Ubuntu
onnx package

onnx 1.13.1-1 source package in Ubuntu

Changelog

onnx (1.13.1-1) unstable; urgency=medium

  [ Debian Janitor ]
  * Apply multi-arch hints. + libonnxifi: Drop Multi-Arch: same.

  [ Mo Zhou ]
  * New upstream version 1.13.1
  * watch: Track the github tags page instead.
  * Rebase existing patches.
  * ONNXIFI is removed by upstream.
  * No longer build static library.
  * Add patch to disable RPATH for shlibs.
  * Update lintian overrides.

 -- Mo Zhou <email address hidden>  Sun, 20 Aug 2023 00:23:57 -0400

Upload details

Uploaded by:
Debian Deep Learning Team
Uploaded to:
Sid
Original maintainer:
Debian Deep Learning Team
Architectures:
any all
Section:
misc
Urgency:
Medium Urgency

See full publishing history Publishing

Series Pocket Published Component Section

Downloads

File Size SHA-256 Checksum
onnx_1.13.1-1.dsc 2.3 KiB a7b42365a65163012b82201e7771ff8e565e87633e4be2ddabf5813c8fe9a425
onnx_1.13.1.orig.tar.gz 10.3 MiB 090d3e10ec662a98a2a72f1bf053f793efc645824f0d4b779e0ce47468a0890e
onnx_1.13.1-1.debian.tar.xz 11.3 KiB 60bf7055c377bf1bff59860de25f34a0806382bc52a710f5096c1f2ececf3bad

No changes file available.

Binary packages built by this source

libonnx-dev: 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.

libonnx-testdata: 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.

libonnx1: 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.

libonnx1-dbgsym: debug symbols for libonnx1
python3-onnx: 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.

python3-onnx-dbgsym: debug symbols for python3-onnx