Python Anaconda: 1) installation; 2) need for Machine Learning

Question

Python Anaconda: 1) installation; 2) need for Machine Learning

Two questions about Python Anaconda

OS Ubuntu 16.04. Do I need to demolish the existing Python and libraries (pandas, numpy, mathplotlib, etc.) before installing PA? If not, will the libraries be updated to new versions? Are there any conflicts?
I started studying Machine Learning. Some citizens write that it is highly desirable to install a PA because there will be a Jupyter Notebook, all installed libraries and a number of other important amenities. The presence of the PA is valid will it greatly simplify life in this sense?

The questions are simple, so I will accept answers like yes\no.

1

python scikit-learn anaconda

Author: Борис Мирский, 2017-06-29

Source

1 answers

score 5 · Accepted Answer

As colleagues have already commented, Anaconda is placed in a separate directory (you decide where) and does not overlap with the "system" Python.

If everything is done correctly, then no difficulties/problems will arise.

Here is an approximate algorithm for installing Anaconda under UNIX*:

Installing Anaconda:

bash Anaconda3-X.X.X-Linux-x86_64.sh

Update conda:

conda update conda

Creating your own VirtualEnv (environment name - ml [machine learning], Python version - 2.7):

conda create -n ml python=2.7 anaconda

PS you can create multiple environments at once/environments for different versions, for example:

conda create -n ml27 python=2.7 anaconda
conda create -n ml35 python=3.5 anaconda
conda create -n ml36 python=3.6 anaconda

Activating the environment:

conda activate ml

Installing additional packages/modules for a specific environment (VirtualEnv):

conda install -n ml [package]

To work in scripts, you can create an environment file (let's call it: $HOME/.ml_env):

export PYTHONPATH=/path/to/my/own/python_libs
export LD_LIBRARY_PATH=$HOME/anaconda3/lib:$ORACLE_HOME/lib
export PATH=$HOME/anaconda3/bin:$PATH:$HOME/bin

Then we add the trace in the SHELL scripts. lines:

#!/bin/bash

source $HOME/.ml_env
source activate ml

Useful ones references:

https://uoa-eresearch.github.io/eresearch-cookbook/recipe/2014/11/20/conda/

Indeed, using Anaconda is very convenient for a number of reasons:

completely independent of the system Python environment, which is easy to configure for yourself, move to another machine, remove or reinstall, and at the same time without affecting or" breaking " the system Python
all installed modules are compatible with each other and tested - Continuum Analytics takes care of this
a lot of useful modules (especially for those who are engaged in machine learning) are already installed by default