anaconda虚拟环境常用操作
在刚接触到服务器的时候，使用服务器的过程中会遇到各种各样的问题。
由于服务器是公用的，可以在服务器上安装anaconda，anaconda可以搭建环境，还可以创建属于自己的虚拟环境。
使用命令 top，即可查看当前的服务器使用状态，如果CPU的使用率超过80%，就不要跑程序了。
退出Ctrl+C。
一、创建虚拟环境相关操作
下面是一些anaconda的一些常用操作：
1、首先，根据服务器的IP、用户名以及密码登录访问服务器；可以通过Xmanager进行访问、连接、传输文件，非常方便；
登录后如下图，系统已安装好anaconda：
2、在服务器系统中安装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。
3、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
在Anaconda配置添加清华镜像：
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/ conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/ conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/ 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/cloud/conda-forge/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
conda config --set show_channel_urls yes
4、创建python虚拟环境
conda create -n env_name python=3.6 其中env_name是自己虚拟环境的名称，可任意命名
同时安装必要的包：
conda create -n env_name numpy matplotlib python=3.6
env_name文件可以在Anaconda安装目录envs文件下找到，使用conda env list命令即可看到下图现有哪些虚拟环境：
5、在虚拟环境中安装额外的包
使用命令conda install -n env_name [package]即可安装package到your_env_name中
比如要在名为env_name的虚拟环境中安装pyltp，就可以使用命令：conda install -n env_name pyltp
6、激活虚拟环境
使用如下命令即可激活创建的虚拟环境
#Linux
source activate your_env_name(虚拟环境名称)
#Windows
activate your_env_name(虚拟环境名称)
此时使用python --version可以检查当前python版本是否为想要的（即虚拟环境的python版本）
7、退出虚拟环境
使用如下命令即可退出创建的虚拟环境
#Linux
source deactivate your_env_name(虚拟环境名称)
#Windows
deactivate your_env_name(虚拟环境名称)
8、删除虚拟环境
#删除环境
conda remove -n your_env_name(虚拟环境名称) --all
#使用命令
conda remove --name $your_env_name $package_name（包名)
在Pycharm中使用虚拟环境
首先查看当前存在的虚拟环境:~$conda env list
在Pycharm中选择:File->setting->Project:XXXX->Project Interperter
可以新建一个，也可以选择上面所创建的虚拟环境。
选择虚拟环境中bin目录下python3.X即可。
二、程序文件传输到服务器
用Xmanager中的Xftp进行传输。首先需在Xftp中新建一个Xftp会话（需输入服务器地址、用户名、密码）进行连接，连接上直接将本地文件拖拽到自己在服务器上建的文件夹即可。
直接从本地（左侧）拖拽文件到服务器（右侧）指定文件夹下：
查看项目树形结构：
三、linux常用命令：
anaconda用法：
查看已经安装的包：
pip list 或者 conda list
安装和更新：
pip install requests
pip install requests –upgrade
或者
conda install requests
conda update requestssudo password root或者su root 获取root权限
cd /home或者cd /文件夹名 进入某一个文件夹
cd .. 返回上一级文件夹
sudo mkdir 文件夹名 创建新的文件夹
mv 文件夹名 /usr/java/ 移动文件夹
tar -xzvf 文件名 解压文件夹
sudo gedit ~/.bashrc 修改配置文件
chmod a+w 文件名 修改只读文件改为读写
ifconfig -a 查看IP
rm ./* 删除某文件夹下的全部文件
unzip 文件名 解压zip文件
四、进入jupyter notebook环境，打开ipthon文件
首先进入虚拟环境中：conda activate tf2.0
再进入.ipython文件所在的目录，并输入 jupyter notebook命令即可
随即打开所需打开的界面：
这样就不会弄混弄乱了。
cmd中具体步骤详情：
(base) C:\Users\Awen>conda list
# packages in environment at D:\Anaconda3:
#
# Name Version Build Channel
_ipyw_jlab_nb_ext_conf 0.1.0 py36he6757f0_0 defaults
alabaster 0.7.10 py36hcd07829_0 defaults
anaconda 5.2.0 py36_3 defaults
anaconda-client 1.6.14 py36_0 defaults
anaconda-navigator 1.8.7 py36_0 defaults
anaconda-project 0.8.2 py36hfad2e28_0 defaults
asn1crypto 0.24.0 py36_0 defaults
astroid 1.6.3 py36_0 defaults
astropy 3.0.2 py36h452e1ab_1 defaults
attrs 18.1.0 py36_0 defaults
babel 2.5.3 py36_0 defaults
backcall 0.1.0 py36_0 defaults
backports 1.0 py36h81696a8_1 defaults
backports.shutil_get_terminal_size 1.0.0 py36h79ab834_2 defaults
beautifulsoup4 4.6.0 py36hd4cc5e8_1 defaults
bitarray 0.8.1 py36hfa6e2cd_1 defaults
bkcharts 0.2 py36h7e685f7_0 defaults
blas 1.0 mkl defaults
blaze 0.11.3 py36h8a29ca5_0 defaults
bleach 2.1.3 py36_0 defaults
blosc 1.14.3 he51fdeb_0 defaults
bokeh 0.12.16 py36_0 defaults
boto 2.48.0 py36h1a776d2_1 defaults
bottleneck 1.2.1 py36hd119dfa_0 defaults
bzip2 1.0.6 hfa6e2cd_5 defaults
ca-certificates 2018.03.07 0 defaults
certifi 2018.4.16 py36_0 defaults
cffi 1.11.5 py36h945400d_0 defaults
chardet 3.0.4 py36h420ce6e_1 defaults
click 6.7 py36hec8c647_0 defaults
cloudpickle 0.5.3 py36_0 defaults
clyent 1.2.2 py36hb10d595_1 defaults
colorama 0.3.9 py36h029ae33_0 defaults
comtypes 1.1.4 py36_0 defaults
conda 4.5.4 py36_0 defaults
conda-build 3.10.5 py36_0 defaults
conda-env 2.6.0 h36134e3_1 defaults
conda-verify 2.0.0 py36h065de53_0 defaults
