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教你从头到尾利用DQN自动玩flappy bird(全程命令提示,GPU+CPU)
作者:骁哲、李伟、July
说明:本文分两部分,第一部分为GPU版教程,第二部分为CPU版教程,两个教程都主要由骁哲编写,李伟校对,而最后跑的是yenchenlin的github开源demo。如遇问题欢迎加Q群交流:472899334。且若探究实验背后原理,请参看此课程:深度学习在线班。
时间:二零一六年十月十三日。
前言
我们在上一篇教程《基于torch学汪峰写歌词、聊天机器人、图像着色/生成、看图说话、字幕生成》中说到:“让每一个人都能玩一把,无限降低初学朋友的实验门槛”,那是否能把难度再次降低呢,比如部分同学不熟悉Linux命令咋整,那是不是不熟悉Linux命令就没法折腾了?然既然是为了让每个人都能玩一把,那就应该尽最大可能照顾到最大多数。
本教程提供全程命令提示,以便让Linux命令暂不熟的同学也能搭建起来。因此,自动玩转flappy bird分三个步骤:
- 不管三七二十一,先把游戏搭建起来
- 搭建起来后,Linux命令后续慢慢熟悉,熟悉后,一通百通,搭建其他实验的环境也会立马顺畅许多
- 取得成就感和安心之后,再细细深究实验背后之原理(当然,深度学习在线班上也会深究实验背后原理)
另本教程省略了ubuntu14.04安装,如果此前没安装过Ubuntu,可以参看《教你从头到尾利用DL学梵高作画》里的第4.1部分。且,本文本一开始只有GPU版的教程,但为照顾到没有GPU的同学,特地在本文第二部分增加CPU版的教程,以让每一个人都能玩。
还是这个事,欢迎更多朋友跟我们一起做实验,一起玩。包括本flappy bird在内的8个实验:梵高作画、文字生成、自动聊天机器人、图像着色、图像生成、看图说话、字幕生成、flappy bird,今2016年内做出这8个实验中的任意一个并在微博上AT@研究者July,便送100上课券,把实验心得发社区 ask.julyed.com 后,再送100上课券。
第一部分、GPU版教程
1.1NVIDIA驱动、CUDA、cudnn安装
下载相应文件,后续 使用,下载地址 :
- NVIDIA驱动 http://www.geforce.cn/drivers (搜索 对应型号)
- CUDA驱动 https://developer.nvidia.com/cuda-toolkit (搜索对应版本)
- CUDNN驱动 https://developer.nvidia.com/cudnn
以下操作均使用root账户
apt-get update (更新源)
apt-get install vim (安装VIM,也可使用 emacs nano)
vi /etc/default/grub (进入grub文件)
启用字符界面登录
将这行 GRUB_CMDLINE_LINUX_DEFAULT="quiet" 中的 quiet 修改为 text
GRUB_CMDLINE_LINUX_DEFAULT="text"
保存退出
update-grub2 (更新一下)
reboot (重启)
1.1.1、 Install NVIDIA Driver 安装NVIDIA驱动
cd /**/**/** (cd到cuda所在文件目录下)
./NVIDIA-Linux-x86_64-367.44.run (安装NVIDIA驱动,此文件需执行权限,chmod +x NVIDIA-Linux-x86_64-367.44.run)
reboot (重启)
1.1.2、 Install CUDA 安装CUDA
cd /**/**/** (cd到cuda所在文件目录下)
./cuda_8.0.27_linux.run (安装CUDA,此文件需执行权限,如遇权限问题 可执行 chmod +x 文件名)
!accept之后第一个选项填写“n”(该选项让你选择是否安装NVIDIA的Driver,之前已经安装过了, 所以不需要),之后一路“绿灯”。
vi /etc/default/grub (打开grub)
启用图形界面登录
将这行 GRUB_CMDLINE_LINUX_DEFAULT="text" 中的 text修改为 quiet
保存退出
update-grub2 (更新一下)
reboot (重启)
1.1.3、 Install cuDNN 安装cuDNN
tar xvzf cudnn-7.5-linux-x64-v5.1-ga.tgz (解压)
将CUDNN解压,将解压出来的文件复制到 CUDA 目录 如下
sudo cp cuda/include/cudnn.h /usr/local/cuda/include (复制)
sudo cp cuda/lib64/libcudnn* /usr/local/cuda/lib64 (复制)
sudo chmod a+r /usr/local/cuda/include/cudnn.h /usr/local/cuda/lib64/libcudnn* (加权限)
CUDA Environment Path 添加CUDA的环境变量
终端中执行
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
export CUDA_HOME=/usr/local/cuda
export PATH="$CUDA_HOME/bin:$PATH"
1.2、源码安装Tensorflow
apt-get install git
Clone the TensorFlow repository 克隆Tensorflow
git clone https://github.com/tensorflow/tensorflow
1.2.1、 Install Bazel 安装Bazel
Install JDK 8 安装JDK8
sudo add-apt-repository ppa:webupd8team/java (添加源)
sudo apt-get update (更新)
