Deep learning environment configuration (3)-win11 miniconda, cuda, cudnn, pip source change

Deep learning environment configuration (deepin20.6)
yolov5 first introduction (win version)

1. Install miniconda:

1. Download: Miniconda3-latest-Windows-x86_64.exe

Then click "next" to install everything.

2. Conda source change

Create a .condarc file in the current user directory ("C:\Users\xxx"):

conda config --set show_channel_urls yes

Add to:

channels:
  - defaults
show_channel_urls: true
default_channels:
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:
  conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  msys2: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  bioconda: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  menpo: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch-lts: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  simpleitk: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud

The result is as follows:
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Clear the index cache:

conda clean -i

3. Create a virtual environment:

In the "Start Menu" you find the image below
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. Enter:

conda create -n yolov5_env python=3.8

tips：

# 查看创建过的虚拟环境
conda env list

# 切换至xxx环境
conda activate xxx
or
conda activate C:\ProgramData\Miniconda3\envs\xxx

# 查看当前环境下安装的第三方库
conda list

# 删除虚拟环境
conda remove -n xxx --all

# 安装第三方库：优先级：conda > pip > 编译

# 删除环境中的某个包
conda remove -n xxx yyy

2. Install cuda and cudnn

cuda download , select 10.2 for graphics cards before 30 series, select 11.1.1 for 30 series graphics cards
cudnn download (requires registration and login) , select the corresponding cudnn according to the cuda version (recommended cuda10.2+cudnn7.6.5, cuda11.1+cudnn8.1.1 )

1. Install cuda:

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Add environment variables:

C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.1\bin
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.1\libnvvp
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.1\lib\x64

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verify:

nvcc -V

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2. Install cudnn:

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Unzip:

Copy to the corresponding cuda installation directory:

3. pip source change reference:

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[global]
index-url = https://pypi.tuna.tsinghua.edu.cn/simple/
[install]
trusted-host = pypi.tuna.tsinghua.edu.cn