console_shortcut 0.1.1 h6bb2dd7_3 defaults
contextlib2 0.5.5 py36he5d52c0_0 defaults
cryptography 2.2.2 py36hfa6e2cd_0 defaults
curl 7.60.0 h7602738_0 defaults
cycler 0.10.0 py36h009560c_0 defaults
cython 0.28.2 py36hfa6e2cd_0 defaults
cytoolz 0.9.0.1 py36hfa6e2cd_0 defaults
dask 0.17.5 py36_0 defaults
dask-core 0.17.5 py36_0 defaults
datashape 0.5.4 py36h5770b85_0 defaults
decorator 4.3.0 py36_0 defaults
distributed 1.21.8 py36_0 defaults
docutils 0.14 py36h6012d8f_0 defaults
entrypoints 0.2.3 py36hfd66bb0_2 defaults
et_xmlfile 1.0.1 py36h3d2d736_0 defaults
fastcache 1.0.2 py36hfa6e2cd_2 defaults
filelock 3.0.4 py36_0 defaults
flask 1.0.2 py36_1 defaults
flask-cors 3.0.4 py36_0 defaults
freetype 2.8 h51f8f2c_1 defaults
get_terminal_size 1.0.0 h38e98db_0 defaults
gevent 1.3.0 py36hfa6e2cd_0 defaults
glob2 0.6 py36hdf76b57_0 defaults
greenlet 0.4.13 py36hfa6e2cd_0 defaults
h5py 2.7.1 py36h3bdd7fb_2 defaults
hdf5 1.10.2 hac2f561_1 defaults
heapdict 1.0.0 py36_2 defaults
html5lib 1.0.1 py36h047fa9f_0 defaults
icc_rt 2017.0.4 h97af966_0 defaults
icu 58.2 ha66f8fd_1 defaults
idna 2.6 py36h148d497_1 defaults
imageio 2.3.0 py36_0 defaults
imagesize 1.0.0 py36_0 defaults
intel-openmp 2018.0.0 8 defaults
ipykernel 4.8.2 py36_0 defaults
ipython 6.4.0 py36_0 defaults
ipython_genutils 0.2.0 py36h3c5d0ee_0 defaults
ipywidgets 7.2.1 py36_0 defaults
isort 4.3.4 py36_0 defaults
itsdangerous 0.24 py36hb6c5a24_1 defaults
jdcal 1.4 py36_0 defaults
jedi 0.12.0 py36_1 defaults
jinja2 2.10 py36h292fed1_0 defaults
jpeg 9b hb83a4c4_2 defaults
jsonschema 2.6.0 py36h7636477_0 defaults
jupyter 1.0.0 py36_4 defaults
jupyter_client 5.2.3 py36_0 defaults
jupyter_console 5.2.0 py36h6d89b47_1 defaults
jupyter_core 4.4.0 py36h56e9d50_0 defaults
jupyterlab 0.32.1 py36_0 defaults
jupyterlab_launcher 0.10.5 py36_0 defaults
kiwisolver 1.0.1 py36h12c3424_0 defaults
lazy-object-proxy 1.3.1 py36hd1c21d2_0 defaults
libcurl 7.60.0 hc4dcbb0_0 defaults
libiconv 1.15 h1df5818_7 defaults
libpng 1.6.34 h79bbb47_0 defaults
libsodium 1.0.16 h9d3ae62_0 defaults
libssh2 1.8.0 hd619d38_4 defaults
libtiff 4.0.9 hb8ad9f9_1 defaults
libxml2 2.9.8 hadb2253_1 defaults
libxslt 1.1.32 hf6f1972_0 defaults
llvmlite 0.23.1 py36hcacf6c6_0 defaults
locket 0.2.0 py36hfed976d_1 defaults
lxml 4.2.1 py36heafd4d3_0 defaults
lzo 2.10 h6df0209_2 defaults
m2w64-gcc-libgfortran 5.3.0 6 defaults
m2w64-gcc-libs 5.3.0 7 defaults
m2w64-gcc-libs-core 5.3.0 7 defaults
m2w64-gmp 6.1.0 2 defaults
m2w64-libwinpthread-git 5.0.0.4634.697f757 2 defaults
markupsafe 1.0 py36h0e26971_1 defaults
matplotlib 2.2.2 py36h153e9ff_1 defaults
mccabe 0.6.1 py36hb41005a_1 defaults
menuinst 1.4.14 py36hfa6e2cd_0 defaults
mistune 0.8.3 py36hfa6e2cd_1 defaults
mkl 2018.0.2 1 defaults
mkl-service 1.1.2 py36h57e144c_4 defaults
mkl_fft 1.0.1 py36h452e1ab_0 defaults
mkl_random 1.0.1 py36h9258bd6_0 defaults
more-itertools 4.1.0 py36_0 defaults
mpmath 1.0.0 py36hacc8adf_2 defaults
msgpack-python 0.5.6 py36he980bc4_0 defaults
msys2-conda-epoch 20160418 1 defaults
multipledispatch 0.5.0 py36_0 defaults
navigator-updater 0.2.1 py36_0 defaults
nbconvert 5.3.1 py36h8dc0fde_0 defaults
nbformat 4.4.0 py36h3a5bc1b_0 defaults
networkx 2.1 py36_0 defaults
nltk 3.3.0 py36_0 defaults
nose 1.3.7 py36h1c3779e_2 defaults
notebook 5.5.0 py36_0 defaults
numba 0.38.0 py36h830ac7b_0 defaults
numexpr 2.6.5 py36hcd2f87e_0 defaults
numpy 1.14.3 py36h9fa60d3_1 defaults
numpy-base 1.14.3 py36h555522e_1 defaults
numpydoc 0.8.0 py36_0 defaults
odo 0.5.1 py36h7560279_0 defaults
olefile 0.45.1 py36_0 defaults
openpyxl 2.5.3 py36_0 defaults
openssl 1.0.2o h8ea7d77_0 defaults
packaging 17.1 py36_0 defaults
pandas 0.23.0 py36h830ac7b_0 defaults
pandoc 1.19.2.1 hb2460c7_1 defaults
pandocfilters 1.4.2 py36h3ef6317_1 defaults
parso 0.2.0 py36_0 defaults
partd 0.3.8 py36hc8e763b_0 defaults
path.py 11.0.1 py36_0 defaults
pathlib2 2.3.2 py36_0 defaults
patsy 0.5.0 py36_0 defaults
