使用Python开源库Couler编写和提交Argo Workflow工作流

article/2025/8/14 21:26:55

Python 是用户在 Kubernetes 上编写机器学习工作流的流行编程语言。

开箱即用时，Argo 并没有为 Python 提供一流的支持。相反，我们提供Java、Golang 和 Python API 客户端[1]。

但这对大多数用户来说还不够。许多用户需要一个抽象层来添加组件和特定于用例的特性。

今天你有两个选择。

KFP 编译器+ Python 客户端

Argo 工作流被用作执行 Kubeflow 流水线的引擎。你可以定义一个 Kubeflow 流水线，并在 Python 中将其直接编译到 Argo 工作流中。

然后你可以使用Argo Python 客户端[2]向 Argo 服务器 API 提交工作流。

这种方法允许你利用现有的 Kubeflow 组件。

安装：

pip3 install kfp
pip3 install argo-workflows

例子：

import kfp as kfp
def flip_coin():return kfp.dsl.ContainerOp(name='Flip a coin',image='python:alpine3.6',command=['python', '-c', """
import random
res = "heads" if random.randint(0, 1) == 0 else "tails"
with open('/output', 'w') as f:f.write(res)"""],file_outputs={'output': '/output'})
def heads():return kfp.dsl.ContainerOp(name='Heads', image="alpine:3.6", command=["sh", "-c", 'echo "it was heads"'])
def tails():return kfp.dsl.ContainerOp(name='Tails', image="alpine:3.6", command=["sh", "-c", 'echo "it was tails"'])
@kfp.dsl.pipeline(name='Coin-flip', description='Flip a coin')
def coin_flip_pipeline():flip = flip_coin()with kfp.dsl.Condition(flip.output == 'heads'):heads()with kfp.dsl.Condition(flip.output == 'tails'):tails()
def main():kfp.compiler.Compiler().compile(coin_flip_pipeline, __file__ + ".yaml")
if __name__ == '__main__':main()

运行这个来创建你的工作流：

apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:generateName: coin-flip-annotations: {pipelines.kubeflow.org/kfp_sdk_version: 1.3.0, pipelines.kubeflow.org/pipeline_compilation_time: '2021-01-21T17:17:54.299235',pipelines.kubeflow.org/pipeline_spec: '{"description": "Flip a coin", "name":"Coin-flip"}'}labels: {pipelines.kubeflow.org/kfp_sdk_version: 1.3.0}
spec:entrypoint: coin-fliptemplates:- name: coin-flipdag:tasks:- name: condition-1template: condition-1when: '"{{tasks.flip-a-coin.outputs.parameters.flip-a-coin-output}}" == "heads"'dependencies: [flip-a-coin]- name: condition-2template: condition-2when: '"{{tasks.flip-a-coin.outputs.parameters.flip-a-coin-output}}" == "tails"'dependencies: [flip-a-coin]- {name: flip-a-coin, template: flip-a-coin}- name: condition-1dag:tasks:- {name: heads, template: heads}- name: condition-2dag:tasks:- {name: tails, template: tails}- name: flip-a-coincontainer:command:- python- -c- "\nimport random\nres = \"heads\" if random.randint(0, 1) == 0 else \"tails\"\\nwith open('/output', 'w') as f:\n    f.write(res)        \n        "image: python:alpine3.6outputs:parameters:- name: flip-a-coin-outputvalueFrom: {path: /output}artifacts:- {name: flip-a-coin-output, path: /output}- name: headscontainer:command: [sh, -c, echo "it was heads"]image: alpine:3.6- name: tailscontainer:command: [sh, -c, echo "it was tails"]image: alpine:3.6arguments:parameters: []serviceAccountName: pipeline-runner

注意，Kubeflow 不支持这种方法。

你可以使用客户端提交上述工作流程如下：

import yaml
from argo.workflows.client import (ApiClient,WorkflowServiceApi,Configuration,V1alpha1WorkflowCreateRequest)
def main():config = Configuration(host="http://localhost:2746")client = ApiClient(configuration=config)service = WorkflowServiceApi(api_client=client)
with open("coin-flip.py.yaml") as f:manifest: dict = yaml.safe_load(f)
del manifest['spec']['serviceAccountName']
service.create_workflow('argo', V1alpha1WorkflowCreateRequest(workflow=manifest))
if __name__ == '__main__':main()

Couler

Couler[3]是一个流行的项目，它允许你以一种平台无感的方式指定工作流，但它主要支持 Argo 工作流（计划在未来支持 Kubeflow 和 AirFlow）：

安装：

pip3 install git+https://github.com/couler-proj/couler

例子：

import couler.argo as couler
from couler.argo_submitter import ArgoSubmitter
def random_code():import random
res = "heads" if random.randint(0, 1) == 0 else "tails"print(res)
def flip_coin():return couler.run_script(image="python:alpine3.6", source=random_code)
def heads():return couler.run_container(image="alpine:3.6", command=["sh", "-c", 'echo "it was heads"'])
def tails():return couler.run_container(image="alpine:3.6", command=["sh", "-c", 'echo "it was tails"'])
result = flip_coin()
couler.when(couler.equal(result, "heads"), lambda: heads())
couler.when(couler.equal(result, "tails"), lambda: tails())
submitter = ArgoSubmitter()
couler.run(submitter=submitter)

这会创建以下工作流程：

apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:generateName: couler-example-
spec:templates:- name: couler-examplesteps:- - name: flip-coin-29template: flip-coin- - name: heads-31template: headswhen: '{{steps.flip-coin-29.outputs.result}} == heads'- name: tails-32template: tailswhen: '{{steps.flip-coin-29.outputs.result}} == tails'- name: flip-coinscript:name: ''image: 'python:alpine3.6'command:- pythonsource: |
import random
res = "heads" if random.randint(0, 1) == 0 else "tails"print(res)- name: headscontainer:image: 'alpine:3.6'command:- sh- '-c'- echo "it was heads"- name: tailscontainer:image: 'alpine:3.6'command:- sh- '-c'- echo "it was tails"entrypoint: couler-examplettlStrategy:secondsAfterCompletion: 600activeDeadlineSeconds: 300

参考资料

使用Python编写和提交Argo工作流 - SegmentFault 思否10人将获赠CNCF商店$100美元礼券！你填了吗？问卷链接（[链接]）作者：Alex CollinsPython 是用户在 Kubernetes 上编写机器学习工作流的流行编程语言。开箱即...https://segmentfault.com/a/1190000039101534[1]