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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

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

# list conda environment
conda info -e

# list all the package in the current environment
conda list

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# 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

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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.

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1. Install JDK 1.8

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

2. 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`
   

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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

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# 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

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# 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

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Machine Setup August 2016

Linux Ubuntu 2016.

• 1080 GTX
• SDK 8.0
• CuDNN 5.1

ENABLE Core dumps

ulimit -c unlimited

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//
//  Swift.IO
//
//  Created by Alec Thomas on 25/02/2015.
//  Updated by Tommie N. Carter, Jr., MBA on 2/12/16.
//  Copyright (c) 2015 SwapOff. All rights reserved.
//
//
//  This file provides a consistent, simple interface to stream-based data sources.
//  It is based on Go's I/O library.