pep8 1.7.1 py36_0 defaults
pickleshare 0.7.4 py36h9de030f_0 defaults
pillow 5.1.0 py36h0738816_0 defaults
pip 10.0.1 py36_0 defaults
pkginfo 1.4.2 py36_1 defaults
pluggy 0.6.0 py36hc7daf1e_0 defaults
ply 3.11 py36_0 defaults
prompt_toolkit 1.0.15 py36h60b8f86_0 defaults
psutil 5.4.5 py36hfa6e2cd_0 defaults
py 1.5.3 py36_0 defaults
pycodestyle 2.4.0 py36_0 defaults
pycosat 0.6.3 py36h413d8a4_0 defaults
pycparser 2.18 py36hd053e01_1 defaults
pycrypto 2.6.1 py36hfa6e2cd_8 defaults
pycurl 7.43.0.1 py36h74b6da3_0 defaults
pyflakes 1.6.0 py36h0b975d6_0 defaults
pygments 2.2.0 py36hb010967_0 defaults
pylint 1.8.4 py36_0 defaults
pyodbc 4.0.23 py36h6538335_0 defaults
pyopenssl 18.0.0 py36_0 defaults
pyparsing 2.2.0 py36h785a196_1 defaults
pyqt 5.9.2 py36h1aa27d4_0 defaults
pysocks 1.6.8 py36_0 defaults
pytables 3.4.3 py36he6f6034_1 defaults
pytest 3.5.1 py36_0 defaults
pytest-arraydiff 0.2 py36_0 defaults
pytest-astropy 0.3.0 py36_0 defaults
pytest-doctestplus 0.1.3 py36_0 defaults
pytest-openfiles 0.3.0 py36_0 defaults
pytest-remotedata 0.2.1 py36_0 defaults
python 3.6.5 h0c2934d_0 defaults
python-dateutil 2.7.3 py36_0 defaults
pytz 2018.4 py36_0 defaults
pywavelets 0.5.2 py36hc649158_0 defaults
pywin32 223 py36hfa6e2cd_1 defaults
pywinpty 0.5.1 py36_0 defaults
pyyaml 3.12 py36h1d1928f_1 defaults
pyzmq 17.0.0 py36hfa6e2cd_1 defaults
qt 5.9.5 vc14he4a7d60_0 [vc14] defaults
qtawesome 0.4.4 py36h5aa48f6_0 defaults
qtconsole 4.3.1 py36h99a29a9_0 defaults
qtpy 1.4.1 py36_0 defaults
requests 2.18.4 py36h4371aae_1 defaults
rope 0.10.7 py36had63a69_0 defaults
ruamel_yaml 0.15.35 py36hfa6e2cd_1 defaults
scikit-image 0.13.1 py36hfa6e2cd_1 defaults
scikit-learn 0.19.1 py36h53aea1b_0 defaults
scipy 1.1.0 py36h672f292_0 defaults
seaborn 0.8.1 py36h9b69545_0 defaults
send2trash 1.5.0 py36_0 defaults
setuptools 39.1.0 py36_0 defaults
simplegeneric 0.8.1 py36_2 defaults
singledispatch 3.4.0.3 py36h17d0c80_0 defaults
sip 4.19.8 py36h6538335_0 defaults
six 1.11.0 py36h4db2310_1 defaults
snappy 1.1.7 h777316e_3 defaults
snowballstemmer 1.2.1 py36h763602f_0 defaults
sortedcollections 0.6.1 py36_0 defaults
sortedcontainers 1.5.10 py36_0 defaults
sphinx 1.7.4 py36_0 defaults
sphinxcontrib 1.0 py36hbbac3d2_1 defaults
sphinxcontrib-websupport 1.0.1 py36hb5e5916_1 defaults
spyder 3.2.8 py36_0 defaults
sqlalchemy 1.2.7 py36ha85dd04_0 defaults
sqlite 3.23.1 h35aae40_0 defaults
statsmodels 0.9.0 py36h452e1ab_0 defaults
sympy 1.1.1 py36h96708e0_0 defaults
tblib 1.3.2 py36h30f5020_0 defaults
terminado 0.8.1 py36_1 defaults
testpath 0.3.1 py36h2698cfe_0 defaults
tk 8.6.7 hcb92d03_3 defaults
toolz 0.9.0 py36_0 defaults
tornado 5.0.2 py36_0 defaults
traitlets 4.3.2 py36h096827d_0 defaults
typing 3.6.4 py36_0 defaults
unicodecsv 0.14.1 py36h6450c06_0 defaults
urllib3 1.22 py36h276f60a_0 defaults
vc 14 h0510ff6_3 defaults
vs2015_runtime 14.0.25123 3 defaults
wcwidth 0.1.7 py36h3d5aa90_0 defaults
webencodings 0.5.1 py36h67c50ae_1 defaults
werkzeug 0.14.1 py36_0 defaults
wheel 0.31.1 py36_0 defaults
widgetsnbextension 3.2.1 py36_0 defaults
win_inet_pton 1.0.1 py36he67d7fd_1 defaults
win_unicode_console 0.5 py36hcdbd4b5_0 defaults
wincertstore 0.2 py36h7fe50ca_0 defaults
winpty 0.4.3 4 defaults
wrapt 1.10.11 py36he5f5981_0 defaults
xlrd 1.1.0 py36h1cb58dc_1 defaults
xlsxwriter 1.0.4 py36_0 defaults
xlwings 0.11.8 py36_0 defaults
xlwt 1.3.0 py36h1a4751e_0 defaults
yaml 0.1.7 hc54c509_2 defaults
zeromq 4.2.5 hc6251cf_0 defaults
zict 0.1.3 py36h2d8e73e_0 defaults
zlib 1.2.11 h8395fce_2 defaults
(base) C:\Users\Awen>
(base) C:\Users\Awen>conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
Warning: 'https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/' already in 'channels' list, moving to the top
(base) C:\Users\Awen>
(base) C:\Users\Awen>conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge/
(base) C:\Users\Awen>conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
