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gnu: python-autograd: Update to 1.7.0.

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* gnu/packages/machine-learning.scm (python-autograd): Update to 1.7.0.
[source]: Use the latest version tag.
[native-inputs]: Remove python-nose, python-setuptools, and
python-wheel; add python-hatchling.

Change-Id: I42cd6b9ce621c1509f459fb947b09d05635fb79b
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Sharlatan Hellseher 2025-03-25 12:01:49 +00:00 • committed by Andreas Enge
parent ab3a8e1096
commit 7b6f6e9604
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1 changed files with 7 additions and 10 deletions
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gnu/packages/machine-learning.scm
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@ -2681,27 +2681,24 @@ Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for Python.")
(license license:expat))) (license license:expat)))
(define-public python-autograd (define-public python-autograd
(let* ((commit "c6d81ce7eede6db801d4e9a92b27ec5d409d0eab")
(revision "0")
(version (git-version "1.5" revision commit)))
(package (package
(name "python-autograd") (name "python-autograd")
(home-page "https://github.com/HIPS/autograd") (version "1.7.0")
(source (origin (source (origin
(method git-fetch) (method git-fetch)
(uri (git-reference (uri (git-reference
(url home-page) (url "https://github.com/HIPS/autograd")
(commit commit))) (commit (string-append "v" version))))
(sha256 (sha256
(base32 (base32
"04kljgydng42xlg044h6nbzxpban1ivd6jzb8ydkngfq88ppipfk")) "1fpnmm3mzw355iq7w751j4mjfcr0yh324cxidba1l22652gg8r8m"))
(file-name (git-file-name name version)))) (file-name (git-file-name name version))))
(version version)
(build-system pyproject-build-system) (build-system pyproject-build-system)
(native-inputs (native-inputs
(list python-nose python-pytest python-setuptools python-wheel)) (list python-hatchling python-pytest))
(propagated-inputs (propagated-inputs
(list python-future python-numpy)) (list python-future python-numpy))
(home-page "https://github.com/HIPS/autograd")
(synopsis "Efficiently computes derivatives of NumPy code") (synopsis "Efficiently computes derivatives of NumPy code")
(description "Autograd can automatically differentiate native Python and (description "Autograd can automatically differentiate native Python and
NumPy code. It can handle a large subset of Python's features, including loops, NumPy code. It can handle a large subset of Python's features, including loops,
@ -2711,7 +2708,7 @@ of derivatives. It supports reverse-mode differentiation
scalar-valued functions with respect to array-valued arguments, as well as scalar-valued functions with respect to array-valued arguments, as well as
forward-mode differentiation, and the two can be composed arbitrarily. The forward-mode differentiation, and the two can be composed arbitrarily. The
main intended application of Autograd is gradient-based optimization.") main intended application of Autograd is gradient-based optimization.")
(license license:expat)))) (license license:expat)))
(define-public lightgbm (define-public lightgbm
(package (package