for data in testloader: images, labels = data if CUDA: images = images.cuda() labels = labels.cuda() outputs = model(images) _, predicted = torch.max(outputs.data, 1) total += labels.size(0) correct += (predicted == labels).sum() print('Accuracy on the test set: %d %%' % ...
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_...
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() else "cpu") # 有GPU先用GPU训练 # 加载数据集 def dataloader(batch_size, workers=2): # 训练集 train_xdata = load('trainX_1024_10c') train_ylabel = load('train...
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') 如果需要指定多张显卡,比如0,1号显卡。 import os os.environ['CUDA_VISIBLE_DEVICES'] = '0,1' 也可以在命令行运行代码时设置显卡: CUDA_VISIBLE_DEVICES=0,1 python train.py 清除显存 torch.cuda.empty_cache() 也可以使用...
matrix_ker(test_a_gpu, test_b_gpu, output_mat_gpu, np.int32(4), block=(2,2,1), grid=(2,2,1))assert( np.allclose(output_mat_gpu.get(), output_mat) ) 我们现在将运行这个程序,并且不出所料地得到以下输出: 现在让我们来看一下 CUDA C 代码,其中包括一个内核和一个设备函数: ...
import os import torch import yaml from ultralytics import YOLO # 导入YOLO模型 from QtFusion.path import abs_path device = "cuda:0" if torch.cuda.is_available() else "cpu" 接着,代码进入了主函数。这里我们设置了工作进程的数量和批次大小,这些都是影响训练速度和内存使用的重要参数。同时,我们指定...
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') train_iterator, valid_iterator = data.BucketIterator.splits( (train_data, valid_data), batch_size = 64, device = device ) 构建CNN 模型 现在我们已经加载了数据,现在可以创建模型了。 我们将使用以下步骤进行操作: 我们希望...
[TestFailure] Resolving exceptions thrown across multipleGraphBolt… Jan 8, 2025 graphbolt [DeprecationWarning] Replacing soon-to-be-deprecatedlazyInitCUDAw… Jan 16, 2025 include/dgl fix(dgl/runtime): add static_cast to fix clang compilation when using… ...
Subpackage:cuda.bindings Thecuda.bindingspackage is a standard set of low-level interfaces, providing full coverage of and access to the CUDA host APIs from Python. The list of available interfaces are: CUDA Driver CUDA Runtime NVRTC nvJitLink ...
.ctags.d .devcontainer .github .vscode android aten benchmarks binaries c10 caffe2 cmake docs functorch mypy_plugins scripts test third_party tools torch torchgen .bazelignore .bazelrc .bazelversion .clang-format .clang-tidy .cmakelintrc ...