[arch-commits] Commit in tensorflow/trunk (PKGBUILD)
Sven-Hendrik Haase
svenstaro at archlinux.org
Sat Apr 7 08:03:43 UTC 2018
Date: Saturday, April 7, 2018 @ 08:03:42
Author: svenstaro
Revision: 314766
upgpkg: tensorflow 1.7.0-2
Modified:
tensorflow/trunk/PKGBUILD
----------+
PKGBUILD | 10 +++++-----
1 file changed, 5 insertions(+), 5 deletions(-)
Modified: PKGBUILD
===================================================================
--- PKGBUILD 2018-04-07 08:02:30 UTC (rev 314765)
+++ PKGBUILD 2018-04-07 08:03:42 UTC (rev 314766)
@@ -6,7 +6,7 @@
pkgname=(tensorflow tensorflow-opt tensorflow-cuda tensorflow-opt-cuda python-tensorflow python-tensorflow-opt python-tensorflow-cuda python-tensorflow-opt-cuda)
pkgver=1.7.0
_pkgver=1.7.0
-pkgrel=1
+pkgrel=2
pkgdesc="Library for computation using data flow graphs for scalable machine learning"
url="https://www.tensorflow.org/"
license=('APACHE')
@@ -147,7 +147,7 @@
}
package_python-tensorflow() {
- depends=(python python-protobuf absl-py)
+ depends=(python-numpy python-protobuf absl-py)
cd ${srcdir}/tensorflow-${_pkgver}
@@ -163,7 +163,7 @@
}
package_python-tensorflow-opt() {
- depends=(python python-protobuf absl-py)
+ depends=(python-numpy python-protobuf absl-py)
conflicts=(python-tensorflow)
provides=(python-tensorflow)
pkgdesc="Library for computation using data flow graphs for scalable machine learning (with CPU optimizations)"
@@ -182,7 +182,7 @@
}
package_python-tensorflow-cuda() {
- depends=(python cuda cudnn python-pycuda python-protobuf absl-py)
+ depends=(python-numpy cuda cudnn python-pycuda python-protobuf absl-py)
conflicts=(python-tensorflow)
provides=(python-tensorflow)
pkgdesc="Library for computation using data flow graphs for scalable machine learning (with CUDA)"
@@ -201,7 +201,7 @@
}
package_python-tensorflow-opt-cuda() {
- depends=(python cuda cudnn python-pycuda python-protobuf absl-py)
+ depends=(python-numpy cuda cudnn python-pycuda python-protobuf absl-py)
conflicts=(python-tensorflow)
provides=(python-tensorflow)
pkgdesc="Library for computation using data flow graphs for scalable machine learning (with CUDA and CPU optimizations)"
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