Ubuntu18.04 安裝 Anaconda3
下載地址：清華大學開源軟件鏡像站
安裝 Anaconda
1）打開terminal；
2）打開下載文件的位置：
cd Downloads/
3）運行 .sh 文件：
bash Anaconda3-5.2.0-Linux-x86_64.sh
4）進入註冊信息頁面，輸入yes；
5）閱讀註冊信息，然後輸入yes；查看文件即將安裝的位置，按enter，即可安裝，如圖5；
7）安裝完成後，收到加入環境變量的提示信息，輸入yes．
8）提示信息“Do you wish to proceed with the installation of Microsoft VSCode? [yes|no]”，輸入no；
9）重啓終端，即可使用Anaconda3；
10）若在終端輸入 python，仍然會顯示Ubuntu自帶的python版本，我們執行：
sudo gedit ~/.bashrc
export PATH="/home/xupp/anaconda3/bin:$PATH"
修改終端的默認 python 爲 anaconda，至此全部完成．
3.驗證安裝是否成功：
方法一：
啓動新終端；查看conda版本：
$ conda --version
如正常顯示則證明安裝成功；
方法二：
啓動新終端；打開Python；
輸入import scipy，未報錯即已成功。
4.其他拓展
列出安裝的包：
$ conda list
更新包：
$ conda update conda
Anaconda文檔：https://docs.continuum.io/
conda的下載源文件目錄
:/home/yourname/.condarc，內容如下：
-
-
修改上述配置文件，刪除上述配置文件 .condarc 中的第三行，然後保存，最終版本文件如下：
-
5
查看是否生效，通過命令 conda info 查看當前配置信息，內容如下，即修改成功，關注 channel URLs 字段內容
-
tensorflow版本與python對應關係，可以下載tensorflow：https://pypi.org/project/tensorflow/#files
python查找：conda search python
python安裝：conda install python=3.6
tensorflow安裝:
(https://pypi.org/project/tensorflow-gpu/2.0.0/#files)
http://mirrors.aliyun.com/pypi/simple/tensorflow-gpu/
需要先創建並激活虛擬環境，再安裝．
(base) zfy@zfy-N9x0TC:~$ conda create -n tensorflow_python_3_7_4 python=3.7
(base) zfy@zfy-N9x0TC:~$ source activate tensorflow_python_3_7_4
conda install tensorflow-gpu=2.0.0
在anaconda下安裝tensorflow-gpu,會默認按張cuda和cudnn,安裝2.0.0版本時安裝的軟件如下：
The following NEW packages will be INSTALLED:
_tflow_select anaconda/pkgs/main/linux-64::_tflow_select-2.1.0-gpu
absl-py anaconda/pkgs/main/linux-64::absl-py-0.8.1-py36_0
astor anaconda/pkgs/main/linux-64::astor-0.8.0-py36_0
blas anaconda/pkgs/free/linux-64::blas-1.0-mkl
c-ares anaconda/pkgs/main/linux-64::c-ares-1.15.0-h7b6447c_1001
cudatoolkit anaconda/pkgs/main/linux-64::cudatoolkit-10.0.130-0
cudnn anaconda/pkgs/main/linux-64::cudnn-7.6.4-cuda10.0_0
cupti anaconda/pkgs/main/linux-64::cupti-10.0.130-0
gast anaconda/pkgs/main/linux-64::gast-0.2.2-py36_0
google-pasta anaconda/pkgs/main/noarch::google-pasta-0.1.8-py_0
grpcio anaconda/pkgs/main/linux-64::grpcio-1.16.1-py36hf8bcb03_1
h5py anaconda/pkgs/main/linux-64::h5py-2.9.0-py36h7918eee_0
hdf5 anaconda/pkgs/main/linux-64::hdf5-1.10.4-hb1b8bf9_0
intel-openmp anaconda/pkgs/main/linux-64::intel-openmp-2019.4-243
keras-applications anaconda/pkgs/main/noarch::keras-applications-1.0.8-py_0
keras-preprocessi~ anaconda/pkgs/main/noarch::keras-preprocessing-1.1.0-py_1
libgfortran-ng anaconda/pkgs/main/linux-64::libgfortran-ng-7.3.0-hdf63c60_0
