print("gpu device name: {}".format(torch.cuda.get_device_name(torch.device("cuda:0"))) gpu device name: NVIDIA GeForce GTX 1080 Ti 7.通过device="cpu:0"指定cpu:0设备 device = torch.Tensor([1,2,3], device="cpu:0").device print("device type: {}".format(device)) device type: ...
(Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:84.) device: torch.device = torch.device(torch._C._get_default_device()), # torch.device('cpu'), bad err=11 in Xbyak::Error Traceback (most recent call last): File "<string>", line 1, in <module> File "/usr/...
get_default_qat_qconfig('fbgemm') torch.quantization.prepare_qat( qat_model, inplace=True ) torch.quantization.convert( qat_model, inplace=True ) 4 剪枝(Pruning) Quantization 是对tensor的精度做优化,Pruning 是从模型参数量角度优化。现在大模型的参数都是B起步,这么大的参数量,移动端设备,比如手机...
numpy.core.multiarray failed to import (Triggered internally at ../torch/csrc/utils/tensor_numpy.cpp:84.) device: torch.device =torch.device(torch._C._get_default_device()),#torch.device('cpu'),.--- Ran 1testin0.000s OK yes (TheUserWarningaboutNumPyis harmless and can be ignored.) The...
torch.get_default_dtype() → torch.dtype Get the current default floating pointtorch.dtype. Example: 代码语言:javascript 复制 >>>torch.get_default_dtype()# initialdefaultforfloating point is torch.float32 torch.float32>>>torch.set_default_dtype(torch.float64)>>>torch.get_default_dtype()#defa...
torch.cuda.current_stream(device=None)[source] 返回给定设备当前选定的流。 参数 device(torch.deviceorint, optional) – 选定的设备。返回当前设备当前选择的流,如果设备为None(默认),则由current_device()给出。 torch.cuda.default_stream(device=None)[source] ...
if :attr:`device` is ``None`` (default).""" torch_npu.npu._lazy_init() with torch_npu.npu.device(device): return torch_npu._C._npu_synchronize() @lru_cache(maxsize=1) def device_count(): return torch_npu._C._npu_getDeviceCount() def...
import argparse import os import paddle import torch import models from models.liif import LIIF device = paddle.get_device() os.environ['CUDA_VISIBLE_DEVICES'] = device.replace('gpu:','') net = models.make({'name': 'liif', 'args': { 'encoder_spec': { 'name': 'rdn', 'args': ...
tkwargs = {"device": torch.device("cuda")ifcudaelsetorch.device("cpu"),"dtype": torch.doubleifdoubleelsetorch.float, } model = self._get_model( batch_shape=batch_shape, num_outputs=num_outputs, n=10, **tkwargs ) self.assertIsInstance(model, FixedNoiseGP) ...
config.gpus_type =torch.cuda.get_device_name() config.gpus_num = torch.cuda.device_count() set_seed(config.seed) local_rank = int(os.environ['LOCAL_RANK']) # start init process torch.distributed.init_process_group(backend='nccl', init_method='env://') ...