anis016

1. login with sudo mode
$ sudo su

2. install virtualenv
$ pip install virtualenv

3. goto the folder where you want to create virtualenvironment
$ cd /home/work/python

4. create virtualenv using a given python version [if omitted, '-p /usr/bin/python3.6' then it is going to take the default one]

anis016

1. Install Python3.6

$ sudo add-apt-repository ppa:jonathonf/python-3.6
$ sudo apt-get update
$ sudo apt-get install python3.6
$ sudo apt install python3.6-dev

2. Install PIP 3.6

First download the file: get-pip.py [source: https://bootstrap.pypa.io/get-pip.py]

anis016

0. Installation requires Git and triggers git clone for each configured repository to ~/.vim/bundle/ by default.
Curl is required for search.

1. Install VIM
$ sudo apt-get remove vim-tiny
$ sudo apt-get update
$ sudo apt-get install vim

2. Install CMake
$ sudo apt-get install CMake

anis016

## Installing MONGODB

Specific Version to install: 3.2.16

Ubuntu 16.04 OS
Source: https://docs.mongodb.com/v3.2/tutorial/install-mongodb-on-ubuntu/

1. Import the public key used by the package management system.
$ sudo apt-key adv --keyserver hkp://keyserver.ubuntu.com:80 --recv EA312927

anis016

# building rl4j for the latest snapshot (mvnrepository still doesn't have the latest build)
# build pipeline >> javacpp -> libnd4j -> nd4j -> DataVec -> deeplearning4j -> rl4j
# https://github.com/deeplearning4j/rl4j/issues/73#issuecomment-335732797

0. note about maven version. it must be latest (running 3.5.0)
1. install cmake
   1.1 wget https://cmake.org/files/v3.11/cmake-3.11.0-rc2-Linux-x86_64.sh
   1.2 chmod a+x cmake-3.6.2-Linux-x86_64.sh
   1.3 mkdir /opt/cmake
   1.4 sudo sh cmake-3.11.0-rc2-Linux-x86_64.sh --prefix=/opt/cmake --skip-license

anis016

// create file:

$ sudo touch /usr/share/applications/pycharm.desktop
$ sudo vim /usr/share/applications/pycharm.desktop

[Desktop Entry]
Version=1.0
Name=PyCharm
GenericName=IDE
Exec="/home/ghost016/IDE/pycharm-community-2019.3.3/bin/pycharm.sh" %f

anis016

3bA62813

https://de.pcpartpicker.com/list/37fp8Y

### caseking (Bought)

9. 750W 80+ Gold Certified Fully-Modular ATX Power Supply - 99.90
https://www.caseking.de/en/bitfenix-fury-80-plus-gold-netzteil-semi-modular-750-watt-nebf-003.html

7. Gigabyte - GeForce GTX 1080 8GB WINDFORCE OC 8G Video Card - 609

anis016

tutorial:
1. https://medium.com/@vivek.yadav/deep-learning-setup-for-ubuntu-16-04-tensorflow-1-2-keras-opencv3-python3-cuda8-and-cudnn5-1-324438dd46f0
2. https://towardsdatascience.com/build-and-setup-your-own-deep-learning-server-from-scratch-e771dacaa252 [for opencv only]

** installing tensorflow-gpu follow above tutorial
** installing pytorch-gpu
# install torch (cuda 9)
$ conda install pytorch torchvision cuda90 -c pytorch
# test gpu install
python -c 'import torch; print(torch.rand(2,3).cuda())'

anis016

import org.joda.time.DateTime;
import org.joda.time.LocalDate;
import org.joda.time.format.DateTimeFormat;
import org.joda.time.format.DateTimeFormatter;

import java.io.IOException;
import java.util.Calendar;
import java.util.Date;

DateTimeFormatter formatter = DateTimeFormat.forPattern( "yyyyMM");

anis016

import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.nn.functional as F
import random
flatten = lambda l: [item for sublist in l for item in sublist]
random.seed(1024)

USE_CUDA = torch.cuda.is_available()
gpus = [0]