libprotobuf anaconda/pkgs/main/linux-64::libprotobuf-3.10.1-hd408876_0
markdown anaconda/pkgs/main/linux-64::markdown-3.1.1-py36_0
mkl anaconda/pkgs/main/linux-64::mkl-2019.4-243
mkl-service anaconda/pkgs/main/linux-64::mkl-service-2.3.0-py36he904b0f_0
mkl_fft anaconda/pkgs/main/linux-64::mkl_fft-1.0.15-py36ha843d7b_0
mkl_random anaconda/pkgs/main/linux-64::mkl_random-1.1.0-py36hd6b4f25_0
numpy anaconda/pkgs/main/linux-64::numpy-1.17.4-py36hc1035e2_0
numpy-base anaconda/pkgs/main/linux-64::numpy-base-1.17.4-py36hde5b4d6_0
opt_einsum anaconda/pkgs/main/noarch::opt_einsum-3.1.0-py_0
protobuf anaconda/pkgs/main/linux-64::protobuf-3.10.1-py36he6710b0_0
scipy anaconda/pkgs/main/linux-64::scipy-1.3.2-py36h7c811a0_0
six anaconda/pkgs/main/linux-64::six-1.13.0-py36_0
tensorboard anaconda/pkgs/main/noarch::tensorboard-2.0.0-pyhb38c66f_1
tensorflow anaconda/pkgs/main/linux-64::tensorflow-2.0.0-gpu_py36h6b29c10_0
tensorflow-base anaconda/pkgs/main/linux-64::tensorflow-base-2.0.0-gpu_py36h0ec5d1f_0
tensorflow-estima~ anaconda/pkgs/main/noarch::tensorflow-estimator-2.0.0-pyh2649769_0
tensorflow-gpu anaconda/pkgs/main/linux-64::tensorflow-gpu-2.0.0-h0d30ee6_0
termcolor anaconda/pkgs/free/linux-64::termcolor-1.1.0-py36_0
werkzeug anaconda/pkgs/main/noarch::werkzeug-0.16.0-py_0
wrapt anaconda/pkgs/main/linux-64::wrapt-1.11.2-py36h7b6447c_0
The following packages will be UPDATED:
certifi anaconda/pkgs/free::certifi-2016.2.28~ --> anaconda/pkgs/main::certifi-2019.11.28-py36_0
setuptools anaconda/pkgs/free::setuptools-36.4.0~ --> anaconda/pkgs/main::setuptools-42.0.2-py36_0
安裝whl包的時候出現“tensorflow_gpu-1.3.0-cp36-cp36m-linux_x86_64.whl is not a supported wheel on this platform”的問題。我們需要下載GPU版的安裝包，在安裝包下載之後，然後手動進入環境，安裝TensorFlow。
具體操作如下(因爲我碰到這樣問題，只能用下面這種方式安裝了)：
source activate tensorflow #激活tensorflow環境（這步操作了，就忽略）
cd /Downloads #切換到whl文件所在文件夾
pip install --ignore-installed --upgrade tensorflow_gpu-1.3.0-cp36-cp36m-linux_x86_64.whl #切記，不要用sudo pip，也不要用pip3，然後--ignore-installed --upgrade等參數也不能省略，否則會出錯。
安裝好tensorflow在終端激活環境後進行測試出現import-im6.q16: not authorized `tf' @ error/constitute.c/WriteImage/1037錯誤
原因是激活環境後要先進入python環境，也就是繼續在終端輸入python進入環境後在輸入import tensorflow as tf就行了
(base) zfy@zfy-N9x0TC:~$ source activate tensorflow
(tensorflow) zfy@zfy-N9x0TC:~$ cd Downloads
(tensorflow) zfy@zfy-N9x0TC:~/Downloads$ pip install --ignore-installed --upgrade tensorflow_gpu-1.14.0-cp37-cp37m-manylinux1_x86_64.whl
(tensorflow) zfy@zfy-N9x0TC:~/Downloads$ import tensorflow as tf
import-im6.q16: not authorized `tf' @ error/constitute.c/WriteImage/1037.
