中文 | English

Installation

注意：对于希望灵活配置以及使用基于CUDA的人工智能算法进行研究的人员来说，裸机安装为最佳选择。确保您的机器运行Ubuntu 22.04 LTS（其他版本未经测试），并已安装Python3.10及以上版本。

Note: For researchers who require flexible configuration and want to utilize CUDA-based AI algorithms, bare-metal installation is the recommended approach. Ensure your machine runs Ubuntu 22.04 LTS (other versions are untested) with Python 3.10 or higher installed.

Prerequisites

Note: If you plan to deploy Mini-SFC in an Anaconda virtual environment (e.g., a development environment named minisfc), the following installation steps should be performed after activating the virtual environment.

conda activate minisfc

Mini-SFC's container-based simulation functionality relies on the Containernet simulator, which requires Docker.

Please ensure Docker is installed on your system. For installation instructions, refer to:Docker Tutorial and Resources。

Then install Containernet. For installation guidance, see:Containernet Installation and Configuration。

After verifying that Containernet's basic functions work properly, you may proceed with Mini-SFC installation.

Installing Mini-SFC

First, clone the Mini-SFC repository:

git clone https://gitee.com/WangXi_Chn/mini_sfc.git

or

git clone https://github.com/wangxichn/mini_sfc.git

Then install the required dependencies using setup.py (recommended to use virtual environment with -e option for developer mode installation):

pip install -e .

After completing these steps, you can begin exploring Mini-SFC's features through the subsequent example documentation.