{
 "cells": [
  {
   "cell_type": "code",
   "id": "initial_id",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "import torch\n",
    "\n",
    "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")"
   ],
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "data = torch.ones(3, 3)\n",
    "print(data.device)"
   ],
   "id": "7a630763614905d",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "device = torch.device(\"cuda:0\")\n",
    "data_gpu = data.to(device)\n",
    "print(data_gpu.device)"
   ],
   "id": "e2a2d8a6d60231c",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "import torch.nn as nn\n",
    "\n",
    "net = nn.Sequential(nn.Linear(3, 3))\n",
    "net.to(device)"
   ],
   "id": "458ea27224fd0061",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "from torch import nn\n",
    "\n",
    "\n",
    "class ASimpleNet(nn.Module):\n",
    "    def __init__(self, layers=3):\n",
    "        super(ASimpleNet, self).__init__()\n",
    "        self.linears = nn.ModuleList([nn.Linear(3, 3, bias=False) for i in range(layers)])\n",
    "\n",
    "    def forward(self, x):\n",
    "        print(\"forward batchsize is: {}\".format(x.size()[0]))\n",
    "        x = self.linears(x)\n",
    "        x = torch.relu(x)\n",
    "        return x"
   ],
   "id": "4859aa95dd22d01d",
   "outputs": [],
   "execution_count": null
  },
  {
   "metadata": {},
   "cell_type": "code",
   "source": [
    "batch_size = 16\n",
    "inputs = torch.randn(batch_size, 3)\n",
    "labels = torch.randn(batch_size, 3)\n",
    "inputs, labels = inputs.to(device), labels.to(device)\n",
    "net = ASimpleNet()\n",
    "net = nn.DataParallel(net)\n",
    "net.to(device)\n",
    "# print(\"CUDA_VISIBLE_DEVICES :{}\".format(os.environ[\"CUDA_VISIBLE_DEVICES\"]))\n",
    "\n",
    "for epoch in range(1):\n",
    "    outputs = net(inputs)"
   ],
   "id": "d3eeb897f7f0ee68",
   "outputs": [],
   "execution_count": null
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
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 },
 "nbformat": 4,
 "nbformat_minor": 5
}