查看自己GPU计算能力，常用GPU显卡表

1. 代码

import os from tensorflow.python.client import device_lib os.environ["TF_CPP_MIN_LOG_LEVEL"] = "99" if __name__ == "__main__": print(device_lib.list_local_devices()) import torch import torch.nn as nn from torch.utils.data import Dataset, DataLoader print(torch.cuda.is_available()) import torch.nn as nn import torch.optim as optim input_size = 5 output_size = 2 batch_size = 30 data_size = 100 device = torch.device("cuda: 0" if torch.cuda.is_available() else "cpu") class RandomDataset(Dataset): def __init__(self, size, length): self.len = length self.data = torch.randn(length, size) def __getitem__(self, index): return self.data[index] def __len__(self): return self.len rand_loader = DataLoader(dataset=RandomDataset(input_size, data_size), batch_size=batch_size, shuffle=True) class Model(nn.Module): # Our model def __init__(self, input_size, output_size): super(Model, self).__init__() self.fc = nn.Linear(input_size, output_size) def forward(self, input): output = self.fc(input) print("\tIn Model: input size", input.size(), "output size", output.size()) return output model = Model(input_size, output_size) if torch.cuda.device_count() >= 1: print("Let's use", torch.cuda.device_count(), "GPUs!") # dim = 0 [30, xxx] -> [10, ...], [10, ...], [10, ...] on 3 GPUs model = nn.DataParallel(model) model.to(device) for data in rand_loader: input = data.to(device) output = model(input) print("Outside: input size", input.size(), "output_size", output.size())

2. 显卡计算能力对照表

GPUCompute CapabilityTesla K803.7Tesla K403.5Tesla K203.5Tesla C20752.0Tesla C2050/C20702.0Tesla P1006.0Tesla P406.1Tesla P46.1Tesla M405.2Tesla K803.7Tesla K403.5Tesla K203.5Tesla K103.0Quadro P60006.1Quadro P50006.1Quadro M6000 24GB5.2Quadro M60005.2Quadro K60003.5Quadro M50005.2Quadro K52003.5Quadro K50003.0Quadro M40005.2Quadro K42003.0Quadro K40003.0Quadro M20005.2Quadro K22005.0Quadro K20003.0Quadro K2000D3.0Quadro K12005.0Quadro K6205.0Quadro K6003.0Quadro K4203.0Quadro 4103.0Quadro Plex 70002.0Quadro K6000M3.0Quadro M5500M5.0Quadro K5200M3.0Quadro K5100M3.0Quadro M5000M5.0Quadro K500M3.0Quadro K4200M3.0Quadro K4100M3.0Quadro M4000M5.0Quadro K3100M3.0Quadro M3000M5.0Quadro K2200M5.0Quadro K2100M3.0Quadro M2000M5.0Quadro K1100M3.0Quadro M1000M5.0Quadro K620M5.0Quadro K610M3.5Quadro M600M5.0Quadro K510M3.5Quadro M500M5.0

… 更全的显卡链接 参考博客