(tensorflow) zfy@zfy-N9x0TC:~$ python
Python 3.7.5 (default, Oct 25 2019, 15:51:11)
[GCC 7.3.0] :: Anaconda, Inc. on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
/home/zfy/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/zfy/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/zfy/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/zfy/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/zfy/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/zfy/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
/home/zfy/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint8 = np.dtype([("qint8", np.int8, 1)])
/home/zfy/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint8 = np.dtype([("quint8", np.uint8, 1)])
/home/zfy/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint16 = np.dtype([("qint16", np.int16, 1)])
/home/zfy/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_quint16 = np.dtype([("quint16", np.uint16, 1)])
/home/zfy/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
_np_qint32 = np.dtype([("qint32", np.int32, 1)])
/home/zfy/anaconda3/envs/tensorflow/lib/python3.7/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.
np_resource = np.dtype([("resource", np.ubyte, 1)])
＞＞
解決方案:
找到/home/hitwh/anaconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/python/framework目錄下的dtypes.py
將_np_quint8 = np.dtype([(“quint8”, np.uint8, 1)])改爲
_np_quint8 = np.dtype([("quint8", np.uint8, (1,))])
然後就正常了
在這裏插入圖片描述
參考鏈接： https://blog.csdn.net/qq_41975844/article/details/99622948
>>> hello = tf.constant('hello,tensorflow')
>>> sess = tf.Session()
2019-12-18 19:56:18.727854: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcuda.so.1
2019-12-18 19:56:18.772612: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-18 19:56:18.772916: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1640] Found device 0 with properties:
name: GeForce GTX 1660 Ti major: 7 minor: 5 memoryClockRate(GHz): 1.59
pciBusID: 0000:01:00.0
2019-12-18 19:56:18.772996: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcudart.so.10.0'; dlerror: libcudart.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/lib/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu:
2019-12-18 19:56:18.773045: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcublas.so.10.0'; dlerror: libcublas.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/lib/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu:
2019-12-18 19:56:18.773102: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcufft.so.10.0'; dlerror: libcufft.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/lib/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu:
2019-12-18 19:56:18.773152: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcurand.so.10.0'; dlerror: libcurand.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/lib/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu:
2019-12-18 19:56:18.773197: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcusolver.so.10.0'; dlerror: libcusolver.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/lib/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu:
2019-12-18 19:56:18.773243: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcusparse.so.10.0'; dlerror: libcusparse.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/lib/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu:
2019-12-18 19:56:18.893324: I tensorflow/stream_executor/platform/default/dso_loader.cc:42] Successfully opened dynamic library libcudnn.so.7
2019-12-18 19:56:18.893416: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1663] Cannot dlopen some GPU libraries. Skipping registering GPU devices...
