问PyTorch get_device_capability()输出解释EN连起来看 运行时间: [GC类型 (原因)] [收集器类型: GC...
在PyTorch中,我们可以使用如下代码获取GPU信息: importtorch defgpu_info() ->str: info ='' foridinrange(torch.cuda.device_count()): p = torch.cuda.get_device_properties(id) info +=f'CUDA:{id}({p.name},{p.total_memory / (1<<20):.0f}MiB)\n' returninfo[:-1] if__name__ =='...
print("The model will be running on", device,"device")# Convert model parameters and buffers to CPU or Cudamodel.to(device)forepochinrange(num_epochs):# loop over the dataset multiple timesrunning_loss =0.0running_acc =0.0fori, (images, labels)inenumerate(train_loader,0):# get the ...
importtorchdefget_gpu_info(): device = torch.device("cuda"iftorch.cuda.is_available()else"cpu")ifdevice.type=="cuda":# 获取当前GPU名字gpu_name = torch.cuda.get_device_name(torch.cuda.current_device())# 获取当前GPU总显存props = torch.cuda.get_device_properties(device) total_memory = pr...
GPU指定 pytorch python指定gpu跑,1、目前主流方法:.to(device)方法(推荐)importtorchimporttime#1.通常用法device=torch.device("cuda"iftorch.cuda.is_available()else"cpu")data=data.to(device)model=model.to(device)'''1.先创建device
打印GPU的总内存和可用内存:print('Total memory:', torch.cuda.get_device_properties(i).total_memory, 'Available memory:', torch.cuda.get_device_properties(i).total_memory - torch.cuda.memory_allocated(i)) 打印模型在GPU上的内存占用:model = ... # your model here print('Model memory on GPU...
torch.cuda.device_count():返回当前可见可用的 GPU 数量 torch.cuda.get_device_name():获取 GPU 名称 torch.cuda.manual_seed():为当前 GPU 设置随机种子 torch.cuda.manual_seed_all():为所有可见 GPU 设置随机种子 torch.cuda.set_device():设置主 GPU 为哪一个物理 GPU,此方法不推荐使用 ...
torch.cuda.empty_cache()# 设置进程可使用的GPU显存最大比例为50%torch.cuda.set_per_process_memory_fraction(0.5,device=0)# 计算总内存total_memory=torch.cuda.get_device_properties(0).total_memoryprint("实际总内存:",round(total_memory/(1024*1024),1),"MB")# 尝试分配大量显存的操作try:# 使用...
C10_CUDA_CHECK(cudaGetDeviceProperties(∝,device_));// we allocate enough address space for 1 1/8 the total memory on the GPU.// This allows for some cases where we have to unmap pages earlier in the// segment to put them at the end.max_handles_=numSegments(prop.totalGlobalMem+prop...