It is necessary to have both the model, and the data on the same device, either CPU or GPU, for the model to process data. Data on CPU and model on GPU, or vice-versa, will result in a Runtime error. You can set a variable device to cuda if it’s available, else it will be … Read more
Here a short answer, formed as another question: Torch, SMORCH ??? PyTorch developed from Torch7. A precursor to the original Torch was a library called SVM-Torch, which was developed around 2001. The SVM stands for Support Vector Machines. SVM-Torch is a decomposition algorithm similar to SVM-Light, but adapted to regression problems, according to this paper. … Read more
In addition to @adeelh’s comment, there is another difference: torch.flatten() results in a .reshape(), and the differences between .reshape() and .view() are: […] torch.reshape may return a copy or a view of the original tensor. You can not count on that to return a view or a copy. Another difference is that reshape() can operate … Read more
nn.ModuleList does not have a forward method, but nn.Sequential does have one. So you can wrap several modules in nn.Sequential and run it on the input. nn.ModuleList is just a Python list (though it’s useful since the parameters can be discovered and trained via an optimizer). While nn.Sequential is a module that sequentially runs the … Read more
You can set the precision options: torch.set_printoptions(precision=10) There are more formatting options on the documentation page, it is very similar to numpy’s.
This is the solution that worked for me. it may work for other Windows users. Just remove/comment the num workers to disable parallel loads
Had the same issue and in my case solution was very easy, however it wasn’t easy to find it. I had to remove and insert nvidia_uvm module. So: > sudo rmmod nvidia_uvm > sudo modprobe nvidia_uvm That’s all. Just before these command collect_env.py reported “Is CUDA available: False”. After: “Is CUDA available: True”
The max_split_size_mb configuration value can be set as an environment variable. The exact syntax is documented, but in short: The behavior of caching allocator can be controlled via environment variable PYTORCH_CUDA_ALLOC_CONF. The format is PYTORCH_CUDA_ALLOC_CONF=<option>:<value>,<option2>:<value2>… Available options: … max_split_size_mb prevents the allocator from splitting blocks larger than this size (in MB). This can help prevent … Read more
As your own answer mentions, log_prob returns the logarithm of the density or probability. Here I will address the remaining points in your question: How is that different from log? Distributions do not have a method log. If they did, the closest possible interpretation would indeed be something like log_prob but it would not be … Read more
From TorchText 0.9.0 Release Notes torchtext.data.Field -> torchtext.legacy.data.Field This means, all features are still available, but within torchtext.legacy instead of torchtext. torchtext.data.Field has been moved to torchtext.legacy.data.Field And the imports would change this way: from torchtext.legacy import data