bradfordcp

/**
 * Based off of the Lucene prolog parser in the wordnet contrib package within the
 * main Lucene project. It has been modified to remove the Lucene bits and generate
 * a synonyms.txt file suitable for consumption by Solr. The idea was mentioned in
 * a sidebar of the book Solr 1.4 Enterprise Search Server by Eric Pugh.
 * 
 * @see <a href="http://lucene.apache.org/java/2_3_2/lucene-sandbox/index.html#WordNet/Synonyms">Lucene Sandbox WordNet page</a>
 * @see <a href="http://svn.apache.org/repos/asf/lucene/dev/trunk/lucene/contrib/wordnet/">SVN Repository of the WordNet contrib</a>
 * @see <a href="https://www.packtpub.com/solr-1-4-enterprise-search-server/book">Solr 1.4 Enterprise Search Server Book</a>
 */

kimchy

curl -XPUT localhost:9200/test

curl -XPUT http://localhost:9200/test/data/_mapping -d '{
    "data" : {        
      "dynamic_templates" : [           
           {
           "string_template" : {
               "match" : "*",
               "match_mapping_type" : "string",
               "mapping" : {

karmi

require 'tire'

# Tire.configure { logger STDERR, level: 'debug' }

Tire.index('movie-titles') do
  delete
  create \
    settings: {
      index: {
        analysis: {

mrflip

Next Steps

• Measure time spend on index, flush, refresh, merge, query, etc. (TD - done)
• Take hot threads snapshots under read+write, read-only, write-only (TD - done)
• Adjust refresh time to 10s (from 1s) and see how load changes (TD)
• Measure time of a rolling restart doing disable_flush and disable_recovery (TD)
• Specify routing on query -- make it choose same node for each shard each time (MD)
• GC new generation size (TD)
• Warmers

• measure before/after of client query time with and without warmers (MD)

linjunpop

Deploy Rails 4 app with Dokku on DigitalOcean

Install dokku

First create a Ubuntu 13.04 x64 droplet on DigitalOcean Control Panel

Then ssh with root account, run this in termianl:

$ wget -qO- https://raw.github.com/progrium/dokku/master/bootstrap.sh | sudo bash

psorianom

# -*- coding: utf-8 -*-
# Authors: Olivier Grisel <olivier.grisel@ensta.org>
# Mathieu Blondel <mathieu@mblondel.org>
# Lars Buitinck <L.J.Buitinck@uva.nl>
# Robert Layton <robertlayton@gmail.com>
#          Jochen Wersdörfer <jochen@wersdoerfer.de>
#          Roman Sinayev <roman.sinayev@gmail.com>
#
# License: BSD 3 clause
"""

joech4n

#!/bin/sh
BUCKETNAME=mybucketname; REGION=us-east-1; for prefix in $(aws s3api list-objects --bucket $BUCKETNAME --delimiter '/' --output text --region $REGION |grep COMMONPREFIX |tail -n+2| awk '{print $2}'); do echo "Totals for $prefix"; aws s3 ls --summarize --human-readable --recursive s3://$BUCKETNAME/$prefix --region $REGION ; done |grep Total

synapticarbors

import numpy as np
import numba as nb

from numba import types
from numba.extending import overload_method


@overload_method(types.Array, 'take')
def array_take(arr, indices):
   if isinstance(indices, types.Array):

ericjang

def sample_gumbel(shape, eps=1e-20): 
  """Sample from Gumbel(0, 1)"""
  U = tf.random_uniform(shape,minval=0,maxval=1)
  return -tf.log(-tf.log(U + eps) + eps)

def gumbel_softmax_sample(logits, temperature): 
  """ Draw a sample from the Gumbel-Softmax distribution"""
  y = logits + sample_gumbel(tf.shape(logits))
  return tf.nn.softmax( y / temperature)

yzh119

from __future__ import print_function
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable

def sample_gumbel(shape, eps=1e-20):
    U = torch.rand(shape).cuda()
    return -Variable(torch.log(-torch.log(U + eps) + eps))