Deep Learning Environment Setup

电脑配置
系统：Ubuntu16.04
GPU:NVIDIA GTX1080

安装过程

1、安装相关依赖项
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libhdf5-serial-dev protobuf-compiler
sudo apt-get install —no-install-recommends libboost-all-dev
sudo apt-get install libopenblas-dev liblapack-dev libatlas-base-dev
sudo apt-get install libgflags-dev libgoogle-glog-dev liblmdb-dev
sudo apt-get install python-skimage ipython python-pil python-h5py ipython python-gflags python-yaml
sudo apt-get install freeglut3-dev build-essential libx11-dev libxmu-dev libxi-dev libgl1-mesa-glx libglu1-mesa libglu1-mesa-dev

2、安装NVIDIA驱动

方法一（很简洁，但很有用）：
直接在Ubuntu系统设置，软件和更新里面，选择中国的服务器源刷新之后，点击附加驱动选项，在Nvidia Corporation选择专有驱动，然后点击应用更改，下载安装完之后重启。

安装完成之后输入以下指令进行验证：

sudo nvidia-smi
若列出了GPU的信息列表则表示驱动安装成功。
Sat Mar 3 08:37:49 2018
+——————————————————————————————————————-+
| NVIDIA-SMI 384.111 Driver Version: 384.111 |
|———————————————-+———————————+———————————+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|=++==============|
| 0 GeForce GTX 1080 Off | 00000000:01:00.0 On | N/A |
| 25% 31C P8 19W / 250W | 217MiB / 8105MiB | 5% Default |
+———————————————-+———————————+———————————+

+——————————————————————————————————————-+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 1044 G /usr/lib/xorg/Xorg 172MiB |
| 0 1762 G compiz 42MiB |
+——————————————————————————————————————-+

3、安装CUDA

首先在官网上(https://developer.nvidia.com/cuda-downloads)下载CUDA
(当前最新版本是9.1,我们需要下载9.0,在搜索中输入cuda9.0,找到下载地址，然后下载)

下载后运行sudo sh cuda_9.0.176_384.81_linux.run

(1) 运行后，首先是协议。按空格到底后，输入accept
Do you accept the previously read EULA?
accept/decline/quit: accept

Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 384.81?
(y)es/(n)o/(q)uit: n

Install the CUDA 9.0 Toolkit?
(y)es/(n)o/(q)uit: y

Enter Toolkit Location

Toolkit location must be an absolute path.
Enter Toolkit Location
default is /usr/local/cuda-9.0 :

Do you want to install a symbolic link at /usr/local/cuda?
(y)es/(n)o/(q)uit: y

Install the CUDA 9.0 Samples?
(y)es/(n)o/(q)uit: y

Enter CUDA Samples Location
[ default is /home/apuser ]:

接下来安装cuda9.0的补丁1，运行sudo sh cuda_9.0.176.1_linux.run

Do you accept the previously read EULA?
accept/decline/quit: accept

Enter CUDA Toolkit installation directory
default is /usr/local/cuda-9.0 :

Installation complete!
Installation directory: /usr/local/cuda-9.0

接下来安装cuda9.0的补丁2，运行sudo sh cuda_9.0.176.2_linux.run

(2)环境变量配置

打开~/.bashrc文件：

sudo gedit ~/.bashrc

将以下内容写入到~/.bashrc尾部：
export PATH=/usr/local/cuda-9.0/bin{PATH}}
export LD_LIBRARY_PATH=”$LD_LIBRARY_PATH:/usr/local/cuda-9.0/lib64:/usr/local/cuda-9.0/extras/CUPTI/lib64”
export CUDA_HOME=/usr/local/cuda-9.0

(3)测试CUDA的sammples

cd ~/NVIDIA_CUDA-9.0_Samples/1_Utilities/deviceQuery
make
./deviceQuery
如果显示一些关于GPU的信息，则说明安装成功。

