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authorFelix Gruber <felgru@posteo.net>2023-11-04 15:07:10 +0000
committerLudovic Courtès <ludo@gnu.org>2023-12-06 23:50:04 +0100
commit0399d5b6101522901b69b985010736505fdf9354 (patch)
tree9e2abe8a188c34fc27b43bb74d4cf49cc6d4d827 /gnu
parent8da64e88108bf8620a3da20f028059fda438754d (diff)
gnu: python-autograd: Update to 1.5.
* gnu/packages/machine-learning.scm (python-autograd): Update to 1.5. [build-system]: Use pyproject-build-system. [arguments]: Remove custom 'check phase. Change-Id: Ic76510b94d268d5dba6e9b0e057fcfca125e424f Signed-off-by: Ludovic Courtès <ludo@gnu.org>
Diffstat (limited to 'gnu')
-rw-r--r--gnu/packages/machine-learning.scm13
1 files changed, 4 insertions, 9 deletions
diff --git a/gnu/packages/machine-learning.scm b/gnu/packages/machine-learning.scm
index 38e586232b..c100e0be6e 100644
--- a/gnu/packages/machine-learning.scm
+++ b/gnu/packages/machine-learning.scm
@@ -1968,9 +1968,9 @@ Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for Python.")
(license license:expat)))
(define-public python-autograd
- (let* ((commit "442205dfefe407beffb33550846434baa90c4de7")
+ (let* ((commit "c6d81ce7eede6db801d4e9a92b27ec5d409d0eab")
(revision "0")
- (version (git-version "0.0.0" revision commit)))
+ (version (git-version "1.5" revision commit)))
(package
(name "python-autograd")
(home-page "https://github.com/HIPS/autograd")
@@ -1981,19 +1981,14 @@ Covariance Matrix Adaptation Evolution Strategy (CMA-ES) for Python.")
(commit commit)))
(sha256
(base32
- "189sv2xb0mwnjawa9z7mrgdglc1miaq93pnck26r28fi1jdwg0z4"))
+ "04kljgydng42xlg044h6nbzxpban1ivd6jzb8ydkngfq88ppipfk"))
(file-name (git-file-name name version))))
(version version)
- (build-system python-build-system)
+ (build-system pyproject-build-system)
(native-inputs
(list python-nose python-pytest))
(propagated-inputs
(list python-future python-numpy))
- (arguments
- `(#:phases (modify-phases %standard-phases
- (replace 'check
- (lambda _
- (invoke "py.test" "-v"))))))
(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,