Deep-Learning/mxnet-notes/1-installation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 安装MXNET\n",
    "\n",
    "由于公司需要，近期需要快速精通mxnet，接下来的几个星期会陆续更新关于mxnet的笔记，提供参考和备忘。第一篇介绍mxnet的安装，mxnet的安装过程十分蛋疼，个人也是摸索了许久才安装成功，期间也是遇到了各种奇奇怪怪的坑，为了避免新人少走弯路，遂将经验总结于此。\n",
    "\n",
    "本笔记分为两部分，第一部分介绍windows下的安装，第二部分介绍linux下的安装。在开始安装以前，默认你已经装好了Anaconda2以及CUDA。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## windows下的安装\n",
    "本人机器配置为Win10 + Cuda 7.5， 后续的安装以此为准。  \n",
    "1.mxnet需要VS2013支持C++ 11特性  \n",
    "在[Visual C++ Compiler Nov 2013 CTP](https://www.microsoft.com/en-us/download/details.aspx?id=41151)下载C++ 11版本的编译器，接着将`C:\\Program Files (x86)\\Microsoft Visual C++ Compiler Nov 2013 CTP`下所有同名目录中的文件覆盖到`C:\\Program Files (x86)\\Microsoft Visual Studio 12.0\\VC`下所有同名目录下对应的文件（覆盖前记得备份）\n",
    "![](http://7xikew.com1.z0.glb.clouddn.com/QQ%E6%88%AA%E5%9B%BE20161026092456.png)\n",
    "\n",
    "2.从github克隆源码  \n",
    "`git clone --recursive https://github.com/dmlc/mxnet`\n",
    "这里提醒注意一定不要忘记--recursive参数，因为mxnet依赖于[DMLC通用工具包](http://dmlc.ml/)，--recursive参数可以自动加载mshadow等依赖。  \n",
    "\n",
    "3.用Cmake生成项目工程文件，并编译项目  \n",
    "打开cmake，`Where is the source code`栏里打开刚才下好的mxnet源代码目录，`Where to build the binaries`栏里指定生成工程文件和编译结果的路径，这里我填的是`C:/mxnet/build`，如图所示：\n",
    "![](http://7xikew.com1.z0.glb.clouddn.com/QQ%E6%88%AA%E5%9B%BE20161026113902.png)\n",
    "接着点击configure，生成配置。\n",
    "![](http://7xikew.com1.z0.glb.clouddn.com/QQ%E6%88%AA%E5%9B%BE20161026100929.png)\n",
    "然后我们点击generate，生成`.sln`项目文件\n",
    "![](http://7xikew.com1.z0.glb.clouddn.com/QQ%E6%88%AA%E5%9B%BE20161026101950.png)\n",
    "找到生成的工程文件`mxnet.sln`，用vs2013打开\n",
    "![](http://7xikew.com1.z0.glb.clouddn.com/QQ%E6%88%AA%E5%9B%BE20161026102039.png)\n",
    "最后，我们在项目mxnet上点击右键->生成，开始编译。\n",
    "![](http://7xikew.com1.z0.glb.clouddn.com/QQ%E6%88%AA%E5%9B%BE20161026114239.png)\n",
    "经过漫长的等待后，mxnet终于编译完成。\n",
    "![](http://7xikew.com1.z0.glb.clouddn.com/QQ%E6%88%AA%E5%9B%BE20161026153134.png)\n",
    "编译完成后，在`C:\\mxnet\\build\\Release`目录下会生成三个文件：libmxnet.dll，libmxnet.exp，libmxnet.lib。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4.安装mxnet的python接口\n",
    "接下来我们到mxnet的源代码目录：`G:\\OpenSource\\mxnet\\python`，运行\n",
    "```\n",
    "python setup.py install\n",
    "```\n",
    "来安装mxnet的python包。\n",
    "![](http://7xikew.com1.z0.glb.clouddn.com/QQ%E6%88%AA%E5%9B%BE20161026153603.png)\n",
    "我们将libmxnet.dll\n",
    "接着，导入mxnet的时候发生了如下的错误：\n",
    "![](http://7xikew.com1.z0.glb.clouddn.com/QQ%E6%88%AA%E5%9B%BE20161026171027.png)\n",
    "\n",
    "通过调试发现问题出在打开`libmxnet.dll`的时候，问题应该出在没有导入依赖的dll文件，但蛋疼的是我也不知道它到底依赖哪一些dll文件。\n",
    "\n",
    "5.安装依赖  \n",
    "通过一番搜索，我找到一个名为dependency walker的软件，用它打开`libmxnet.dll`，我们看到还缺少的dll文件有哪些（图中的问号）\n",
    "![](http://7xikew.com1.z0.glb.clouddn.com/QQ%E6%88%AA%E5%9B%BE20161026161615.png)\n",
    "这些均能dll在mxnet的release tab下找到，下载完成后将其解压到mxnet的pthon安装目录`C:\\Anaconda2\\Lib\\site-packages\\mxnet-0.7.0-py2.7.egg\\mxnet`下。将这些文件放入目录后，我们测试一下能不能导入"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mxnet version is:0.7.0\n"
     ]
    }
   ],
   "source": [
    "import ctypes\n",
    "ctypes._dlopen(r\"C:\\Anaconda2\\Lib\\site-packages\\mxnet-0.7.0-py2.7.egg\\mxnet\\cudart64_75.dll\")\n",
    "ctypes._dlopen(r\"C:\\Anaconda2\\Lib\\site-packages\\mxnet-0.7.0-py2.7.egg\\mxnet\\cublas64_75.dll\")\n",
