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Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python
This issue is to suggest refactoring of the code for QR decomposition. Currently, a quite elaborate algorithm is used for QR. The following suggestions might turn out to be actually...
### What happened? Manually triggered GH Action [Get latest PyTorch version main branch](https://github.com/helmholtz-analytics/heat/actions/workflows/pytorch-latest-main.yml) fails. ### Code snippet triggering the error _No response_ ### Error message or erroneous outcome ```shell /usr/bin/git...
Currently, the code wrapping `mpi4py` for `torch.Tensor`'s is contained in `heat/core/communication.py` and not fully separated from constructions related to the `DNDarray`-class (e.g., computation of chunk sizes is also introduced there)....
**Related** #1097 #1218 **Feature functionality** The current (#1218) implementation of `ht.fft.fft()` is only distributed if the input DNDarray is n-dimensional (n>1). Research suitable algorithm and implement distributed 1-D FFT of...
**Related** This issue is complements Heat's Dataloader (implemented in datatools.py) and can be used alongside Heat's Dataset **Feature functionality** Enables the conversion of images stored in the Pytorch ImageFolder format...
### What happened? The Python version vs. PyTorch version cross-checks we run after a PR is approved are failing. Something has changed in the jobs set up that we couldn't...
It is well-known that convolution is (up to constants and conventions) the same as applying Fourier transform, a pointwise multiplication, and inverse Fourier transform. Since we will soon have a...
See #1104 for the general problem/the idea of batched linear algebra (focussing on the case of matmul for batches of matrices). This issue is intended to do the same for...
In Pytorch, most linear algebra routines can be applied to batches of matrices. In Heat it would be nice to have a similar rule how to "define" linear algebra (`matmul`,`qr`,`cholesky`,`svd`...
**Feature functionality** Will result in significant performance enhancement. Probably requires expanding the tests.