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DL-with-Python-and-PyTorch2/pytorch-05/pytorch-05-05.ipynb
2024-01-08 00:44:52 +08:00

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5.7GPU加速"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.7.1 单GPU加速"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"#for batch_idx, (img, label) in enumerate(train_loader):\n",
"# img=img.to(device)\n",
"# label=label.to(device)\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#实例化网络\n",
"#model = Net()\n",
"#model.to(device) #使用序号为0的GPU\n",
"#或model.to(device1) #使用序号为1的GPU"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 5.7.2 多GPU加速"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.datasets import load_boston\n",
"from sklearn.model_selection import train_test_split\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"import torch\n",
"import torch.nn as nn\n",
"import torch.nn.functional as F\n",
"import torch.optim as optim\n",
"\n",
"\n",
"\n",
"boston = load_boston()\n",
"X,y = (boston.data, boston.target)\n",
"dim = X.shape[1]\n",
"\n",
"\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)\n",
"#组合训练数据及标签\n",
"myset = list(zip(X_train,y_train))"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#对训练数据进行标准化\n",
"mean=X_train.mean(axis=0)\n",
"std=X_train.std(axis=0)\n",
"X_train-=mean\n",
"X_train/=std\n",
"\n",
"X_test-=mean\n",
"X_test/=std"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#实例化模型\n",
"device_ids =[0,1,2,3]\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"dtype = torch.FloatTensor\n",
"#train_data=nn.DataParallel(torch.from_numpy(X_train).type(dtype))\n",
"train_data=torch.from_numpy(X_train).type(dtype).to(device)\n",
"train_target=torch.from_numpy(y_train).type(dtype).to(device)\n",
"test_data=torch.from_numpy(X_test).type(dtype).to(device)\n",
"test_target=torch.from_numpy(y_test).type(dtype).to(device)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from torch.utils import data\n",
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"dtype = torch.FloatTensor\n",
"\n",
"train_loader = data.DataLoader(myset,batch_size=128,shuffle=True)\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"构建网络"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"class Net1(nn.Module):\n",
" \"\"\"\n",
" 使用sequential构建网络Sequential()函数的功能是将网络的层组合到一起\n",
" \"\"\"\n",
" def __init__(self, in_dim, n_hidden_1, n_hidden_2, out_dim):\n",
" super(Net1, self).__init__()\n",
" self.layer1 = torch.nn.Sequential(nn.Linear(in_dim, n_hidden_1))\n",
" self.layer2 = torch.nn.Sequential(nn.Linear(n_hidden_1, n_hidden_2))\n",
" self.layer3 = torch.nn.Sequential(nn.Linear(n_hidden_2, out_dim))\n",
" \n",
" \n",
" def forward(self, x):\n",
" x1 = F.relu(self.layer1(x))\n",
" x1 = F.relu(self.layer2(x1))\n",
" x2 = self.layer3(x1)\n",
" #显示每个GPU分配的数据大小\n",
" print(\"\\tIn Model: input size\", x.size(),\"output size\", x2.size())\n",
" return x2\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Let's use 2 GPUs\n"
]
},
{
"data": {
"text/plain": [
"DataParallel(\n",
" (module): Net1(\n",
" (layer1): Sequential(\n",
" (0): Linear(in_features=13, out_features=16, bias=True)\n",
" )\n",
" (layer2): Sequential(\n",
" (0): Linear(in_features=16, out_features=32, bias=True)\n",
" )\n",
" (layer3): Sequential(\n",
" (0): Linear(in_features=32, out_features=1, bias=True)\n",
" )\n",
" )\n",
")"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
"#实例化网络\n",
"model = Net1(13, 16, 32, 1)\n",
"if torch.cuda.device_count() > 1:\n",
" print(\"Let's use\", torch.cuda.device_count(), \"GPUs\")\n",
" # dim = 0 [64, xxx] -> [32, ...], [32, ...] on 2GPUs\n",
" model = nn.DataParallel(model)\n",
"\n",
"model.to(device)\n"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"optimizer_orig = torch.optim.Adam(model.parameters(), lr=0.01)\n",
"loss_func = torch.nn.MSELoss()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"模型训练,并可视化损失值"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
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"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"Outside: input size torch.Size([20, 13]) output_size torch.Size([20, 1])\n",
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"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"Outside: input size torch.Size([20, 13]) output_size torch.Size([20, 1])\n",
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"\n",
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"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"Outside: input size torch.Size([20, 13]) output_size torch.Size([20, 1])\n",
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"\tIn Model: input size\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
" torch.Size([10, 13]) output size torch.Size([10, 1])\n",
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"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"Outside: input size torch.Size([20, 13]) output_size torch.Size([20, 1])\n",
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"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"Outside: input size torch.Size([20, 13]) output_size torch.Size([20, 1])\n",
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"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"Outside: input size torch.Size([20, 13]) output_size torch.Size([20, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"Outside: input size torch.Size([20, 13]) output_size torch.Size([20, 1])\n",
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"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n"
]
},
{
"name": "stdout",
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"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"Outside: input size torch.Size([20, 13]) output_size torch.Size([20, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
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"Outside: input size torch.Size([20, 13]) output_size torch.Size([20, 1])\n",
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"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"Outside: input size torch.Size([20, 13]) output_size torch.Size([20, 1])\n",
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"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"Outside: input size torch.Size([20, 13]) output_size torch.Size([20, 1])\n",
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"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
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"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
"\n",
"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"Outside: input size torch.Size([20, 13]) output_size torch.Size([20, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
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"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
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"\tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
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"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
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"\tIn Model: input size \tIn Model: input size torch.Size([64, 13]) output size torch.Size([64, 1])\n",
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" output size torch.Size([64, 1])\n",
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"Outside: input size torch.Size([128, 13]) output_size torch.Size([128, 1])\n",
"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n",
"\tIn Model: input size torch.Size([10, 13]) output size torch.Size([10, 1])\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Outside: input size torch.Size([20, 13]) output_size torch.Size([20, 1])\n"
]
}
],
"source": [
"from tensorboardX import SummaryWriter\n",
"writer = SummaryWriter(log_dir='logs')\n",
"for epoch in range(100): \n",
" model.train()\n",
" for data,label in train_loader:\n",
" input = data.type(dtype).to(device)\n",
" label = label.type(dtype).to(device)\n",
" output = model(input) \n",
" loss = loss_func(output, label)\n",
" # 反向传播\n",
" optimizer_orig.zero_grad()\n",
" loss.backward()\n",
" optimizer_orig.step()\n",
" print(\"Outside: input size\", input.size() ,\"output_size\", output.size())\n",
" writer.add_scalar('train_loss_paral',loss, epoch)"
]
}
],
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