    "ctypes._dlopen(r\"cudnn64_5.dll\")\n",
    "ctypes._dlopen(r\"C:\\Anaconda2\\Lib\\site-packages\\mxnet-0.7.0-py2.7.egg\\mxnet\\libopenblas.dll\")\n",
    "ctypes._dlopen(r\"C:\\Anaconda2\\Lib\\site-packages\\mxnet-0.7.0-py2.7.egg\\mxnet\\opencv_world300.dll\")\n",
    "ctypes._dlopen(r\"C:\\Anaconda2\\Lib\\site-packages\\mxnet-0.7.0-py2.7.egg\\mxnet\\opencv_core2413.dll\")\n",
    "ctypes._dlopen(r\"vcomp120.dll\")\n",
    "ctypes._dlopen(r\"kernel32.dll\")\n",
    "import mxnet as mx\n",
    "print \"mxnet version is:%s\"%mx.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上面的代码中，我们需要手动的载入mxnet依赖的动态链接库才能导入，目前还不清楚为什么它不会自动载入，这个问题留待以后解决，目前可以先把上段代码加入到mxnet的初始化代码中。接着我们跑一跑examples/image-classification/train_mnist这个例子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:start with arguments Namespace(batch_size=128, data_dir='G:/OpenSource/mxnet/example/image-classification/mnist/', gpus='0', kv_store='local', load_epoch=None, lr=0.1, lr_factor=1, lr_factor_epoch=1, model_prefix=None, network='mlp', num_epochs=10, num_examples=60000, save_model_prefix=None)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-10-26 19:37:38,994 Node[0] start with arguments Namespace(batch_size=128, data_dir='G:/OpenSource/mxnet/example/image-classification/mnist/', gpus='0', kv_store='local', load_epoch=None, lr=0.1, lr_factor=1, lr_factor_epoch=1, model_prefix=None, network='mlp', num_epochs=10, num_examples=60000, save_model_prefix=None)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:root:Start training with [gpu(0)]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-10-26 19:37:42,038 Node[0] Start training with [gpu(0)]\n"
     ]
    },
    {
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      "2016-10-26 19:38:08,348 Node[0] Epoch[9] Batch [250]\tSpeed: 27586.22 samples/sec\tTrain-top_k_accuracy_5=1.000000\n"
     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-10-26 19:38:08,349 Node[0] Epoch[9] Batch [250]\tSpeed: 27586.22 samples/sec\tTrain-top_k_accuracy_10=1.000000\n"
     ]
    },
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      "2016-10-26 19:38:08,351 Node[0] Epoch[9] Batch [250]\tSpeed: 27586.22 samples/sec\tTrain-top_k_accuracy_20=1.000000\n"
     ]
    },
    {
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     ]
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     ]
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      "INFO:root:Epoch[9] Resetting Data Iterator\n"
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      "2016-10-26 19:38:09,355 Node[0] Epoch[9] Resetting Data Iterator\n"
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      "INFO:root:Epoch[9] Time cost=2.179\n"
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      "2016-10-26 19:38:09,358 Node[0] Epoch[9] Time cost=2.179\n"
     ]
    },
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      "INFO:root:Epoch[9] Validation-accuracy=0.973958\n"
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      "2016-10-26 19:38:09,525 Node[0] Epoch[9] Validation-top_k_accuracy_10=1.000000\n"
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      "2016-10-26 19:38:09,526 Node[0] Epoch[9] Validation-top_k_accuracy_20=1.000000\n"
     ]
    }
   ],
   "source": [
