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Diffstat (limited to '')
| -rw-r--r-- | misc/py-torchao/Makefile | 32 | ||||
| -rw-r--r-- | misc/py-torchao/distinfo | 5 | ||||
| -rw-r--r-- | misc/py-torchao/pkg-descr | 4 |
3 files changed, 41 insertions, 0 deletions
diff --git a/misc/py-torchao/Makefile b/misc/py-torchao/Makefile new file mode 100644 index 000000000000..afe8d2314df5 --- /dev/null +++ b/misc/py-torchao/Makefile @@ -0,0 +1,32 @@ +PORTNAME= torchao +DISTVERSIONPREFIX= v +DISTVERSION= 0.13.0 +CATEGORIES= misc # machine-learning +PKGNAMEPREFIX= ${PYTHON_PKGNAMEPREFIX} + +MAINTAINER= yuri@FreeBSD.org +COMMENT= PyTorch: Package for applying ao techniques to GPU models +WWW= https://docs.pytorch.org/ao/stable/index.html \ + https://github.com/pytorch/ao + +LICENSE= BSD3CLAUSE +LICENSE_FILE= ${WRKSRC}/LICENSE + +PY_DEPENDS= ${PYNUMPY} \ + ${PYTHON_PKGNAMEPREFIX}pytorch>0:misc/py-pytorch@${PY_FLAVOR} +BUILD_DEPENDS= ${PY_DEPENDS} +RUN_DEPENDS= ${PY_DEPENDS} + +USES= python +USE_PYTHON= distutils autoplist pytest + +USE_GITHUB= yes +GH_ACCOUNT= pytorch +GH_PROJECT= ao +GH_TUPLE= NVIDIA:cutlass:e51efbf:cutlass/third_party/cutlass + +NO_ARCH= yes + +# tests fail with: caught unexpected SystemExit! + +.include <bsd.port.mk> diff --git a/misc/py-torchao/distinfo b/misc/py-torchao/distinfo new file mode 100644 index 000000000000..8e8a224bd2e6 --- /dev/null +++ b/misc/py-torchao/distinfo @@ -0,0 +1,5 @@ +TIMESTAMP = 1758232597 +SHA256 (pytorch-ao-v0.13.0_GH0.tar.gz) = 3d2aac7c2dcc9bb7aabe5d9cf8bd508bac2b7e0e4582e162932bf4667a079d0c +SIZE (pytorch-ao-v0.13.0_GH0.tar.gz) = 7937501 +SHA256 (NVIDIA-cutlass-e51efbf_GH0.tar.gz) = cbd9e9512cb85c6e6ea56f54cf9d83d879bc607a4b4f5180764291652ca30970 +SIZE (NVIDIA-cutlass-e51efbf_GH0.tar.gz) = 33083009 diff --git a/misc/py-torchao/pkg-descr b/misc/py-torchao/pkg-descr new file mode 100644 index 000000000000..08e1fee9729d --- /dev/null +++ b/misc/py-torchao/pkg-descr @@ -0,0 +1,4 @@ +torchao is analogous to a great API in Keras to view the visualization +of the model which is very helpful while debugging your network. +The aim is to provide information complementary to, what is not provided +by print(your_model) in PyTorch. |