sudo apt-get install oracle-java8-installer (安装)
Add Bazel distribution URI as a package source (one time setup) (将Bazel的URL添加为源)
echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
curl https://bazel.io/bazel-release.pub.gpg | sudo apt-key add -
Update and install Bazel 更新并下载Bazel
sudo apt-get update && sudo apt-get install bazel
sudo apt-get upgrade bazel
1.2.2、 Install other dependencies 安装其他依赖
sudo apt-get install python-numpy swig python-dev python-wheel python-pip
Configure the installation 配置 (这里注意configure后面的提示,提示已经给出)
./configure(在 clone 下来的 tensorflow目录执行)
Please specify the location of python. [Default is /usr/bin/python]:
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] N
No Google Cloud Platform support will be enabled for TensorFlow
Do you wish to build TensorFlow with GPU support? [y/N] y
GPU support will be enabled for TensorFlow
Please specify which gcc nvcc should use as the host compiler. [Default is /usr/bin/gcc]:
Please specify the Cuda SDK version you want to use, e.g. 7.0. [Leave empty to use system default]: 8.0 (此处根据实际情况修改)
Please specify the location where CUDA 7.5 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify the cuDNN version you want to use. [Leave empty to use system default]: 5 (此处根据实际情况修改)
Please specify the location where cuDNN 5 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
Please specify a list of comma-separated Cuda compute capabilities you want to build with.
You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
Please note that each additional compute capability significantly increases your build time and binary size.
[Default is: "3.5,5.2"]:3.0(这个值视机器配置而定,配置越高值越高,参考 https://developer.nvidia.com/cuda-gpus#collapse4,而3.0通用)
Setting up Cuda include
Setting up Cuda lib
Setting up Cuda bin
Setting up Cuda nvvm
Setting up CUPTI include
Setting up CUPTI lib64
Configuration finished
1.2.3、 Create the pip package and install 创建pip包并且安装
bazel build -c opt //tensorflow/tools/pip_package:build_pip_package (笔者用公司网提示error,翻墙后问题解决)
bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg(笔者安装过程中出现ImportError:No module named setuptools,解决办法:apt-get install python-pip,安装python-pip就行了)
sudo pip install /tmp/tensorflow_pkg/tensorflow-0.11.0rc0-py2-none-any.whl
1.2.4、 Setting up TensorFlow for Development 编译设置Tensorflow
bazel build -c opt //tensorflow/tools/pip_package:build_pip_package
bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
mkdir _python_build
cd _python_build
ln -s ../bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/* .
ln -s ../tensorflow/tools/pip_package/* .
