Device cuda 1. When I run the training (trainer. device('cuda:0') device = torch. Here's a quick example: Changing default device # Created On: Mar 15, 2023 | Last Updated: Jun 07, 2023 | Last Verified: Nov 05, 2024 It is common practice to write PyTorch code in a device-agnostic way, and then switch between CPU and CUDA depending on what hardware is available. Apr 10, 2024 · Hello! It seems like you've encountered an issue where specifying device=1 doesn't switch the GPU being used. train ()), I get the error: RuntimeError: Expected all tensors to be on the same device, but found at least two devices, cuda:1 and cuda:0. eval(). By default, if you don't explicitly set your device in PyTorch, it'll use device=0. To use a different GPU, you need to ensure that your model and tensors are moved to the target device. cuda. Jul 30, 2025 · PyTorch is a popular open - source deep learning framework known for its flexibility and dynamic computational graph. to("cuda:0") and why this Jul 24, 2020 · Setting CUDA_VISIBLE_DEVICES=1 mean your script will only see one GPU which is GPU1. For example if you do: CUDA_VISIBLE_DEVICES=2,4,5, your script will see 3 GPUs with index 0, 1 and 2. py. device('cuda') Thanks! Jan 26, 2022 · Err, first you can try move with torch. load(modelFile)) model = model. If that does not works, You can set CUDA_VISIBLE_DEVICES=1 when convert your model with cuda:0 and do inference on cuda:1. See full list on blog. It seems that "cuda", which afaik defaults to "cuda:0" is hard coded somewhere in the model. . One of the key features that makes PyTorch powerful for training deep learning models is its support for CUDA, which allows models to leverage the parallel computing capabilities of NVIDIA GPUs. load_state_dict(torch. I don’t understand the behaviour when trying to load this model onto another, say cuda:0. In this blog, we will explore the concept of PyTorch device IDs in the context of Mar 5, 2025 · When moving a model to a GPU, it should be possible to select the GPU if multiple devices are present. So I am not sure if these two methods will work. I do not have a host with multiple devices for now. I will try it ASAP. This Aug 17, 2020 · If I only have one gpu does doing either of the below mean that the same gpu will be used? device = torch. This means that if your system has devices 0, 1 and 2, and if CUDA_VISIBLE_DEVICES is set to “0,2”, then when a client connects to the server it will see the remapped devices - device 0 and a device 1. Feb 17, 2020 · I have a model class myModel, which I pre-train on device cuda:1 and then save to file modelFile. However, inside your script it will be cuda:0 and not cuda:1. csdn. I would like to make sure if I understand the difference between these two command correctly. device("cuda") it makes the device to be a GPU without particularly specifying the device name (0,1,2,3). Typically, to do this you might have used if-statements and cuda() calls to do this: Jan 8, 2018 · How do I check if PyTorch is using the GPU? The nvidia-smi command can detect GPU activity, but I want to check it directly from inside a Python script. This should propagate down the code to ensure that all tensors are moved to that specific device. Can someone help me understand what is going on behind the scenes when one has the following: model = myModel() model. device(device): to the beginning of create_trt_engine in tensorrt/utils. Jan 15, 2024 · My system has two cuda cards which I both want to use. net May 27, 2019 · I assumed if I use torch. When CUDA_VISIBLE_DEVICES is set before launching the control daemon, the devices will be remapped by the MPS server. Because it only see one GPU and its index start at 0. nshc setym mtcit poulut zyqf egglzv odb agfmoho uoejp wdgpe