本步骤经笔者亲身实践，集百家所长，能实现Caffe在NVIDIA GPU下进行计算。
安装开发所需要的一些基本包
sudo apt-get install build-essential # basic requirement
sudo apt-get install vim cmake git # tools
sudo apt-get install libprotobuf-dev libleveldb-dev libsnappy-dev libopencv-dev libboost-all-dev libhdf5-serial-dev libgflags-dev libgoogle-glog-dev liblmdb-dev protobuf-compiler libatlas-base-dev #required by caffe
| #! /bin/bash | |
| sudo apt-get update | |
| sudo apt-get install -y software-properties-common debconf-utils | |
| sudo add-apt-repository -y ppa:webupd8team/java | |
| sudo apt-get update | |
| sudo echo "oracle-java8-installer shared/accepted-oracle-license-v1-1 select true" | sudo debconf-set-selections | |
| sudo apt-get install -y oracle-java8-installer |
| Source: http://datahugger.org/datascience/setting-up-hadoop-v2-with-spark-v1-on-osx-using-homebrew/ | |
| This post builds on the previous setup Hadoop (v1) guide, to explain how to setup a single node Hadoop (v2) cluster with Spark (v1) on OSX (10.9.5). | |
| Apache Hadoop is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Rather than rely on hardware to deliver high-availability, the library itself is designed to detect and handle failures at the application layer, so delivering a highly-available service on top of a cluster of computers, each of which may be prone to failures. The Apache Hadoop framework is composed of the following core modules: | |
| HDFS (Distributed File System): a distributed file-system that stores data on commodity machines, providing very high aggregate bandwidth across the cluster. | |
| YARN (Yet A |
Download binary from oracle: http://www.oracle.com/technetwork/database/database-technologies/r/roracle/downloads/index.html
The run the following command in r, substituting the file path:
setwd('xxxxx') # set to path of download
install.packages('ROracle_1.1-11.zip', repos = NULL)| library(caret) | |
| library(C50) | |
| library(mlbench) | |
| fitControl <- trainControl(method = "repeatedcv", | |
| number = 10, | |
| repeats = 10, returnResamp="all") | |
| # Choose the features and classes | |
| data(PimaIndiansDiabetes2) |
| require(caret) | |
| #load some data | |
| data(USArrests) | |
| ### Prepare Data (postive observations) | |
| # add a column to be the strata. In this case it is states, it can be sites, or other locations | |
| # the original data has 50 rows, so this adds a state label to 10 consecutive observations | |
| USArrests$state <- c(rep(c("PA","MD","DE","NY","NJ"), each = 5)) | |
| # this replaces the existing rownames (states) with a simple numerical index |
| # The MIT License (MIT) | |
| # Copyright (c) 2016 Vladimir Ignatev | |
| # | |
| # Permission is hereby granted, free of charge, to any person obtaining | |
| # a copy of this software and associated documentation files (the "Software"), | |
| # to deal in the Software without restriction, including without limitation | |
| # the rights to use, copy, modify, merge, publish, distribute, sublicense, | |
| # and/or sell copies of the Software, and to permit persons to whom the Software | |
| # is furnished to do so, subject to the following conditions: | |
| # |
| # restore_packages.R | |
| # | |
| # installs each package from the stored list of packages | |
| # source: http://hlplab.wordpress.com/2012/06/01/transferring-installed-packages-between-different-installations-of-r/ | |
| load("~/installed_packages.rda") | |
| for (count in 1:length(installedpackages)) { | |
| install.packages(installedpackages[count]) | |
| } |
| library(ggplot2) | |
| # ggplot theme to be used | |
| plotTheme <- function() { | |
| theme( | |
| panel.background = element_rect( | |
| size = 3, | |
| colour = "black", | |
| fill = "white"), | |
| axis.ticks = element_line( |