4、配置cuDNN

首先去官网(https://developer.nvidia.com/rdp/cudnn-download)下载cuDNN,可能需要注册一个账号才能下载。
由于本人的显卡是GTX1080,
下载cudnn-9.0-linux-x64-v7.tgz下载后进行解压
解压后把相应的文件拷贝到对应的CUDA目录下即可

sudo cp cuda/include/cudnn.h /usr/local/cuda-9.0/include
sudo cp cuda/lib64/libcudnn /usr/local/cuda-9.0/lib64/
sudo chmod a+r /usr/local/cuda-9.0/include/cudnn.h
sudo chmod a+r /usr/local/cuda-9.0/lib64/libcudnn

卸载命令：
sudo /usr/local/cuda-9.0/bin/uninstall_cuda_9.0.pl

5、matlab的安装与配置(可选)
在网盘上下载安装包 https://pan.baidu.com/share/init?surl=mhSXTfq 密码:79sb

挂载：
sudo mkdir /media/matlab
sudo mount -o loop R2016b_glnxa64_dvd1.iso /media/matlab/
安装：
cd /media/matlab
ls # 这里会看到install
cd ..
sudo /media/matlab/install

安装到一半,提示拔出 dvd1 ,然后插入 dvd2 对话框 *
新打开个终端 sudo mount -o loop R2016b_glnxa64_dvd2.iso /media/matlab/

激活：
cd /usr/local/MATLAB/R2016b/bin
sudo ./matlab #运行matlab，弹出激活对话框，选择用不联网的方法进行激活，加载license_standalone.lic文件

cd ~/Downloads/software/Linux_matlab16b/Matlab 2016b Linux64 Crack/R2016b/bin/glnxa64
sudo cp lib* /usr/local/MATLAB/R2016b/bin/glnxa64/

运行：
cd /usr/local/MATLAB/R2016b/bin
./matlab

设置快捷方式:
sudo gedit /usr/share/applications/Matlab2016b.desktop

[Desktop Entry]
Encoding=UTF-8
Name=Matlab 2016b
Comment=MATLAB
Exec=/usr/local/MATLAB/R2016b/bin/matlab
Icon=/usr/local/MATLAB/R2016b/toolbox/shared/dastudio/resources/MatlabIcon.png
Terminal=true
StartupNotify=true
Type=Application
Categories=Application;

6、安装opencv3.4.1

（1）下载opencv3.4.1 并解压

wget https://github.com/Itseez/opencv/archive/3.4.1.zip
unzip opencv-3.4.1.zip

（2）安装依赖项：
sudo apt-get install —assume-yes libopencv-dev build-essential cmake git libgtk2.0-dev pkg-config python-dev python-numpy libdc1394-22 libdc1394-22-dev libjpeg-dev libpng12-dev libtiff5-dev libjasper-dev libavcodec-dev libavformat-dev libswscale-dev libxine2-dev libgstreamer0.10-dev libgstreamer-plugins-base0.10-dev libv4l-dev libtbb-dev libqt4-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev x264 v4l-utils unzip

sudo apt-get install ffmpeg libopencv-dev libgtk-3-dev python-numpy python3-numpy libdc1394-22 libdc1394-22-dev libjpeg-dev libpng12-dev libtiff5-dev libjasper-dev libavcodec-dev libavformat-dev libswscale-dev libxine2-dev libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev libv4l-dev libtbb-dev qtbase5-dev libfaac-dev libmp3lame-dev libopencore-amrnb-dev libopencore-amrwb-dev libtheora-dev libvorbis-dev libxvidcore-dev x264 v4l-utils unzip

sudo apt-get install liblapacke-dev checkinstall

(3)在终端中cd到opencv文件夹下，然后：
mkdir build #新建一个build文件夹，编译的工程都在这个文件夹里
cd build/
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local -D WITH_TBB=ON -D WITH_V4L=ON -D WITH_QT=ON -D WITH_OPENGL=ON -DCUDA_NVCC_FLAGS=”-D_FORCE_INLINES” ..

cmake成功后，会出现如下结果，提示配置和生成成功：
— Configuring done
— Generating done
— Build files have been written to: /home/apuser/Downloads/software/opencv-3.4.1/build

