sengertwd

echo "This is our provisioning script"
apt-get update
apt-get install -y python-software-properties python g++ make
add-apt-repository ppa:chris-lea/node.js
apt-get update
apt-get install -y nodejs
npm install -g grunt-cli

kolodny

export const doTheThing = () => (dispatch, getState) => {
  const users = getState();
  dispatch({
    type: 'THE_THING',
    users,
  });
};

vgaidarji

pre.highlight,
.highlight pre { background-color: #272822; }
.highlight .hll { background-color: #22282A }
.highlight .c { color: #99AA8A } /* Comment */
.highlight .err { color: #960050; background-color: #1e0010 } /* Error */
.highlight .k { color: #93C763 } /* Keyword */
.highlight .l { color: #ae81ff } /* Literal */
.highlight .n { color: #F1F2F3 } /* Name */
.highlight .o { color: #E8E2B7 } /* Operator */
.highlight .p { color: #F1F2F3 } /* Punctuation */

mrcasals

$ brew uninstall postgresql
$ gem uninstall pg # ALL OF THEM
$ rm -fr /usr/local/var/postgres
$ launchctl unload -w ~/Library/LaunchAgents/homebrew.mxcl.postgresql.plist
$ pg_ctl -D /usr/local/var/postgres stop -s -m fast # WE SHOULD HAVE ALL postgres SERVERS & PROCESSES STOPPED BY NOW
$ brew install postgres
$ env ARCHFLAGS="-arch x86_64" gem install pg
$ echo "DONE." >> /dev/null

avloss

TensorFlow Serving in 10 minutes!

TensorFlow SERVING is Googles' recommended way to deploy TensorFlow models. Without proper computer engineering background, it can be quite intimidating, even for people who feel comfortable with TensorFlow itself. Few things that I've found particularly hard were:

• Tutorial examples have C++ code (which I don't know)
• Tutorials have Kubernetes, gRPG, Bezel (some of which I saw for the first time)
• It needs to be compiled. That process takes forever!

After all, it worked just fine. Here I present an easiest possible way to deploy your models with TensorFlow Serving. You will have your self-built model running inside TF-Serving by the end of this tutorial. It will be scalable, and you will be able to query it via REST.

jychstar

import argparse
import sys

from tensorflow.examples.tutorials.mnist import input_data
from time import time
t0 = time()

import tensorflow as tf

tf.summary.FileWriterCache.clear()

saintedlama

/* Based on Sublime Text's Monokai theme */

.cm-s-monokai.CodeMirror {background: #272822; color: #f8f8f2;}
.cm-s-monokai div.CodeMirror-selected {background: #49483E !important;}
.cm-s-monokai .CodeMirror-gutters {background: #272822; border-right: 0px;}
.cm-s-monokai .CodeMirror-linenumber {color: #d0d0d0;}
.cm-s-monokai .CodeMirror-cursor {border-left: 1px solid #f8f8f0 !important;}

.cm-s-monokai span.cm-comment {color: #75715e;}
.cm-s-monokai span.cm-atom {color: #ae81ff;}

olivierlacan

Launch Sublime Text 2 from the Mac OS X Terminal

Sublime Text 2 ships with a CLI called subl (why not "sublime", go figure). This utility is hidden in the following folder (assuming you installed Sublime in /Applications like normal folk. If this following line opens Sublime Text for you, then bingo, you're ready.

open /Applications/Sublime\ Text\ 2.app/Contents/SharedSupport/bin/subl

You can find more (official) details about subl here: http://www.sublimetext.com/docs/2/osx_command_line.html

Installation

hmldd

# coding:utf-8

from elasticsearch import Elasticsearch
import json

# Define config
host = "127.0.0.1"
port = 9200
timeout = 1000
index = "index"

hugobowne

class MyStreamListener(tweepy.StreamListener):
    def __init__(self, api=None):
        super(MyStreamListener, self).__init__()
        self.num_tweets = 0
        self.file = open("tweets.txt", "w")

    def on_status(self, status):
        tweet = status._json
        self.file.write( json.dumps(tweet) + '\n' )
        self.num_tweets += 1