Python实现简单的http服务器程序

主要的需求是这样的，需要调用服务器端的程序获取GPU服务器的信息并且返回给前端显示出来，那么就需要完成一个服务器端的程序，获取服务器的数据之后将数据返回（以JSON格式）。

效果如下图：






页面没有内容是因为服务程序还没有启动。下面完成服务器程序：

#!/usr/bin/python

from bottle import route,run,template
import os
from bottle import get,post,request
import re

gpu_info_dict = {}

@route('/')
@route('/xiandao')
def func():
os.system("./deviceQuery > gpu_info")
os.system(" nvidia-smi >> gpu_info")

fs = open('gpu_info','r')
i = 1
for line in fs.readlines():
a = line.strip().split(":")
if i == 7:
gpu_info_dict['device'] = a[-1].strip()
elif i == 9:
gpu_info_dict['cuda_version_number'] = '"'+a[-1].strip()+'"'
elif i == 10:
gpu_info_dict['global_memory'] = '"'+a[-1].strip()+'"'
elif i == 11:
gpu_info_dict['total_cores'] = '"'+a[-1].strip()+'"'
elif i == 12:
gpu_info_dict['gpu_clock_rate'] = '"'+a[-1].strip()+'"'
elif i == 13:
gpu_info_dict['mem_clock_rate'] = '"'+a[-1].strip()+'"'
elif i == 14:
gpu_info_dict['mem_bus_width'] = '"'+a[-1].strip()+'"'
elif i == 19:
gpu_info_dict['constant mem'] = '"'+a[-1].strip()+'"'
elif i == 20:
gpu_info_dict['shared_mem'] = '"'+a[-1].strip()+'"'
elif i == 21:
gpu_info_dict['registers_available'] = '"'+a[-1].strip()+'"'
elif i == 50:
l1 = line[19:32].strip().split("/")
gpu_info_dict['power_used'] = '"'+l1[0].strip()+'"'
gpu_info_dict['power_capacity'] = '"'+l1[1].strip()+'"'
l2 = line[34:55].strip().split("/")
gpu_info_dict['mem_used'] = '"'+l2[0].strip()+'"'
gpu_info_dict['mem_capacity'] = '"'+l2[1].strip()+'"'
i += 1

#生成json格式的字符串并返回
json = "{"
for i in gpu_info_dict:
json += '"'+i+'"'+":"+gpu_info_dict[i]+","
json += "}"
return json

run(host='172.16.1.20',port=8088,debug=True)

1)bottle是一个应用于小网页应用的快速简单的框架（http://yunpan.cn/cytIgzQXPjeaS （提取码：8e71））。

2)13/14行是调用程序和命令，将获取gpu的信息并重定向到文件gpu_info中去。生成如下文件：

./deviceQuery Starting...

CUDA Device Query (Runtime API) version (CUDART static linking)

Detected 1 CUDA Capable device(s)

Device 0: "Tesla K40m"
CUDA Driver Version / Runtime Version          5.5 / 5.5
CUDA Capability Major/Minor version number:    3.5
Total amount of global memory:                 11520 MBytes (12079136768 bytes)
(15) Multiprocessors, (192) CUDA Cores/MP:     2880 CUDA Cores
GPU Clock rate:                                876 MHz (0.88 GHz)
Memory Clock rate:                             3004 Mhz
Memory Bus Width:                              384-bit
L2 Cache Size:                                 1572864 bytes
Maximum Texture Dimension Size (x,y,z)         1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
Maximum Layered 1D Texture Size, (num) layers  1D=(16384), 2048 layers
Maximum Layered 2D Texture Size, (num) layers  2D=(16384, 16384), 2048 layers
Total amount of constant memory:               65536 bytes
Total amount of shared memory per block:       49152 bytes
Total number of registers available per block: 65536
Warp size:                                     32
Maximum number of threads per multiprocessor:  2048
Maximum number of threads per block:           1024
Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
Max dimension size of a grid size    (x,y,z): (2147483647, 65535, 65535)
Maximum memory pitch:                          2147483647 bytes
Texture alignment:                             512 bytes
Concurrent copy and kernel execution:          Yes with 2 copy engine(s)
Run time limit on kernels:                     No
Integrated GPU sharing Host Memory:            No
Support host page-locked memory mapping:       Yes
Alignment requirement for Surfaces:            Yes
Device has ECC support:                        Enabled
Device supports Unified Addressing (UVA):      Yes
Device PCI Bus ID / PCI location ID:           3 / 0
Compute Mode:
< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >

deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 5.5, CUDA Runtime Version = 5.5, NumDevs = 1, Device0 = Tesla K40m
Result = PASS
Tue Jan 13 21:18:02 2015
+------------------------------------------------------+
| NVIDIA-SMI 5.319.37   Driver Version: 319.37         |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Tesla K40m          Off  | 0000:03:00.0     Off |                    0 |
| N/A   27C    P0    61W / 235W |       69MB / 11519MB |     99%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Compute processes:                                               GPU Memory |
|  GPU       PID  Process name                                     Usage      |
|=============================================================================|
|  No running compute processes found                                         |
+-----------------------------------------------------------------------------+

3)那么现在就需要对这个文件中的关键信息进行提取，然后形成JSON格式的字符串返回。

运行程序：



学习一下nohup命令：

nohup 的用途就是让提交的命令忽略 hangup 信号，让程序在后台运行。nohup
的使用是十分方便的，只需在要处理的命令前加上 nohup 即可，标准输出和标准错误缺省会被重定向到 nohup.out 文件中。一般我们可在结尾加上"&"来将命令同时放入后台运行，也可用

">filename 2>&1"

来更改缺省的重定向文件名。

[root@pvcent107 ~]# nohup ping www.ibm.com &
[1] 3059
nohup: appending output to `nohup.out'
[root@pvcent107 ~]# ps -ef |grep 3059
root      3059   984  0 21:06 pts/3    00:00:00 ping www.ibm.com
root      3067   984  0 21:06 pts/3    00:00:00 grep 3059

端口映射：

可以观察到run.py中的ip=172.16.1.20（serverA），port=8088。这个ip不能直接访问，需要跳板机（ip：http://172.21.7.224）才能访问，因此需要建立一个跳板机到serverA的一个映射，这样访问跳板机某个端口的时候就相当于去访问serverA的某个端口对应的应用程序。

那么在服务器程序启动的情况下，就可以通过网页进行IP访问了：





成功得到了数据:)

作者：忆之独秀

邮箱：leaguenew@qq.com

注明出处：http://blog.csdn.net/lavorange/article/details/42684851