    "import ctypes\n",
    "ctypes._dlopen(r\"C:\\Anaconda2\\Lib\\site-packages\\mxnet-0.7.0-py2.7.egg\\mxnet\\cudart64_75.dll\")\n",
    "ctypes._dlopen(r\"C:\\Anaconda2\\Lib\\site-packages\\mxnet-0.7.0-py2.7.egg\\mxnet\\cublas64_75.dll\")\n",
    "ctypes._dlopen(r\"cudnn64_5.dll\")\n",
    "ctypes._dlopen(r\"C:\\Anaconda2\\Lib\\site-packages\\mxnet-0.7.0-py2.7.egg\\mxnet\\libopenblas.dll\")\n",
    "ctypes._dlopen(r\"C:\\Anaconda2\\Lib\\site-packages\\mxnet-0.7.0-py2.7.egg\\mxnet\\opencv_world300.dll\")\n",
    "ctypes._dlopen(r\"C:\\Anaconda2\\Lib\\site-packages\\mxnet-0.7.0-py2.7.egg\\mxnet\\opencv_core2413.dll\")\n",
    "ctypes._dlopen(r\"vcomp120.dll\")\n",
    "ctypes._dlopen(r\"kernel32.dll\")\n",
    "import mxnet as mx\n",
    "import argparse\n",
    "import os, sys\n",
    "import logging\n",
    "\n",
    "def _download(data_dir):\n",
    "    if not os.path.isdir(data_dir):\n",
    "        os.system(\"mkdir \" + data_dir)\n",
    "    os.chdir(data_dir)\n",
    "    if (not os.path.exists('train-images-idx3-ubyte')) or \\\n",
    "       (not os.path.exists('train-labels-idx1-ubyte')) or \\\n",
    "       (not os.path.exists('t10k-images-idx3-ubyte')) or \\\n",
    "       (not os.path.exists('t10k-labels-idx1-ubyte')):\n",
    "        os.system(\"wget http://data.dmlc.ml/mxnet/data/mnist.zip\")\n",
    "        os.system(\"unzip -u mnist.zip; rm mnist.zip\")\n",
    "    os.chdir(\"..\")\n",
    "\n",
    "def get_loc(data, attr={'lr_mult':'0.01'}):\n",
    "    \"\"\"\n",
    "    the localisation network in lenet-stn, it will increase acc about more than 1%,\n",
    "    when num-epoch >=15\n",
    "    \"\"\"\n",
    "    loc = mx.symbol.Convolution(data=data, num_filter=30, kernel=(5, 5), stride=(2,2))\n",
    "    loc = mx.symbol.Activation(data = loc, act_type='relu')\n",
    "    loc = mx.symbol.Pooling(data=loc, kernel=(2, 2), stride=(2, 2), pool_type='max')\n",
    "    loc = mx.symbol.Convolution(data=loc, num_filter=60, kernel=(3, 3), stride=(1,1), pad=(1, 1))\n",
    "    loc = mx.symbol.Activation(data = loc, act_type='relu')\n",
    "    loc = mx.symbol.Pooling(data=loc, global_pool=True, kernel=(2, 2), pool_type='avg')\n",
    "    loc = mx.symbol.Flatten(data=loc)\n",
    "    loc = mx.symbol.FullyConnected(data=loc, num_hidden=6, name=\"stn_loc\", attr=attr)\n",
    "    return loc\n",
    "\n",
    "def get_mlp():\n",
    "    \"\"\"\n",
    "    multi-layer perceptron\n",
    "    \"\"\"\n",
    "    data = mx.symbol.Variable('data')\n",
    "    fc1  = mx.symbol.FullyConnected(data = data, name='fc1', num_hidden=128)\n",
    "    act1 = mx.symbol.Activation(data = fc1, name='relu1', act_type=\"relu\")\n",
    "    fc2  = mx.symbol.FullyConnected(data = act1, name = 'fc2', num_hidden = 64)\n",
    "    act2 = mx.symbol.Activation(data = fc2, name='relu2', act_type=\"relu\")\n",
    "    fc3  = mx.symbol.FullyConnected(data = act2, name='fc3', num_hidden=10)\n",
    "    mlp  = mx.symbol.SoftmaxOutput(data = fc3, name = 'softmax')\n",
    "    return mlp\n",
    "\n",
    "def get_lenet(add_stn=False):\n",
    "    \"\"\"\n",
    "    LeCun, Yann, Leon Bottou, Yoshua Bengio, and Patrick\n",
    "    Haffner. \"Gradient-based learning applied to document recognition.\"\n",
    "    Proceedings of the IEEE (1998)\n",
