594, 0.9490, 0.6551]], device=‘cuda:0’)