| #!/usr/bin/env python | |
| # encoding: utf-8 | |
| from selenium import webdriver | |
| import unittest | |
| import nose | |
| from nose.plugins.multiprocess import MultiProcess | |
| import new | |
| import json | |
| import httplib |
| function KnowledgeMapInitGlobals() { | |
| window.KnowledgeMapGlobals = { | |
| colors: { | |
| blue: "#0080C9", | |
| green: "#8EBE4F", | |
| red: "#E35D04", | |
| gray: "#FFFFFF" | |
| }, |
| This playbook has been removed as it is now very outdated. |
WAL-E needs to be installed on all machines, masters and slaves.
Only one machine, the master, writes WAL segments via continuous archiving. The configuration for the master postgresql.conf is:
archive_mode = on
archive_command = 'envdir /etc/wal-e.d/env wal-e wal-push %p'
archive_timeout = 60
| # Load the MNIST digit recognition dataset into R | |
| # http://yann.lecun.com/exdb/mnist/ | |
| # assume you have all 4 files and gunzip'd them | |
| # creates train$n, train$x, train$y and test$n, test$x, test$y | |
| # e.g. train$x is a 60000 x 784 matrix, each row is one digit (28x28) | |
| # call: show_digit(train$x[5,]) to see a digit. | |
| # brendan o'connor - gist.github.com/39760 - anyall.org | |
| load_mnist <- function() { | |
| load_image_file <- function(filename) { |