深度学习神经网络——TensorFlow安装及tf2连接服务器连接不到的问题

目录

一、安装TensorFlow

二、问题：tf2连接服务器连接不到的问题

一、安装TensorFlow

1.设置清华源：https://mirror.tuna.tsinghua.edu.cn/help/anaconda/

2.Anaconda propt中创建虚拟环境：
先用Notepad 编辑tensorflow2.0.0.yml文件

文件内容为如下，没有文件可以打开记事本自行输入保存一下：（保存为tensorflow2.0.0.yml）

name: tf2
channels:
– defaults
dependencies:
– _tflow_select=2.2.0=eigen
– absl-py=0.15.0=pyhd3eb1b0_0
– aiohttp=3.7.4.post0=py36h2bbff1b_2
– astor=0.8.1=py36haa95532_0
– async-timeout=3.0.1=py36haa95532_0
– attrs=21.4.0=pyhd3eb1b0_0
– backcall=0.2.0=pyhd3eb1b0_0
– blas=1.0=mkl
– blinker=1.4=py36haa95532_0
– brotlipy=0.7.0=py36h2bbff1b_1003
– ca-certificates=2022.3.29=haa95532_0
– cachetools=4.2.2=pyhd3eb1b0_0
– certifi=2021.5.30=py36haa95532_0
– cffi=1.14.6=py36h2bbff1b_0
– chardet=4.0.0=py36haa95532_1003
– charset-normalizer=2.0.4=pyhd3eb1b0_0
– click=8.0.3=pyhd3eb1b0_0
– colorama=0.4.4=pyhd3eb1b0_0
– cryptography=3.4.7=py36h71e12ea_0
– cycler=0.11.0=pyhd3eb1b0_0
– decorator=5.1.1=pyhd3eb1b0_0
– entrypoints=0.3=py36_0
– freetype=2.10.4=hd328e21_0
– gast=0.2.2=py36_0
– google-auth-oauthlib=0.4.4=pyhd3eb1b0_0
– google-pasta=0.2.0=pyhd3eb1b0_0
– grpcio=1.35.0=py36hc60d5dd_0
– h5py=2.10.0=py36h5e291fa_0
– hdf5=1.10.4=h7ebc959_0
– icc_rt=2019.0.0=h0cc432a_1
– icu=58.2=ha925a31_3
– idna=3.3=pyhd3eb1b0_0
– idna_ssl=1.1.0=py36haa95532_0
– importlib-metadata=4.8.1=py36haa95532_0
– intel-openmp=2022.0.0=haa95532_3663
– ipykernel=5.3.4=py36h5ca1d4c_0
– ipython=7.16.1=py36h5ca1d4c_0
– ipython_genutils=0.2.0=pyhd3eb1b0_1
– jedi=0.17.0=py36_0
– joblib=1.0.1=pyhd3eb1b0_0
– jpeg=9d=h2bbff1b_0
– jupyter_client=7.1.2=pyhd3eb1b0_0
– jupyter_core=4.8.1=py36haa95532_0
– keras-applications=1.0.8=py_1
– keras-preprocessing=1.1.2=pyhd3eb1b0_0
– kiwisolver=1.3.1=py36hd77b12b_0
– libpng=1.6.37=h2a8f88b_0
– libprotobuf=3.17.2=h23ce68f_1
– libtiff=4.2.0=hd0e1b90_0
– lz4-c=1.9.3=h2bbff1b_1
– markdown=3.3.4=py36haa95532_0
– matplotlib=3.3.4=py36haa95532_0
– matplotlib-base=3.3.4=py36h49ac443_0
– mkl=2020.2=256
– mkl-service=2.3.0=py36h196d8e1_0
– mkl_fft=1.3.0=py36h46781fe_0
– mkl_random=1.1.1=py36h47e9c7a_0
– multidict=5.1.0=py36h2bbff1b_2
– nest-asyncio=1.5.1=pyhd3eb1b0_0
– numpy=1.19.2=py36hadc3359_0
– numpy-base=1.19.2=py36ha3acd2a_0
– oauthlib=3.2.0=pyhd3eb1b0_0
– olefile=0.46=py36_0
– openssl=1.1.1n=h2bbff1b_0
– opt_einsum=3.3.0=pyhd3eb1b0_1
– pandas=1.1.5=py36hd77b12b_0
– parso=0.8.3=pyhd3eb1b0_0
– pickleshare=0.7.5=pyhd3eb1b0_1003
– pillow=8.3.1=py36h4fa10fc_0
– pip=21.2.2=py36haa95532_0
– prompt-toolkit=3.0.20=pyhd3eb1b0_0
– protobuf=3.17.2=py36hd77b12b_0
– pyasn1=0.4.8=pyhd3eb1b0_0
– pyasn1-modules=0.2.8=py_0
– pycparser=2.21=pyhd3eb1b0_0
– pygments=2.11.2=pyhd3eb1b0_0
– pyjwt=2.1.0=py36haa95532_0
– pyopenssl=21.0.0=pyhd3eb1b0_1
– pyparsing=3.0.4=pyhd3eb1b0_0
– pyqt=5.9.2=py36h6538335_2
– pyreadline=2.1=py36_1
– pysocks=1.7.1=py36haa95532_0
– python=3.6.5=h0c2934d_0
– python-dateutil=2.8.2=pyhd3eb1b0_0
– pytz=2021.3=pyhd3eb1b0_0
– pywin32=228=py36hbaba5e8_1
– pyzmq=22.2.1=py36hd77b12b_1
– qt=5.9.7=vc14h73c81de_0
– requests=2.27.1=pyhd3eb1b0_0
– requests-oauthlib=1.3.0=py_0
– rsa=4.7.2=pyhd3eb1b0_1
– scikit-learn=0.24.1=py36hf11a4ad_0
– scipy=1.5.2=py36h9439919_0
– seaborn=0.11.2=pyhd3eb1b0_0
– setuptools=58.0.4=py36haa95532_0
– sip=4.19.8=py36h6538335_0
– six=1.16.0=pyhd3eb1b0_1
– sqlite=3.38.2=h2bbff1b_0
– tensorboard=2.4.0=pyhc547734_0
– tensorboard-plugin-wit=1.6.0=py_0
– tensorflow=2.0.0=eigen_py36h457aea3_0
– tensorflow-base=2.0.0=eigen_py36h01553b8_0
– tensorflow-estimator=2.0.0=pyh2649769_0
– termcolor=1.1.0=py36haa95532_1
– threadpoolctl=2.2.0=pyh0d69192_0
– tk=8.6.11=h2bbff1b_0
– tornado=6.1=py36h2bbff1b_0
– traitlets=4.3.3=py36haa95532_0
– typing-extensions=4.1.1=hd3eb1b0_0
– typing_extensions=4.1.1=pyh06a4308_0
– urllib3=1.26.8=pyhd3eb1b0_0
– vc=14.2=h21ff451_1
– vs2015_runtime=14.27.29016=h5e58377_2
– wcwidth=0.2.5=pyhd3eb1b0_0
– werkzeug=0.16.1=py_0
– wheel=0.37.1=pyhd3eb1b0_0
– win_inet_pton=1.1.0=py36haa95532_0
– wincertstore=0.2=py36h7fe50ca_0
– wrapt=1.12.1=py36he774522_1
– xz=5.2.5=h62dcd97_0
– yarl=1.6.3=py36h2bbff1b_0
– zipp=3.6.0=pyhd3eb1b0_0
– zlib=1.2.12=h8cc25b3_0
– zstd=1.4.9=h19a0ad4_0
– pip:
– google-auth==1.35.0
prefix: C:\Users\86157\anaconda3\envs\tf2