(4) 然后make编译就可以了
make -j4

(5) 安装
sudo make install

安装好的opencv版本查看
pkg-config —modversion opencv

(6) 更新环境变量并测试
sudo /bin/bash -c ‘echo “/usr/local/lib” > /etc/ld.so.conf.d/opencv.conf’
sudo ldconfig
sudo apt-get update

python

import cv2
cv2.version
‘3.2.0’

7、安装升级pip并安装Jupyter

sudo apt install python-pip

If you have Python 3 installed (which we recommended):

python3 -m pip install —upgrade pip
python3 -m pip install jupyter

If you have Python 2 installed:

python -m pip install —upgrade pip
python -m pip install jupyter

Congratulations, you have installed Jupyter Notebook! To run the notebook, run the following command at the Terminal (Mac/Linux) or Command Prompt (Windows):

jupyter notebook

8、安装编译caffe
（1）从github上获取caffe：

git clone https://github.com/BVLC/caffe.git

（2）进入caffe目录，并复制出所需的Makefile.config文件

cp Makefile.config.example Makefile.config

（3）修改Makefile.config文件

gedit Makefile.config

根据个人情况修改文件：

a.若使用cudnn，则

将 #USE_CUDNN := 1
修改成： USE_CUDNN := 1

b.若使用的opencv版本是3的，则

将 #OPENCV_VERSION := 3
修改为： OPENCV_VERSION := 3

c.若要使用python来编写layer，则

将 #WITH_PYTHON_LAYER := 1
修改为 WITH_PYTHON_LAYER := 1

d. 重要的一项 :
将# Whatever else you find you need goes here.下面的
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib
修改为：
INCLUDE_DIRS := $(PYTHON_INCLUDE) /usr/local/include /usr/include/hdf5/serial
LIBRARY_DIRS := $(PYTHON_LIB) /usr/local/lib /usr/lib /usr/lib/x86_64-linux-gnu /usr/lib/x86_64-linux-gnu/hdf5/serial
这是因为ubuntu16.04的文件包含位置发生了变化，尤其是需要用到的hdf5的位置，所以需要更改这一路径.

e.使用matlab接口
将 # MATLAB_DIR := /usr/local
修改为 MATLAB_DIR := /usr/local/MATLAB/R2016b

（4）编译
make all -j8 #-j根据自己电脑配置决定
make test
make runtest

（5）编译caffe的python接口
sudo apt-get install python-pydot
sudo make pycaffe

编译完成后，添加路径到环境变量

gedit ~/.bashrc

添加上caffe的路径：

export PYTHONPATH=/home/apuser/deeplearning/caffe-master/python:$ PYTHONPATH

测试：
python

Python 2.7.14 |Anaconda custom (64-bit)| (default, Oct 16 2017, 17:29:19)
[GCC 7.2.0] on linux2
Type “help”, “copyright”, “credits” or “license” for more information.

import caffe

或者使用
make pytest

python caffe报错：No module named google.protobuf.internal
则使用下面命令
sudo pip install easydict
sudo pip install protobuf

（6）配置caffe的Matlab接口

编译接口:

sudo apt-get install libmatio-dev
make matcaffe
make mattest

如果你在运行上面命令时,遇到如下错误:libstdc++.so.6 version
‘GLIBCXX_3.4.21’ not found,说明你的Matlab库不匹配。你需要在启动
Matlab之前运行如下命令

sudo rm /usr/local/MATLAB/R2016b/sys/os/glnxa64/libstdc++.so.6
sudo ln -s /usr/lib/x86_64-linux-gnu/libstdc++.so.6 /usr/local/MATLAB/R2016b/sys/os/glnxa64/libstdc++.so.6

运行错误:

error while loading shared libraries: libcudart.so.8.0: cannot open shared object file: No such file or directory

