Consider this blog post model:
:
'''Hooks for lettuce to run a test pylons server''' | |
import threading | |
from os import getcwd | |
import pylons.test | |
from paste.deploy.loadwsgi import loadapp | |
from paste.httpserver import serve | |
from paste.script.appinstall import SetupCommand |
global | |
log 127.0.0.1 local0 notice | |
maxconn 50000 | |
daemon | |
stats socket /tmp/proxystats level admin | |
defaults | |
log global | |
mode http | |
option httplog | |
option dontlognull |
# -*- mode: ruby -*- | |
# vi: set ft=ruby : | |
# David Lutz's Multi VM Vagrantfile | |
# inspired from Mark Barger's https://gist.github.com/2404910 | |
boxes = [ | |
{ :name => :web, :role => 'web_dev', :ip => '192.168.33.1', :ssh_port => 2201, :http_fwd => 9980, :cpus =>4, :shares => true }, | |
{ :name => :data, :role => 'data_dev', :ip => '192.168.33.2', :ssh_port => 2202, :mysql_fwd => 9936, :cpus =>4 }, | |
{ :name => :railsapp, :role => 'railsapp_dev', :ip => '192.168.33.3', :ssh_port => 2203, :http_fwd => 9990, :cpus =>1} | |
] |
This tutorial guides you through creating your first Vagrant project.
We start with a generic Ubuntu VM, and use the Chef provisioning tool to:
Afterwards, we'll see how easy it is to package our newly provisioned VM
def runserver(port=5000, profile_log=None): | |
"""Runs a development server.""" | |
from gevent.wsgi import WSGIServer | |
from werkzeug.serving import run_with_reloader | |
from werkzeug.debug import DebuggedApplication | |
from werkzeug.contrib.profiler import ProfilerMiddleware | |
port = int(port) | |
if profile_log: |
#!/usr/bin/env python | |
""" | |
How to use it: | |
1. Just `kill -2 PROCESS_ID` or `kill -15 PROCESS_ID` , The Tornado Web Server Will shutdown after process all the request. | |
2. When you run it behind Nginx, it can graceful reboot your production server. | |
3. Nice Print in http://weibo.com/1682780325/zgkb7g8k7 | |
""" |
import sys | |
from pyspark.context import SparkContext | |
from numpy import array, random as np_random | |
from sklearn import linear_model as lm | |
from sklearn.base import copy | |
N = 10000 # Number of data points | |
D = 10 # Numer of dimensions | |
ITERATIONS = 5 |
(function() { | |
var script, | |
scripts = document.getElementsByTagName('script')[0]; | |
function load(url) { | |
script = document.createElement('script'); | |
script.async = true; | |
script.src = url; | |
scripts.parentNode.insertBefore(script, scripts); |