2024-02-29 09:03:48 +00:00
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#
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# spec file for package python-cotengra
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#
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# Copyright (c) 2024 SUSE LLC
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#
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# All modifications and additions to the file contributed by third parties
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# remain the property of their copyright owners, unless otherwise agreed
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# upon. The license for this file, and modifications and additions to the
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# file, is the same license as for the pristine package itself (unless the
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# license for the pristine package is not an Open Source License, in which
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# case the license is the MIT License). An "Open Source License" is a
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# license that conforms to the Open Source Definition (Version 1.9)
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# published by the Open Source Initiative.
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# Please submit bugfixes or comments via https://bugs.opensuse.org/
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#
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Name: python-cotengra
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Accepting request 1177286 from home:glaubitz:branches:devel:languages:python:numeric
- Update to version 0.6.2
* Fix final, output contractions being mistakenly marked as not tensordot-able.
* When `implementation="autoray"` don't require a backend to have both
`einsum` and `tensordot`, instead fallback to `cotengra`'s own.
- from version 0.6.1
* The number of workers initialized (for non-distributed pools) is now set to,
in order of preference, 1. the environment variable `COTENGRA_NUM_WORKERS`,
2. the environment variable `OMP_NUM_THREADS`, or 3. `os.cpu_count()`.
* Add [RandomGreedyOptimizer](cotengra.pathfinders.path_basic.RandomGreedyOptimizer)
which is a lightweight and performant randomized greedy optimizer, eschewing both
hyper parameter tuning and full contraction tree construction, making it suitable
for very large contractions (10,000s of tensors+).
* Add [optimize_random_greedy_track_flops](cotengra.pathfinders.path_basic.optimize_\
random_greedy_track_flops) which runs N trials of (random) greedy path optimization,
whilst computing the FLOP count simultaneously. This or its accelerated rust counterpart
in `cotengrust` is the driver for the above optimizer.
* Add `parallel="threads"` backend, and make it the default for `RandomGreedyOptimizer`
when `cotengrust` is present, since its version of `optimize_random_greedy_track_flops`
releases the GIL.
* Significantly improve both the speed and memory usage of [`SliceFinder`](cotengra.slicer.SliceFinder)
* Alias `tree.total_cost()` to `tree.combo_cost()`
- from version 0.6.0
* All input node legs and pre-processing steps are now calculated lazily,
allowing slicing of indices including those 'simplified' away {issue}`31`.
* Make [`tree.peak_size`](cotengra.ContractionTree.peak_size) more accurate,
by taking max assuming left, right and parent intermediate tensors are all
present at the same time.
* Add simulated annealing tree refinement (in `path_simulated_annealing.py`),
based on "Multi-Tensor Contraction for XEB Verification of
Quantum Circuits" by Gleb Kalachev, Pavel Panteleev, Man-Hong Yung
OBS-URL: https://build.opensuse.org/request/show/1177286
OBS-URL: https://build.opensuse.org/package/show/devel:languages:python:numeric/python-cotengra?expand=0&rev=5
2024-05-28 15:14:08 +00:00
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Version: 0.6.2
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2024-02-29 09:03:48 +00:00
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Release: 0
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Summary: Hyper optimized contraction trees for large tensor networks and einsums
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License: Apache-2.0
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URL: https://github.com/jcmgray/cotengra
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Source: https://files.pythonhosted.org/packages/source/c/cotengra/cotengra-%{version}.tar.gz
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BuildRequires: %{python_module pip}
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BuildRequires: %{python_module setuptools >= 45}
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BuildRequires: %{python_module setuptools_scm >= 6.2}
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BuildRequires: %{python_module wheel}
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BuildRequires: fdupes
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BuildRequires: python-rpm-macros
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Requires: python-autoray
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Recommends: python-cotengrust
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Recommends: python-cytoolz
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Recommends: python-kahypar
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Recommends: python-networkx
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Recommends: python-numpy
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Recommends: python-opt-einsum
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Recommends: python-optuna
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Recommends: python-ray
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# SECTION test requirements
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BuildRequires: %{python_module altair}
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BuildRequires: %{python_module autoray}
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BuildRequires: %{python_module dask}
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BuildRequires: %{python_module matplotlib}
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BuildRequires: %{python_module networkx}
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BuildRequires: %{python_module numpy}
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BuildRequires: %{python_module opt-einsum}
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BuildRequires: %{python_module pytest-xdist}
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BuildRequires: %{python_module pytest}
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BuildRequires: %{python_module seaborn}
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BuildRequires: %{python_module tqdm}
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# /SECTION
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BuildArch: noarch
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%python_subpackages
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%description
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A python library for contracting tensor networks or einsum expressions involving large numbers of tensors.
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Some of the key feautures of cotengra include:
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* drop-in einsum replacement
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* an explicit contraction tree object that can be flexibly built, modified and visualized
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* a 'hyper optimizer' that samples trees while tuning the generating meta-paremeters
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* dynamic slicing for massive memory savings and parallelism
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* support for hyper edge tensor networks and thus arbitrary einsum equations
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* paths that can be supplied to numpy.einsum, opt_einsum, quimb among others
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* performing contractions with tensors from many libraries via cotengra, even if they don't provide einsum
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or tensordot but do have (batch) matrix multiplication
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%prep
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%autosetup -p1 -n cotengra-%{version}
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sed -i '/addopts/d' pyproject.toml
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rm cotengra/.gitattributes
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# Note: ignore the rust files for now gh#jcmgray/cotengra#30
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%build
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%pyproject_wheel
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%install
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%pyproject_install
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%python_expand %fdupes %{buildroot}%{$python_sitelib}
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%check
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%pytest -n auto
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%files %{python_files}
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%doc README.md
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%license LICENSE.md
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%{python_sitelib}/cotengra
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%{python_sitelib}/cotengra-%{version}.dist-info
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%changelog
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