csanz

#Drone Specs

Space Specs

• Flat Area
• Great wifi
• Hopefully High ceilings without any crazy wires (eg. 120ft high ceilings in Berlin)

Spare parts

pprett

"""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

johnantoni

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

jboner

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

dblock

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

gonz

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

zh

require 'em-redis'
require 'redis'
require 'redis/distributed'
require "fiber_pool"

class Redis
  class Distributed

    def initialize(urls, options = {})
      @tag = options.delete(:tag) || /^\{(.+?)\}/

IPrism

#!/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

carlosantoniodasilva

#!/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"