解决：

将相应的库文件复制到/usr/lib

sudo cp /usr/local/cuda-9.0/lib64/libcudart.so.9.0 /usr/local/lib/libcudart.so.9.0 && sudo ldconfig

sudo cp /usr/local/cuda-9.0/lib64/libcublas.so.9.0 /usr/local/lib/libcublas.so.9.0 && sudo ldconfig

sudo cp /usr/local/cuda-9.0/lib64/libcurand.so.9.0 /usr/local/lib/libcurand.so.9.0 && sudo ldconfig

sudo cp /usr/local/cuda-9.0/lib64/libcudnn.so.7 /usr/local/lib/libcudnn.so.7 && sudo ldconfig

ps. ldconfig命令是一个动态链接库管理命令，是为了让动态链接库为系统共享

10、Bazel安装：

Using Bazel custom APT repository (recommended)
（1）Install JDK 8

Install JDK 8 by using:

sudo apt-get install openjdk-8-jdk

On Ubuntu 14.04 LTS you’ll have to use a PPA:

add-apt-repository ppa:webupd8team/java

sudo apt-get update && sudo apt-get install oracle-java8-installer

（2） Add Bazel distribution URI as a package source (one time setup)

echo “deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8” | sudo tee /etc/apt/sources.list.d/bazel.list
curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add -

If you want to install the testing version of Bazel, replace stable with testing.
（3）Install and update Bazel

sudo apt-get update && sudo apt-get install bazel

Once installed, you can upgrade to a newer version of Bazel with:

sudo apt-get upgrade bazel

11、tensorflow安装：

(1)Clone the TensorFlow repository
$ git clone https://github.com/tensorflow/tensorflow

(2)Prepare environment for Linux
$ sudo apt-get install python-numpy python-dev python-pip python-wheel

(3)configure the installation:

$ cd tensorflow # cd to the top-level directory created
$ ./configure

You have bazel 0.11.1 installed.
Please specify the location of python. [Default is /usr/bin/python]:

Found possible Python library paths:
/usr/local/lib/python2.7/dist-packages
/usr/lib/python2.7/dist-packages
/home/apuser/deeplearning/caffe-master/python
Please input the desired Python library path to use. Default is [/usr/local/lib/python2.7/dist-packages]

Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]: n
No jemalloc as malloc support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]: n
No Google Cloud Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Hadoop File System support? [Y/n]: n
No Hadoop File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Amazon S3 File System support? [Y/n]: n
No Amazon S3 File System support will be enabled for TensorFlow.

Do you wish to build TensorFlow with Apache Kafka Platform support? [y/N]: n
No Apache Kafka Platform support will be enabled for TensorFlow.

Do you wish to build TensorFlow with XLA JIT support? [y/N]: n
No XLA JIT support will be enabled for TensorFlow.

Do you wish to build TensorFlow with GDR support? [y/N]: n
No GDR support will be enabled for TensorFlow.

Do you wish to build TensorFlow with VERBS support? [y/N]: n
No VERBS support will be enabled for TensorFlow.

Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]: n
No OpenCL SYCL support will be enabled for TensorFlow.

Do you wish to build TensorFlow with CUDA support? [y/N]: y
CUDA support will be enabled for TensorFlow.

Please specify the CUDA SDK version you want to use, e.g. 7.0. [Leave empty to default to CUDA 9.0]:

Please specify the location where CUDA 9.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]: /usr/local/cuda-9.0

Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]:

Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda-9.0]:

Do you wish to build TensorFlow with TensorRT support? [y/N]: n
No TensorRT support will be enabled for TensorFlow.

Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size. [Default is: 6.1]

Do you want to use clang as CUDA compiler? [y/N]: n
nvcc will be used as CUDA compiler.

Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:

Do you wish to build TensorFlow with MPI support? [y/N]: n
No MPI support will be enabled for TensorFlow.

Please specify optimization flags to use during compilation when bazel option “—config=opt” is specified [Default is -march=native]:

Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]: n
Not configuring the WORKSPACE for Android builds.

