Linux Notes#07# Building a Machine Learning Environment

Environment: Debian 8.8 64-bit, also suitable for win10

The basic steps:

Install Python
Install the necessary libraries
test

1. Install Python

Continue to build a Python environment and choose a compromise version Python 3.4

sudo apt-get install python3

At this time, there will be a problem: how to use pip when both Python3 and Python2 are installed? The default installed pip corresponds to python 2.7

Use the simplest and most straightforward solution:

apt-get purge -y python3-pip
apt-get install python3-pip

Check if it is installed:

root@xkfx:/opt/python# python3 -m pip -V
pip 1.5.6 from /usr/lib/python3/dist-packages (python 3.4)

Update to the latest version:

python3 -m pip install --upgrade pip

To install the module to the corresponding Python version with pip (specify the Python version):

python3 -m pip install module name

PS. -m is probably the meaning of the module module

2. Install the necessary libraries

ipython：

root@xkfx:~# python3 -m pip install ipython

root@xkfx:/opt/python# ipython
Python 3.4.2 (default, Oct 8 2014, 10:45:20) 
Type 'copyright', 'credits' or 'license' for more information
IPython 6.3.1 -- An enhanced Interactive Python. Type '?' for help.

In [1]: exit

matplotlib ：

root@xkfx:~# python3 -m pip install matplotlib

root@xkfx:~# python3
Python 3.4.2 (default, Oct 8 2014, 10:45:20) 
[GCC 4.9.1] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import xxxxxx
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ImportError: No module named 'xxxxxx'
>>> import matplotlib

numpy：

root@xkfx:~# python3 -m pip install numpy
Looking in indexes: http://mirrors.aliyun.com/pypi/simple/
Requirement already satisfied: numpy in /usr/local/lib/python3.4/dist-packages (1.14.2)
root@xkfx:~# python3
Python 3.4.2 (default, Oct 8 2014, 10:45:20) 
[GCC 4.9.1] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import numpy

sklearn ：

root@xkfx:~# python3 -m pip install scipy

root@xkfx:~# python3 -m pip install sklearn

root@xkfx:~# python3
Python 3.4.2 (default, Oct 8 2014, 10:45:20) 
[GCC 4.9.1] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import sklearn

tensorflow：

root@xkfx:~# python3 -m pip install tensorflow

root@xkfx:~# python3
Python 3.4.2 (default, Oct 8 2014, 10:45:20) 
[GCC 4.9.1] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> print(tf.__version__)
1.7.0

pandas：

python3 -m pip install pandas 

 Command "/usr/bin/python3 -m pip install --ignore-installed --no-user --prefix /tmp/pip-build-env-kfazvlty http://mirrors.aliyun.com/pypi/packages/1b/d2/22cde5ea9af055f81814f9f2545f5ed8a053eb749c08d186b369959189a8/wheel-0.31.0-py2.py3-none-any.whl#md5=240d714477a715bcd90e94cb2c44f28c http://mirrors.aliyun.com/pypi/packages/20/d7/04a0b689d3035143e2ff288f4b9ee4bf6ed80585cc121c90bfd85a1a8c2e/setuptools-39.0.1-py2.py3-none-any.whl#md5=ca299c7acd13a72e1171a3697f2b99bc http://mirrors.aliyun.com/pypi/packages/70/25/1e1521e6ce2cf78ff4a8b06fbc2cd513ce004ec337000eddfe016fdf3fc6/Cython-0.28.2-cp34-cp34m-manylinux1_x86_64.whl#md5=e277ba5fdbbaab6e3434bdcf58e41d6a http://mirrors.aliyun.com/pypi/packages/fc/1b/a1717502572587c724858862fd9b98a66105f3a3443225bda9a1bd16ee14/numpy-1.9.3-cp34-cp34m-manylinux1_x86_64.whl#md5=e1130c8f540a759d79ba5e8960f6915a http://mirrors.aliyun.com/pypi/packages/02/64/c6c1c24ff4dbcd789fcfdb782e343ac23c074f6b8b03e818ff60eb0f937f/numpy-1.12.1-cp34-cp34m-manylinux1_x86_64.whl#md5=6288d4e9cfea859e03dc82879539d029 http://mirrors.aliyun.com/pypi/packages/1b/ee/f65826b2880f67652c21326565b4c166c7cdb1019f84b82af65e625475cd/numpy-1.13.1-cp34-cp34m-manylinux1_x86_64.whl#md5=c51520d0d3836c91cba18d1fa8cf299c" failed with error code 1 in None

Since pandas was not successfully installed anyway under debian

I finally decided to build the environment on windows. . .

3. Test

Run the following code, and it will be ok if there is no error.

import math

from IPython import display
from matplotlib import cm
from matplotlib import gridspec
from matplotlib import pyplot as plt
import numpy as np
import pandas as pd
from sklearn import metrics
import tensorflow as tf
from tensorflow.python.data import Dataset

tf.logging.set_verbosity(tf.logging.ERROR)
pd.options.display.max_rows = 10
pd.options.display.float_format = '{:.1f}'.format