@jiapei-nexera You can use the ts-config command and pass the config.properties file , where you can specify GPU specific information. You can refer to this section https://pytorch.org/serve/configuration.html#limit-gpu-usage and https://pytorch.org/serve/configuration.html#nvidia-control-visib...
import torch from pytorch_quantization import nn as quant_nn from pytorch_quantization import quant_modules quant_nn.TensorQuantizer.use_fb_fake_quant = True quant_modules.initialize() model = MLP().eval() torch.onnx.export( model.cuda(), torch.rand(10240, 512).cuda(), "MLP_explicit_quant...
Another example would be thefaiss-cpuandfaiss-gpupackages - I want to specify that I need one of them, and which based on some argument. Impact This allows a team of data scientists to use Poetry to manage dependencies for non-package mode repositories using different os (darwin, linux, wi...
[W ProcessGroupNCCL.cpp:1569] Rank 1 using best-guess GPU 1 to perform barrier as devices used by this process are currently unknown. This can potentially cause a hang if this rank to GPU mapping is incorrect.Specify device_ids in barrier() to force use of a particular device. Maybe ...
GPU 7: Tesla V100-SXM2-32GB Nvidia driver version: 418.40.04 cuDNN version: Could not collect HIP runtime version: N/A MIOpen runtime version: N/A Versions of relevant libraries: [pip3] numpy==1.21.2 [pip3] pytorch-lightning==1.5.5 ...
No CUDA: conda install pytorch-cpu torchvision-cpu -c pytorch I believe that NVIDIA and Anaconda handle things differently. I have zero thoughts on which way is correct, but I thought it would be useful to start such a conversation around this. My hope is that we can come to some consens...
I'm training yolov5x to predict one class. I created a custom yolo5x.yaml where nc == 1. And I want to train my model using pretrained coco weights. And When I load coco pretrained weights like this : --weight yolov5x.pt, I receive an er...
Docker requires a GPU with at least 16GB of VRAM, and all acceleration features are enabled by default. Before running this Docker, you can use the following command to check if your device supports CUDA acceleration on Docker. docker run --rm --gpus=all nvidia/cuda:12.1.0-base-ubuntu22.04...
Docker requires a GPU with at least 16GB of VRAM, and all acceleration features are enabled by default. Before running this Docker, you can use the following command to check if your device supports CUDA acceleration on Docker. docker run --rm --gpus=all nvidia/cuda:12.1.0-base-ubuntu22.04...