from time import time @cuda.jit def gpu_add(a, b, result, n): idx = cuda.threadIdx.x + cuda.blockDim.x * cuda.blockIdx.x if idx < n : result[idx] = a[idx] + b[idx] def main(): n = 20000000 x = np.arange(n).astype(np.int32) y = 2 * x # 拷贝数据到设备端 x_...
importtorch# 检测GPU是否可用iftorch.cuda.is_available():device=torch.device("cuda")print("GPU可用")else:device=torch.device("cpu")print("GPU不可用")# 将模型移动到GPUmodel=Model().to(device)# 定义损失函数和优化器criterion=torch.nn.CrossEntropyLoss()optimizer=torch.optim.SGD(model.parameters(...
import torch from joblib import dump, load import torch.utils.data as Data import numpy as np import pandas as pd import torch import torch.nn as nn # 参数与配置 torch.manual_seed(100) # 设置随机种子,以使实验结果具有可重复性 device = torch.device("cuda" if torch.cuda.is_available() ...
import torch import torch.nn as nn import torch.nn.functional as F import torch.optim as optim from torchvision import datasets, transforms BATCH_SIZE=512 #大概需要2G的显存 EPOCHS=20 # 总共训练批次 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") train_loader = torch...
model.fc = nn.Linear(num_ftrs,10)# 将模型移动到GPU(如果可用)device = torch.device("cuda:0"iftorch.cuda.is_available()else"cpu") model = model.to(device) 步骤4:定义损失函数和优化器 我们选择交叉熵损失函数(Cross Entropy Loss)作为模型训练的损失函数,并使用Adam优化器进行优化。
importtorchfromjoblibimportdump, loadimporttorch.utils.data asDataimportnumpyasnpimportpandasaspdimporttorchimporttorch.nnasnn# 参数与配置torch.manual_seed(100)# 设置随机种子,以使实验结果具有可重复性device = torch.device("cuda"iftorch.cuda.is_available()else"cpu")# 有GPU先用GPU训练# 加载数据集def...
x = x.reshape((0, -1)) x = F.tanh(self.fc1(x)) x = F.tanh(self.fc2(x)) return xnet = Net()# 初始化与优化器定义# set the context on GPU is available otherwise CPUctx = [mx.gpu() if mx.test_utils.list_gpus() else mx.cpu()]...
(), ) )ifargs.dry_run:breakdeftest(model, device, test_loader): model.eval() test_loss =0correct =0withtorch.no_grad():fordata, targetintest_loader: data, target = data.to(device), target.to(device) output = model(data) test_loss += F.nll_loss( output, target, reduction="...
# 输入维度 hidden_dim = 128 output_dim = len(label_encoder.classes_) # 输出维度 model = TextClassifier(input_dim, hidden_dim, output_dim) optimizer = optim.Adam(model.parameters(), lr=0.001) criterion = nn.CrossEntropyLoss() device = torch.device("cuda" if torch.cuda.is_available() ...
如果CUDA可以用,让我们首先定义下我们的设备为第一个可见的cuda设备。 device = torch.device("cuda:0"iftorch.cuda.is_available()else"cpu")# 假设在一台CUDA机器上运行,那么这里将输出一个CUDA设备号:print(device) 输出: cuda:0 本节剩余部分都会假定设备就是台CUDA设备。接着这些方法会递归地遍历所有模块...