Parents (Total: 5)

The searched file hash is included in 5 parent files which include package known and seen by metalookup. A sample is included below:

Key	Value
MD5	2A526CE4BCA476571C6E73CB2FDAEC63
PackageArch	x86_64
PackageDescription	The libadwaitaqt-devel package contains libraries and header files for developing applications that use libadwaitaqt1.
PackageName	libadwaitaqt-devel
PackageRelease	26.8
PackageVersion	1.4.0
SHA-1	17F370E157E5414BD83E2355D9E339E92BF5CE20
SHA-256	331BD2F56FB179B762921EA4B6655E19C696C475B2E4A21452B7A5B50A21FFE3

Key	Value
FileSize	17784
MD5	F495DEA2EF315F714778A5DB2BBB1C5B
PackageDescription	Xilinx Intermediate Representation (XIR) for deep learning algorithms (develop) Xilinx Intermediate Representation (XIR) is a graph based intermediate representation of the AI algorithms which is well designed for compilation and efficient deployment of the Domain-specific Processing Unit (DPU) on the FPGA platform. Advanced users can apply Whole Application Acceleration to benefit from the power of FPGA by extending the XIR to support customized IP in Vitis AI flow. . XIR includes Op, Tensor, Graph and Subgraph libraries, which providing a clear and flexible representation for the computational graph. For now, it's the foundation for the Vitis AI quantizer, compiler, runtime and many other tools. XIR provides in-memory format, and file format for different usage. The in-memory format XIR is a Graph object, and the file format is a xmodel. A Graph object can be serialized to a xmodel while the xmodel can be deserialized to the Graph object. . In the Op library, there's a well-defined set of operators to cover the wildly used deep learning frameworks, e.g. TensorFlow, Pytorch and Caffe, and all of the built-in operators for DPU. This enhences the expression ability and achieves one of the core goals of eliminating the difference between these frameworks and providing a unified representation for users and developers. . XIR also provides a Python APIs which is named PyXIR. It enables Python users to fully access XIR and benefits in a pure Python environment, e.g. co-develop and integrate users' Python project with the current XIR based tools without massive dirty work to fix the gap between two languages. . This package provides the development environment for XIR.
PackageMaintainer	Punit Agrawal <punit@debian.org>
PackageName	libxir-dev
PackageSection	libdevel
PackageVersion	1.4-1
SHA-1	71CAAD514F16B585AECA7FD835365F9B07C14589
SHA-256	3B69F59FFBDADA50346035841C72EC04FC79F8228C29B0028BF518FF81985A90

Key	Value
MD5	AD2156F1A6BE1FF763279FAB6D5DD332
PackageArch	x86_64
PackageDescription	The libadwaitaqt-devel package contains libraries and header files for developing applications that use libadwaitaqt1.
PackageName	libadwaitaqt-devel
PackageRelease	26.9
PackageVersion	1.4.0
SHA-1	A11B5F4727DD155921757FCFA078C4BCDC2F3194
SHA-256	E02C9AEB3335C263F48F2EABE0CA55C5ABDB2BFDC09B583839160D1223461B38

Key	Value
FileSize	17780
MD5	6CBD745A71FDB3AFF5EEF8CD5D77F1A7
PackageDescription	Xilinx Intermediate Representation (XIR) for deep learning algorithms (develop) Xilinx Intermediate Representation (XIR) is a graph based intermediate representation of the AI algorithms which is well designed for compilation and efficient deployment of the Domain-specific Processing Unit (DPU) on the FPGA platform. Advanced users can apply Whole Application Acceleration to benefit from the power of FPGA by extending the XIR to support customized IP in Vitis AI flow. . XIR includes Op, Tensor, Graph and Subgraph libraries, which providing a clear and flexible representation for the computational graph. For now, it's the foundation for the Vitis AI quantizer, compiler, runtime and many other tools. XIR provides in-memory format, and file format for different usage. The in-memory format XIR is a Graph object, and the file format is a xmodel. A Graph object can be serialized to a xmodel while the xmodel can be deserialized to the Graph object. . In the Op library, there's a well-defined set of operators to cover the wildly used deep learning frameworks, e.g. TensorFlow, Pytorch and Caffe, and all of the built-in operators for DPU. This enhences the expression ability and achieves one of the core goals of eliminating the difference between these frameworks and providing a unified representation for users and developers. . XIR also provides a Python APIs which is named PyXIR. It enables Python users to fully access XIR and benefits in a pure Python environment, e.g. co-develop and integrate users' Python project with the current XIR based tools without massive dirty work to fix the gap between two languages. . This package provides the development environment for XIR.
PackageMaintainer	Punit Agrawal <punit@debian.org>
PackageName	libxir-dev
PackageSection	libdevel
PackageVersion	1.4-1
SHA-1	E06549795FB614B3B0555275202F0A37F48D1ABB
SHA-256	9E8CE57F35ECFA912F906E672437205C9A95A965C0E81890CFCCB558B2D16D38

Key	Value
MD5	63FBAC6A0FDAF79871C102EFEAFF8967
PackageArch	x86_64
PackageDescription	The libadwaitaqt-devel package contains libraries and header files for developing applications that use libadwaitaqt1.
PackageMaintainer	https://bugs.opensuse.org
PackageName	libadwaitaqt-devel
PackageRelease	1.4
PackageVersion	1.4.0
SHA-1	1D7EF8AACCA7FEFE7420F151BDEB3DE0B6DD0800
SHA-256	90F8EBA7EEE2BB0723BEDD8F889C9A6532732F188F7C71F35FDEF44154599B01