It looks like PyTorch’s caching allocator reserves some fixed amount of memory even if there are no tensors, and this allocation is triggered by the first CUDA memory access (torch.cuda.empty_cache() deletes unused tensor from the cache, but the cache itself still uses some memory). Even with a tiny 1-element tensor, after del and torch.cuda.empty_cache(), GPUtil.showUtilization(all=True) … Read more
Update 3: Since late 2020, torch-mlir project has come a long way and now supports all major Operating systems. Using torch-mlir you can now use your AMD, NVIDIA or Intel GPUs with the latest version of Pytorch. You can download the binaries for your OS from here. Update 2: Since October 21, 2021, You can … Read more
Yes, you can emulate one by another, the reason for having them separate is efficiency. LSTMCell is a cell that takes arguments: Input of shape batch × input dimension; A tuple of LSTM hidden states of shape batch x hidden dimensions. It is a straightforward implementation of the equations. LSTM is a layer applying an … Read more
For nn.CrossEntropyLoss the target has to be a single number from the interval [0, #classes] instead of a one-hot encoded target vector. Your target is [1, 0], thus PyTorch thinks you want to have multiple labels per input which is not supported. Replace your one-hot-encoded targets: [1, 0] –> 0 [0, 1] –> 1
I also had the same issue. And running this => a=torch.cuda.FloatTensor(), gave the assertion error AssertionError: Torch not compiled with CUDA enabled . …which kind of cleared that i was running pytorch without cuda. Steps: Make sure you have un-installed Pytorch by invoking the following command: pip uninstall torch Go to https://pytorch.org/get-started/locally/ and select your … Read more
Unfortunately, you can’t right now. And I don’t think it is a good idea since it will make PyTorch clumsy. A popular workaround could convert it into numeric types using sklearn. Here is a short example: from sklearn import preprocessing import torch labels = [‘cat’, ‘dog’, ‘mouse’, ‘elephant’, ‘pandas’] le = preprocessing.LabelEncoder() targets = le.fit_transform(labels) … Read more
You can list all the available GPUs by doing: >>> import torch >>> available_gpus = [torch.cuda.device(i) for i in range(torch.cuda.device_count())] >>> available_gpus [<torch.cuda.device object at 0x7f2585882b50>]
it may be because type of weight initialization you are using otherwise this should not happen try with same initializer in both the models
By “combine them” I assume you mean to concatenate the two inputs. Assuming you concat along the second dimension: import torch from torch import nn class TwoInputsNet(nn.Module): def __init__(self): super(TwoInputsNet, self).__init__() self.conv = nn.Conv2d( … ) # set up your layer here self.fc1 = nn.Linear( … ) # set up first FC layer self.fc2 = … Read more
Since Python 3.7 you can use a built-in breakpoint function. If this is not available, you can use import pdb pdb.set_trace() instead. If you want to execute the next line you can try n (next) instead of s (step).