    "    \"\"\"\n",
    "    data = mx.symbol.Variable('data')\n",
    "    if(add_stn):\n",
    "        data = mx.sym.SpatialTransformer(data=data, loc=get_loc(data), target_shape = (28,28),\n",
    "                                         transform_type=\"affine\", sampler_type=\"bilinear\")\n",
    "    # first conv\n",
    "    conv1 = mx.symbol.Convolution(data=data, kernel=(5,5), num_filter=20)\n",
    "    tanh1 = mx.symbol.Activation(data=conv1, act_type=\"tanh\")\n",
    "    pool1 = mx.symbol.Pooling(data=tanh1, pool_type=\"max\",\n",
    "                              kernel=(2,2), stride=(2,2))\n",
    "    # second conv\n",
    "    conv2 = mx.symbol.Convolution(data=pool1, kernel=(5,5), num_filter=50)\n",
    "    tanh2 = mx.symbol.Activation(data=conv2, act_type=\"tanh\")\n",
    "    pool2 = mx.symbol.Pooling(data=tanh2, pool_type=\"max\",\n",
    "                              kernel=(2,2), stride=(2,2))\n",
    "    # first fullc\n",
    "    flatten = mx.symbol.Flatten(data=pool2)\n",
    "    fc1 = mx.symbol.FullyConnected(data=flatten, num_hidden=500)\n",
    "    tanh3 = mx.symbol.Activation(data=fc1, act_type=\"tanh\")\n",
    "    # second fullc\n",
    "    fc2 = mx.symbol.FullyConnected(data=tanh3, num_hidden=10)\n",
    "    # loss\n",
    "    lenet = mx.symbol.SoftmaxOutput(data=fc2, name='softmax')\n",
    "    return lenet\n",
    "\n",
    "def get_iterator(data_shape):\n",
    "    def get_iterator_impl(args, kv):\n",
    "        data_dir = args.data_dir\n",
    "        \"\"\"\n",
    "        if '://' not in args.data_dir:\n",
    "            _download(args.data_dir)\n",
    "        \"\"\"\n",
    "        flat = False if len(data_shape) == 3 else True\n",
    "\n",
    "        train           = mx.io.MNISTIter(\n",
    "            image       = data_dir + \"train-images-idx3-ubyte\",\n",
    "            label       = data_dir + \"train-labels-idx1-ubyte\",\n",
    "            input_shape = data_shape,\n",
    "            batch_size  = args.batch_size,\n",
    "            shuffle     = True,\n",
    "            flat        = flat,\n",
    "            num_parts   = kv.num_workers,\n",
    "            part_index  = kv.rank)\n",
    "\n",
    "        val = mx.io.MNISTIter(\n",
    "            image       = data_dir + \"t10k-images-idx3-ubyte\",\n",
    "            label       = data_dir + \"t10k-labels-idx1-ubyte\",\n",
    "            input_shape = data_shape,\n",
    "            batch_size  = args.batch_size,\n",
    "            flat        = flat,\n",
    "            num_parts   = kv.num_workers,\n",
    "            part_index  = kv.rank)\n",
    "\n",
    "        return (train, val)\n",
    "    return get_iterator_impl\n",
    "\n",
    "def parse_args():\n",
    "    parser = argparse.ArgumentParser(description='train an image classifer on mnist')\n",
    "    parser.add_argument('--network', type=str, default='mlp',\n",
    "                        choices = ['mlp', 'lenet', 'lenet-stn'],\n",
    "                        help = 'the cnn to use')\n",
    "    parser.add_argument('--data-dir', type=str, default='mnist/',\n",
    "                        help='the input data directory')\n",
    "    parser.add_argument('--gpus', type=str,\n",
    "                        help='the gpus will be used, e.g \"0,1,2,3\"')\n",
