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Source: onnx
Section: science
Homepage: https://onnx.ai
Standards-Version: 4.7.3
Vcs-Git: https://salsa.debian.org/deeplearning-team/onnx.git
Vcs-Browser: https://salsa.debian.org/deeplearning-team/onnx
Maintainer: Debian Deep Learning Team <debian-ai@lists.debian.org>
Uploaders: Mo Zhou <lumin@debian.org>
Build-Depends: debhelper-compat (= 12),
dh-exec,
dh-python,
dpkg-dev (>= 1.22.5),
cmake,
libprotobuf-dev,
protobuf-compiler,
nanobind-dev,
python3-all,
python3-all-dev,
python3-numpy <!nocheck>,
python3-protobuf <!nocheck>,
python3-nanobind,
python3-pytest <!nocheck>,
python3-pytest-runner,
python3-tabulate <!nocheck>
Package: python3-onnx
Architecture: any
Depends: ${misc:Depends}, ${python3:Depends}, ${shlibs:Depends}
Provides: ${python3:Provides}
Description: Open Neural Network Exchange (ONNX) (Python)
Open Neural Network Exchange (ONNX) is the first step toward an open ecosystem
that empowers AI developers to choose the right tools as their project evolves.
ONNX provides an open source format for AI models. It defines an extensible
computation graph model, as well as definitions of built-in operators and
standard data types. Initially onnx focuses on the capabilities needed for
inferencing (evaluation).
.
Caffe2, PyTorch, Microsoft Cognitive Toolkit, Apache MXNet and other tools are
developing ONNX support. Enabling interoperability between different frameworks
and streamlining the path from research to production will increase the speed
of innovation in the AI community.
.
This package contains the Python interface.
Package: libonnx1l
Section: libs
Replaces: libonnx1,
libonnx1t64
Breaks: libonnx1 (<< ${source:Version}),
libonnx1t64 (<< ${source:Version})
Architecture: any
Multi-Arch: same
Depends: ${misc:Depends}, ${shlibs:Depends}
Description: Open Neural Network Exchange (ONNX) (libs)
Open Neural Network Exchange (ONNX) is the first step toward an open ecosystem
that empowers AI developers to choose the right tools as their project evolves.
ONNX provides an open source format for AI models. It defines an extensible
computation graph model, as well as definitions of built-in operators and
standard data types. Initially onnx focuses on the capabilities needed for
inferencing (evaluation).
.
Caffe2, PyTorch, Microsoft Cognitive Toolkit, Apache MXNet and other tools are
developing ONNX support. Enabling interoperability between different frameworks
and streamlining the path from research to production will increase the speed
of innovation in the AI community.
.
This package contains the shared objects.
Package: libonnx-dev
Section: libdevel
Architecture: any
Multi-Arch: same
Depends: libonnx1l (= ${binary:Version}), ${misc:Depends}
Description: Open Neural Network Exchange (ONNX) (dev)
Open Neural Network Exchange (ONNX) is the first step toward an open ecosystem
that empowers AI developers to choose the right tools as their project evolves.
ONNX provides an open source format for AI models. It defines an extensible
computation graph model, as well as definitions of built-in operators and
standard data types. Initially onnx focuses on the capabilities needed for
inferencing (evaluation).
.
Caffe2, PyTorch, Microsoft Cognitive Toolkit, Apache MXNet and other tools are
developing ONNX support. Enabling interoperability between different frameworks
and streamlining the path from research to production will increase the speed
of innovation in the AI community.
.
This package contains the development files.
Package: libonnx-testdata
Architecture: all
Multi-Arch: foreign
Depends: ${misc:Depends}
Description: Open Neural Network Exchange (ONNX) (test data)
Open Neural Network Exchange (ONNX) is the first step toward an open ecosystem
that empowers AI developers to choose the right tools as their project evolves.
ONNX provides an open source format for AI models. It defines an extensible
computation graph model, as well as definitions of built-in operators and
standard data types. Initially onnx focuses on the capabilities needed for
inferencing (evaluation).
.
Caffe2, PyTorch, Microsoft Cognitive Toolkit, Apache MXNet and other tools are
developing ONNX support. Enabling interoperability between different frameworks
and streamlining the path from research to production will increase the speed
of innovation in the AI community.
.
This package contains the test data.
|
