Would JS be faster than Python when it comes to Pytorch? For example, I seriously doubt that would be the case for Numpy, since it's a wrapper for C code, with the ability to use Fortran libraries for optimization.
There is often a lot of work that has to be done before and after PyTorch gets involved. For example the code I'm working on right now involves reading and parsing a bunch of files, filtering and extracting a bunch of data based on various criteria, formatting that data, passing it to a PyTorch model and then taking the results from PyTorch, validating it, reformatting it and then writing it to disk. The PyTorch part is probably as fast as it can get, but most of the overall runtime is spent doing all that other stuff and if you can speed that up then that is a clear win in many cases.
The benefit of having it in JS is not speed but portability and access to the JS ecosystem of tools. Having the code run in the browser without needing a complex setup is a huge benefit for sharing demos. Node.js provides a way to use native code as well and it's quite commonly used https://github.com/nodejs/node-gyp so there's no reason you couldn't use those same or similar libraries in a JS implementation.
C running on the CPU isn't fast enough for ML. You need to run on GPUs or TPUs if you're serious.
Yes, most of the tensor operations in PyTorch do their math in native code. However, Python still does orchestration and other tasks like data loading and because it is so slow it still ends up causing a ton of overhead in many cases despite offloading most of the work. It's very common for the GPU to sit idle between kernels while Python spins. So JavaScript being faster could still be a big advantage.
Very much the opposite, since this is pure JS. PyTorch uses tuned native-code and GPU components for the heavy lifting and can in some cases compile Python code using PyTorch JIT / torch.compiler / torch.fx.
