#Drone Specs
- Flat Area
- Great wifi
- Hopefully High ceilings without any crazy wires (eg. 120ft high ceilings in Berlin)
#!/bin/bash | |
APP_NAME="your-app-name-goes-here" | |
APP_PATH=/home/deploy/${APP_NAME} | |
# Production environment | |
export RAILS_ENV="production" | |
# This loads RVM into a shell session. Uncomment if you're using RVM system wide. | |
# [[ -s "/usr/local/lib/rvm" ]] && . "/usr/local/lib/rvm" |
#!/bin/bash | |
### BEGIN INIT INFO | |
# Provides: APPLICATION | |
# Required-Start: $all | |
# Required-Stop: $network $local_fs $syslog | |
# Default-Start: 2 3 4 5 | |
# Default-Stop: 0 1 6 | |
# Short-Description: Start the APPLICATION unicorns at boot | |
# Description: Enable APPLICATION at boot time. | |
### END INIT INFO |
require 'em-redis' | |
require 'redis' | |
require 'redis/distributed' | |
require "fiber_pool" | |
class Redis | |
class Distributed | |
def initialize(urls, options = {}) | |
@tag = options.delete(:tag) || /^\{(.+?)\}/ |
gonz@bamboo:~ > git clone git://github.com/maccman/juggernaut.git | |
Cloning into juggernaut... | |
remote: Counting objects: 558, done. | |
remote: Compressing objects: 100% (258/258), done. | |
remote: Total 558 (delta 309), reused 481 (delta 255) | |
Receiving objects: 100% (558/558), 576.74 KiB | 135 KiB/s, done. | |
Resolving deltas: 100% (309/309), done. | |
gonz@bamboo:~ > cd juggernaut |
module Mongoid | |
class Criteria | |
def each_by(by, &block) | |
idx = 0 | |
total = 0 | |
set_limit = options[:limit] | |
while ((results = ordered_clone.limit(by).skip(idx)) && results.any?) | |
results.each do |result| | |
return self if set_limit and set_limit >= total |
Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
username: vagrant | |
password: vagrant | |
sudo apt-get update | |
sudo apt-get install build-essential zlib1g-dev git-core sqlite3 libsqlite3-dev | |
sudo aptitude install mysql-server mysql-client | |
sudo nano /etc/mysql/my.cnf |
"""Parallel grid search for sklearn's GradientBoosting. | |
This script uses IPython.parallel to run cross-validated | |
grid search on an IPython cluster. Each cell on the parameter grid | |
will be evaluated ``K`` times - results are stored in MongoDB. | |
The procedure tunes the number of trees ``n_estimators`` by averaging | |
the staged scores of the GBRT model averaged over all K folds. | |
You need an IPython ipcluster to connect to - for local use simply |
<!DOCTYPE html> | |
<html> | |
<head><title>ChamberedTest</title></head> | |
<script type="text/javascript" src="js/chambered.js"></script> | |
<style type="text/css"> | |
canvas, img { | |
image-rendering: optimizeSpeed; | |
image-rendering: -moz-crisp-edges; | |
image-rendering: -webkit-optimize-contrast; |