Access to raw C++ tensor

I’d like to share tensors between Python and .NET,
e.g. have a batch generator written in .NET and pass those batches to a Python model loaded through Python.NET.

Currently there’s no way to unsafely get raw C++ Torch tensor pointer from TorchSharp, and pass it to Python.

A workaround is to access the raw data (tensor.bytes) and use that to construct a tensor in Python, but it results in unnecessary copying and is inefficient, especially if the source and target tensors are on GPU.

So the ask in this ticket is to get access to raw tensor pointer.

My target is actually to get a full-blown Python PyTorch tensor (the PyObject wrapper of the the C++ tensor), but I assume that’s beyond the scope of TorchSharp.

[NiklasGustafsson]

I have nothing against Python (although I’m personally a big fan of Julia for numeric workloads) and I think Python.NET is really great for the scenarios it’s intended to serve.

That said, I want TorchSharp to remain simple and straightforward. It’s a library that is aware of its role and not trying to be something more than it is – .NET bindings to libtorch. Therefore, it must remain free of any runtime dependencies beyond .NET and whatever supported accelerator HW requires.

[lostmsu]

As long as we don’t introduce a Python VM dependency into TorchSharp…

Yeah, my feed is full of links to LeCun’s recent comment on Python slowing deep learning research down due to GIL.

I also noticed, that they tried and failed to make simple multithreading work efficiently with nn.DataParallel due to it, and now recommend DistributedDataParallel, which is magnitude more complicated to setup, and likely uses some arcane magic internally like passing Python objects across process boundaries with big rapid-firing footguns.