naviocean

#!/bin/bash
apt-get -y update
apt-get -y install nginx-extras build-essential libpcre3-dev libssl-dev libgeoip-dev libpq-dev libxslt1-dev libgd2-xpm-dev
wget -c https://openresty.org/download/openresty-1.11.2.4.tar.gz
tar zxvf openresty-1.11.2.4.tar.gz
cd openresty-1.11.2.4

./configure \
--sbin-path=/usr/sbin/nginx \
--conf-path=/etc/nginx/nginx.conf \

naviocean

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

naviocean

# $ nvida-docker run -it naviocean/convert

# Download base image
FROM ubuntu:16.04

# Set environment variables
ENV PATH "/usr/local/cuda-8.0/bin:$PATH"
ENV LD_LIBRARY_PATH "/usr/local/cuda-8.0/lib:$LD_LIBRARY_PATH"

# Set keyboard configuration in advance of installing CUDA

naviocean

#!/bin/bash

# Install CUDA Toolkit 10
wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-repo-ubuntu1804_10.0.130-1_amd64.deb
sudo apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/7fa2af80.pub && sudo apt update
sudo dpkg -i cuda-repo-ubuntu1804_10.0.130-1_amd64.deb

sudo apt update
sudo apt install -y cuda

naviocean

# tested on AWS p2.xlarge August 29, 2018

# install CUDA
sudo apt-get update && sudo apt-get install wget -y --no-install-recommends
CUDA_URL="https://developer.nvidia.com/compute/cuda/9.2/Prod2/local_installers/cuda-repo-ubuntu1604-9-2-local_9.2.148-1_amd64"
wget -c ${CUDA_URL} -O cuda.deb
sudo dpkg --install cuda.deb
sudo apt-key add /var/cuda-repo-9-2-local/7fa2af80.pub
sudo apt-get update
sudo apt-get install -y cuda

naviocean

var mediaJSON = { "categories" : [ { "name" : "Movies",
        "videos" : [ 
		    { "description" : "Big Buck Bunny tells the story of a giant rabbit with a heart bigger than himself. When one sunny day three rodents rudely harass him, something snaps... and the rabbit ain't no bunny anymore! In the typical cartoon tradition he prepares the nasty rodents a comical revenge.\n\nLicensed under the Creative Commons Attribution license\nhttp://www.bigbuckbunny.org",
              "sources" : [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/BigBuckBunny.mp4" ],
              "subtitle" : "By Blender Foundation",
              "thumb" : "images/BigBuckBunny.jpg",
              "title" : "Big Buck Bunny"
            },
            { "description" : "The first Blender Open Movie from 2006",
              "sources" : [ "http://commondatastorage.googleapis.com/gtv-videos-bucket/sample/ElephantsDream.mp4" ],

naviocean

Multiple SSH Keys settings for different github account

create different public key

create different ssh key according the article Mac Set-Up Git

$ ssh-keygen -t rsa -C "your_email@youremail.com"

naviocean

#!/bin/bash
VERSION=3.3.0 
PYTHON=/usr/bin/python3

if [ $1 == "arm" ]; then
	echo "Compiling for ARM (Raspberry Pi)"

	sudo apt-get install gcc-arm-linux-gnueabi
	sudo apt-get install gcc-arm-linux-gnueabihf
	

naviocean

Moved to git repository: https://github.com/denji/nginx-tuning

NGINX Tuning For Best Performance

For this configuration you can use web server you like, i decided, because i work mostly with it to use nginx.

Generally, properly configured nginx can handle up to 400K to 500K requests per second (clustered), most what i saw is 50K to 80K (non-clustered) requests per second and 30% CPU load, course, this was 2 x Intel Xeon with HyperThreading enabled, but it can work without problem on slower machines.

You must understand that this config is used in testing environment and not in production so you will need to find a way to implement most of those features best possible for your servers.

naviocean

#!/bin/sh
# Cleanup docker files: untagged containers and images.
#
# Use `docker-cleanup -n` for a dry run to see what would be deleted.

untagged_containers() {
    # Print containers using untagged images: $1 is used with awk's print: 0=line, 1=column 1.
    # NOTE: "[0-9a-f]{12}" does not work with GNU Awk 3.1.7 (RHEL6).
    # Ref: https://github.com/blueyed/dotfiles/commit/a14f0b4b#commitcomment-6736470
    docker ps -a | tail -n +2 | awk '$2 ~ "^[0-9a-f]+$" {print $'$1'}'