2019-12-18 19:56:18.894144: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2019-12-18 19:56:18.922259: I tensorflow/core/platform/profile_utils/cpu_utils.cc:94] CPU Frequency: 2904000000 Hz
2019-12-18 19:56:18.922891: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55848db69460 executing computations on platform Host. Devices:
2019-12-18 19:56:18.922910: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): <undefined>, <undefined>
2019-12-18 19:56:18.922986: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1181] Device interconnect StreamExecutor with strength 1 edge matrix:
2019-12-18 19:56:18.922997: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1187]
2019-12-18 19:56:18.995509: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:1005] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2019-12-18 19:56:18.995842: I tensorflow/compiler/xla/service/service.cc:168] XLA service 0x55848f0ac480 executing computations on platform CUDA. Devices:
2019-12-18 19:56:18.995860: I tensorflow/compiler/xla/service/service.cc:175] StreamExecutor device (0): GeForce GTX 1660 Ti, Compute Capability 7.5
>>> sess.run(hello)
b'hello,tensorflow'
>>> sess.close()
上面出現了so文件找不到的錯誤
錯誤例子如下：
2019-12-18 19:56:18.773045: I tensorflow/stream_executor/platform/default/dso_loader.cc:53] Could not dlopen library 'libcublas.so.10.0'; dlerror: libcublas.so.10.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: /usr/local/cuda/lib64:/lib/x86_64-linux-gnu:/usr/lib/x86_64-linux-gnu:
主要錯誤信息爲Could not dlopen library 'libcublas.so.10.0'。造成這樣的原因是鏈接文件不對造成的。
這種問題很好解決，下面我羅列一些常發生這樣錯誤的so文件解決辦法，首先大家要確定報錯的so文件名稱是什麼，例如上面報錯的是libcublas.so.10.0這個文件，那麼就找到對應的libcublas庫文件，然後在/usr/local/cuda-10.1/lib64/目錄下創建一個bcublas.so.10.0連接文件即可。
一般缺失的so文件都在/usr/local/cuda/lib64/目錄下，有一些特別的在/usr/lib/x86_64-linux-gnu/目錄下。
libcudart
sudo ln -s /usr/local/cuda/lib64/libcudart.so.10.1 /usr/local/cuda/lib64/libcudart.so.10.0
libcufft
sudo ln -s /usr/local/cuda/lib64/libcufft.so.10.1.168 /usr/local/cuda/lib64/libcufft.so.10.0
libcurand
sudo ln -s /usr/local/cuda/lib64/libcurand.so.10.1.168 /usr/local/cuda/lib64/libcurand.so.10.0
libcusolver
sudo ln -s /usr/local/cuda/lib64/libcusolver.so.10.1.168 /usr/local/cuda/lib64/libcusolver.so.10.0
libcusparse
sudo ln -s /usr/local/cuda/lib64/libcusparse.so.10.1.168 /usr/local/cuda/lib64/libcusparse.so.10.0
libcublas
root@zfy-N9x0TC:/home/zfy# sudo rm -rf /usr/local/cuda/lib64/libcublas.so.10.0
root@zfy-N9x0TC:/home/zfy# sudo ln -s /usr/lib/x86_64-linux-gnu/libcublas.so.10.1.0.105 /usr/local/cuda/lib64/libcublas.so.10.0
如果/usr/lib/x86_64-linux-gnu/目錄下沒有libcublas庫，可以在/usr/local/cuda10.1/targets/x86_64-linux/lib/查找libcublas庫。
查看numa是否支持
apt-get install numa
使用命令
numactl --hardware
(base) root@zfy-N9x0TC:/home/zfy# numactl --hardware
available: 1 nodes (0)
node 0 cpus: 0 1 2 3 4 5
node 0 size: 15875 MB
node 0 free: 9592 MB
node distances:
node 0
0: 10
有多個node 即爲numa系統.
1.配置一下anaconda的鏡像：
conda config --add channels https://mirrors.ustc.edu.cn/anaconda/pkgs/free/
conda config --set show_channel_urls yes
2.創建環境，輸入
conda create -n tensorflow-gpu python=3.6 ＃這裏的tensorflow-gpu是環境的名字
3.進入環境
conda activate tensorflow-gpu
4.下載tensorflow-gpu
pip install --upgrade --ignore-installed tensorflow-gpu
5.然後進入Python測試環境是否配置成功：
python
import tensorflow as tf #這裏的tensorflow不能寫成環境的名字
hello = tf.constant('Hello, TensorFlow!')
