Kyriakos Chatzidimitriou kyrcha

Accessible from the web: http://hostname:8081 through mongo-express and you can connect with a mongodb client like robo 3T in hostname:27017.

To run:

docker-compose -f mongo.yml up

Checkout all the packages in the default conda environment:

conda list

Upgrading conda and packages:

conda upgrade conda
conda upgrade --all

Installing packages examples:

Instructions of how to set-up environments etc.

Complete guide on how to deploy a node app in an Ubuntu server using flightplan can be found here:

	const Octokit = require('@octokit/rest')
	const rp = require('request-promise')
	const fs = require('fs')

	function sleep (ms) {
	return new Promise(resolve => setTimeout(resolve, ms))
	}

	const baseUrl = 'https://api.github.com'
	const octokit = new Octokit({ auth: process.env.GITHUB_TOKEN })

	# editorconfig.org
	root = true

	[*]
	indent_style = space
	indent_size = 2
	end_of_line = lf
	charset = utf-8
	trim_trailing_whitespace = true
	insert_final_newline = true

	from multiprocessing import Pool
	import redis
	import math
	import json
	from random import seed
	from random import gauss

	# Atomic operations
	def sum(x):
	r = redis.Redis(host='localhost', port=6379, db=0)

	# function needed for visualization purposes
	sigmoid = function(params, x) {
	params[1] / (1 + exp(-params[2] * (x - params[3])))
	}

	x = 1:53
	y = c(0,0,0,0,0,0,0,0,0,0,0,0,0,0.1,0.18,0.18,0.18,0.33,0.33,0.33,0.33,0.41,
	0.41,0.41,0.41,0.41,0.41,0.5,0.5,0.5,0.5,0.68,0.58,0.58,0.68,0.83,0.83,0.83,
	0.74,0.74,0.74,0.83,0.83,0.9,0.9,0.9,1,1,1,1,1,1,1)

	# Transforming a 60000x784 dataframe to a 60000x28x28x1 4D tensor for Keras modelling
	train_img = X_train.values.reshape(X_train.shape[0], 28, 28, 1)