How to Install and Uninstall libxir2 Package on Kali Linux

Last updated: May 20,2024

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

1. Install "libxir2" package

This guide let you learn how to install libxir2 on Kali Linux


    
        
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                sudo apt update
            
                    
    
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                sudo apt install
            
                        
                libxir2
            
                    
    
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2. Uninstall "libxir2" package

Please follow the guidance below to uninstall libxir2 on Kali Linux:


    
        
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                sudo apt remove
            
                        
                libxir2
            
                    
    
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                sudo apt autoclean && sudo apt autoremove
            
                    
    
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3. Information about the libxir2 package on Kali Linux

Package: libxir2
Source: xir
Version: 2.5-1
Installed-Size: 10758
Maintainer: Debian Xilinx Package Maintainers
Architecture: amd64
Depends: libc6 (>= 2.32), libgcc-s1 (>= 3.4), libgoogle-glog0v6 (>= 0.6.0), libprotobuf23 (>= 3.12.4), libstdc++6 (>= 11), libunilog2 (>= 2.5)
Size: 1446060
SHA256: 3055e1da75a91ef2fa96a697316800d456861ba6539bd212b0d42ee02344fe77
SHA1: baa273efe948c41ebed88338abbcf739c2bf4cdb
MD5sum: e54ffbc7d4a488ca699a745ee05f3d5c
Description: Xilinx Intermediate Representation (XIR) for deep learning algorithms (runtime)
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 runtime environment for XIR.
Description-md5: bde3c057ea2b72e21e07b07c52e8ead2
Multi-Arch: same
Homepage: https://github.com/Xilinx/Vitis-AI
Section: libs
Priority: optional
Filename: pool/main/x/xir/libxir2_2.5-1_amd64.deb