Warning: 'https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/' already in 'channels' list, moving to the top
(base) C:\Users\Awen>
(base) C:\Users\Awen>conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/
Warning: 'https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2/' already in 'channels' list, moving to the top
(base) C:\Users\Awen>
(base) C:\Users\Awen>conda config --set show_channel_urls yes
(base) C:\Users\Awen>conda env list
# conda environments:
#
base * D:\Anaconda3
(base) C:\Users\Awen>conda create -n tf2.0 python=3.6.5
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.5.4
latest version: 4.8.3
Please update conda by running
$ conda update -n base conda
## Package Plan ##
environment location: D:\Anaconda3\envs\tf2.0
added / updated specs:
- python=3.6.5
The following packages will be downloaded:
package | build
---------------------------|-----------------
pip-9.0.1 | py36_1 1.7 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
wincertstore-0.2 | py36_0 14 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
python-3.6.5 | 1 19.5 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
vc-14 | 0 703 B https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
setuptools-36.4.0 | py36_1 534 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
vs2015_runtime-14.0.25420 | 0 2.0 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
certifi-2016.2.28 | py36_0 214 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
wheel-0.29.0 | py36_0 129 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
------------------------------------------------------------
Total: 24.0 MB
The following NEW packages will be INSTALLED:
certifi: 2016.2.28-py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
pip: 9.0.1-py36_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
python: 3.6.5-1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
setuptools: 36.4.0-py36_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
vc: 14-0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
vs2015_runtime: 14.0.25420-0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
wheel: 0.29.0-py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
wincertstore: 0.2-py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
Proceed ([y]/n)? y
Downloading and Extracting Packages
pip-9.0.1 | 1.7 MB | ############################################################################## | 100%
wincertstore-0.2 | 14 KB | ############################################################################## | 100%
python-3.6.5 | 19.5 MB | ############################################################################## | 100%
vc-14 | 703 B | ############################################################################## | 100%
setuptools-36.4.0 | 534 KB | ############################################################################## | 100%
vs2015_runtime-14.0. | 2.0 MB | ############################################################################## | 100%
certifi-2016.2.28 | 214 KB | ############################################################################## | 100%
wheel-0.29.0 | 129 KB | ############################################################################## | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
#
# To activate this environment, use
#
# $ conda activate tf2.0
#
# To deactivate an active environment, use
#
# $ conda deactivate
(base) C:\Users\Awen>conda env list
# conda environments:
#
base * D:\Anaconda3
tf2.0 D:\Anaconda3\envs\tf2.0
(base) C:\Users\Awen>conda activate tf2.0
(tf2.0) C:\Users\Awen>conda install tensorflow==2.0
Solving environment: done
==> WARNING: A newer version of conda exists. <==
current version: 4.5.4
latest version: 4.8.3
Please update conda by running
$ conda update -n base conda
## Package Plan ##
environment location: D:\Anaconda3\envs\tf2.0
added / updated specs:
- tensorflow==2.0
