# Conda
## 下载安装miniconda
```bash
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
chmod +x Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
source ~/.bashrc

You have chosen to not have conda modify your shell scripts at all.
To activate conda's base environment in your current shell session:

eval "$(/root/miniconda3/bin/conda shell.YOUR_SHELL_NAME hook)"

To install conda's shell functions for easier access, first activate, then:

conda init

If you'd prefer that conda's base environment not be activated on startup,
   set the auto_activate_base parameter to false:
```
如果想要一进入tetminal就激活环境，可以`conda config --set auto_activate_base false`。

## 快速使用
```bash
conda create -n torch113 python=3.10 numpy matplotlib pandas requests
conda activate torch
conda install pytorch torchvision pytorch-cuda=11.6 -c pytorch -c nvidia
conda install pytorch==1.13.1 torchvision==0.14.1 pytorch-cuda=11.7 -c pytorch -c nvidia

deactivate
```

## 查看信息
```bash
conda info
conda config --show

conda config --add envs_dirs dir
conda config --remove envs_dirs dir

# 查看源
conda config --show channels
# 移除源
conda config --remove channels CHANNELS
# 添加源
conda config --add channels CHANNELS
# 显示源
conda config --set show_channel_urls yes

conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/

conda config --set auto_activate_base false

conda config --set proxy_servers.http http://id:pw@address:port
conda config --set proxy_servers.https https://id:pw@address:port
```

## Create
```bash
conda install python==3.10
# direct
conda create -n torch113 python=3.10 numpy matplotlib pandas requests
# conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.6 -c pytorch -c conda-forge
# clone
conda create -n traget_env_name --clone source_env_name
# from yaml
conda env create -f environment.yaml
```

## Activate 
```bash
conda activate torch
deactivate
```

## Export environment
```bash
conda env export > environment.yaml
# conda env create -f environment.yaml
```

## Install packages
```bash
conda install pytorch torchvision pytorch-cuda=11.6 -c pytorch -c nvidia
conda install pytorch==1.13.1 torchvision==0.14.1 pytorch-cuda=11.7 -c pytorch -c nvidia
```


## 进阶使用
### pip安装位置
```bash
python -m site
export PYTHONUSERBASE=/mnt/cache/chenrenjie/.conda/envs/torch113

```
### 配置环境变量
[the section on managing environments in the official documentation](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#macos-and-linux)

```bash
echo $CONDA_PREFIX

cd $CONDA_PREFIX
mkdir -p etc/conda/activate.d
vim etc/conda/activate.d/env_vars.sh

cd etc/conda/deactivate.d
```

### 切换cuda/gcc版本
```
export PATH=/mnt/cache/share/gcc/gcc-7.3.0/bin:$PATH
export LD_LIBRARY_PATH=/mnt/cache/share/gcc/gcc-7.3.0/lib64:/mnt/cache/share/gcc/mpc-0.8.1/lib:$LD_LIBRARY_PATH

export CUDA_HOME=/mnt/cache/share/cuda-11.7
export PATH=/mnt/cache/share/cuda-11.7/bin::$PATH
export LD_LIBRARY_PATH=/mnt/cache/share/cuda-11.7/lib64:$LD_LIBRARY_PATH
```

### 非root配置

当普通用户利用ssh登录后：
```bash
# 1. 执行init 这里前面的文件位置就换成自己的文件位置
/opt/miniconda3/bin/conda init bash
# 2. 执行
source ~/.bashrc
```

### 查看python pkg 安装位置
```bash
python -m site

vim ~/.condarc #进入家目录下的conda配置文件.condarc

#按i键进入编辑模式，在末端另起一行写入以下命令：
envs_dirs:
  - /full/path/to/your/new/dir/for/new/environments/
pkgs_dirs:
  - /full/path/to/your/new/dir/for/new/packages/

sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys 3B4FE6ACC0B21F32

conda config --show-sources
# 修改 .condarc 文件
channels:
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
  - https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
  - defaults
show_channel_urls: true
default_channels:
  - http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
  - http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
  - http://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
custom_channels:
  conda-forge: http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  msys2: http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  bioconda: http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  menpo: http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  pytorch: http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
  simpleitk: http://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
ssl_verify: false
```