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Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python
The current ht.histogram() function calls the ht.histc() function which uses PyTorch. It should work more like NumPy instead. _Originally posted by @mtar in https://github.com/helmholtz-analytics/heat/issues/547#issuecomment-717151678_
**Feature functionality** `ht.average(x, weights=None, axis=None, returned=False)` (PR #352) has the same constraints as numpy.average(), among them the following: - if axis is not None, the weights tensor must be 1D...
**Feature functionality** It is planned to implement a thin provisiong layer above the existing MPI communication API that allows for nearly seemless integration of the MPI calls with the PyTorch...
**Related** #340 **Feature functionality** https://docs.scipy.org/doc/numpy/reference/generated/numpy.in1d.html https://docs.scipy.org/doc/numpy/reference/generated/numpy.isin.html
**Related** #343 QR, possibly #336, testing -> #390 **Feature functionality** Calculate the singular value decomposition of a matrix
Any chance for supporting `savez`/`savez_compressed` and loading NpzFiles? _Originally posted by @fschlimb in https://github.com/helmholtz-analytics/heat/issues/101#issuecomment-843967591_ References: - https://numpy.org/doc/stable/reference/generated/numpy.savez.html - https://numpy.org/doc/stable/reference/generated/numpy.savez_compressed.html
**Feature functionality** redistribute can be used to clean up the concatenate function, reduce the complexity of the code, and clarify what it does to a 3rd party.
**Feature functionality** GitHub introduced the GitHub Actions. This is a CI/CD pipeline similar to the one GitLab already provides. The benefit is that each build can be split into smaller...
**Related** This is mostly related to the current LA efforts. **Feature functionality** We need some functions that can generate matrices of arbitrary size that have certain unique features that allow...
**Description** Currently the random array returned by `ht.random.rand` is different depending on the split of the tensor. For example: ``` >>> ht.random.seed(1) >>> a = ht.random.randn(4, 4, split=0) >>> ht.random.seed(1)...