简易教程：如何在 Linux 上安装 SWIG (linux swig安装)

Title: A Simple Guide to Installing SWIG on Linux

SWIG, or the Simplified Wrapper and Interface Generator, is a powerful tool for interfacing between programming languages. It can be especially useful for developers who work with multiple programming languages and need to create interfaces to facilitate communication between them. In this guide, we will walk through the steps for installing SWIG on Linux.

Step 1: Check Your System

Before we begin, it’s important to make sure that your system meets the minimum requirements for installing SWIG. Specifically, you will need:

– A Linux distribution (such as Ubuntu or CentOS) that includes a package management system

– Basic knowledge of the command line interface (CLI) in Linux

– Root access (or sudo privileges) on your machine

Once you’ve confirmed that your system meets these requirements, you’re ready to move on to the next step.

Step 2: Install SWIG

The easiest way to install SWIG on Linux is through the package management system of your distribution. The following examples demonstrate how to install SWIG on Ubuntu (using apt) and CentOS (using yum).

Ubuntu:

$ sudo apt-get update

$ sudo apt-get install swig

CentOS:

$ sudo yum update

$ sudo yum install swig

Note that the package name may be slightly different depending on your distribution. For example, on Fedora you would use dnf instead of yum:

Fedora:

$ sudo dnf update

$ sudo dnf install swig

Step 3: Verify the Installation

Once SWIG is installed, you can verify that it is working correctly by running the swig command in the CLI. This should display the help information for SWIG, which includes a list of commands and options.

$ swig -h

If you see this help information, then SWIG is installed and ready to use.

Step 4: Get Started

Now that you have SWIG installed and verified, you’re ready to start using it for your programming projects. SWIG has extensive documentation avlable on their website, including tutorials, reference guides, and examples.

Conclusion:

In summary, the process for installing SWIG on Linux is strghtforward and can be completed quickly using your distribution’s package management system. Once you have SWIG installed and verified, you can start using its powerful capabilities for interfacing between different programming languages. With its ease of use and comprehensive documentation, SWIG is an invaluable tool for developers working in a multi-language programming environment.

相关问题拓展阅读：

• 因tensorflow版本问题造成的error

因tensorflow版本问题造成的error

1.

“`

Traceback (most recent call last): File “model_VQA.py”, line 2, inimport tensorflow as tf File “/home/supermicro/.local/lib/python2.7/site-packages/tensorflow/__init__.py”, line 23, infrom tensorflow.python import * File “/home/supermicro/.local/lib/python2.7/site-packages/tensorflow/python/__init__.py”, line 48, infrom tensorflow.python import pywrap_tensorflow File “/home/supermicro/.local/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py”, line 28, in _pywrap_tensorflow = swig_import_helper()

 空洞 File “/home/supermicro/.local/lib/python2.7/site-packages/tensorflow/python/pywrap_tensorflow.py”, line 24, in swig_import_helper

    _mod = imp.load_module(‘_pywrap_tensorflow’, fp, pathname, description)

ImportError: libcudart.so.7.5: cannot open shared object file: No such file or directory

“`

这个问题有可能是因为没有

“`$ cd /usr/local/cuda/lib64

$ $ln -s libcudart.so libcudart.so.7.5

“`另一个原因就是tensorflow版本不对，没有安装gpu的版本（我的环境是python2.7,cuda8.0,opencv3.1.0安装的tensorflow-gpu 1.0.1“`

pip install tensorflow-gpu==1.0.1 –user“`）

2.“`*** Error in `python’: free(): invalid next size (fast): 0x00007f297c***

======= Backtrace: =========

/lib/x86_64-linux-gnu/libc.so.6(+0x777e5)

/lib/x86_64-linux-gnu/libc.so.6(+0x8037a)

/lib/x86_64-linux-gnu/libc.so.6(cfree+0x4c)

/usr/lib/nvidia-390/libnvidia-fatbinaryloader.so.390.48(+0x1d05a)

/usr/lib/nvidia-390/libnvidia-fatbinaryloader.so.390.48(+0x11c73)

……“`以上问题也是tensorflow版本斗改枯问题在我安装完正确版本的tensorflow的版本后自动消失。记得pip list看看有没有其他版本的tensorflow要先卸载其他版本的tensorflow哦！

最后tucao一句，配置环境真让人抓狂，一搞就大半天，不过半路出家也只能摸歼纤着石头过河啦！

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