Anaconda

Failed to import any qt binding

二次信任 提交于 2020-04-10 09:45:58
(zhouying) F:\TensorflowProject\ObjectDetection> python -m pip uninstall matplotlib Found existing installation: matplotlib 3.2.1 Uninstalling matplotlib-3.2.1: Would remove: n:\anaconda3\install\envs\zhouying\lib\site-packages\matplotlib-3.2.1-py3.6-win-amd64.egg Proceed (y/n)? y Successfully uninstalled matplotlib-3.2.1 (zhouying) F:\TensorflowProject\ObjectDetection> (zhouying) F:\TensorflowProject\ObjectDetection> (zhouying) F:\TensorflowProject\ObjectDetection> (zhouying) F:\TensorflowProject\ObjectDetection> (zhouying) F:\TensorflowProject\ObjectDetection> (zhouying) F:\TensorflowProject

Why does Tensorflow warn about AVX2 while I am using MKL?

僤鯓⒐⒋嵵緔 提交于 2020-04-10 04:45:10
问题 I am using Tensorflow's Anaconda distribution with MKL support. from tensorflow.python.framework import test_util test_util.IsMklEnabled() This code prints True . However, when I compile my Keras model I still get Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 This is not the behavior I was expecting. 回答1: The MKL-DNN portions of the tensorflow execution (which is the main acceleration provided by MKL engineers) are JIT'ed at runtime. So the

Why does Tensorflow warn about AVX2 while I am using MKL?

╄→гoц情女王★ 提交于 2020-04-10 04:38:07
问题 I am using Tensorflow's Anaconda distribution with MKL support. from tensorflow.python.framework import test_util test_util.IsMklEnabled() This code prints True . However, when I compile my Keras model I still get Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2 This is not the behavior I was expecting. 回答1: The MKL-DNN portions of the tensorflow execution (which is the main acceleration provided by MKL engineers) are JIT'ed at runtime. So the

Kernel error after updating Spyder in anaconda [duplicate]

荒凉一梦 提交于 2020-04-10 03:37:20
问题 This question already has answers here : Kernel error after updating to Spyder 4.1 (3 answers) Closed 21 days ago . I updated Spyder to version 4.1.0 (together with all other packages in anaconda). Spyder itself works fine however the kernel is not working. I get the following error and can't figure out how to solve it: An error ocurred while starting the kernel The error is: Traceback (most recent call last): File "C:\Users\20172010\AppData\Local\Continuum\anaconda3\lib\site‑packages\spyder

Kernel error after updating Spyder in anaconda [duplicate]

独自空忆成欢 提交于 2020-04-10 03:36:11
问题 This question already has answers here : Kernel error after updating to Spyder 4.1 (3 answers) Closed 21 days ago . I updated Spyder to version 4.1.0 (together with all other packages in anaconda). Spyder itself works fine however the kernel is not working. I get the following error and can't figure out how to solve it: An error ocurred while starting the kernel The error is: Traceback (most recent call last): File "C:\Users\20172010\AppData\Local\Continuum\anaconda3\lib\site‑packages\spyder

三分钟教会你Python数据分析—数据导入,小白基础入门必看内容

ⅰ亾dé卋堺 提交于 2020-04-09 19:00:01
前言 文的文字及图片来源于网络,仅供学习、交流使用,不具有任何商业用途,版权归原作者所有,如有问题请及时联系我们以作处理。 作者:小白 PS:如有需要Python学习资料的小伙伴可以加点击下方链接自行获取http://t.cn/A6Zvjdun 今天我们来学习数据导入,需要使用到Pandas模块。 模块相当于是一个文件夹,它能够用于存放同个主题相关的Python代码段,包括定义函数、类和变量。 例如将数据处理相关的函数都存放在一个模块里,将数据分析相关的函数都存放在一个模块里,将数据可视化相关的函数都存放在一个模块里。这样就可以在其他有需要的地方进行调用,在编写程序的时候,经常需要引用其它模块。 你可以将Excel菜单中的每个选项卡 看成是一个模块,例如数据选项卡下就包含了数据获取和转换、连接、排序和筛选、数据工具等功能。只是它以图形界面的方式供我们选择使用。 而Python中的模块一般看不见摸不着,需要在使用的时候进行导入使用,所以对初学者来说不算太友好,这也是大家认为Python难学的原因之一。 另外Python中模块的概念,在 Java 中称之为包(package),而在 C\C++中则称之为库(library),Java和C\C++都是程序员常用的编程语言,所以有些程序员就会把Python 中的模块混淆称之为包和库,你只要明白它们说的是一回事即可。 原来是这么回事

