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Home Page: https://heat.readthedocs.io/
License: MIT License
Distributed tensors and Machine Learning framework with GPU and MPI acceleration in Python
Home Page: https://heat.readthedocs.io/
License: MIT License
We need to support function astype
Required at
Everytime I edit edit tensor.py
and run pytest, pytest prints a warning that the docstring of ht.floor contains an unknown escape sequence \lfloor
. Mysteriously, this warning does not show up in the second run of pytest, which might explain why this was not caught in CI yet.
inside Tensor.binop
:
Case requried: Both arrays distributed along different axes.
Possible design:
import heat as ht
a = ht.zeros((10,2,), device='gpu<:id>')
import heat as ht
ht.device('gpu<:id>')
#alternative
with ht.gpu(<':id'>):
ht.zeros()
This should be done from successful Travis CI build. Possibly only tagged versions. See https://docs.travis-ci.com/user/deployment/pypi/ for details.
Need to set MACOSX_DEPLOYMENT_TARGET.
Similar to numpy, it would be nice to have a array factory function to create tensors from local data.
Major implementation challenge is how to deal with imbalanced data distributions/initial passed arrays. Do we need to balance the data?
The unit test that checks whether ht.randn
throws a ValueError
depends on the fact that pytorch
shall detect malformed input. Unfortunately, this is not the case for my pytorch installation (trying to print the resulting tensor, however, results in an infinite loop, such that we can be sure that pytorch does not magically know how to handle negative dimensions ... )
For completeness the pytest output:
with self.assertRaises(ValueError):
> ht.randn(-1, 3, dtype=ht.float64)
E AssertionError: ValueError not raised
heat/core/tests/test_tensor.py:278: AssertionError
There seems to be already some all_ints
variable available in ht.randn
, which could be augmented to also check for positive arguments?
We want to automatically test our code base for
As far as I know, travis-ci supports MPI (https://travis-ci.org/).
The function is a general purpose function and does not really belong into the clustering algorithm.
PyPI requires both a description and long_description to be set, with the former being used for listing a project among others and the latter for the detailed project page.
Type: Classification, Ground Truth
Size: Should fit on GPU, max 6 GB
All factory function like asarray, zeros, ones etc must support a dtype argument as in
Apparently, our coverage report is not generated again on every build. It seems like the 'coverage' executable is not among the cached contents for build, but the Python library is.
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