The following packages will be downloaded:
package | build
---------------------------|-----------------
certifi-2020.4.5.1 | py36h9f0ad1d_0 151 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
m2w64-libwinpthread-git-5.0.0.4634.697f757| 2 30 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
openssl-1.1.1f | hfa6e2cd_0 4.7 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
keras-preprocessing-1.1.0 | py_0 33 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
wrapt-1.12.1 | py36h68a101e_1 44 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libblas-3.8.0 | 14_mkl 3.5 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
six-1.14.0 | py_1 13 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
tensorflow-2.0.0 |mkl_py36h781710d_0 4 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
msys2-conda-epoch-20160418 | 1 2 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
protobuf-3.11.4 | py36h97ec31f_1 584 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
m2w64-gcc-libgfortran-5.3.0| 6 340 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
intel-openmp-2019.4 | 245 1.7 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
numpy-1.18.1 | py36h48dd78f_1 4.7 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
h5py-2.9.0 |py36hf098a70_1000 915 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
absl-py-0.9.0 | py36h9f0ad1d_1 162 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
_tflow_select-2.3.0 | mkl 3 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
tensorboard-2.0.0 | pyhb38c66f_1 3.3 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
python_abi-3.6 | 1_cp36m 4 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
keras-applications-1.0.8 | py_1 30 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
grpcio-1.23.0 | py36hc6b9980_1 1.0 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libmklml-2019.0.5 | 0 21.4 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
pyreadline-2.1 | py36_0 139 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
blas-1.0 | mkl 6 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
libprotobuf-3.11.4 | h1a1b453_0 2.2 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
scipy-1.3.1 | py36h29ff71c_0 14.4 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
mkl-2019.4 | 245 157.5 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
gast-0.2.2 | py_0 10 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
werkzeug-0.16.1 | py_0 255 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
liblapack-3.8.0 | 14_mkl 3.5 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
setuptools-46.1.3 | py36h9f0ad1d_0 660 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
m2w64-gcc-libs-core-5.3.0 | 7 213 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
mkl-service-2.3.0 | py36hfa6e2cd_0 52 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
tensorflow-estimator-2.0.0 | pyh2649769_0 272 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
icc_rt-2019.0.0 | h0cc432a_1 9.4 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
m2w64-gcc-libs-5.3.0 | 7 518 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
vc-14.1 | h869be7e_1 6 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
m2w64-gmp-6.1.0 | 2 689 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
opt_einsum-3.2.0 | py_0 49 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
astor-0.7.1 | py_0 22 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
zlib-1.2.11 | h2fa13f4_1006 236 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
vs2015_runtime-14.16.27012 | h30e32a0_1 2.2 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
hdf5-1.10.4 |nompi_hcc15c50_1106 34.9 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
ca-certificates-2020.4.5.1 | hecc5488_0 184 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
markdown-3.2.1 | py_0 61 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
