{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4.6 GPU计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:15.123349Z",
     "start_time": "2019-03-17T08:12:14.979997Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sun Mar 17 16:12:15 2019       \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| NVIDIA-SMI 390.48                 Driver Version: 390.48                    |\r\n",
      "|-------------------------------+----------------------+----------------------+\r\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\r\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\r\n",
      "|===============================+======================+======================|\r\n",
      "|   0  GeForce GTX 1050    Off  | 00000000:01:00.0 Off |                  N/A |\r\n",
      "| 20%   40C    P5    N/A /  75W |   1213MiB /  2000MiB |     23%      Default |\r\n",
      "+-------------------------------+----------------------+----------------------+\r\n",
      "                                                                               \r\n",
      "+-----------------------------------------------------------------------------+\r\n",
      "| Processes:                                                       GPU Memory |\r\n",
      "|  GPU       PID   Type   Process name                             Usage      |\r\n",
      "|=============================================================================|\r\n",
      "|    0      1235      G   /usr/lib/xorg/Xorg                           434MiB |\r\n",
      "|    0      2095      G   compiz                                       171MiB |\r\n",
      "|    0      2660      G   /opt/teamviewer/tv_bin/TeamViewer              5MiB |\r\n",
      "|    0      4166      G   /proc/self/exe                               397MiB |\r\n",
      "|    0     13274      C   /home/tss/anaconda3/bin/python               191MiB |\r\n",
      "+-----------------------------------------------------------------------------+\r\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi # 对Linux/macOS用户有效"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:15.512222Z",
     "start_time": "2019-03-17T08:12:15.124792Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4.1\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "from torch import nn\n",
    "\n",
    "print(torch.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.1 计算设备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:15.539276Z",
     "start_time": "2019-03-17T08:12:15.513205Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.is_available() # cuda是否可用"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:15.543795Z",
     "start_time": "2019-03-17T08:12:15.540338Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.device_count() # gpu数量"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:15.551451Z",
     "start_time": "2019-03-17T08:12:15.544964Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.current_device() # 当前设备索引, 从0开始"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:15.555020Z",
     "start_time": "2019-03-17T08:12:15.552387Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'GeForce GTX 1050'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.cuda.get_device_name(0) # 返回gpu名字"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.2 `Tensor`的GPU计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:15.562186Z",
     "start_time": "2019-03-17T08:12:15.556621Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2, 3])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.tensor([1, 2, 3])\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:17.441336Z",
     "start_time": "2019-03-17T08:12:15.563813Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2, 3], device='cuda:0')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = x.cuda(0)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:17.449383Z",
     "start_time": "2019-03-17T08:12:17.445193Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:17.454548Z",
     "start_time": "2019-03-17T08:12:17.450268Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 2, 3], device='cuda:0')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n",
    "\n",
    "x = torch.tensor([1, 2, 3], device=device)\n",
    "# or\n",
    "x = torch.tensor([1, 2, 3]).to(device)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:17.467441Z",
     "start_time": "2019-03-17T08:12:17.455495Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([1, 4, 9], device='cuda:0')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = x**2\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:17.470297Z",
     "start_time": "2019-03-17T08:12:17.468866Z"
    }
   },
   "outputs": [],
   "source": [
    "# z = y + x.cpu()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4.6.3 模型的GPU计算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:17.474763Z",
     "start_time": "2019-03-17T08:12:17.471348Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cpu')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net = nn.Linear(3, 1)\n",
    "list(net.parameters())[0].device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:17.478553Z",
     "start_time": "2019-03-17T08:12:17.475677Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda', index=0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "net.cuda()\n",
    "list(net.parameters())[0].device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-03-17T08:12:17.957448Z",
     "start_time": "2019-03-17T08:12:17.479843Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[-0.5574],\n",
       "        [-0.3792]], device='cuda:0', grad_fn=<ThAddmmBackward>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = torch.rand(2,3).cuda()\n",
    "net(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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