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library(foreach)
library(iterators)
library(doParallel)
library(tcltk)

# Choose number of iterations
n <- 1000

cl <- makeCluster(8)

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1. Install Environment (All Hosts)
1.1. Install CentOS-6.5-x86_64-minimal 1 master and 3 slave (static ip: 192.168.10.100 - 192.168.10.103)
# yum install wget -y
1.2. Setup hosts
# vi /etc/hosts
192.168.10.100   master.kxlab.org
192.168.10.101   slave1.kxlab.org
192.168.10.102   slave2.kxlab.org
192.168.10.103   slave3.kxlab.org
# hostname -f

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Caffe + Ubuntu 12.04 64bit + CUDA 6.5 配置说明

本步骤能实现用Intel核芯显卡来进行显示， 用NVIDIA GPU进行计算。

1. 安装开发所需的依赖包

安装开发所需要的一些基本包

sudo apt-get install build-essential
sudo apt-get install vim cmake git
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev

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##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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class AdjacencyListGraph(object):
    def __init__(self):
        self.node = {}
        self.adj = {}

    def add_node(self, node, **attrs):
        if node not in self.adj:
            self.adj[node] = {}
            self.node[node] = attrs

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import cntk
import cntk.ops as C
import numpy as np

from sklearn.preprocessing import OneHotEncoder

from cntk.blocks import default_options, Input   # Building blocks
from cntk.initializer import glorot_uniform
from cntk.layers import Dense                    # Layers
from cntk.learner import sgd, learning_rate_schedule, UnitType

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import dshelper as dsh
import instrumentdb as idb
import logging
import numpy as np
import os
import pandas as pd
import psutil
import sys
import tensorflow as tf
import time

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import dshelper as dsh
import numpy as np
import os
import pandas as pd
import sys
import time

from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis as QDA

from sqlalchemy import create_engine, MetaData