google-pasta-0.2.0 | pyh8c360ce_0 42 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
termcolor-1.1.0 | py36_0 8 KB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
libcblas-3.8.0 | 14_mkl 3.5 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
tensorflow-base-2.0.0 |mkl_py36hd1d5974_0 41.9 MB https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
------------------------------------------------------------
Total: 315.7 MB
The following NEW packages will be INSTALLED:
_tflow_select: 2.3.0-mkl https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
absl-py: 0.9.0-py36h9f0ad1d_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
astor: 0.7.1-py_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
blas: 1.0-mkl https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
ca-certificates: 2020.4.5.1-hecc5488_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
gast: 0.2.2-py_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
google-pasta: 0.2.0-pyh8c360ce_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
grpcio: 1.23.0-py36hc6b9980_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
h5py: 2.9.0-py36hf098a70_1000 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
hdf5: 1.10.4-nompi_hcc15c50_1106 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
icc_rt: 2019.0.0-h0cc432a_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
intel-openmp: 2019.4-245 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
keras-applications: 1.0.8-py_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
keras-preprocessing: 1.1.0-py_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libblas: 3.8.0-14_mkl https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libcblas: 3.8.0-14_mkl https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
liblapack: 3.8.0-14_mkl https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
libmklml: 2019.0.5-0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
libprotobuf: 3.11.4-h1a1b453_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
m2w64-gcc-libgfortran: 5.3.0-6 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
m2w64-gcc-libs: 5.3.0-7 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
m2w64-gcc-libs-core: 5.3.0-7 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
m2w64-gmp: 6.1.0-2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
m2w64-libwinpthread-git: 5.0.0.4634.697f757-2 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
markdown: 3.2.1-py_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
mkl: 2019.4-245 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
mkl-service: 2.3.0-py36hfa6e2cd_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
msys2-conda-epoch: 20160418-1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/msys2
numpy: 1.18.1-py36h48dd78f_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
openssl: 1.1.1f-hfa6e2cd_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
opt_einsum: 3.2.0-py_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
protobuf: 3.11.4-py36h97ec31f_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
pyreadline: 2.1-py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
python_abi: 3.6-1_cp36m https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
scipy: 1.3.1-py36h29ff71c_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
six: 1.14.0-py_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
tensorboard: 2.0.0-pyhb38c66f_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
tensorflow: 2.0.0-mkl_py36h781710d_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
tensorflow-base: 2.0.0-mkl_py36hd1d5974_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
tensorflow-estimator: 2.0.0-pyh2649769_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
termcolor: 1.1.0-py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
werkzeug: 0.16.1-py_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
