pip 安裝依賴 設定加速

http://www.zyglz.com/index.php/archives/18.html
阿里雲源：https://mirrors.aliyun.com/pypi/simple
騰訊雲源：https://mirrors.cloud.tencent.com/pypi/simple
豆瓣源：https://pypi.doubanio.com/simple
清華源：https://pypi.tuna.tsinghua.edu.cn/simple
華為源：https://repo.huaweicloud.com/simple


2.生成requirements.txt檔案：
3.安裝requirements.txt依賴：
python 
 
-m pip install --upgrade pip
pip freeze > requirements.txt
pip install -r requirements.txt

pip 指定包的目錄
方法一
指定安裝numpy包到固定資料夾下，比如這裡“資料夾”是安裝路徑

pip install -t 資料夾 numpy
pip3 install tensorflow==2.0.0 -i https://pypi.tuna.tsinghua.edu.cn/simple


#自定義依賴安裝包的路徑
USER_SITE = null
#自定義的啟用Python指令碼的路徑
USER_BASE = null
我這裡修改為

USER_SITE 
 
= "D:\program\Anaconda\envs\py36\Lib\site-packages"
USER_BASE = "D:\program\Anaconda\envs\py36\Scripts"
驗證
python -m site 


yum install python3-dev python3-pip python3-venv

yum install zlib-devel bzip2-devel openssl-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel db4-devel libpcap-devel xz-devel

conda 安裝



 
# 獲得最新的miniconda安裝包；
wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
# 安裝到自己的HOME目錄software/miniconda3中，這個目錄在安裝前不能存在；
sh Miniconda3-latest-Linux-x86_64.sh -b -p ${HOME}/software/miniconda3
# 安裝成功後刪除安裝包
rm -f Miniconda3-latest-Linux-x86_64.sh
# 將環境變數寫入~/.bashrc檔案中；
echo "export PATH=${HOME}/software/miniconda3/bin:\$PATH" >> ~/.bashrc
# 退出重新登入或者執行以下命令
source ~/.bashrc

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

conda config --set show_channel_urls yes

#GPU 版
conda create -n tf-gpu-2.3.0 tensorflow-gpu==2.3.0 -y


#CPU 版
conda create -n tf-2.3.0 tensorflow==2.3.0 -y
source activate tf-2.3.0
python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])))"

import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
#＃保證sess.run()能夠正常執行 2.0版本的
import tensorflow as tf
tf.__version__
tf.compat.v1.disable_eager_execution()　
hello = tf.constant('hello,tensorflow')
sess= tf.compat.v1.Session()
print(sess.run(hello))



#匯入包
from hyperlpr import *
#匯入OpenCV庫
import cv2
#讀入圖片
image = cv2.imread("D:\\tensorflow\\lpr-master\\test-imgs\\1.jpg")
cv2.imshow("Image", image）
#識別結果
print(HyperLPR_plate_recognition(image))



source deactivate

win

pip install tensorflow-1.6.0-cp36-cp36m-win_amd64.whl

pip install tensorflow-2.3.0-cp38-cp38m-win_amd64.whl