#neural-style Installation
This guide will walk you through the setup for neural-style
on AWS.
First we need to install torch, following the installation instructions here:
#neural-style Installation
This guide will walk you through the setup for neural-style
on AWS.
First we need to install torch, following the installation instructions here:
# "Colorizing B/W Movies with Neural Nets", | |
# Network/Code Created by Ryan Dahl, hacked by samim.io to work with movies | |
# BACKGROUND: http://tinyclouds.org/colorize/ | |
# DEMO: https://www.youtube.com/watch?v=_MJU8VK2PI4 | |
# USAGE: | |
# 1. Download TensorFlow model from: http://tinyclouds.org/colorize/ | |
# 2. Use FFMPEG or such to extract frames from video. | |
# 3. Make sure your images are 224x224 pixels dimension. You can use imagemagicks "mogrify", here some useful commands: | |
# mogrify -resize 224x224 *.jpg | |
# mogrify -gravity center -background black -extent 224x224 *.jpg |
#!/usr/bin/env python3 | |
# | |
# A pre-commit hook to verify PEP8 conformity using flake8 | |
# and to run all available unit tests | |
import subprocess | |
import sys | |
import os | |
import glob |
g2.2xlarge or better
https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-ffba7b94
Start a g2.2xlarge or better (GPU instance) with https://console.aws.amazon.com/ec2/v2/home?region=us-east-1#LaunchInstanceWizard:ami=ami-ffba7b94 | |
Login, username is ubuntu | |
Update a bunch of stuff and make sure cudnn R2 is used: | |
luarocks install image | |
luarocks install loadcaffe | |
luarocks install torch | |
export LD_LIBRARY_PATH=/home/ubuntu/torch-distro/install/lib:/home/ubuntu/torch-distro/install/lib:/home/ubuntu/cudnn-6.5-linux-x64-v2-rc2 |
Table of Contents generated with DocToc
A brief description of streaming protocols and formats here
var io = require('socket.io-client')("http://localhost:3001") | |
var Promise = require("bluebird") | |
io.emitAsync = Promise.promisify(io.emit) | |
io.emitAsync("report", { | |
"name": "thomas" | |
}).then(function(data){ | |
console.log(data) | |
}).catch(function(e){ | |
console.log(e.message) |