From b616dbc56b8bcba8a7dd1c01aeb67ed60ba0d041 Mon Sep 17 00:00:00 2001 From: Andrew W Elble Date: Wed, 21 Oct 2020 14:37:05 -0400 Subject: New package: py-autograd (#19425) * py-autograd * add missing dependencies --- .../repos/builtin/packages/py-autograd/package.py | 27 ++++++++++++++++++++++ 1 file changed, 27 insertions(+) create mode 100644 var/spack/repos/builtin/packages/py-autograd/package.py (limited to 'var') diff --git a/var/spack/repos/builtin/packages/py-autograd/package.py b/var/spack/repos/builtin/packages/py-autograd/package.py new file mode 100644 index 0000000000..86aba0132d --- /dev/null +++ b/var/spack/repos/builtin/packages/py-autograd/package.py @@ -0,0 +1,27 @@ +# 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')) -- cgit v1.2.3-60-g2f50