    "    parser.add_argument('--num-examples', type=int, default=60000,\n",
    "                        help='the number of training examples')\n",
    "    parser.add_argument('--batch-size', type=int, default=128,\n",
    "                        help='the batch size')\n",
    "    parser.add_argument('--lr', type=float, default=.1,\n",
    "                        help='the initial learning rate')\n",
    "    parser.add_argument('--model-prefix', type=str,\n",
    "                        help='the prefix of the model to load/save')\n",
    "    parser.add_argument('--save-model-prefix', type=str,\n",
    "                        help='the prefix of the model to save')\n",
    "    parser.add_argument('--num-epochs', type=int, default=10,\n",
    "                        help='the number of training epochs')\n",
    "    parser.add_argument('--load-epoch', type=int,\n",
    "                        help=\"load the model on an epoch using the model-prefix\")\n",
    "    parser.add_argument('--kv-store', type=str, default='local',\n",
    "                        help='the kvstore type')\n",
    "    parser.add_argument('--lr-factor', type=float, default=1,\n",
    "                        help='times the lr with a factor for every lr-factor-epoch epoch')\n",
    "    parser.add_argument('--lr-factor-epoch', type=float, default=1,\n",
    "                        help='the number of epoch to factor the lr, could be .5')\n",
    "    return parser.parse_args(['--gpus', '0', '--data-dir', 'G:/OpenSource/mxnet/example/image-classification/mnist/'])\n",
    "\n",
    "if __name__ == '__main__':\n",
    "    args = parse_args()\n",
    "\n",
    "    if args.network == 'mlp':\n",
    "        data_shape = (784, )\n",
    "        net = get_mlp()\n",
    "    elif args.network == 'lenet-stn':\n",
    "        data_shape = (1, 28, 28)\n",
    "        net = get_lenet(True)\n",
    "    else:\n",
    "        data_shape = (1, 28, 28)\n",
    "        net = get_lenet()\n",
    "\n",
    "    # kvstore\n",
    "    kv = mx.kvstore.create(args.kv_store)\n",
    "\n",
    "    # logging\n",
    "    head = '%(asctime)-15s Node[' + str(kv.rank) + '] %(message)s'\n",
    "    \n",
    "    logger = logging.getLogger()\n",
    "    formatter = logging.Formatter(head)\n",
    "    stdout_handler = logging.StreamHandler(sys.stdout)\n",
    "    stdout_handler.setFormatter(formatter)\n",
    "\n",
    "    logger.addHandler(stdout_handler)\n",
    "\n",
    "    logger.setLevel(logging.INFO)\n",
    "    logger.info('start with arguments %s', args)\n",
    "    \n",
    "    # load model\n",
    "    model_prefix = args.model_prefix\n",
    "    if model_prefix is not None:\n",
    "        model_prefix += \"-%d\" % (kv.rank)\n",
    "    model_args = {}\n",
    "    if args.load_epoch is not None:\n",
    "        assert model_prefix is not None\n",
    "        tmp = mx.model.FeedForward.load(model_prefix, args.load_epoch)\n",
    "        model_args = {'arg_params' : tmp.arg_params,\n",
    "                      'aux_params' : tmp.aux_params,\n",
    "                      'begin_epoch' : args.load_epoch}\n",
    "    # save model\n",
    "    save_model_prefix = args.save_model_prefix\n",
    "    if save_model_prefix is None:\n",