wrapt: 1.12.1-py36h68a101e_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
zlib: 1.2.11-h2fa13f4_1006 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
The following packages will be UPDATED:
certifi: 2016.2.28-py36_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free --> 2020.4.5.1-py36h9f0ad1d_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
setuptools: 36.4.0-py36_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free --> 46.1.3-py36h9f0ad1d_0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
vc: 14-0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free --> 14.1-h869be7e_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
vs2015_runtime: 14.0.25420-0 https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free --> 14.16.27012-h30e32a0_1 https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud/conda-forge
Proceed ([y]/n)? y
Downloading and Extracting Packages
certifi-2020.4.5.1 | 151 KB | ############################################################################## | 100%
m2w64-libwinpthread- | 30 KB | ############################################################################## | 100%
openssl-1.1.1f | 4.7 MB | ############################################################################## | 100%
keras-preprocessing- | 33 KB | ############################################################################## | 100%
wrapt-1.12.1 | 44 KB | ############################################################################## | 100%
libblas-3.8.0 | 3.5 MB | ############################################################################## | 100%
six-1.14.0 | 13 KB | ############################################################################## | 100%
tensorflow-2.0.0 | 4 KB | ############################################################################## | 100%
msys2-conda-epoch-20 | 2 KB | ############################################################################## | 100%
protobuf-3.11.4 | 584 KB | ############################################################################## | 100%
m2w64-gcc-libgfortra | 340 KB | ############################################################################## | 100%
intel-openmp-2019.4 | 1.7 MB | ############################################################################## | 100%
numpy-1.18.1 | 4.7 MB | ############################################################################## | 100%
h5py-2.9.0 | 915 KB | ############################################################################## | 100%
absl-py-0.9.0 | 162 KB | ############################################################################## | 100%
_tflow_select-2.3.0 | 3 KB | ############################################################################## | 100%
tensorboard-2.0.0 | 3.3 MB | ############################################################################## | 100%
python_abi-3.6 | 4 KB | ############################################################################## | 100%
keras-applications-1 | 30 KB | ############################################################################## | 100%
grpcio-1.23.0 | 1.0 MB | ############################################################################## | 100%
libmklml-2019.0.5 | 21.4 MB | ############################################################################## | 100%
pyreadline-2.1 | 139 KB | ############################################################################## | 100%
blas-1.0 | 6 KB | ############################################################################## | 100%
libprotobuf-3.11.4 | 2.2 MB | ############################################################################## | 100%
scipy-1.3.1 | 14.4 MB | ############################################################################## | 100%
mkl-2019.4 | 157.5 MB | ############################################################################# | 100%
gast-0.2.2 | 10 KB | ############################################################################## | 100%
