John D. Pope johndpope

Machine Setup August 2016

Linux Ubuntu 2016.

1080 GTX
SDK 8.0
CuDNN 5.1

ENABLE Core dumps

ulimit -c unlimited

Install Conda https://conda.io/docs/installation.html

Cheatsheet https://conda.io/docs/_downloads/conda-cheatsheet.pdf

//OSX or Linux
conda create -n tensorflow python=3.5
source activate tensorflow

Install JDK 1.8

(optional) use https://github.com/robbyrussell/oh-my-zsh https://code.visualstudio.com/

See if JAVA_HOME ENV variable is set to 1.8. If not, add this line in your ~/.zshrc:
```
export JAVA_HOME=`/usr/libexec/java_home -v 1.8`
```

Download & Run MongoDB

Grab the latest stable release from the download page
Unzip the archive to a folder called ${MONGO_HOME} on your disk
Create a folder /data/db on the same drive/partition
Go to ${MONGO_HOME}/bin
Execute the binary mongod (or mongod.exe on Windows)

For a more detailed explanation, see the MongoDB installation guide.

Using GraphQL with the Apollo iOS client

Were you ever annoyed when working with a REST API because the endpoints didn't give you the data you needed for the views in your app? Getting the right information either required multiple server requests or you had to bug the backend developers to adjust the API? Worry no more, GraphQL and Apollo to the rescue! 🚀

GraphQL is a new API design paradigm that was open-sourced by Facebook in 2015. It introduces a new era for APIs by eliminating a lot of the ineffencies with today's de-facto standard REST. In contrast to REST, GraphQL APIs only expose a single endpoint and the consumer of the API can precisely specify what data they require with every request.

In this tutorial, you're going to build an iPhone app that helps users plan which iOS conferences they'd like to attend. You'll setup your own GraphQL server and interact with it from the app using the [Apollo iOS Client]

	# Create a simple TF Graph
	# By Omid Alemi - Jan 2017
	# Works with TF <r1.0

	import tensorflow as tf

	I = tf.placeholder(tf.float32, shape=[None,3], name='I') # input
	W = tf.Variable(tf.zeros_initializer(shape=[3,2]), dtype=tf.float32, name='W') # weights
	b = tf.Variable(tf.zeros_initializer(shape=[2]), dtype=tf.float32, name='b') # biases
	O = tf.nn.relu(tf.matmul(I, W) + b, name='O') # activation / output

	# Create a simple TF Graph
	# By Omid Alemi - Jan 2017
	# Works with TF <r1.0

	import tensorflow as tf

	I = tf.placeholder(tf.float32, shape=[None,3], name='I') # input
	W = tf.Variable(tf.zeros_initializer(shape=[3,2]), dtype=tf.float32, name='W') # weights
	b = tf.Variable(tf.zeros_initializer(shape=[2]), dtype=tf.float32, name='b') # biases
	O = tf.nn.relu(tf.matmul(I, W) + b, name='O') # activation / output

	# chmod +x karhoo_setup.sh
	#!/bin/bash

	#NODE_VERSION=0.4.7
	#NPM_VERSION=1.0.94

	# Examples
	# https://github.com/donnemartin/dev-setup/blob/master/brew.sh
	# https://gist.github.com/phatblat/1713458#file-heroku-node-init-sh
	# https://gist.github.com/zenorocha/7159780

	conda info
	conda info --all
	conda info --license

	# list conda environment
	conda info -e

	# list all the package in the current environment
	conda list

	ubuntu@ip-172-31-14-185:~/sfm-docker$ cat docker-compose.yml
	version: "2"
	services:
	db:
	image: gwul/sfm-ui-db:1.2.0
	environment:
	- POSTGRES_PASSWORD
	- TZ
	logging:
	driver: json-file