Preconfigured Bazel build configs. You can use any of the below by adding “—config=<>” to your build command. See tools/bazel.rc for more details.
—config=mkl # Build with MKL support.
—config=monolithic # Config for mostly static monolithic build.
Configuration finished

(4)Build the pip package:

仅 CPU 支持，无 GPU 支持：
$ bazel build —config=opt //tensorflow/tools/pip_package:build_pip_package
有 GPU 支持：
$ bazel build —config=opt —config=cuda //tensorflow/tools/pip_package:build_pip_package

生成 pip 安装包
$ bazel-bin/tensorflow/tools/pip_package/build_pip_package ~/tmp/tensorflow_pkg

(5) Install the pip package

$ sudo pip install ~/tmp/tensorflow_pkg/tensorflow-1.7.0-cp27-cp27mu-linux_x86_64.whl

$ sudo pip install —upgrade

(6)Validate your installation

$ python

Python

import tensorflow as tf
hello = tf.constant(‘Hello, TensorFlow!’)
sess = tf.Session()
print(sess.run(hello))

注意：切换这两种不同的编译配置时, 使用 “bazel clean” 清理环境.

12、安装Android Studio

Setting up Android Studio takes just a few clicks. (You should have already downloaded Android Studio.)

To install Android Studio on Linux, proceed as follows:

(1)Unpack the .zip file you downloaded to an appropriate location for your applications, such as within /usr/local/ for your user profile, or /opt/ for shared users.
(2)To launch Android Studio, open a terminal, navigate to the android-studio/bin/ directory, and execute studio.sh.
(3)Select whether you want to import previous Android Studio settings or not, then click OK.
(4)The Android Studio Setup Wizard guides you though the rest of the setup, which includes downloading Android SDK components that are required for development.

Tip: To make Android Studio available in your list of applications, select Tools > Create Desktop Entry from the Android Studio menu bar.

Required libraries for 64-bit machines:

If you are running a 64-bit version of Ubuntu, you need to install some 32-bit libraries with the following command:

sudo apt-get install libc6:i386 libncurses5:i386 libstdc++6:i386 lib32z1 libbz2-1.0:i386

If you are running 64-bit Fedora, the command is:

sudo yum install zlib.i686 ncurses-libs.i686 bzip2-libs.i686

10、安装Anaconda2（可选）

从官网下载所需的linux版本的可执行文件，执行如下命令：

bash Anaconda2-5.0.1-Linux-x86_64.sh

所有选项默认即可。

安装protobuf的依赖：

conda install protobuf == 2.6.1

11、gcc 版本切换（可选）：

1.产看你的gcc版本 ls /usr/bin/gcc -l
2.查看当前GCC版本 gcc —version
3.下载安装所需版本的gcc
sudo add-apt-repository ppa:ubuntu-toolchain-r/test
sudo apt-get update
sudo apt-get install gcc-4.9 g++-4.9(保留原来的5.0版本，便于快速切换)
sudo update-alternatives —install /usr/bin/gcc gcc /usr/bin/gcc-5 30
sudo update-alternatives —install /usr/bin/gcc gcc /usr/bin/gcc-4.9 40
sudo update-alternatives —install /usr/bin/g++ g++ /usr/bin/g++-5 30
sudo update-alternatives —install /usr/bin/g++ g++ /usr/bin/g++-4.9 40

sudo update-alternatives —config gcc
有 2 个候选项可用于替换 gcc (提供 /usr/bin/gcc)。

选择 路径 优先级 状态

• 0 /usr/bin/gcc-4.9 20 自动模式
  1 /usr/bin/gcc-5 10 手动模式
  2 /usr/bin/gcc-4.9 20 手动模式

sudo update-alternatives —config g++
有 2 个候选项可用于替换 g++ (提供 /usr/bin/g++)。

选择 路径 优先级 状态

• 0 /usr/bin/g++-4.9 20 自动模式
  1 /usr/bin/g++-5 10 手动模式
  2 /usr/bin/g++-4.9 20 手动模式

Python 环境管理：

创建一个名为python35的环境，指定Python版本是3.5（不用管是3.5.x，conda会为我们自动寻找3.5.x中的最新版本）

conda create —name python35 python=3.5

安装好后，使用activate激活某个环境

source activate python35

此时，再次输入

python —version

可以得到Python 3.4.5 :: Anaconda 4.1.1 (64-bit)，即系统已经切换到了3.4的环境

如果想返回默认的python 2.7环境，运行

source deactivate python35 # for Linux & Mac

删除一个已有的环境

conda remove —name python35 —all

查看已安装的环境:
conda info -e

conda的一些常用操作如下：

查看当前环境下已安装的包

conda list

查看某个指定环境的已安装包

conda list -n python35

查找package信息

conda search numpy

安装package

conda install -n python35 numpy

如果不用-n指定环境名称，则被安装在当前活跃环境

也可以通过-c指定通过某个channel安装

更新package

conda update -n python35 numpy

删除package

conda remove -n python35 numpy

问题说明：

make mattest 错误 —#error This file was generated by a newer version of protoc
原因： it is because of the wrong version of protobuf
首先你要卸载，并重装其要求的protobuf版本（2.6.1），之后重新编译就好了

