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这篇CFSDN的博客文章支持python的分布式计算框架Ray详解由作者收集整理,如果你对这篇文章有兴趣,记得点赞哟.
项目地址:https://github.com/ray-project/ray 。
Ray为构建分布式应用程序提供了一个简单、通用的API。Ray是一种分布式执行框架,便于大规模应用程序和利用先进的机器学习库.
Ray通过以下方式完成这项任务:
为构建和运行分布式应用程序提供简单的原语.
使最终用户能够并行化单个机器代码,而代码更改很少到零.
在核心Ray之上包含大量应用程序、库和工具,以支持复杂的应用程序.
安装方式比较简单: pip install ray==1.4.1 。
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[root@node2 ~]
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in
/
usr
/
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6
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site
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packages (
from
google
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api
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core<
2.0
.
0
,>
=
1.0
.
0
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>opencensus
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>ray[default]) (
2020.1
)
Collecting cachetools<
5.0
,>
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is
not
installed.
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installed.
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is
not
installed.
Installing collected packages: pyasn1, setuptools, rsa, pyparsing, pyasn1
-
modules, protobuf, multidict, immutables, cachetools, yarl, packaging, idna
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auth, contextvars, attrs, async
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timeout, pyrsistent, psutil, opencensus
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context, nvidia
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core, blessings, aiohttp, redis, pyyaml, pydantic, py
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|
下面是不使用分布式的代码示例:
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|
# test_ray.py
import
ray
import
cv2
import
numpy
import
time
#@ray.remote
def
test(img):
orb
=
cv2.AKAZE_create()
kb
=
orb.detect(img,
None
)
kp, des
=
orb.compute(img, kp)
return
des
# 不使用注解的方式
test_remote
=
ray.remote(test)
# 函数test 加注解时使用方式
# test_remote = test.remote(test)
img
=
cv2.imread(
"/tmp/test.jpg"
)
start_time
=
time.time()
for
i
in
range
(
10
):
test(img)
print
(time.time()
-
start_time)
|
运行:python test_ray.py 。
如果需要启动集群模式,则需要先启动服务,选择一台机器作为主服务器,然后按虾米那命令启动:
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|
[root@node1 ~]
# ray start --head --port=8888
Local node IP:
192.168
.
0.81
2021
-
07
-
28
09
:
58
:
44
,
051
INFO services.py:
1274
-
-
View the Ray dashboard at http:
/
/
127.0
.
0.1
:
8265
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Ray runtime started.
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Next
steps
To connect to this Ray runtime
from
another node, run
ray start
-
-
address
=
'192.168.0.81:8888'
-
-
redis
-
password
=
'5241590000000000'
Alternatively, use the following Python code:
import
ray
ray.init(address
=
'auto'
, _redis_password
=
'5241590000000000'
)
If connection fails, check your firewall settings
and
network configuration.
To terminate the Ray runtime, run
ray stop
|
可以看到里面提示具体的用法。此时通过ray的web界面,端口8265访问,看到已有一台机器.
然后在另外两台机启动客户端节点,保证各个节点的ray版本一致,查看版本:
# pip freeze |grep ray 。
版本不一致的话,要升级为一致:pip install -U ray, 此处选择的版本为1.4.1 。
从节点启动方式(密码可以在主节点启动时显示):
1
|
ray start
-
-
address
=
'192.168.0.81:8888'
-
-
redis
-
password
=
'5241590000000000'
|
改写程序,用于分布式:
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2
3
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5
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8
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|
# test_ray.py
import
ray
import
cv2
import
numpy
import
time
@ray
.remote
def
test(img):
orb
=
cv2.AKAZE_create()
kb
=
orb.detect(img,
None
)
kp, des
=
orb.compute(img, kp)
return
des
# 不使用注解的方式
#test_remote = ray.remote(test)
# 函数test 加注解时使用方式
test_remote
=
test.remote(test)
if
__name__
=
=
"__main__"
:
ray.init(address
=
'auto'
, _redis_password
=
'5241590000000000'
)
start_time
=
time.time()
img
=
cv2.imread(
"/tmp/test.jpg"
)
futures
=
[test_remote.remote(img)
for
i
in
range
(
100
)]
ray.get(futures)
start_time
=
time.time()
print
(time.time()
-
start_time)
|
在主节点运行程序,程序会在3台机器上启动。停止ray服务的方式:ray stop. 。
到此这篇关于支持python的分布式计算框架Ray的文章就介绍到这了,更多相关python的分布式计算框架Ray内容请搜索我以前的文章或继续浏览下面的相关文章希望大家以后多多支持我! 。
原文链接:https://blog.csdn.net/Ajinnv/article/details/119153287 。
最后此篇关于支持python的分布式计算框架Ray详解的文章就讲到这里了,如果你想了解更多关于支持python的分布式计算框架Ray详解的内容请搜索CFSDN的文章或继续浏览相关文章,希望大家以后支持我的博客! 。
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实例如下: ? 1
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Linux的网络虚拟化是LXC项目中的一个子项目,LXC包括文件系统虚拟化,进程空间虚拟化,用户虚拟化,网络虚拟化,等等,这里使用LXC的网络虚拟化来模拟多个网络环境。 本文从基本的网络设备讲
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