helxsz

How to setup Let's Encrypt for Nginx on Ubuntu 18.04 (including IPv6, HTTP/2 and A+ SLL rating)

---

Virtual hosts

Let's say you want to host domains first.com and second.com.

Create folders for their files:

helxsz

/**
 * Created by leo6104 (github.com/leo6104)
 * You can use this nodejs script on angular v5/v4/v2 project.
 * 1. Place this gist file `ng-update-v6.js` to angular project's root path
 * 2. use command `node ng-update-v6.js .angular-cli.json` 
 * 3. check angular.json file (created by ng-update-v6.js)
 **/
const fs = require('fs');
const path = require('path');

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const Web3      = require('web3');
const ZeroEx    = require('0x.js').ZeroEx;
const BigNumber = require('bignumber.js');

// Default provider for TestRPC
const provider = new Web3.providers.HttpProvider('http://localhost:8545')

// Calling constructor
const zeroEx = new ZeroEx(provider);

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var Web3 = require('web3');
var util = require('ethereumjs-util');
var tx = require('ethereumjs-tx');
var lightwallet = require('eth-lightwallet');
var txutils = lightwallet.txutils;
var web3 = new Web3(
    new Web3.providers.HttpProvider('https://rinkeby.infura.io/')
);
var address = '0x8D68583e625CAaE969fA9249502E105a21435EbF';
var key = '1ce642301e680f60227b9d8ffecad474f15155b6d8f8a2cb6bde8e85c8a4809a';

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pragma solidity ^0.4.0;
contract Ballot {

    struct Voter {
        uint weight;
        bool voted;
        uint8 vote;
        address delegate;
    }
    struct Proposal {

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'''
texture image path are relative to the blend file directory. run from command line like this:
 
texture=img/2012720989_c.jpg blender -b mug.blend --python texture_change.py -F PNG -s 1 -e 1 -j 1 -t 0 -a
''''
import os

image_file = os.getenv('texture')
if not image_file:
	image_file="img/2012720989_c.jpg"

helxsz

##VGG16 model for Keras

This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.

It has been obtained by directly converting the Caffe model provived by the authors.

Details about the network architecture can be found in the following arXiv paper:

Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

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# HED batch processing script; modified from https://github.com/s9xie/hed/blob/master/examples/hed/HED-tutorial.ipynb
# Step 1: download the hed repo: https://github.com/s9xie/hed
# Step 2: download the models and protoxt, and put them under {caffe_root}/examples/hed/
# Step 3: put this script under {caffe_root}/examples/hed/
# Step 4: run the following script: 
#       python batch_hed.py --images_dir=/data/to/path/photos/ --hed_mat_dir=/data/to/path/hed_mat_files/
# The code sometimes crashes after computation is done. Error looks like "Check failed: ... driver shutting down". You can just kill the job. 
# For large images, it will produce gpu memory issue. Therefore, you better resize the images before running this script. 
# Step 5: run the MATLAB post-processing script "PostprocessHED.m" 
# https://github.com/phillipi/pix2pix/blob/master/scripts/edges/batch_hed.py

helxsz

This document describes how to install the combination of 14.04 + CUDA 7.5 + Tensorflow. This combination is the easiest to install without anything like compilation from sources etc.

Download and install Ubuntu 14.04 or 16.04

http://releases.ubuntu.com/14.04/ http://releases.ubuntu.com/16.04/

Install CUDA 8

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# install dependencies
sudo apt-get update
sudo apt-get install -y build-essential
sudo apt-get install -y cmake
sudo apt-get install -y libgtk2.0-dev
sudo apt-get install -y pkg-config
sudo apt-get install -y python-numpy python-dev
sudo apt-get install -y libavcodec-dev libavformat-dev libswscale-dev
sudo apt-get install -y libjpeg-dev libpng12-dev libtiff5-dev libjasper-dev