Tensorflow使用GPU训练
确认显卡驱动正确安装：
(notebook) [wuhf@aps ~]$ nvidia-smi
Thu Aug 20 18:07:33 2020
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 430.50 Driver Version: 430.50 CUDA Version: 10.1 |
|-------------------------------+----------------------+----------------------+
| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |
|===============================+======================+======================|
| 0 GeForce GTX 1080 Off | 00000000:82:00.0 Off | N/A |
| 27% 29C P8 6W / 180W | 113MiB / 8119MiB | 0% Default |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: GPU Memory |
| GPU PID Type Process name Usage |
|=============================================================================|
| 0 37575 C ...uhf/miniconda3/envs/notebook/bin/python 103MiB |
+-----------------------------------------------------------------------------+
CUDA版本和Tensorflow版本有对应关系，TF2.0可以使用CUDA 10.1，安装TF2.0版本，查看conda 源中的TF ：
(notebook) [wuhf@aps ~]$ conda search tensorflow |grep 2.0.0
tensorflow 2.0.0 eigen_py27hec4e49e_0 pkgs/main
tensorflow 2.0.0 eigen_py36ha83d16c_0 pkgs/main
tensorflow 2.0.0 eigen_py37hce6be7f_0 pkgs/main
tensorflow 2.0.0 gpu_py27hb041a2f_0 pkgs/main
tensorflow 2.0.0 gpu_py36h6b29c10_0 pkgs/main
tensorflow 2.0.0 gpu_py37h768510d_0 pkgs/main
tensorflow 2.0.0 mkl_py27h68eb67f_0 pkgs/main
tensorflow 2.0.0 mkl_py36hef7ec59_0 pkgs/main
tensorflow 2.0.0 mkl_py37h66b46cc_0 pkgs/mai
一定要安装 gpu的build，指定build安装方法：
conda install {project}={version}={build}
执行命令：
conda install tensorflow=2.0.0=gpu_py36h6b29c10_0
然后来执行python代码测试TF是否正常：
import tensorflow as tf
tf.test.is_gpu_available()
tf.test.gpu_device_name()
输出：
Type "help", "copyright", "credits" or "license" for more information.
>>> import tensorflow as tf
>>> tf.test.is_gpu_available()
True
>>> tf.test.gpu_device_name()
2020-08-20 18:16:13.857330: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1618] Found device 0 with properties:
'/device:GPU:0'
>>>
如果安装很慢，可使用国内源，在用户目录下新建.condarc文件，内容如下：
channels:
- defaults
show_channel_urls: true
channel_alias: https://mirrors.tuna.tsinghua.edu.cn/anaconda
default_channels:
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/r
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/pro
- https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/msys2
custom_channels:
conda-forge: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
msys2: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
bioconda: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
menpo: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
pytorch: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
simpleitk: https://mirrors.tuna.tsinghua.edu.cn/anaconda/cloud
使用pip安装
不管是gpu还是cpu版本的tf，安装文件都会复制到{python}/lib/python3.7/site-packages/tensorflow 这样后安装就会覆盖新安装的。
关键点总结：
- tensorflow一定要安装带gpu的build
- tensorflow-gpu这个不用安装
- tensorflow的版本要和cuda版本匹配，可以去官网查看对应关系
资料：
Tensorflow与CUDA之间的关系： https://tensorflow.google.cn/install/gpu
