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高级软工大作业,包括前端、后端和deepcube模型。

License: MIT License

Python 17.26% C++ 12.20% Makefile 0.33% Shell 0.74% Perl 6 0.06% C 38.23% CWeb 30.31% HTML 0.81% Dockerfile 0.06%

deepcube-project's Introduction

deepcube-project

高级软工大作业,包括前端、后端和deepcube模型。

模型环境配置

1)系统依赖库安装:
apt-get update && apt-get install -y --no-install-recommends "build-essential=12.4ubuntu1" "libboost-all-dev=1.65.1.0ubuntu1" "libboost-dev=1.65.1.0ubuntu1" && rm -rf /var/lib/apt/lists/*

2)开发平台(安装了 conda):
conda install --yes python==2.7.5 tensorflow-gpu==1.8.0 && conda clean --yes --all

3)平台依赖库:
pip install --upgrade dm-sonnet==1.10 matplotlib==2.2.3

载入容器并进行端口映射,使得能够在虚拟机外部通过浏览器访问 : docker run -idt -d 8000:8000 bash

注意,win 进入容器的命令:winpty docker exec -it 容器名 bash

系统依赖django安装命令:pip install django

django为python2

进入django项目目录,启动django: python manage.py runserver 0.0.0.0:8000

浏览器中访问的是运行容器的虚拟机的ip:8000

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