diff options
Diffstat (limited to 'misc/py-opt-einsum')
| -rw-r--r-- | misc/py-opt-einsum/Makefile | 26 | ||||
| -rw-r--r-- | misc/py-opt-einsum/distinfo | 3 | ||||
| -rw-r--r-- | misc/py-opt-einsum/pkg-descr | 9 |
3 files changed, 0 insertions, 38 deletions
diff --git a/misc/py-opt-einsum/Makefile b/misc/py-opt-einsum/Makefile deleted file mode 100644 index afc1a532cd9d..000000000000 --- a/misc/py-opt-einsum/Makefile +++ /dev/null @@ -1,26 +0,0 @@ -PORTNAME= opt-einsum -DISTVERSION= 3.4.0 -CATEGORIES= misc python # machine-learning -MASTER_SITES= PYPI -PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} -DISTNAME= ${PORTNAME:S/-/_/}-${PORTVERSION} - -MAINTAINER= yuri@FreeBSD.org -COMMENT= Optimized Einsum: A tensor contraction order optimizer -WWW= https://github.com/dgasmith/opt_einsum - -LICENSE= MIT -LICENSE_FILE= ${WRKSRC}/LICENSE - -BUILD_DEPENDS= ${PYTHON_PKGNAMEPREFIX}hatch-fancy-pypi-readme>=0:devel/py-hatch-fancy-pypi-readme@${PY_FLAVOR} \ - ${PYTHON_PKGNAMEPREFIX}hatch-vcs>0:devel/py-hatch-vcs@${PY_FLAVOR} \ - ${PYTHON_PKGNAMEPREFIX}hatchling>0:devel/py-hatchling@${PY_FLAVOR} - -USES= python -USE_PYTHON= pep517 autoplist pytest - -NO_ARCH= yes - -# tests as of 3.4.0: 7736 passed, 155 skipped in 76.00s (0:01:16) - -.include <bsd.port.mk> diff --git a/misc/py-opt-einsum/distinfo b/misc/py-opt-einsum/distinfo deleted file mode 100644 index 856c1d93e171..000000000000 --- a/misc/py-opt-einsum/distinfo +++ /dev/null @@ -1,3 +0,0 @@ -TIMESTAMP = 1755493435 -SHA256 (opt_einsum-3.4.0.tar.gz) = 96ca72f1b886d148241348783498194c577fa30a8faac108586b14f1ba4473ac -SIZE (opt_einsum-3.4.0.tar.gz) = 63004 diff --git a/misc/py-opt-einsum/pkg-descr b/misc/py-opt-einsum/pkg-descr deleted file mode 100644 index 7e1e65cc14a1..000000000000 --- a/misc/py-opt-einsum/pkg-descr +++ /dev/null @@ -1,9 +0,0 @@ -Optimized einsum can significantly reduce the overall execution time of -einsum-like expressions (e.g., np.einsum, dask.array.einsum, pytorch.einsum, -tensorflow.einsum, ) by optimizing the expression's contraction order and -dispatching many operations to canonical BLAS, cuBLAS, or other specialized -routines. - -Optimized einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch, -Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as -potentially any library which conforms to a standard API. |