andreas pushed a commit to branch python-team
in repository guix.
commit 1ee3c0cc630819b6791ad0dd7283cba29420b625
Author: Sharlatan Hellseher <sharlata...@gmail.com>
AuthorDate: Tue Mar 25 12:01:49 2025 +0000
gnu: python-autograd: Update to 1.7.0.
* 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
---
gnu/packages/machine-learning.scm | 17 +++++++----------
1 file changed, 7 insertions(+), 10 deletions(-)
diff --git a/gnu/packages/machine-learning.scm
b/gnu/packages/machine-learning.scm
index 72444383fb..d0439fde45 100644
--- a/gnu/packages/machine-learning.scm
+++ b/gnu/packages/machine-learning.scm
@@ -2681,27 +2681,24 @@ Covariance Matrix Adaptation Evolution Strategy
(CMA-ES) for Python.")
(license license:expat)))
(define-public python-autograd
- (let* ((commit "c6d81ce7eede6db801d4e9a92b27ec5d409d0eab")
- (revision "0")
- (version (git-version "1.5" revision commit)))
(package
(name "python-autograd")
- (home-page "https://github.com/HIPS/autograd";)
+ (version "1.7.0")
(source (origin
(method git-fetch)
(uri (git-reference
- (url home-page)
- (commit commit)))
+ (url "https://github.com/HIPS/autograd";)
+ (commit (string-append "v" version))))
(sha256
(base32
- "04kljgydng42xlg044h6nbzxpban1ivd6jzb8ydkngfq88ppipfk"))
+ "1fpnmm3mzw355iq7w751j4mjfcr0yh324cxidba1l22652gg8r8m"))
(file-name (git-file-name name version))))
- (version version)
(build-system pyproject-build-system)
(native-inputs
- (list python-nose python-pytest python-setuptools python-wheel))
+ (list python-hatchling python-pytest))
(propagated-inputs
(list python-future python-numpy))
+ (home-page "https://github.com/HIPS/autograd";)
(synopsis "Efficiently computes derivatives of NumPy code")
(description "Autograd can automatically differentiate native Python and
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
forward-mode differentiation, and the two can be composed arbitrarily. The
main intended application of Autograd is gradient-based optimization.")
- (license license:expat))))
+ (license license:expat)))
(define-public lightgbm
(package