python setup.py develop
1.2.5、 Train your first TensorFlow neural net model 测试Tensorflow
cd tensorflow/models/image/mnist
export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64"
export CUDA_HOME=/usr/local/cuda (这里重新添加环境变量是因为笔者安装过程中提示未能找到CUDA)
python convolutional.py(笔者这里出现AttributeError:type object 'NewBase' has no attribute 'is_abstract'问题,解决办法:sudo pip install six --upgrade -- target="/usr/lib/python2.7/dist-packages")
1.3、安装OpenCV
Download OpenCV 下载OpenCV
浏览器打开 http://opencv.org/
右侧下载Linux版本的OpenCV
cd到下载目录
unzip opencv-2.4.13.zip
cd opencv-2.4.13
mkdir release
sudo apt-get install build-essential cmake libgtk2.0-dev pkg-config python-dev python-numpy libavcodec-dev libavformat-dev libswscale-dev
cd release
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
sudo make install
1.4、下载FlappyBird
Download DeepLearningFlappyBird 下载FlappyBird
git clone --recursive https://github.com/yenchenlin/DeepLearningFlappyBird
1.5、 安装pygame
Install pygame 安装pygame
wget http://www.pygame.org/ftp/pygame-1.9.1release.tar.gz 下载pygame
sudo apt-get install libsdl1.2-dev (SDL安装)
sudo apt-get install libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev libsdl-gfx1.2-dev libsdl-net1.2-dev libsdl-sge-dev libsdl-sound1.2-dev libportmidi-dev libsmpeg-dev (安装其他依赖包)
cd pygame-1.9.1release
python config.py
run deep_q_network.py
python deep_q_network.py 运行deep_q_network.py (笔者这里报错:AttributeError:'module' object has no attribute 'stack',解决办法:sudo apt-get install python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose)
git clone git://github.com/numpy/numpy.git numpy (笔者这里运行了一下cd numpy;python setup.py install,发现报错缺少cython于是执行后面的命令:apt-get install cython)
cd numpy
python setup.py install
1.6、GPU版的执行程序
全部安装完后,再次执行
python deep_q_network.py
画面卡住等待一下,GPU、CUDA在运行需要时间..
稍等片刻,奇迹出现了,飞鸟开始自动飞、自动上下跳跃、自动穿过障碍,要知道纯人工手动玩很难坚持9s!
静态图片可能看不出啥效果,视频见这:http://weibo.com/1580904460/EcxQh6em0。
至此,这个曾虐遍全球无数人的游戏,就这样在我们手里,利用深度学习自动玩转了!无不体现深度学习的神奇与魅力。
第二部分、CPU版教程
以下操作均使用root账户
2.1、 安装Tensorflow
源码安装方式
Clone the TensorFlow repository 克隆Tensorflow
git clone https://github.com/tensorflow/tensorflow
2.1.1、 Install Bazel 安装Bazel
Install JDK 8 安装JDK8
sudo add-apt-repository ppa:webupd8team/java (添加源)
sudo apt-get update (更新)
sudo apt-get install oracle-java8-installer (安装)
Add Bazel distribution URI as a package source (one time setup) (将Bazel的URL添加为源)
echo "deb [arch=amd64] http://storage.googleapis.com/bazel-apt stable jdk1.8" | sudo tee /etc/apt/sources.list.d/bazel.list
curl https://bazel.io/bazel-release.pub.gpg | sudo apt-key add -
Update and install Bazel 更新并下载Bazel
sudo apt-get update && sudo apt-get install bazel
sudo apt-get upgrade bazel
2.1.2、 Install other dependencies 安装其他依赖
sudo apt-get install python-numpy swig python-dev python-wheel python-pip
Configure the installation 配置 (这里注意configure后面的提示,提示已经给出)
./configure [在 clone 下来的 tensorflow
目录执行]
标注颜色的字体 需要手动输入
Please specify the location of python. [Default is /usr/bin/python]:
Do you wish to build TensorFlow with Google Cloud Platform support? [y/N] N
No Google Cloud Platform support will be enabled for TensorFlow
Do you wish to build TensorFlow with GPU support? [y/N] N
Configuration finished
2.1.3、 Create the pip package and install 创建pip包并且安装
bazel build -c opt //tensorflow/tools/pip_package:build_pip_package (笔者用公司网提示error,翻墙后问题解决)
bazel build -c opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
bazel-bin/tensorflow/tools/pip_package/build_pip_package /tmp/tensorflow_pkg(笔者安装过程中出现ImportError:No module named setuptools,解决办法:apt-get install python-pip,安装python-pip就行了。)
sudo pip install /…/…/tensorflow-0.11.0rc0-py27-none-any.whl
上述tensorflow...whl文件 下载地址 https://github.com/tensorflow/tensorflow#installation
选择 Linux_CPU-only中的对应版本
2.1.4、 Setting up TensorFlow for Development 编译设置Tensorflow
bazel build -c opt //tensorflow/tools/pip_package:build_pip_package
mkdir _python_build
cd _python_build
ln -s ../bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/* .
