爲pointnet++的運行搭建環境ubuntu18.04+cuda10.0+cudnn7.4.2+anaconda3+tensorflow-gpu1.13.1（超超超超簡單的版本！！輕輕鬆鬆搭建好！

重裝好幾回了！沒有人比我更懂重裝（不是

如今我默認你們都才裝好ubuntu18.04，就是幹！請注意！我這裏是經過安裝cuda來安裝顯卡驅動！想要單獨安裝顯卡驅動（好比英偉達官網下載run文件或者經過ubuntu-drivers devices來安裝系統推薦的驅動版本）的同窗請看其餘教程！可是(◔◡◔)重裝屢次的我以爲，反正都要裝cuda，因此經過cuda安裝nvidia是最簡單不過啦~
注：sudo是獲取臨時root權限，因此咱們開局直接進root
如今咱們來看下大體流程：
cuda（順便安裝顯卡驅動）–> cudnn --> anaconda3 -->搭建環境–>安裝tensorflow-gpu

python

換源（加快下載速度
使用root權限：
sudo -s
備份源碼：
cp /etc/apt/sources.list /etc/apt/sources.list.bak
替換源列表內容：
gedit /etc/apt/sources.list
打開list後，將如下內容替換掉原來的：

linux

# 默認註釋了源碼鏡像以提升 apt update 速度，若有須要可自行取消註釋
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

# 預發佈軟件源，不建議啓用
# 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

記得點保存
更新列表：
apt-get update
OK，換源成功！

ubuntu

禁用系統自帶的顯卡驅動
打開系統禁用列表：
gedit /etc/modprobe.d/blacklist.conf
經過添加如下代碼，將nouveau拉入黑名單！哼，咱們不和它玩兒！：
blacklist nouveau
options nouveau modset=0
而後更新下咱們修改的內容，讓它生效：
update-initramfs -u
重啓：
reboot
再看看這玩意兒還敢出來不：
lsmod | grep nouveau
OK，沒有任何輸出（它怕了它怕了哈哈

bash
安裝相關依賴
安裝gcc（記得進入root模式哦：
apt install build-essential

ionic

安裝cuda（安裝它對應的顯卡驅動
寶貝們乖乖去官網下載哦～
—>指路http://developer.nvidia.com/cuda-downloads
到安裝文件目錄下運行.run文件（萌新小妙招～輸入cd再空一格，將存放run文件的文件夾拖入終端，再回車，就能夠進入安裝目錄啦～而後輸入ls還能夠查看目錄下的文件哦）：
sh cuda_10.0.130_410.48_linux.run
舒適提示：記得替換爲本身的cuda文件名
安裝過程當中，輸入accept
若是以前沒有裝顯卡驅動，那麼在安裝cuda的過程當中能夠在這裏安裝哦（是我本人了

測試

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

不要選擇openGL！ui

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

關於這個服務（可y可n：url

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

後面的問題都y或者enter默認，來看看結果：spa

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

安裝完成後，須要添加環境變量：
gedit ~/.bashrc
在文件最後加入如下代碼（記得改爲本身的cuda版本哦

命令行

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

添加並保存，將文件生效：
source ~/.bashrc
最後咱們查看下cuda的版本信息以及nvidia驅動信息：
nvcc -V
cuda的版本信息以下：

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驅動信息查詢：
nvidia-smi
查詢結果以下：

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                                                 |
	+-----------------------------------------------------------------------------+

安裝cudnn
去官網下載壓縮包
—>指路https://developer.nvidia.com/rdp/cudnn-archive
下載好後，咱們來解壓它（此時壓縮包在你的下載目錄下：
首先進入下載目錄，而後開始解壓：
tar -zxvf cudnn-10.0-linux-x64-v7.4.2.24.tgz
解壓結果以下：
```
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
```
而後咱們須要把cudnn移動到cuda中：
cp -P cuda/lib64/libcudnn* /usr/local/cuda-10.0/lib64/
cp cuda/include/cudnn.h /usr/local/cuda-10.0/include/

爲全部用戶設置讀取權限（記得改爲你本身的版本號！
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
結果以下（個人是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)
```
安裝anaconda3
沒有下載的寶貝，去清華源（速度賊快
請看路—>https://mirrors.tuna.tsinghua.edu.cn/help/anaconda/
進入下載文件的目錄中運行：
bash Anaconda3-2020.02-Linux-x86_64.sh
爲anaconda加入環境變量：
gedit ~/.bashrc
在bashrc的最後加入（記得修改成本身的用戶名：
```
export PATH="/home/yy/anaconda3/bin:$PATH"
```
最後別忘更新下：
source ~/.bashrc
搭建環境
確保本身在root模式下！建立環境（tf是我本身命名的，你們根據本身喜愛改～：
conda create -n tf python=3.7
激活剛剛咱們建立的環境：
source activate tf
激活後，咱們的命令行開頭就有環境名啦～說明此時咱們正處於tf這個環境中：
```
root@yy:~# source activate tf
(tf) root@yy:~#
```

安裝tensorflow-gpu
在激活環境中輸入（直接用pip太慢了，因此我後面加上了清華源連接：
pip install tensorflow-gpu==1.13.1 -i https://pypi.tuna.tsinghua.edu.cn/simple
網很差的時候可能就會全紅，就會像下面同樣報錯read timed out，不要緊多安幾回，總有網順的時候：

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.

安裝完畢後，進入python再輸入import tensorflow as tf測試下：

(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，報錯了耶，不要捉雞！先輸入quit()退出python，
再在命令行輸入：
ldconfig /usr/local/cuda-10.0/lib64
結果以下：

>>> 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～報錯解除！此時咱們查看下numpy的版本：

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

好像版本過高啦，咱們下降下版本：
pip install -U numpy==1.16.0 -i https://pypi.tuna.tsinghua.edu.cn/simple
到這裏就所有結束啦～

我跑下pointnet++康康
**作個小測試，只跑一個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!徹底莫得問題！

(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
.......這裏太多了，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