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作者：Alex Collins
Python 是用户在 Kubernetes 上编写机器学习工作流的风行编程语言。
开箱即用时，Argo 并没有为 Python 提供一流的反对。相同，咱们提供 Java、Golang 和 Python API 客户端。
但这对大多数用户来说还不够。许多用户须要一个形象层来增加组件和特定于用例的个性。
明天你有两个抉择。
KFP 编译器 + Python 客户端
Argo 工作流被用作执行 Kubeflow 流水线的引擎。你能够定义一个 Kubeflow 流水线，并在 Python 中将其间接编译到 Argo 工作流中。
而后你能够应用 Argo Python 客户端向 Argo 服务器 API 提交工作流。
这种办法容许你利用现有的 Kubeflow 组件。
装置：
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()运行这个来创立你的工作流：
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-flip
templates:
- name: coin-flip
dag:
tasks:
- name: condition-1
template: condition-1
when: '"{{tasks.flip-a-coin.outputs.parameters.flip-a-coin-output}}" == "heads"'
dependencies: [flip-a-coin]
- name: condition-2
template: condition-2
when: '"{{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-1
dag:
tasks:
- {name: heads, template: heads}
- name: condition-2
dag:
tasks:
- {name: tails, template: tails}
- name: flip-a-coin
container:
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:alpine3.6
outputs:
parameters:
- name: flip-a-coin-output
valueFrom: {path: /output}
artifacts:
- {name: flip-a-coin-output, path: /output}
- name: heads
container:
command: [sh, -c, echo "it was heads"]
image: alpine:3.6
- name: tails
container:
command: [sh, -c, echo "it was tails"]
image: alpine:3.6
arguments:
parameters: []
serviceAccountName: pipeline-runner留神，Kubeflow 不反对 这种办法。
你能够应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 是一个风行的我的项目，它容许你以一种平台无感的形式指定工作流，但它次要反对 Argo 工作流（打算在将来反对 Kubeflow 和 AirFlow）：
装置：
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)这会创立以下工作流程：
apiVersion: argoproj.io/v1alpha1
kind: Workflow
metadata:
generateName: couler-example-
spec:
templates:
- name: couler-example
steps:
- - name: flip-coin-29
template: flip-coin
- - name: heads-31
template: heads
when: '{{steps.flip-coin-29.outputs.result}} == heads'
- name: tails-32
template: tails
when: '{{steps.flip-coin-29.outputs.result}} == tails'
- name: flip-coin
script:
name: ''image:'python:alpine3.6'
command:
- python
source: |
import random
res = "heads" if random.randint(0, 1) == 0 else "tails"
print(res)
- name: heads
container:
image: 'alpine:3.6'
command:
- sh
- '-c'
- echo "it was heads"
- name: tails
container:
image: 'alpine:3.6'
command:
- sh
- '-c'
- echo "it was tails"
entrypoint: couler-example
ttlStrategy:
secondsAfterCompletion: 600
activeDeadlineSeconds: 300
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