2018年1月8日 · 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.

2020年4月3日 · I tried installing torch with CUDA 11.8 and conda somehow installed it on Python=3.7.4 which is not supported. I just needed Python=3.8.10 and everything worked fine.

2018年5月26日 · If you would like to ensure your splits have balanced classes, you can use train_test_split from sklearn. Assuming you have wrapped your data in a custom Dataset object: from torch.utils.data import DataLoader, Subset from sklearn.model_selection import train_test_split TEST_SIZE = 0.1 BATCH_SIZE = 64 SEED = 42 # generate indices: instead …

2023年4月2日 · I want to set up a stable-diffusion environment in Windows10. I downloaded the recommended graphics card driver version and cuda version, but running webui-user-bat still generates an error: Torch ...

2018年11月12日 · I want to do some timing comparisons between CPU & GPU as well as some profiling and would like to know if there's a way to tell pytorch to not use the GPU and instead use the CPU only? I realize I could install another CPU-only pytorch, but hoping there's an easier way.

2021年11月14日 · test_dataloader = DataLoader(test_dataset, batch_size=16) Note: Converting all labels to torch.long instead of float32 because the label should be integer scalar in F.nll_loss method.

2018年1月9日 · Starting with PyTorch 0.4.1 there is the detect_anomaly context manager, which automatically inserts assertions equivalent to assert not torch.isnan(grad).any() between all steps of backward propagation. It's very useful when issues arise during backward pass.

2022年3月30日 · I'm using google colab free Gpu's for experimentation and wanted to know how much GPU Memory available to play around, torch.cuda.memory_allocated () returns the current GPU memory occupied, but how do we determine total available memory using PyTorch.

2019年6月29日 · A simple and profound explanation is that use of with torch.no_grad() behaves just like a loop where everything written in it will have there requires_grad argument set as False although temporarily. So there is no need to specify anything beyond this if you need to stop the backpropagation from gradients of certain variables or functions. However, torch.detach() …

2021年4月5日 · I created a pyTorch Model to classify images. I saved it once via state_dict and the entire model like that: torch.save(model.state_dict(), "model1_statedict") torch.save(model, "model1_complete") How can i use these models? I'd like to check them with some images to see if they're good. I am loading the model with: model = torch.load(path_model)