wangwenchao

package main

import (
	"bytes"
	"fmt"
	"io"
	"log"
	"mime/multipart"
	"net/http"
	"os"

wangwenchao

// --- Compiling ---
$ wget http://download.redis.io/releases/redis-2.8.3.tar.gz
$ tar xzvf redis-2.8.3.tar.gz
$ cd redis-2.8.3
$ make
$ make install

// --- or using yum ---
$ rpm -Uvh http://download.fedoraproject.org/pub/epel/6/x86_64/epel-release-6-8.noarch.rpm
$ rpm -Uvh http://rpms.famillecollet.com/enterprise/remi-release-6.rpm

wangwenchao

#!/bin/sh
# example: sh  delete_es_by_day.sh logstash-kettle-log logsdate 30
 
index_name=$1
daycolumn=$2
savedays=$3
format_day=$4
 
if [ ! -n "$savedays" ]; then
  echo "the args is not right,please input again...."

wangwenchao

INTRO

Concurrency is a domain I have wanted to explore for a long time because the locks and the race conditions have always intimidated me. I recall somebody suggesting concurrency patterns in golang because they said "you share the data and not the variables".

Amused by that, I searched for "concurrency in golang" and bumped into this awesome slide by Rob Pike: https://talks.golang.org/2012/waza.slide#1 which does a great job of explaining channels, concurrency patterns and a mini-architecture of load-balancer (also explains the above one-liner).

Let's dig in:

Goroutines

wangwenchao

import subprocess
import datetime
import numpy as np

THREAD_NUM=4

def get_video_info(fileloc) :
    command = ['ffprobe',
               '-v', 'fatal',
               '-show_entries', 'stream=width,height,r_frame_rate,duration',

wangwenchao

Intro

There is a lot of buzz around neural-nets and how cool they are for image-recognition and text-recognition. There are a lot of sources out there to learn tensorflow and their application to solve these set of problems.

I'd reommend a course offered by Google on Udacity which teaches all about this stuff: https://classroom.udacity.com/courses/ud730.

This course doesn't cover the details of mathematics behind neural-nets and so doesn't this gist. However, the instructors do go into the intuition, breaking down a complex problem like visual recognition into abstract concepts and writing a program to predict some tensors and ultimately those concepts.

Through this gist I've tried to cover a very basic introduction to the tensorflow framework, the data structures it uses and it's application in a predictive-text model.

What is a tensor?

wangwenchao

Add config/puma.ruby 
--------------------
 
cd /home/redmine/redmine
sudo -u redmine -H curl --output config/puma.rb https://gist.github.com/thanhhh/5610668/raw/fdfe2a865c3a0afe912c8784c971ea7ca3e64cfd/puma.rb
 
Install Init Script
--------------------
Download the init script (will be /etc/init.d/redmine):
 

wangwenchao

Simple multimedia streams analyzer
usage: ffprobe [OPTIONS] [INPUT_FILE]

Main options:
-L                  show license
-h topic            show help
-? topic            show help
-help topic         show help
--help topic        show help
-version            show version

wangwenchao

How to setup your own CA with OpenSSL

For educational reasons I've decided to create my own CA. Here is what I learned.

First things first

Lets get some context first.

wangwenchao

#!/bin/bash
vim-cmd vmsvc/getallvms | grep -i hostname | cut -d ' ' -f 1 | xargs vim-cmd vmsvc/get.guest | grep ipAddress | sed -n 1p | cut -d '"' -f 2
# or
ssh esxi.example.com /bin/vim-cmd vmsvc/get.guest $(ssh esxi.example.com /bin/vim-cmd vmsvc/getallvms | grep -i hostname | cut -d ' ' -f 1) | grep ipAddress | sed -n 1p | cut -d '"' -f 2