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@alq666
alq666 / service-checklist.md
Created September 12, 2016 14:32 — forked from acolyer/service-checklist.md
Internet Scale Services Checklist

Internet Scale Services Checklist

A checklist for designing and developing internet scale services, inspired by James Hamilton's 2007 paper "On Desgining and Deploying Internet-Scale Services."

Basic tenets

  • Does the design expect failures to happen regularly and handle them gracefully?
  • Have we kept things as simple as possible?
@andreicristianpetcu
andreicristianpetcu / ansible-summary.md
Created May 30, 2016 19:25
This is an ANSIBLE Cheat Sheet from Jon Warbrick

An Ansible summary

Jon Warbrick, July 2014, V3.2 (for Ansible 1.7)

Configuration file

intro_configuration.html

First one found from of

@rschuman
rschuman / install_tmux_centos.md
Last active July 26, 2018 12:51
Installation of most recent libevent2 and tmux on CentOS. Compile flags borrowed from https://gist.github.com/sturadnidge/4185338
@zacstewart
zacstewart / classifier.py
Last active March 27, 2023 15:59
Document Classification with scikit-learn
import os
import numpy
from pandas import DataFrame
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.cross_validation import KFold
from sklearn.metrics import confusion_matrix, f1_score
NEWLINE = '\n'
@bluszcz
bluszcz / gist:5150795
Last active December 14, 2015 21:28
Building Ardour 3 on Ubuntu 12.04
wget http://lv2plug.in/spec/lv2-1.0.0.tar.bz2
tar -xjf lv2-1.0.0.tar.bz2
cd lv2-1.0.0/
./waf configure
./waf build
sudo ./waf install
cd ../
wget http://download.drobilla.net/serd-0.14.0.tar.bz2 http://download.drobilla.net/sord-0.8.0.tar.bz2 http://download.drobilla.net/sratom-0.2.0.tar.bz2 http://download.drobilla.net/lilv-0.14.2.tar.bz2 http://download.drobilla.net/suil-0.6.4.tar.bz2
tar -zxf serd-0.14.0.tar.bz2
tar -xjf serd-0.14.0.tar.bz2
@ogrisel
ogrisel / .gitignore
Last active December 14, 2015 16:29
Demo Notebook + Helper For Distributed Model Selection with IPython.parallel and scikit-learn
*.pyc
*.npy
*.pkl
*.mmap