为pointnet++的运行搭建环境ubuntu18.04+cuda10.0+cudnn7.4.2+anaconda3+tensorflow-gpu1.13.1（超超超超简单的版本！！轻轻松松搭建好！

重装好几次了！没有人比我更懂重装（不是

现在我默认大家都才装好ubuntu18.04，就是干！请注意！我这里是通过安装cuda来安装显卡驱动！想要单独安装显卡驱动（比如英伟达官网下载run文件或者通过ubuntu-drivers devices来安装系统推荐的驱动版本）的同学请看其他教程！但是(◔◡◔)重装多次的我觉得，反正都要装cuda，所以通过cuda安装nvidia是最简单不过啦~
注：sudo是获取临时root权限，所以我们开局直接进root
现在我们来看下大致流程：
cuda（顺便安装显卡驱动）–> cudnn --> anaconda3 -->搭建环境–>安装tensorflow-gpu

1. 换源（加快下载速度
   使用root权限：
   sudo -s
   备份源码：
   cp /etc/apt/sources.list /etc/apt/sources.list.bak
   替换源列表内容：
   gedit /etc/apt/sources.list
   打开list后，将以下内容替换掉原来的：

   # 默认注释了源码镜像以提高 apt update 速度，如有需要可自行取消注释
   deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic main restricted universe multiverse
   # deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic main restricted universe multiverse
   deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-updates main restricted universe multiverse
   # deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-updates main restricted universe multiverse
   deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-backports main restricted universe multiverse
   # deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-backports main restricted universe multiverse
   deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-security main restricted universe multiverse
   # deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-security main restricted universe multiverse
   
   # 预发布软件源，不建议启用
   # deb https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-proposed main restricted universe multiverse
   # deb-src https://mirrors.tuna.tsinghua.edu.cn/ubuntu/ bionic-proposed main restricted universe multiverse
   

   记得点保存
   更新列表：
   apt-get update
   OK，换源成功！

2. 禁用系统自带的显卡驱动
   打开系统禁用列表：
   gedit /etc/modprobe.d/blacklist.conf
   通过添加以下代码，将nouveau拉入黑名单！哼，我们不和它玩儿！：
   blacklist nouveau
options nouveau modset=0
   然后更新下我们修改的内容，让它生效：
   update-initramfs -u
   重启：
   reboot
   再看看这玩意儿还敢出来不：
   lsmod | grep nouveau
   OK，没有任何输出（它怕了 它怕了哈哈

3. 安装相关依赖
   安装gcc（记得进入root模式哦：
   apt install build-essential

4. 安装cuda（安装它对应的显卡驱动
   宝贝们乖乖去官网下载哦～
   —>指路http://developer.nvidia.com/cuda-downloads
   到安装文件目录下运行.run文件（萌新小妙招～输入cd再空一格，将存放run文件的文件夹拖入终端，再回车，就可以进入安装目录啦～然后输入ls还可以查看目录下的文件哦）：
   sh cuda_10.0.130_410.48_linux.run
   温馨提示：记得替换为自己的cuda文件名
   安装过程中，输入accept
   如果之前没有装显卡驱动，那么在安装cuda的过程中可以在这里安装哦（是我本人了

   Install NVIDIA Accelerated Graphics Driver for Linux-x86_64 410.48?
   (y)es/(n)o/(q)uit: y
   

   不要选择openGL！

   Do you want to install the OpenGL libraries?
   (y)es/(n)o/(q)uit [ default is yes ]: n
   

   关于这个服务（可y可n：

   Do you want to run nvidia-xconfig?
   This will update the system X configuration file so that the NVIDIA X driver
   is used. The pre-existing X configuration file will be backed up.
   This option should not be used on systems that require a custom
   X configuration, such as systems with multiple GPU vendors.
   (y)es/(n)o/(q)uit [ default is no ]: n
   

   后面的问题都y或者enter默认，来看看结果：

   ===========
   = Summary =
   ===========
   Driver:   Installed
   Toolkit:  Installed in /usr/local/cuda-10.0
   Samples:  Installed in /home/yy, but missing recommended libraries
   

