rabelais88

스타트업에 traefik + docker-swarm을 추천하는 이유

• Traefik: Reverse-proxy router
• Docker-swarm: Docker-supported orchestrator

왜 Docker-Swarm인가?

• kubernetes(이하 k8) 는 무겁고 느리다. docker-swarm(이하 DS) 은 docker 최신버전에 기본으로 탑재되어 나온다. 또한 helm이나 별도의 관리 cli(kubeadm, kubectl)를 설치할 필요가 없다.

• k8 은 세팅도 어렵다: 세팅하기가 워낙 까다로워 실제로 운영하기 적절한 경우는 devops 팀을 가진 최소 20명 이상의 대규모 사이즈 팀이다. 한 명이 작업하는 것이 아주 불가능한 것은 아니지만, 긴급수정시에는 많이 위험해질 수 있다. 총원이 10명이 되지 않는 우리팀 같은 경우는 한 사람이 급하게 기능수정을 해야될 일이 많다.

ziadoz

#!/usr/bin/env bash
# https://developers.supportbee.com/blog/setting-up-cucumber-to-run-with-Chrome-on-Linux/
# https://gist.github.com/curtismcmullan/7be1a8c1c841a9d8db2c
# https://stackoverflow.com/questions/10792403/how-do-i-get-chrome-working-with-selenium-using-php-webdriver
# https://stackoverflow.com/questions/26133486/how-to-specify-binary-path-for-remote-chromedriver-in-codeception
# https://stackoverflow.com/questions/40262682/how-to-run-selenium-3-x-with-chrome-driver-through-terminal
# https://askubuntu.com/questions/760085/how-do-you-install-google-chrome-on-ubuntu-16-04

# Versions
CHROME_DRIVER_VERSION=`curl -sS https://chromedriver.storage.googleapis.com/LATEST_RELEASE`

rondomondo

#!/usr/bin/env python
import os
import time
import json
import base64
import requests
import argparse

from base64 import urlsafe_b64decode, b64decode
from Crypto.Hash import SHA256, SHA512

makuk66

This is a demo of how to talk between programs running on a Mac, and programs running under Docker on a VM on the same Mac as installed by Docker Toolbox.

Just add a route to your containers using the VM as the gateway. Demo:

• on the mac, run Docker Quickstart Terminal from the latest Docker Toolbox. This launches the virtualbox VM, and configures your shell
• in that shell, figure out the IP address of the VM:

mak@crab 493 ~ $ docker-machine inspect default -f '{{ .Driver.IPAddress }} '
192.168.99.100 

xadhix-zz

var xLBD = {};
xLBD.c = function (){
xLBD.f = unescape(document.querySelector("[flashvars]").getAttribute("flashvars")).substring(7);
xLBD.f = JSON.parse(xLBD.f.substring(0, xLBD.f.lastIndexOf("}") + 1)).video_data[0].hd_src;
xLBD.a = "<div style='position:absolute;top:100px;height:300px;left:15%;background:#fff;border:10px solid #000;font-size:5em;padding:100px;'>Click <a download='lookback.mp4' href='"+xLBD.f+"'>here<\/a> to download your lookBack video.</div>";
document.body.innerHTML += xLBD.a;
}
if(document.readyState == "complete")
xLBD.c();
else window.onload = xLBD.c;

darjeeling

Docker 치트 시트

왜 Docker를 사용해야하는가?

Why Should I Care (For Developers)

"나에게 Docker의 매력은 간단히 격리된 환경을 만들 수 있다는 것과, 그러한 환경을 재사용할 수 있다는 점이다."런타임 환경을 한 번 만들어 패키지로 만들면, 이 패키지를 다른 어떤 머신에서도 다시 사용할 수 있다. 또한 여기서 실행되는 모든 것은 마치 가상머신과 같이 호스트로부터 격리되어있다. 무엇보다도 이런 모든 일들이 빠르고 간단히 가능하다.

TL;DR, 지금 바로 Docker 개발 환경 구축하기

bnlucas

When going to /search/artist/<query_string> using the documents below you get:

	/search/artist/m	- this returns both Macklemore and Against Me!
	/search/artist/a	- this returns both Against Me! and AWOLNATION
	/search/artist/ma	- this returns only Macklemore
	/search/artist/aw	- this returns only AWOLNATION
	/search/artist/me	- this returns only Against Me!


Adding 'AWOLNATION' to models.Artist produces:

shlomibabluki

# coding=UTF-8
from __future__ import division
import re

# This is a naive text summarization algorithm
# Created by Shlomi Babluki
# April, 2013


class SummaryTool(object):

adamalton

<h1>Page 1</h1>
<a href="/myview/?page={{previous_page_number}}">Previous page</a>
<a href="/myview/?page={{next_page_number}}">Next page</a>
{% for object in results %}
    {{object}} {#whatever you're displaying #}
{% endfor %}

emeeks

The range sliders at the top change the values for the force-directed algorithm and the buttons load new graphs and apply various techniques. This will hopefully serve as a tool for teaching network analysis and visualization principles during my Gephi courses and general Networks in the Humanities presentations.

Notice this includes a pretty straightforward way to load CSV node and edge lists as exported from Gephi.

It also includes a pathfinding algorithm built for the standard data structure of force-directed networks in D3. This requires the addition of .id attributes for the nodes, however.

Now with Clustering Coefficients!

Also, it loads images for nodes but the images are not in the gist. The code also refers to different network types but the data files on Gist only refer to the transportation network.