sess = tf.Session()
print(sess.run(hello))
6.退出
先退出python
exit()
再推出anaconda，
conda deactivate
Anaconda創建環境：
conda create -n py36 python=3.6
刪除環境
conda remove -n py36 --all
激活環境
conda activate py36
退出環境
conda deactivate
修改源方法：
臨時使用：
可以在使用pip的時候在後面加上-i參數，指定pip源
eg: pip install scrapy -i https://pypi.tuna.tsinghua.edu.cn/simple --trusted-host pypi.tuna.tsinghua.edu.cn
永久修改：
linux:
修改 ~/.pip/pip.conf (沒有就創建一個)， 內容如下：
[global]
index-url = https://pypi.tuna.tsinghua.edu.cn/simple
windows:
直接在user目錄中創建一個pip目錄，如：C:\Users\xx\pip，新建文件pip.ini，內容如下
[global]
index-url = https://pypi.tuna.tsinghua.edu.cn/simple
Anaconda 鏡像使用幫助
Anaconda 是一個用於科學計算的 Python 發行版，支持 Linux, Mac, Windows, 包含了衆多流行的科學計算、數據分析的 Python 包。
Anaconda 安裝包可以到 https://mirrors.tuna.tsinghua.edu.cn/anaconda/archive/ 下載。
TUNA 還提供了 Anaconda 倉庫與第三方源（conda-forge、msys2、pytorch等，查看完整列表）的鏡像，各系統都可以通過修改用戶目錄下的 .condarc 文件:
channels:
- defaults
show_channel_urls: true
default_channels:
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
custom_channels:
conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
msys2: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
bioconda: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
menpo: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
pytorch: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
simpleitk: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
即可添加 Anaconda Python 免費倉庫。Windows 用戶無法直接創建名爲 .condarc 的文件，可先執行 conda config --set show_channel_urls yes 生成該文件之後再修改。
在已安裝conda機器上配置國內鏡像站
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --set show_channel_urls yes
添加清華鏡像源
複製，粘貼，運行以下代碼：
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --set show_channel_urls yes
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/
2019/5/5更新
由於清華鏡像源關閉，需恢復官方源
conda config --remove-key channels
2019/6/22更新
Annaconda感受到了廣大同胞在gayhub上的強烈呼聲，鏡像源又可以用了。
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
conda config --set show_channel_urls yes
清華大學——Anaconda 鏡像使用幫助
用戶沒有對anaconda3文件夾的讀寫權限解決方案：
造成其原因可能是由於在安裝anaconda時使用了管理員權限。最簡單的方法就是撤銷對這個文件夾權限限制，定位到anaconda3同級文件夾下打開終端執行如下代碼即可：
sudo chmod 777 -R anaconda3
報錯解決方案：W: 目標 Sources (main/source/Sources) 在 /etc/apt/sources.list:2 和 /etc/apt/sources.list:7 中被配置了多次
vim /etc/apt/sources.list
查看第2行和第7行，發現重複。
第2行是 deb-src http://archive.ubuntu.com/ubuntu xenial main restricted
第7行是 deb-src http://archive.ubuntu.com/ubuntu xenial restricted
刪除配置重複的部分。
修改ubuntu系統源
1. https://www.cnblogs.com/cymwill/p/10293205.html
2. https://blog.csdn.net/xiangxianghehe/article/details/80112149
ubuntu18.04 卸載與安裝anaconda
1、刪除Anaconda3文件夾
rm -rf ~/anaconda3
2、刪除Anaconda·配置的環境變量
sudo gedit ~/.bashrc
將末尾的此行刪除
# added by Anaconda3 installer
export PATH="/home/Vselfdom/anaconda3/bin:$PATH"
此處的Vselfdom應爲你的實際用戶名
3、更新環境變量，使更改生效
source ~/.bashrc
ｕbuntu18.04降級gcc到4.8
(1). 下載gcc/g++ 4.8
$ sudo apt-get install -y gcc-4.8
$ sudo apt-get install -y g++-4.8
1
2
(2). 鏈接gcc/g++實現降級
$ cd /usr/bin
$ sudo rm gcc
$ sudo ln -s gcc-4.8 gcc
$ sudo rm g++
$ sudo ln -s g++-4.8 g++