svn commit: r494091 - in head/math: . py-autograd
Ruslan Makhmatkhanov
rm at FreeBSD.org
Wed Feb 27 22:11:17 UTC 2019
Author: rm
Date: Wed Feb 27 22:11:15 2019
New Revision: 494091
URL: https://svnweb.freebsd.org/changeset/ports/494091
Log:
Autograd can automatically differentiate native Python and Numpy code. It can
handle a large subset of Python's features, including loops, ifs, recursion and
closures, and it can even take derivatives of derivatives of derivatives. It
supports reverse-mode differentiation (a.k.a. backpropagation), which means it
can efficiently take gradients of 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.
WWW: https://github.com/HIPS/autograd
Added:
head/math/py-autograd/
head/math/py-autograd/Makefile (contents, props changed)
head/math/py-autograd/distinfo (contents, props changed)
head/math/py-autograd/pkg-descr (contents, props changed)
Modified:
head/math/Makefile
Modified: head/math/Makefile
==============================================================================
--- head/math/Makefile Wed Feb 27 22:09:42 2019 (r494090)
+++ head/math/Makefile Wed Feb 27 22:11:15 2019 (r494091)
@@ -690,6 +690,7 @@
SUBDIR += py-algopy
SUBDIR += py-altgraph
SUBDIR += py-apgl
+ SUBDIR += py-autograd
SUBDIR += py-basemap
SUBDIR += py-basemap-data
SUBDIR += py-bayesian-optimization
Added: head/math/py-autograd/Makefile
==============================================================================
--- /dev/null 00:00:00 1970 (empty, because file is newly added)
+++ head/math/py-autograd/Makefile Wed Feb 27 22:11:15 2019 (r494091)
@@ -0,0 +1,22 @@
+# $FreeBSD$
+
+PORTNAME= autograd
+DISTVERSION= 1.2
+CATEGORIES= math python
+MASTER_SITES= CHEESESHOP
+PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX}
+
+MAINTAINER= rm at FreeBSD.org
+COMMENT= Efficiently computes derivatives of numpy code
+
+LICENSE= MIT
+
+RUN_DEPENDS= ${PYNUMPY} \
+ ${PYTHON_PKGNAMEPREFIX}future>=0.15.2:devel/py-future@${PY_FLAVOR}
+
+USES= python
+USE_PYTHON= autoplist distutils
+
+NO_ARCH= yes
+
+.include <bsd.port.mk>
Added: head/math/py-autograd/distinfo
==============================================================================
--- /dev/null 00:00:00 1970 (empty, because file is newly added)
+++ head/math/py-autograd/distinfo Wed Feb 27 22:11:15 2019 (r494091)
@@ -0,0 +1,3 @@
+TIMESTAMP = 1551302910
+SHA256 (autograd-1.2.tar.gz) = a08bfa6d539b7a56e7c9f4d0881044afbef5e75f324a394c2494de963ea4a47d
+SIZE (autograd-1.2.tar.gz) = 32540
Added: head/math/py-autograd/pkg-descr
==============================================================================
--- /dev/null 00:00:00 1970 (empty, because file is newly added)
+++ head/math/py-autograd/pkg-descr Wed Feb 27 22:11:15 2019 (r494091)
@@ -0,0 +1,10 @@
+Autograd can automatically differentiate native Python and Numpy code. It can
+handle a large subset of Python's features, including loops, ifs, recursion and
+closures, and it can even take derivatives of derivatives of derivatives. It
+supports reverse-mode differentiation (a.k.a. backpropagation), which means it
+can efficiently take gradients of 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.
+
+WWW: https://github.com/HIPS/autograd
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