卸载办法：

remove

sudo apt-get remove libprotobuf-dev protobuf-compiler
sudo apt-get remove libprotobuf-lite8 libprotoc8
sudo apt-get remove python-protobuf
sudo pip uninstall protobuf

如果安装了anaconda

conda uninstall protobuf

如果出现错误：

E:Encountered a section with no Package: header,

输入以下命令：
sudo rm /var/lib/apt/lists/* -vf
sudo apt-get update

重新安装

reinstall

sudo apt-get install libprotobuf-dev protobuf-compiler

if anaconda

conda install -c anaconda protobuf=2.6.1
或者
/home/username/anaconda2/bin/pip install protobuf == 2.6.1

附注：

查看当前protoc版本：protoc —version

查看protoc安装位置：which protoc

查找protoc相关文件：sudo find / -name protoc

sudo apt-get install python-pydot

conda install -c https://conda.anaconda.org/anaconda protobuf

Anaconda+用conda创建python虚拟环境

conda可以理解为一个工具，也是一个可执行命令，其核心功能是包管理与环境管理。包管理与pip的使用类似，环境管理则允许用户方便地安装不同版本的python并可以快速切换。 conda的设计理念——conda将几乎所有的工具、第三方包都当做package对待，甚至包括python和conda自身 Anaconda则是一个打包的集合，里面预装好了conda、某个版本的python、众多packages、科学计算工具等等。

1、首先在所在系统中安装Anaconda。可以打开命令行输入conda -V检验是否安装以及当前conda的版本。

2、conda常用的命令。

1）conda list 查看安装了哪些包。
2）conda env list 或 conda info -e 查看当前存在哪些虚拟环境
3）conda update conda 检查更新当前conda

3、创建Python虚拟环境。

 使用 conda create -n your_env_name python=X.X（2.7、3.6等） anaconda 命令创建python版本为X.X、名字为your_env_name的虚拟环境。your_env_name文件可以在Anaconda安装目录envs文件下找到。

指定python版本为2.7，注意至少需要指定python版本或者要安装的包# 后一种情况下，自动安装最新python版本

conda create -n env_name python=2.7

同时安装必要的包

conda create -n env_name numpy matplotlib python=2.7

4、使用激活(或切换不同python版本)的虚拟环境。

打开命令行输入python --version可以检查当前python的版本。
使用如下命令即可 激活你的虚拟环境(即将python的版本改变)。
Linux:  source activate your_env_name(虚拟环境名称)
Windows: activate your_env_name(虚拟环境名称)

这是再使用python —version可以检查当前python版本是否为想要的。

5、对虚拟环境中安装额外的包。

使用命令conda install -n your_env_name [package]即可安装package到your_env_name中

6、关闭虚拟环境(即从当前环境退出返回使用PATH环境中的默认python版本)。

使用如下命令即可。

deactivate env_name，也可以使用activate root切回root环境
Linux下使用 source deactivate

7、删除虚拟环境。

移除环境

使用命令conda remove -n your_env_name(虚拟环境名称) —all， 即可删除。

删除环境中的某个包。

使用命令conda remove —name $your_env_name $package_name 即可。

8、设置国内镜像

如果需要安装很多packages，你会发现conda下载的速度经常很慢，因为Anaconda.org的服务器在国外。所幸的是，清华TUNA镜像源有Anaconda仓库的镜像，我们将其加入conda的配置即可：

添加Anaconda的TUNA镜像

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

TUNA的help中镜像地址加有引号，需要去掉

设置搜索时显示通道地址

conda config —set show_channel_urls yes