    "        save_model_prefix = model_prefix\n",
    "    checkpoint = None if save_model_prefix is None else mx.callback.do_checkpoint(save_model_prefix)\n",
    "\n",
    "    # data\n",
    "    (train, val) = get_iterator(data_shape)(args, kv)\n",
    "\n",
    "    # train\n",
    "    devs = mx.cpu() if args.gpus is None else [\n",
    "        mx.gpu(int(i)) for i in args.gpus.split(',')]\n",
    "\n",
    "    epoch_size = args.num_examples / args.batch_size\n",
    "\n",
    "    if args.kv_store == 'dist_sync':\n",
    "        epoch_size /= kv.num_workers\n",
    "        model_args['epoch_size'] = epoch_size\n",
    "\n",
    "    if 'lr_factor' in args and args.lr_factor < 1:\n",
    "        model_args['lr_scheduler'] = mx.lr_scheduler.FactorScheduler(\n",
    "            step = max(int(epoch_size * args.lr_factor_epoch), 1),\n",
    "            factor = args.lr_factor)\n",
    "\n",
    "    if 'clip_gradient' in args and args.clip_gradient is not None:\n",
    "        model_args['clip_gradient'] = args.clip_gradient\n",
    "\n",
    "    # disable kvstore for single device\n",
    "    if 'local' in kv.type and (\n",
    "            args.gpus is None or len(args.gpus.split(',')) is 1):\n",
    "        kv = None\n",
    "\n",
    "    model = mx.model.FeedForward(\n",
    "        ctx                = devs,\n",
    "        symbol             = net,\n",
    "        num_epoch          = args.num_epochs,\n",
    "        learning_rate      = args.lr,\n",
    "        momentum           = 0.9,\n",
    "        wd                 = 0.00001,\n",
    "        initializer        = mx.init.Xavier(factor_type=\"in\", magnitude=2.34),\n",
    "        **model_args)\n",
    "\n",
    "    eval_metrics = ['accuracy']\n",
    "    ## TopKAccuracy only allows top_k > 1\n",
    "    for top_k in [5, 10, 20]:\n",
    "        eval_metrics.append(mx.metric.create('top_k_accuracy', top_k = top_k))\n",
    "\n",
    "    model.fit(\n",
    "        X                  = train,\n",
    "        eval_data          = val,\n",
    "        eval_metric        = eval_metrics,\n",
    "        kvstore            = kv,\n",
    "        batch_end_callback = [mx.callback.Speedometer(args.batch_size, 50)],\n",
    "        epoch_end_callback = checkpoint)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可以看到，验证集上的准确率接近于1，这说明我们的安装过程是成功的。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "总结一下期间遇到的错误：  \n",
    "1.\n",
    ">CMake Error: The following variables are used in this project, but they are set to NOTFOUND.  \n",
    "Please set them or make sure they are set and tested correctly in the CMake files:  \n",
    "CUDA_cublas_LIBRARY (ADVANCED)  \n",
    "    linked by target \"mxnet\" in directory G:/OpenSource/mxnet  \n",
    "    linked by target \"mxnet\" in directory G:/OpenSource/mxnet  \n",
    "CUDA_cublas_device_LIBRARY (ADVANCED)  \n",
    "    linked by target \"mxnet\" in directory G:/OpenSource/mxnet  \n",
    "    linked by target \"mxnet\" in directory G:/OpenSource/mxnet  \n",
    "CUDA_curand_LIBRARY (ADVANCED)  \n",
    "    linked by target \"mxnet\" in directory G:/OpenSource/mxnet  \n",
    "    linked by target \"mxnet\" in directory G:/OpenSource/mxnet  \n",
    "    \n",
    "解决方法：\n",
