01.12.2016 · RuntimeError: Input type (torch.cuda.FloatTensor) and weight type (torch.FloatTensor) should be the same Zhenye-Na/image-similarity-using-deep-ranking#2. Closed. resistor pushed a commit to resistor/pytorch that referenced this issue on Mar 13, 2020. Support for multiple cuda devices and cache code ( pytorch#283)
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27.11.2020 · AttributeError: module 'torch' has no attribute 'cuda' I tried to update PyTorch and install the last version 1.7.0 with CUDA 11.0 support. After that, I noticed some version discrepancies. nvidia-smi shows CUDA version 11.0 but nvcc -V shows 9.1.
torch.cuda. This package adds support for CUDA tensor types, that implement the same function as CPU tensors, but they utilize GPUs for computation. It is lazily initialized, so you can always import it, and use is_available () to determine if your system supports CUDA.
25.08.2020 · 优点：需要把模型放在Cuda ... ‘torch.Size’ object has no attribute ‘numel’，如下图所示。 ... 卷积层加3个全连接层，话不多说，直接上代码： import torch from torch import nn from torchstat import stat class AlexNet(nn.Module): def __init__(self, num_classes): ...
Hi,. Thank you for posting your questions. First of all use torch.cuda.is_available() to detemine the CUDA availability also we need more details to figure ...
07.03.2018 · Hi, torch.cuda.empty_cache() (EDITED: fixed function name) will release all the GPU memory cache that can be freed. If after calling it, you still have some memory that is used, that means that you have a python variable (either torch Tensor or torch Variable) that reference it, and so it cannot be safely released as you can still access it.
29.12.2020 · CUDA runtime version: No CUDA GPU models and configuration: No CUDA Nvidia driver version: No CUDA cuDNN version: No CUDA HIP runtime version: N/A MIOpen runtime version: N/A. Versions of relevant libraries: [pip3] numpy==1.19.2 [pip3] torch==1.7.1 [pip3] torchvision==0.2.1 [conda] blas 1.0 mkl
I tried to run the code in the Quick Start. When I got to this step, optimizer = torch.optim.Adam(task.parameters(), lr=1e-3) solver = core.Engine(task, train_set, valid_set, test_set, optimizer, batch_size=1024) solver.train(num_epoch=5...