mohitsethi

#!/bin/bash
# herein we backup our indexes! this script should run at like 6pm or something, after logstash
# rotates to a new ES index and theres no new data coming in to the old one. we grab metadatas,
# compress the data files, create a restore script, and push it all up to S3.
TODAY=`date +"%Y.%m.%d"`
INDEXNAME="logstash-$TODAY" # this had better match the index name in ES
INDEXDIR="/usr/local/elasticsearch/data/logstash/nodes/0/indices/"
BACKUPCMD="/usr/local/backupTools/s3cmd --config=/usr/local/backupTools/s3cfg put"
BACKUPDIR="/mnt/es-backups/"
YEARMONTH=`date +"%Y-%m"`

mohitsethi

echo "Connecting to 192.168.1.100"
$Server="192.168.1.100"
$User="Administrator"
$Password="AdminPassword"
cmdkey /generic:TERMSRV/$Server /user:$User /pass:$Password
mstsc /v:$Server

mohitsethi

package main

import (
    "fmt"
    "log"
    "net"
    "net/mail"
    "net/smtp"
    "crypto/tls"
)

mohitsethi

import cv2

# Windows dependencies
# - Python 2.7.6: http://www.python.org/download/
# - OpenCV: http://opencv.org/
# - Numpy -- get numpy from here because the official builds don't support x64:
#   http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy

# Mac Dependencies
# - brew install python

mohitsethi

#!/usr/bin/env python
#
# Extracts email addresses from one or more plain text files.
#
# Notes:
# - Does not save to file (pipe the output to a file if you want it saved).
# - Does not check for duplicates (which can easily be done in the terminal).
#
# (c) 2013  Dennis Ideler <ideler.dennis@gmail.com>

mohitsethi

FROM ubuntu:14.04

# Replace 1000 with your user / group id
RUN export uid=1000 gid=1000 && \
    mkdir -p /home/developer && \
    echo "developer:x:${uid}:${gid}:Developer,,,:/home/developer:/bin/bash" >> /etc/passwd && \
    echo "developer:x:${uid}:" >> /etc/group && \
    echo "developer ALL=(ALL) NOPASSWD: ALL" > /etc/sudoers.d/developer && \
    chmod 0440 /etc/sudoers.d/developer && \
    chown ${uid}:${gid} -R /home/developer

mohitsethi

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.

mohitsethi

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os.path
import re
import sys
import tarfile

import numpy as np

mohitsethi

var auth = {
  user: "USERNAME",
  pass: "PASSWORD"
};

function ParallelRunner (list, func, runners) {
  function createSlots(runners) {
    var slots = [];
    for (var i = 0; i < runners; i++) {
      slots.push( objReturner() );

mohitsethi

XVFB=/usr/bin/Xvfb
XVFBARGS=":1 -screen 0 1024x768x24 -ac +extension GLX +render -noreset"
PIDFILE=/var/run/xvfb.pid
case "$1" in
  start)
    echo -n "Starting virtual X frame buffer: Xvfb"
    start-stop-daemon --start --quiet --pidfile $PIDFILE --make-pidfile --background --exec $XVFB -- $XVFBARGS
    echo "."
    ;;
  stop)