detection.ConvertToTensor(cuda_input_frame) self.torch_detection.DetectAndDraw(self.TopFrame(), cuda_tensor, 'Pytorch only (no VPI)', self.scores_threshold) # -------- Bottom Frame: With VPI --------- vpi_output
conda install pytorch==1.12.1 torchvision==0.13.1 cudatoolkit=11.3 -c pytorch -c conda-forge The installation is success but the torch.cuda.is_available return false, so the inference result is not as expected. After using jtop to monitor, you can find the GPU utilization is also zero. H...
this lib seems to be able to test pytroch 1.5 with cuda 10.0 and pytorch seems to detect the gpu. 👍 3 amogkam mentioned this issue Jul 15, 2020 [ray][tune] Ray does not make GPU available to Trainables ray-project/ray#9503 Closed 2 tasks Snoeprol commented Mar 7, 2022 ...
loss = F.cross_entropy(outputs, labels) # 报错--RuntimeError: element 0 of tensors does not require grad and does not have a loss = loss.requires_grad_() loss.backward() ## 问题3 问题展示 Pytorch RuntimeError: Expected tensor for argument #1 'indices' to have scalar type Long; but...
I cant install it by “pip3 install torchvision” cause it would collecting torch(from torchvision), and PyTorch does not currently provide packages for PyPI. Please help me out. Thanks a lot Hi buptwlr, run the commands below to install torchvision. It is installed from source: ...
如果没有准备好, 就放在 GraphTask 中的 not_ready 中。 如果graph_task->outstanding_tasks <= 0 则退出循环。即执行完了 GraphTask 所有的 Node。 主进程进行阻塞等待,等待 graph_task->future_result_,即工作线程结束。 具体代码如下: 代码语言:javascript ...
inference: trt_engine: /path/to/engine/file dataset: data: samples_per_gpu:16test: data_prefix: /raid/ImageNet2012/ImageNet2012/val classes: /raid/ImageNet2012/classnames.txt Use the following command to run classification (PyTorch) engine inference: ...
If using or upgrading to a 3-part-version driver, for example, a driver that takes the format of xxx.yy.zz, you will receive a Failed to detect NVIDIA driver version. message. This is due to a known bug in the entry point script's parsing of 3-part driver versions. This message is...
Please note that the basic installation is not standalone, as it does not provide a deep learning framework, which is required for the package to run. We try to keep framework-specific dependencies to a minimum. You can install framework-specific builds as follows: ...
// // CUDA tensor can be supported if the MPI used is CUDA-aware MPI, and // ProcessGroupMPI will automatically detect this support. class ProcessGroupMPI : public ProcessGroup { public: class WorkMPI : public ProcessGroup::Work { public: explicit WorkMPI( std::vector<at::Tensor> output...