    "参考[github](https://github.com/dmlc/mxnet/issues/2521)上的issue，换成64位编译器。  \n",
    "\n",
    "2.无法打开包括文件:opencv2\\opencv.hpp  \n",
    "解决方法：  \n",
    "1）打开项目属性页，配置一栏中，将Debug改为Release  \n",
    "2）在项目属性页的VC++标签页中的包含目录选项中加入opencv的头文件路径`G:\\opencv\\build\\include`  \n",
    "3）在项目属性页的VC++标签页中的可执行文件目录选项中加入opencv的bin目录所在路径`G:\\opencv\\build\\x64\\vc12\\bin`  \n",
    "\n",
    "3.错误\t5373\terror LNK2001: 无法解析的外部符号 \"int __cdecl cv::_interlockedExchangeAdd(int *,int)\"(?_interlockedExchangeAdd@cv@@YAHPEAHH@Z)  \n",
    "解决方法：  \n",
    "在项目属性页的标签页中的链接器下的附加依赖项属性中加入opencv的库文件`G:\\opencv\\build\\x64\\vc12\\lib\\opencv_core2413.lib`\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## linux下的安装\n",
    "\n",
    "1.从github克隆源码  \n",
    "`git clone --recursive https://github.com/dmlc/mxnet`\n",
    "这里提醒注意一定不要忘记--recursive参数，因为mxnet依赖于DMLC、MSHADOW等，--recursive参数可以自动下载这些依赖。\n",
    "\n",
    "2.安装atlas，opencv  \n",
    "`sudo apt-get install -y build-essential libatlas-base-dev libopencv-dev`\n",
    "\n",
    "3.修改mxnet/make/config.mk  \n",
    "做如下修改：  \n",
    "```sh\n",
    "USE_CUDA = 1\n",
    "USE_CUDA_PATH = /usr/local/cuda\n",
    "USE_CUDNN = 1\n",
    "USE_NVRTC = 1\n",
    "\n",
    "```\n",
    "\n",
    "\n",
    "4.到mxnet源码目录下执行\n",
    "`make -j4`  \n",
    "其中`-j4`表示用4个线程加速编译  \n",
    "\n",
    "5.编译完成后，在mxnet/lib文件夹下会生成`libmxnet.a`和`libmxnet.so`，将`libmxnet.so`拷贝到`/usr/lib`下：  \n",
    "`cp lib/libmxnet.so /usr/lib`\n",
    "然后将`/usr/lib`加入环境变量，打开`~/.bashrc`，加入：  \n",
    "`export LD_LIBRARY_PATH=/usr/lib:$LD_LIBRARY_PATH`  \n",
    "\n",
    "6.进入mxnet/python目录，执行  \n",
    "`python setup.py install`\n",
    "安装mxnet的python模块  \n",
    "到此，安装就完成了\n",
    "\n",
    "### 遇到的错误\n",
    "1.version `GOMP_4.0' not found   \n",
    "这是由于我在config.mk中设置了`USE_OPENMP=1`而我的电脑里没装openmp导致的  \n",
    "解决方法：  \n",
    "把USE_OPENMP改为0，重新编译  \n",
    "\n",
    "2.GLIBCXX_3.4.20 not found  \n",
    "这是由于anaconda自带的libstdc++.so.6与编译器/usr/lib/x86_64-linux-gnu/libstdc++.so.6冲突了，anaconda自带的是旧版的  \n",
    "```\n",
    "hschen@hschen-Inspiron-7557:~/Workspace/mxnet$ strings /home/hschen/anaconda2/bin/../lib/libstdc++.so.6|grep GLIBC\n",
    "GLIBCXX_3.4\n",
    "GLIBCXX_3.4.1\n",
    "GLIBCXX_3.4.2\n",
    "GLIBCXX_3.4.3\n",
    "GLIBCXX_3.4.4\n",
    "GLIBCXX_3.4.5\n",
    "GLIBCXX_3.4.6\n",
    "GLIBCXX_3.4.7\n",
    "GLIBCXX_3.4.8\n",
    "GLIBCXX_3.4.9\n",
    "GLIBCXX_3.4.10\n",
    "GLIBCXX_3.4.11\n",
    "GLIBCXX_3.4.12\n",
    "GLIBCXX_3.4.13\n",
    "GLIBCXX_3.4.14\n",
    "GLIBCXX_3.4.15\n",
    "GLIBCXX_3.4.16\n",
    "GLIBCXX_3.4.17\n",
    "GLIBCXX_3.4.18\n",
    "GLIBCXX_3.4.19\n",
    "GLIBC_2.3\n",
    "GLIBC_2.2.5\n",
    "GLIBC_2.3.2\n",
    "GLIBCXX_FORCE_NEW\n",
    "GLIBCXX_DEBUG_MESSAGE_LENGTH\n",
    "```\n",
    "其中没有GLIBCXX_3.4.20这个版本，解决办法是  \n",
    "```sh\n",
    "mv /home/hschen/anaconda2/bin/../lib/libstdc++.so.6 /home/hschen/anaconda2/bin/../lib/libstdc++.so.6.oldver\n",
    "cp /usr/lib/x86_64-linux-gnu/libstdc++.so.6 /home/hschen/anaconda2/bin/../lib/libstdc++.so.6\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 参考\n",
    "[mxnet配置安装](https://github.com/zhubuntu/MXNet-Learning-Note/blob/master/mxnet%E5%AE%89%E8%A3%85%E9%85%8D%E7%BD%AE.md)  \n",
    "https://github.com/dmlc/mxnet/issues/655"
   ]
  }
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