Check devices(cuda and mlu) is available with patch_environment . Move clear_environment and patch_environment into src/accelerate/utils/environment.py to avoid circular import . Before submitting This PR fixes a typo or improves the docs (you can dismiss the other checks if that's the case)...
"""cuda.check_cuda_available()ifnotself._cpu:returnself d = self.__dict__withcuda.get_device(device):fornameinself._params: d[name].to_gpu()fornameinself._persistent: value = d[name]ifisinstance(value, numpy.ndarray): d[name] = cuda.to_gpu(value) self._cpu =Falsereturnself 开发...
开发者ID:corochann,项目名称:SeRanet,代码行数:18, deftrain(epoch=10, batch_size=32, gpu=False):ifgpu: cuda.check_cuda_available() xp = cuda.cupyifgpuelsenp td = TrainingData(LABEL_FILE, img_root=IMAGES_ROOT, image_property=IMAGE_PROP)# make mean imageifnotos.path.isfile(MEAN_IMAGE_...
defcheck_cuda_driver_version():# 获取当前CUDA驱动程序版本try:output=subprocess.check_output(["nvcc","--version"])version_str=output.decode("utf-8")version_lines=version_str.split("\n")forlineinversion_lines:if"release"inline:version=line.split()[-1]returnversionexcept(subprocess.CalledProcessE...
as pltimport timeimport osimport cv2import nvidia_smiimport copyfrom PIL import Imagefrom torch.utils.data import Dataset,DataLoaderimport torch.utils.checkpoint as checkpointfrom tqdm import tqdmimport shutilfrom torch.utils.checkpoint import checkpoint_sequentialdevice="cuda" if torch.cuda.is_available...
这通常发生在PyTorch检测到CUDA环境有问题,但您可能仍然想继续运行程序而不使用GPU。 2. 检查PyTorch的GPU支持 在禁用检查之前,建议先确认PyTorch是否支持GPU以及CUDA是否已正确安装。您可以通过以下Python代码片段来检查PyTorch的CUDA支持情况: python import torch # 检查CUDA是否可用 if torch.cuda.is_available(): ...
pythonCopy codeimporttorchimporttorch.nnasnnimporttorch.backends.cudnnascudnn# 检查是否有可用的GPU设备device=torch.device("cuda"iftorch.cuda.is_available()else"cpu")# 加载模型model=MyModel().to(device)# 检查是否为cuDNN加速的模式ifdevice.type=='cuda':# 设置cuDNN为benchmark模式,以获得最佳性能...
iftorch.cuda.is_available():device=torch.device("cuda")# 使用GPUelse:device=torch.device("cpu")# 使用CPU 1. 2. 3. 4. 在这段代码中,我们首先使用torch.cuda.is_available()函数检查GPU是否可用。如果GPU可用,我们将使用cuda设备,否则将使用cpu设备。这将决定我们在训练过程中使用的设备类型。
1. 找到调用的地方进行修改,例如: vim ./model/retriever/filtering/contriver.py 1. 修改里面的调用GPU地方内容为: self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu") 1.
iftorch.cuda.is_available():gpu_model=no_gpu_model.cuda()try:print(gpu_model.forward(batch.cuda()).size())exceptTypeErrorase:print(e)# OUT: torch.Size([10, 1]) It is the second case, I would want some feedback weather the variables should be send to cuda or not based on the mo...