werkzeug-0.16.1 | 255 KB | ############################################################################## | 100%
liblapack-3.8.0 | 3.5 MB | ############################################################################## | 100%
setuptools-46.1.3 | 660 KB | ############################################################################## | 100%
m2w64-gcc-libs-core- | 213 KB | ############################################################################## | 100%
mkl-service-2.3.0 | 52 KB | ############################################################################## | 100%
tensorflow-estimator | 272 KB | ############################################################################## | 100%
icc_rt-2019.0.0 | 9.4 MB | ############################################################################## | 100%
m2w64-gcc-libs-5.3.0 | 518 KB | ############################################################################## | 100%
vc-14.1 | 6 KB | ############################################################################## | 100%
m2w64-gmp-6.1.0 | 689 KB | ############################################################################## | 100%
opt_einsum-3.2.0 | 49 KB | ############################################################################## | 100%
astor-0.7.1 | 22 KB | ############################################################################## | 100%
zlib-1.2.11 | 236 KB | ############################################################################## | 100%
vs2015_runtime-14.16 | 2.2 MB | ############################################################################## | 100%
hdf5-1.10.4 | 34.9 MB | ############################################################################## | 100%
ca-certificates-2020 | 184 KB | ############################################################################## | 100%
markdown-3.2.1 | 61 KB | ############################################################################## | 100%
google-pasta-0.2.0 | 42 KB | ############################################################################## | 100%
termcolor-1.1.0 | 8 KB | ############################################################################## | 100%
libcblas-3.8.0 | 3.5 MB | ############################################################################## | 100%
tensorflow-base-2.0. | 41.9 MB | ############################################################################## | 100%
Preparing transaction: done
Verifying transaction: done
Executing transaction: done
(tf2.0) C:\Users\Awen>python
Python 3.6.5 | packaged by conda-forge | (default, Apr 6 2018, 16:13:55) [MSC v.1900 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> print(tf.__version__)
2.0.0
>>> exit()
(tf2.0) C:\Users\Awen>cd C:\Users\Awen\Desktop\NLP\code\01
(tf2.0) C:\Users\Awen\Desktop\NLP\code\01>jupyter notebook
[I 00:56:53.808 NotebookApp] JupyterLab beta preview extension loaded from D:\Anaconda3\lib\site-packages\jupyterlab
[I 00:56:53.809 NotebookApp] JupyterLab application directory is D:\Anaconda3\share\jupyter\lab
[I 00:56:54.154 NotebookApp] Serving notebooks from local directory: C:\Users\Awen\Desktop\NLP\code\01
[I 00:56:54.154 NotebookApp] 0 active kernels
[I 00:56:54.154 NotebookApp] The Jupyter Notebook is running at:
[I 00:56:54.154 NotebookApp] http://localhost:8888/?token=fb3cb1703589524627dced286d8ca5e190efbe203ea2468b
[I 00:56:54.155 NotebookApp] Use Control-C to stop this server and shut down all kernels (twice to skip confirmation).
[C 00:56:54.156 NotebookApp]
Copy/paste this URL into your browser when you connect for the first time,
to login with a token:
http://localhost:8888/?token=fb3cb1703589524627dced286d8ca5e190efbe203ea2468b&token=fb3cb1703589524627dced286d8ca5e190efbe203ea2468b
[I 00:56:54.521 NotebookApp] Accepting one-time-token-authenticated connection from ::1
参考链接：https://blog.csdn.net/CampusAmour/article/details/83215524