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在Notebook中与自己部署的中国象棋AI对战！

案例目标

提供一种可视化的方式，与自己在ModelArts中部署的中国象棋AI对战

案例内容介绍

本次案例需要先在AI Gallery订阅《中国象棋AlphaZero模型》，并利用该模型前往ModelArts控制台部署在线服务。本案例将调用部署好的在线服务获取AI动作，直接部署模型将调用内置模型。

如果想要先训练自己的中国象棋模型再与之对战，可先在AI Gallery中订阅《CChess中国象棋》算法，并在ModelArts中进行训练。训练完成后获取权重地址，在《中国象棋AlphaZero模型》中导入模型并部署在线服务。详见《训练自己的象棋AI模型并与之对战》

注意事项

本案例运行环境为 PyTorch-1.4，且建议使用 CPU 运行，请查看《ModelAtrs JupyterLab 硬件规格使用指南》了解切换硬件规格的方法；
如果您是第一次使用 JupyterLab，请查看《ModelAtrs JupyterLab使用指导》了解使用方法；
如果您在使用 JupyterLab 过程中碰到报错，请参考《ModelAtrs JupyterLab常见问题解决办法》尝试解决问题；

实验开始

1. 程序初始化

第1步：安装基础依赖

要确保所有依赖都安装成功后，再执行之后的代码。如果某些模块因为网络原因导致安装失败，直接重试一次即可。

!pip install ipympl==0.8.8

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Installing collected packages: pyrsistent, importlib-resources, attrs, jsonschema, fastjsonschema, webencodings, soupsieve, nbformat, tinycss2, pandocfilters, nbclient, mistune, jupyterlab-pygments, defusedxml, bleach, beautifulsoup4, argon2-cffi-bindings, terminado, Send2Trash, prometheus-client, nbconvert, argon2-cffi, notebook, widgetsnbextension, jupyterlab-widgets, ipywidgets, ipympl
Successfully installed Send2Trash-1.8.0 argon2-cffi-21.3.0 argon2-cffi-bindings-21.2.0 attrs-21.4.0 beautifulsoup4-4.11.1 bleach-5.0.0 defusedxml-0.7.1 fastjsonschema-2.15.3 importlib-resources-5.8.0 ipympl-0.8.8 ipywidgets-7.7.0 jsonschema-4.6.0 jupyterlab-pygments-0.2.2 jupyterlab-widgets-1.1.0 mistune-0.8.4 nbclient-0.6.3 nbconvert-6.5.0 nbformat-5.4.0 notebook-6.4.12 pandocfilters-1.5.0 prometheus-client-0.14.1 pyrsistent-0.18.1 soupsieve-2.3.2.post1 terminado-0.15.0 tinycss2-1.1.1 webencodings-0.5.1 widgetsnbextension-3.6.0
WARNING: You are using pip version 21.2.2; however, version 22.1.2 is available.
You should consider upgrading via the '/home/ma-user/anaconda3/envs/PyTorch-1.4/bin/python -m pip install --upgrade pip' command.

第2步: 下载依赖包

import os
import moxing as mox
if not os.path.exists('cchess_gameplay_v2'):
    mox.file.copy('obs://modelarts-labs-bj4-v2/course/modelarts/reinforcement_learning/cchess_gameplay/cchess_gameplay_v2.zip', 
                  'cchess_gameplay_v2.zip')
    os.system('unzip cchess_gameplay_v2.zip')

INFO:root:Using MoXing-v2.0.1.rc0.ffd1c0c8-ffd1c0c8
INFO:root:Using OBS-Python-SDK-3.20.9.1

2. 填写参数

"""
cchess_config      说明
    mcts_playouts  选填参数，Int类型，默认值100，推荐取值范围100-1600，数值越低，表示AI的思考步数越少，反之表示越多
    service_id     必填参数，String类型，在线服务ID
"""
cchess_config = {
    "mcts_playouts": 200,
    "service_id": "d915e7bc-84ce-4d6f-8752-21dd317edc0d",  # 请在此处填写您的在线服务ID，如"d915e7bc-84ce-4d6f-8752-21dd317edc0d"
}

3. 开始对战

每下完一步后适当晃动鼠标

%matplotlib widget

from modelarts.session import Session
from modelarts.model import Predictor

from cchess_gameplay_v2.cchess_gameplay_client import ChessGamePlay
if __name__ == "__main__":
    session = Session()
    predictor_instance = Predictor(session, service_id=cchess_config["service_id"])
    gp = ChessGamePlay(cchess_config, predictor_instance)
    gp.start_game()

INFO:matplotlib.font_manager:generated new fontManager

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炼丹小童 25个月以前

https://fileserver.developer.huaweicloud.com/FileServer/getFile/cmtybbs/00f/2d4/1f5/08e8a7ad4a00f2d41f5bc00a59e8fe52.20230129061752.39311022218675363185119066304880:20230129062102:2400:D7A4A1FB9F21BC54ACFF93C8BC76CEB71931E843754D6A35D10D262C6EC0E379.png

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炼丹小童 25个月以前

INFO:matplotlib.font_manager:generated new fontManager /home/ma-user/anaconda3/envs/PyTorch-1.4/lib/python3.7/site-packages/requests/__init__.py:104: RequestsDependencyWarning: urllib3 (1.26.12) or chardet (5.0.0)/charset_normalizer (2.0.12) doesn't match a supported version! RequestsDependencyWarning) Error displaying widget: model not found 最后一步报错

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hw71255899 37个月以前

学习到很多

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运气男孩 41个月以前

牛的

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xuzhipeng 42个月以前

很不错，学习了

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