Result for 017EC79D5C208495B80277B2AC4605AA8525D707

Search result
Graph view
Parent (29)

Query result

Key Value
FileNamesnap-hashlookup-import/app/machine-learning/python-packages/onnx/backend/test/data/node/test_reduce_sum_square_negative_axes_keepdims_random_expanded/test_data_set_0/output_0.pb
FileSize43
MD5FC666F6BDA36D47375D022C6C615F46D
SHA-1017EC79D5C208495B80277B2AC4605AA8525D707
SHA-2564FEB9DD68CA76CFAD4EA4F286D40EF62AC50A02B2AD0DF92FFC4892B1B61B819
SHA-512953283E3F750F17C939295FC042700063639B9D5845ED26E37E5F3C9DB4A6B68479034AA50F1BA65940C48811EBBA6B121EBF6549D68BE414276FA63B5DFB427
SSDEEP3:0kflfsanjunlPnn:0kVJnj4Pnn
TLSH
insert-timestamp1721647814.033244
mimetypeapplication/octet-stream
sourcesnap:RE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_143
hashlookup:parent-total29
hashlookup:trust100

Network graph view

Parents (Total: 29)

The searched file hash is included in 29 parent files which include package known and seen by metalookup. A sample is included below:

Key Value
FileSize1590008
MD5A23094C66F78BD23919B95DF2FF95B30
PackageDescriptionOpen 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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamelibonnx-testdata
PackageSectionscience
PackageVersion1.7.0+dfsg-1build1
SHA-12428B3C2B31703E3771FE916B13A0B53F7B20B58
SHA-256882DEAFAB8DAC6EAD553CDC5EB731E1CA6E940D7612170762DFD2523585AAB86
Key Value
MD5EF2C611450005D453BC3935EF67BA304
PackageArchx86_64
PackageDescriptionOpen format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners.
PackageNamepython3-onnx
PackageReleaselp150.3.2
PackageVersion1.6.0
SHA-12B568070E4C6A69EC806F01353724EA4540FED59
SHA-2567D2F7CB8C0B860A764BECDF6101899D73AAF6424654B0848B20C4B6D69195F32
Key Value
FileSize1581044
MD5E63DB2CA2B2992E1A5E8AD9A836ABD16
PackageDescriptionOpen 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.
PackageMaintainerUbuntu Developers <ubuntu-devel-discuss@lists.ubuntu.com>
PackageNamelibonnx-testdata
PackageSectionscience
PackageVersion1.7.0+dfsg-3build1
SHA-130CDDB0286DFEBE94840415FBC6F601DF9F88E0A
SHA-256BBCFB8B2F65913732C81B9CF20636F627AED1D42EA1E7AE6ED4E51245F9F70D1
Key Value
MD586FB1BE86E57C54AF6262D4E7D2AF4C9
PackageArchx86_64
PackageDescriptionOpen format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners.
PackageNamepython3-onnx
PackageRelease2.113
PackageVersion1.8.1
SHA-137EBC54D3C428044AB8218D8E67B5CD65AF1D240
SHA-2565B7D2783BC22F5AA80D060CAF404F287BFCFA68287A1FE2AD038EBEC6B6FD2DE
Key Value
SHA-14191E3DDAFE273F7A207AB8E218FF4A8D68A4397
snap-authoritycanonical
snap-filenameRE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_87.snap
snap-idRE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_87
snap-nameimmich-distribution
snap-publisher-idixIKmdMaUVa6JDwEaVTIIgQJOq9ghsjH
snap-signkeyBWDEoaqyr25nF5SNCvEv2v7QnM9QsfCc0PBMYD_i2NGSQ32EF2d4D0hqUel3m8ul
snap-timestamp2023-03-12T15:53:44.592658Z
source-urlhttps://api.snapcraft.io/api/v1/snaps/download/RE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_87.snap
Key Value
MD53D24FA6CD4AA40119C15188AEA7C80BA
PackageArchi586
PackageDescriptionOpen format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners.
PackageNamepython3-onnx
PackageRelease6.4
PackageVersion1.6.0
SHA-141A30FA8DD2137FFFCD5A7881601D57FDAAEDA3D
SHA-2566A5FF53DBFAEA135330A29B505A75077864B70B89310C3ED041BE5D442843E57
Key Value
SHA-141E93F2D7BD5C35A1361A0E98B20BB5F3C94E1C8
snap-authoritycanonical
snap-filenameRE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_143.snap
snap-idRE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_143
snap-nameimmich-distribution
snap-publisher-idixIKmdMaUVa6JDwEaVTIIgQJOq9ghsjH
snap-signkeyBWDEoaqyr25nF5SNCvEv2v7QnM9QsfCc0PBMYD_i2NGSQ32EF2d4D0hqUel3m8ul
snap-timestamp2023-03-12T15:53:44.592658Z
source-urlhttps://api.snapcraft.io/api/v1/snaps/download/RE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_143.snap
Key Value
SHA-1435D923A9226CF8DD45A8E0C86E475148C79AEB2
snap-authoritycanonical
snap-filenameRE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_108.snap
snap-idRE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_108
snap-nameimmich-distribution
snap-publisher-idixIKmdMaUVa6JDwEaVTIIgQJOq9ghsjH
snap-signkeyBWDEoaqyr25nF5SNCvEv2v7QnM9QsfCc0PBMYD_i2NGSQ32EF2d4D0hqUel3m8ul
snap-timestamp2023-03-12T15:53:44.592658Z
source-urlhttps://api.snapcraft.io/api/v1/snaps/download/RE98ktTqRPHHtxUS9I7T7NQXPIxYHG4q_108.snap
Key Value
MD5D94ADCD2FFACF30F3294D77E21B45729
PackageArchx86_64
PackageDescriptionOpen format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners.
PackageNamepython3-onnx
PackageRelease2.83
PackageVersion1.8.1
SHA-1442E09E88FF7C4220EFF6E1B7E3A5218FDAA0496
SHA-256ECE8556CD8081B282ABCC5F3C3FDBE3E4B068400AA379DAA92B201DDEB68CFC2
Key Value
MD54968FBDE3FAADCE4D785DB8019E3BA23
PackageArchi586
PackageDescriptionOpen format to represent deep learning models. With ONNX, AI developers can more easily move models between state-of-the-art tools and choose the combination that is best for them. ONNX is developed and supported by a community of partners.
PackageMaintainerhttps://bugs.opensuse.org
PackageNamepython38-onnx
PackageRelease1.10
PackageVersion1.8.1
SHA-1527D2BF4F7F9FAEF88C1F4F157FFCEF52875A0AE
SHA-256B161199B6B4B874CC5564379D12DE2FC460B2F57BF6BD0300BC4B381C8D56112





metalookup is a service from miwakeru.com