修改文件中最后一行

prefix: C:\Users\86157\anaconda3\envs\tf2 为prefix: 你指定的虚拟环境的路径

打开anaconda prompt 输入命令 ：

conda env create -f tensorflow2.0.0.yml -n tf2

注意：-f后为TensorFlow2.0.0.yml文件的完整文件路径

环境创建好后进行激活，进入tf2虚拟环境

命令：conda activate tf2

1. 进入tf2虚拟环境执行如下安装：

为不同的虚拟环境设置

conda install ipykernel  ​​​​​​​

注： tf2为当前虚拟环境名称 “tf2″为jupyter notebook中显示的内核名称

到此，安装完成。启动jupyter notebook

二、问题：tf2连接服务器连接不到的问题

jupyter 启动提示This event loop is already running

解决办法：

conda activate tf2 # 切换到tf2虚拟环境
conda list # 查看一下自己 ipykernel 为4.6.1，由于版本问题造成不匹配无法连接
pip install –ignore-installed ipykernel –upgrade # 进行强制更新

更新完成，此时可能会出现报错：ERROR: pip’s dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.

tensorflow 2.0.0 requires tensorboard

原因：

tensorboard版本不匹配与TensorFlow，我们使用的TensorFlow版本为2.0.0，而tensorboard版本为2.4.0

解决办法：

pip install tensorboard==2.0.0 # 将tensorboard版本更新为2.0.0

重新启动jupyter notebook

查看jupyter版本信息

!jupyter –version

查看TensorFlow版本及安装路径：

import tensorflow as tf
tf.version # 此命令为获取安装的tensorflow版本
print(tf.version) # 输出版本
tf.path #查看tensorflow安装路径
print(tf.path)

Original: https://blog.csdn.net/weixin_46474921/article/details/124614211
Author: Siobhan. 明鑫
Title: 深度学习神经网络——TensorFlow安装及tf2连接服务器连接不到的问题