How to Install and Uninstall libxir-utils Package on Kali Linux

Last updated: July 04,2024

Deprecated! Installation of this package may no longer be supported.

1. Install "libxir-utils" package

Please follow the step by step instructions below to install libxir-utils on Kali Linux


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                libxir-utils
            
                    
    
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2. Uninstall "libxir-utils" package

This guide covers the steps necessary to uninstall libxir-utils on Kali Linux:


    
        
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                sudo apt remove
            
                        
                libxir-utils
            
                    
    
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            $
        

        
            
                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the libxir-utils package on Kali Linux

Package: libxir-utils
Source: xir
Version: 2.5-1
Installed-Size: 242
Maintainer: Debian Xilinx Package Maintainers
Architecture: amd64
Depends: libc6 (>= 2.4), libgcc-s1 (>= 3.0), libgoogle-glog0v6 (>= 0.6.0), libprotobuf23 (>= 3.12.4), libstdc++6 (>= 11), libxir2 (= 2.5-1)
Size: 48348
SHA256: d5df7f349ba0b2e8e8fbb41b5d1e05b3d6ce18962cada7a1d927fe18290529a7
SHA1: dd4dad2132cc30c3c304288be346e34153fe3bb7
MD5sum: e9bf8071cdc4df1811c18878b3c1eb37
Description: Xilinx Intermediate Representation (XIR) for deep learning algorithms (utils)
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 contains the utilities from XIR.
Description-md5: b6df97dd7f6fda23ff690dcb84ec5ed7
Homepage: https://github.com/Xilinx/Vitis-AI
Section: utils
Priority: optional
Filename: pool/main/x/xir/libxir-utils_2.5-1_amd64.deb