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
FileSize	17780
MD5	88C9A20600F39D062C5079AE08057D8B
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	EAFA2210DB1574B4A819219B5073C852B7B5C448
SHA-256	BD35110F67DE170B0E02860B6942AD28ADD2F8D5B51FD983F8BA9E06C4EE7FC2

Key	Value
MD5	6F73B425021EA0EF353E52F548923994
PackageArch	i586
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	6F854CCE07DDD358ADB692E9F80570A36D44516D
SHA-256	9C9AF1A79981C7EA25455DF9C80776C9B94540A701C40CD46F915BF6BB174E6D

Key	Value
MD5	3A6A305A28A24F74D80FF35A04993B7D
PackageArch	i586
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	3F4DBC604AEDB0DC0B27EBE6B13C0239EFDDAB7E
SHA-256	152F6671C053B3FC4AFCBB098E512C6D34214943D37E435E3ECC122B13088554

Key	Value
FileSize	17792
MD5	34448452CD36D29A180283B5674B9C10
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	F885C353E29C716A972B96AE0DA9059D51D48F47
SHA-256	F56447C4F86D5E5110315893313648889917A793E6DBACA8F04BE70D820F5D00

Key	Value
MD5	BA9E3F13139CAF161FF24B560E8DFC6A
PackageArch	i586
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	6E4D0B82910A525FB21AD0BA44277305B9761BDF
SHA-256	E0A329047D01F483A02E4A03D0285C05D1FF5C1DBDA6C99C8BCD247272683389