ln -s ../tensorflow/tools/pip_package/* .
python setup.py develop
2.1.5、 Train your first TensorFlow neural net model 测试Tensorflow
cd tensorflow/models/image/mnist
python convolutional.py(笔者这里出现AttributeError:type object 'NewBase' has no attribute 'is_abstract'问题,解决办法:sudo pip install six --upgrade --
target="/usr/lib/python2.7/dist-packages")
2.2、安装OpenCV
Download OpenCV 下载OpenCV
浏览器打开 http://opencv.org/
右侧下载Linux版本的OpenCV
cd到下载目录
unzip opencv-2.4.13.zip
cd opencv-2.4.13
mkdir release
sudo apt-get install build-essential cmake libgtk2.0-dev pkg-config python-dev python-numpy libavcodec-dev libavformat-dev libswscale-dev
cd release
cmake -D CMAKE_BUILD_TYPE=RELEASE -D CMAKE_INSTALL_PREFIX=/usr/local ..
sudo make install
2.3、 下载FlappyBird
Download DeepLearningFlappyBird 下载FlappyBird
git clone --recursive https://github.com/yenchenlin/DeepLearningFlappyBird
2.4、 安装pygame
Install pygame 安装pygame
wget http://www.pygame.org/ftp/pygame-1.9.1release.tar.gz 下载pygame
sudo apt-get install libsdl1.2-dev (SDL安装)
sudo apt-get install libsdl-image1.2-dev libsdl-mixer1.2-dev libsdl-ttf2.0-dev libsdl-gfx1.2-dev libsdl-net1.2-dev libsdl-sge-dev libsdl-sound1.2-dev libportmidi-dev libsmpeg-dev (安装其他依赖包)
cd pygame-1.9.1release
python config.py
run deep_q_network.py
python deep_q_network.py 运行deep_q_network.py (笔者这里报错:AttributeError:'module' object has no attribute 'stack',解决办法:
sudo apt-get install python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas
python-sympy python-nose
git clone git://github.com/numpy/numpy.git numpy (笔者这里运行了一下cd numpy;python setup.py install,发现报
错缺少cython于是执行后面的命令)
apt-get install cython
cd numpy
python setup.py install)
2.6、CPU版的执行程序
全部安装完后,再次执行
python deep_q_network.py
画面不会卡顿,cpu版本会立刻出现结果(笔者这里第一次时候没有执行root,会报错,同学们切记执行上条命令前一定要root了!)
PS:按照FlappyBird安装教程安装,可以直接应用在”学梵高作画”的教程里,两者可以通用!
参考文献
- 教你从头到尾利用DL学梵高作画:GTX 1070 cuda 8.0 tensorflow gpu版
- 5月深度学习班学员小蔡同学写的简易教程:用MAC DQN玩Flappy Bird
- https://github.com/yenchenlin/DeepLearningFlappyBird
后记
七月在线开发/市场团队,二零一六年十月十三日。
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