   安装完成后，需要添加环境变量：
   gedit ~/.bashrc
   在文件最后加入以下代码（记得改成自己的cuda版本哦

   export PATH="/usr/local/cuda-10.0/bin:$PATH"
   export LD_LIBRARY_PATH="/usr/lcoal/cuda-10.0/lib64:$LD_LIBRARY_PATH"
   

   添加并保存，将文件生效：
   source ~/.bashrc
   最后我们查看下cuda的版本信息以及nvidia驱动信息：
   nvcc -V
   cuda的版本信息如下：

   nvcc: NVIDIA (R) Cuda compiler driver
   Copyright (c) 2005-2018 NVIDIA Corporation
   Built on Sat_Aug_25_21:08:01_CDT_2018
   Cuda compilation tools, release 10.0, V10.0.130
   

   nvidia驱动信息查询：
   nvidia-smi
   查询结果如下：

   	Wed Aug 12 15:59:46 2020       
   	+-----------------------------------------------------------------------------+
   	| NVIDIA-SMI 410.48                 Driver Version: 410.48                    |
   	|-------------------------------+----------------------+----------------------+
   	| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
   	| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
   	|===============================+======================+======================|
   	|   0  Graphics Device     Off  | 00000000:01:00.0 Off |                  N/A |
   	| N/A   41C    P0    N/A /  N/A |      0MiB /  3020MiB |      1%      Default |
   	+-------------------------------+----------------------+----------------------+                                                         
   	+-----------------------------------------------------------------------------+
   	| Processes:                                                       GPU Memory |
   	|  GPU       PID   Type   Process name                             Usage      |
   	|=============================================================================|
   	|  No running processes found                                                 |
   	+-----------------------------------------------------------------------------+
   

5. 安装cudnn
   去官网下载压缩包
   —>指路https://developer.nvidia.com/rdp/cudnn-archive
   下载好后，我们来解压它（此时压缩包在你的下载目录下：
   首先进入下载目录，然后开始解压：
   tar -zxvf cudnn-10.0-linux-x64-v7.4.2.24.tgz
   解压结果如下：

   cuda/include/cudnn.h
   cuda/NVIDIA_SLA_cuDNN_Support.txt
   cuda/lib64/libcudnn.so
   cuda/lib64/libcudnn.so.7
   cuda/lib64/libcudnn.so.7.4.2
   cuda/lib64/libcudnn_static.a
   

   然后我们需要把cudnn移动到cuda中：
   cp -P cuda/lib64/libcudnn* /usr/local/cuda-10.0/lib64/
cp cuda/include/cudnn.h /usr/local/cuda-10.0/include/

   为所有用户设置读取权限（记得改成你自己的版本号！
   chmod a+r /usr/local/cuda-10.0/include/cudnn.h
chmod a+r /usr/local/cuda-10.0/lib64/libcudnn*
   查看cudnn版本信息：
   cat /usr/local/cuda/include/cudnn.h | grep CUDNN_MAJOR -A 2
   结果如下（我的是7.4.2：

   #define CUDNN_MAJOR 7
   #define CUDNN_MINOR 4
   #define CUDNN_PATCHLEVEL 2
   --
   #define CUDNN_VERSION (CUDNN_MAJOR * 1000 + CUDNN_MINOR * 100 + CUDNN_PATCHLEVEL)
   

6. 安装anaconda3
   没有下载的宝贝，去清华源（速度贼快
   请看路—>https://mirrors.tuna.tsinghua.edu.cn/help/anaconda/
   进入下载文件的目录中运行：
   bash Anaconda3-2020.02-Linux-x86_64.sh
   为anaconda加入环境变量：
   gedit ~/.bashrc
   在bashrc的最后加入（记得修改为自己的用户名：

   export PATH="/home/yy/anaconda3/bin:$PATH"
   

   最后别忘更新下：
   source ~/.bashrc

7. 搭建环境
   确保自己在root模式下！创建环境（tf是我自己命名的，大家根据自己喜好改～：
   conda create -n tf python=3.7
   激活刚刚我们创建的环境：
   source activate tf
   激活后，我们的命令行开头就有环境名啦～说明此时我们正处于tf这个环境中：

   root@yy:~# source activate tf
   (tf) root@yy:~#
   

8. 安装tensorflow-gpu
   在激活环境中输入（直接用pip太慢了，所以我后面加上了清华源链接：
   pip install tensorflow-gpu==1.13.1 -i https://pypi.tuna.tsinghua.edu.cn/simple
   网不好的时候可能就会全红，就会像下面一样报错read timed out，没关系多安几次，总有网顺的时候：

