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authorAndrew W Elble <aweits@rit.edu>2020-10-21 14:37:05 -0400
committerGitHub <noreply@github.com>2020-10-21 13:37:05 -0500
commitb616dbc56b8bcba8a7dd1c01aeb67ed60ba0d041 (patch)
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New package: py-autograd (#19425)
* py-autograd * add missing dependencies
Diffstat (limited to 'var')
-rw-r--r--var/spack/repos/builtin/packages/py-autograd/package.py27
1 files changed, 27 insertions, 0 deletions
diff --git a/var/spack/repos/builtin/packages/py-autograd/package.py b/var/spack/repos/builtin/packages/py-autograd/package.py
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+# Copyright 2013-2020 Lawrence Livermore National Security, LLC and other
+# Spack Project Developers. See the top-level COPYRIGHT file for details.
+#
+# SPDX-License-Identifier: (Apache-2.0 OR MIT)
+
+
+class PyAutograd(PythonPackage):
+ """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. For more information, check out the tutorial and the
+ examples directory."""
+
+ homepage = "https://github.com/HIPS/autograd"
+ url = "https://pypi.io/packages/source/a/autograd/autograd-1.3.tar.gz"
+
+ version('1.3', sha256='a15d147577e10de037de3740ca93bfa3b5a7cdfbc34cfb9105429c3580a33ec4')
+
+ depends_on('py-setuptools', type='build')
+ depends_on('py-future@0.15.2:', type=('build', 'run'))
+ depends_on('py-numpy@1.12:', type=('build', 'run'))