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new d3d157bc61 gnu: Add python-torchdiffeq.
d3d157bc61 is described below
commit d3d157bc61c4a6a3fac11e33d26f6f2a72a24151
Author: Navid Afkhami <[email protected]>
AuthorDate: Wed May 21 16:01:01 2025 +0200
gnu: Add python-torchdiffeq.
* gnu/packages/machine-learning.scm (python-torchdiffeq): New variable.
Change-Id: Ic2ab73250b60f1733d2721ebd6d3abae719c5a1f
---
gnu/packages/machine-learning.scm | 31 +++++++++++++++++++++++++++++++
1 file changed, 31 insertions(+)
diff --git a/gnu/packages/machine-learning.scm
b/gnu/packages/machine-learning.scm
index 837aa02efa..bd090d63b1 100644
--- a/gnu/packages/machine-learning.scm
+++ b/gnu/packages/machine-learning.scm
@@ -2554,6 +2554,37 @@ forward-mode differentiation, and the two can be
composed arbitrarily. The
main intended application of Autograd is gradient-based optimization.")
(license license:expat)))
+(define-public python-torchdiffeq
+ ;; There are neither releases nor tags.
+ (let ((commit "a88aac53cae738addee44251288ce5be9a018af3")
+ (revision "0"))
+ (package
+ (name "python-torchdiffeq")
+ (version (git-version "0.2.5" revision commit))
+ (source
+ (origin
+ (method git-fetch)
+ (uri (git-reference
+ (url "https://github.com/rtqichen/torchdiffeq";)
+ (commit commit)))
+ (file-name (git-file-name name version))
+ (sha256
+ (base32 "0c2zqbdxqvd5abfpk0im6rcy1ij39xvrmixc6l9znb6bhcxk2jra"))))
+ (build-system pyproject-build-system)
+ (arguments
+ (list
+ #:test-flags
+ '(list "-k" "not test_seminorm" "tests/run_all.py")))
+ (propagated-inputs (list python-numpy python-scipy python-pytorch))
+ (native-inputs (list python-pytest python-setuptools))
+ (home-page "https://github.com/rtqichen/torchdiffeq";)
+ (synopsis "ODE solvers and adjoint sensitivity analysis in PyTorch")
+ (description
+ "This tool provides ordinary differential equation solvers implemented
+in PyTorch. Backpropagation through ODE solutions is supported using the
+adjoint method for constant memory cost.")
+ (license license:expat))))
+
(define-public lightgbm
(package
(name "lightgbm")
