支持python的分布式计算框架Ray详解

项目地址：https://github.com/ray-project/ray

1、简介

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。

2、安装

安装方式比较简单： pip install ray==1.4.1

[root@node2 ~]# pip install 'ray[default]'
Looking in indexes: https://mirrors.aliyun.com/pypi/simple/
Collecting ray[default]
  Downloading https://mirrors.aliyun.com/pypi/packages/13/ec/f727ddd3fbcdc6102eace62c9d5dd9d9ad8112d40eeb7de8783676aca24d/ray-1.4.1-cp36-cp36m-manylinux2014_x86_64.whl (51.6 MB)
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Collecting aiohttp
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Requirement already satisfied: dataclasses in /usr/local/lib/python3.6/site-packages (from ray[default]) (0.8)
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Requirement already satisfied: requests in /usr/local/lib/python3.6/site-packages (from ray[default]) (2.24.0)
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Requirement already satisfied: click>=7.0 in /usr/local/lib/python3.6/site-packages (from ray[default]) (7.1.2)
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Collecting colorful
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Requirement already satisfied: six>=1.5.2 in /usr/local/lib/python3.6/site-packages (from grpcio>=1.28.1->ray[default]) (1.15.0)
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Collecting multidict<7.0,>=4.5
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Collecting idna-ssl>=1.0
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Requirement already satisfied: chardet<5.0,>=2.0 in /usr/local/lib/python3.6/site-packages (from aiohttp->ray[default]) (3.0.4)
Collecting yarl<2.0,>=1.0
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Collecting nvidia-ml-py3>=7.352.0
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Collecting psutil
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Collecting blessings>=1.6
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Collecting pyrsistent>=0.14.0
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Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.6/site-packages (from jsonschema->ray[default]) (4.3.0)
Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.6/site-packages (from importlib-metadata->jsonschema->ray[default]) (3.4.1)
Collecting opencensus-context==0.1.2
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Collecting google-api-core<2.0.0,>=1.0.0
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Collecting contextvars
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Collecting google-auth<2.0dev,>=1.25.0
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Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/site-packages (from requests->ray[default]) (2020.6.20)
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Using legacy 'setup.py install' for contextvars, since package 'wheel' is not installed.
Installing collected packages: pyasn1, setuptools, rsa, pyparsing, pyasn1-modules, protobuf, multidict, immutables, cachetools, yarl, packaging, idna-ssl, googleapis-common-protos, google-auth, contextvars, attrs, async-timeout, pyrsistent, psutil, opencensus-context, nvidia-ml-py3, hiredis, google-api-core, blessings, aiohttp, redis, pyyaml, pydantic, py-spy, prometheus-client, opencensus, numpy, msgpack, jsonschema, grpcio, gpustat, filelock, colorama, aioredis, aiohttp-cors, ray, colorful
  Attempting uninstall: setuptools
    Found existing installation: setuptools 39.2.0
    Uninstalling setuptools-39.2.0:
      Successfully uninstalled setuptools-39.2.0
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Successfully installed aiohttp-3.7.4.post0 aiohttp-cors-0.7.0 aioredis-1.3.1 async-timeout-3.0.1 attrs-21.2.0 blessings-1.7 cachetools-4.2.2 colorama-0.4.4 colorful-0.5.4 contextvars-2.4 filelock-3.0.12 google-api-core-1.31.0 google-auth-1.33.1 googleapis-common-protos-1.53.0 gpustat-0.6.0 grpcio-1.39.0 hiredis-2.0.0 idna-ssl-1.1.0 immutables-0.15 jsonschema-3.2.0 msgpack-1.0.2 multidict-5.1.0 numpy-1.19.5 nvidia-ml-py3-7.352.0 opencensus-0.7.13 opencensus-context-0.1.2 packaging-21.0 prometheus-client-0.11.0 protobuf-3.17.3 psutil-5.8.0 py-spy-0.3.7 pyasn1-0.4.8 pyasn1-modules-0.2.8 pydantic-1.8.2 pyparsing-2.4.7 pyrsistent-0.18.0 pyyaml-5.4.1 ray-1.4.1 redis-3.5.3 rsa-4.7.2 setuptools-57.4.0 yarl-1.6.3

3、单机

下面是不使用分布式的代码示例：

# 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

4、集群

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：

[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)：

ray start --address='192.168.0.81:8888' --redis-password='5241590000000000'

改写程序，用于分布式：

# 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.

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