   File "/home/yy/anaconda3/envs/tf/lib/python3.7/site-packages/pip/_vendor/urllib3/response.py", line 576, in stream
   data = self.read(amt=amt, decode_content=decode_content)
   File "/home/yy/anaconda3/envs/tf/lib/python3.7/site-packages/pip/_vendor/urllib3/response.py", line 541, in read
   raise IncompleteRead(self._fp_bytes_read, self.length_remaining)
   File "/home/yy/anaconda3/envs/tf/lib/python3.7/contextlib.py", line 130, in __exit__
   self.gen.throw(type, value, traceback)
   File "/home/yy/anaconda3/envs/tf/lib/python3.7/site-packages/pip/_vendor/urllib3/response.py", line 442, in _error_catcher
   raise ReadTimeoutError(self._pool, None, "Read timed out.")
   pip._vendor.urllib3.exceptions.ReadTimeoutError: HTTPSConnectionPool(host='pypi.tuna.tsinghua.edu.cn', port=443): Read timed out.
   

   安装完毕后，进入python再输入import tensorflow as tf测试下：

   (tf) root@yy:~# python
   Python 3.7.7 (default, May  7 2020, 21:25:33) 
   [GCC 7.3.0] :: Anaconda, Inc. on linux
   Type "help", "copyright", "credits" or "license" for more information.
   >>> import tensorflow as tf
   Traceback (most recent call last):
     File "/home/yy/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow.py", line 58, in <module>
       from tensorflow.python.pywrap_tensorflow_internal import *
     File "/home/yy/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in <module>
       _pywrap_tensorflow_internal = swig_import_helper()
     File "/home/yy/anaconda3/envs/tf/lib/python3.7/site-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
       _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
     File "/home/yy/anaconda3/envs/tf/lib/python3.7/imp.py", line 242, in load_module
       return load_dynamic(name, filename, file)
     File "/home/yy/anaconda3/envs/tf/lib/python3.7/imp.py", line 342, in load_dynamic
       return _load(spec)
   ImportError: libcublas.so.10.0: cannot open shared object file: No such file or directory
   

   哇噢，报错了耶，不要捉鸡！先输入quit()退出python，
   再在命令行输入：
   ldconfig /usr/local/cuda-10.0/lib64
   结果如下：

   >>> quit()
   (tf) root@yy:~# ldconfig /usr/local/cuda-10.0/lib64
   (tf) root@yy:~# python
   Python 3.7.7 (default, May  7 2020, 21:25:33) 
   [GCC 7.3.0] :: Anaconda, Inc. on linux
   Type "help", "copyright", "credits" or "license" for more information.
   >>> import tensorflow as tf
   >>> 
   

   呼～报错解除！此时我们查看下numpy的版本：

   >>> import numpy
   >>> numpy.__version__
   '1.19.1'
   

   好像版本太高啦，我们降低下版本：
   pip install -U numpy==1.16.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
   到这里就全部结束啦～

我跑下pointnet++康康
**做个小测试，只跑一个epoch

parser.add_argument('--num_point', type=int, default=1024, help='Point Number [default: 1024]')
parser.add_argument('--max_epoch', type=int, default=1, help='Epoch to run [default: 251]')
parser.add_argument('--batch_size', type=int, default=8, help='Batch Size during training [default: 16]')

very good!完全莫得问题！

(tf) root@yy:/media/yy/Data/ipython_jupyter/pointnet2123# python train.py
**** EPOCH 000 ****
2020-08-12 17:13:44.277590
---- batch: 050 ----
mean loss: 3.805058
accuracy: 0.127500
 ---- batch: 100 ----
mean loss: 3.299858
accuracy: 0.205000
.......这里太多了，省略掉.........
 ---- batch: 1200 ----
mean loss: 1.797384
accuracy: 0.492500
2020-08-12 17:18:01.698818
---- EPOCH 000 EVALUATION ----
eval mean loss: 1.345066
eval accuracy: 0.606969
eval avg class acc: 0.502087
Model saved in file: log/model.ckpt