几十行代码构建一个前后端分离的目标检测演示网站

蹲街弑〆低调 提交于 2020-04-08 21:17:29
开源代码链接: https://github.com/AIZOOTech/flask-object-detection 测试环境搭建: 一. 下载安装 Anaconda 1. 下载 官放下载网站: https://www.anaconda.com/distribution/#download-section 2. 安装 下载完成后双击安装即可(笔者的安装路径为:E:\anaconda3\) 3. 配置环境变量 E:\anaconda3\Scripts 4. 测试是否安装成功 运行cmd 输入 conda --version 如下图说明安装成功 二、使用 Anaconda 管理python开发环境 打开cmd 1. 安装python36 并激活3.6版本 为了避免可能发生的错误, 我们在命令行输入 conda upgrade --all 先把所有工具包进行升级 conda upgrade --all conda create --name python36 python=3.6.10 conda activate python36 3. 安装所需库 conda install pillow conda install flask conda install tensorflow==1.12 三、下载源码并运行测试 1. 启动后端 git clone https://github

Centos7 安装 Anaconda3 及 常用的conda命令

北慕城南 提交于 2020-04-08 12:52:48
1.下载linux安装包(.sh文件): Anaconda官网 --> Download Site --> Anaconda3-2019.03-Linux-x86_64.sh 2.安装Anaconda3到(/usr/anconda3)目录下: # bash Anaconda3-2019.03-Linux-x86_64.sh 输入 yes ,回车,接受anaconda的条款。 输入你需要安装的地址 /usr/anconda3 ,回车。 安装完成初始化conda(一般更新当前用户的bashrc),输入 yes ,回车。 4.添加(/usr/anconda3/bin)到系统环境变量文件(/etc/profile)中: # vi /etc/profile 在profile文件结尾加入一行: export PATH=/usr/anaconda3/bin:$PATH 使其立即生效: 当前用户生效: $ source /etc/profile root用户生效: # source /etc/profile 或重启系统生效。 5.测试是否安装成功: 打印版本号 conda --version 或 conda -V 6.【补充】root模式下,终端前部分出现了(base)字样??? 查看 $ vi ~/.bashrc 可知,原因就是aconda自动加入了命令到 .bashrc中,

Conda-Anaconda的虚拟环境和包管理功能

隐身守侯 提交于 2020-04-08 12:30:45
Anaconda是一个用于大数据和科学计算的Python工具包,其中的包管理工具为conda,与python标准的pip包管理工具类似,但是融合了virtualenv的虚拟环境管理功能。这里介绍conda的虚拟环境创建和管理的功能。 Managing environments Contents Managing environments Export the environment file Use environment from file Build identical conda environments Build identical conda environments with URLs Create an environment Change environments (activate/deactivate) Create a separate environment List all environments Verify current environment Clone an environment Remove an environment Share an environment Saved environment variables With conda, you can create, export, list, remove, and update

Activate/deactivate conda virtualenvs on entering/leaving directories

风格不统一 提交于 2020-04-07 06:14:29
问题 pyenv-virtualenv offers a nice way of activating the environment on the very instant of entering or leaving the directory which contains a .python-version text file which specifies the environment to activate. It works for the directory it is in and all directories contained in it. The environment is deactivated once we change the directory to something above it. This allows to easily switch between projects or analyses using different python versions (just by changing the directories). Is