Server - 配置 Kubernetes 多任务流程 KubeFlow Pipeline 的开发教程

news2024/11/16 5:29:30

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KFP

Kubernetes 的 KFP（Kubeflow Pipelines）是一个平台，用于构建和部署可扩展和可移植的机器学习（ML）工作流。允许用户以简单、可复用和可组合的方式定义机器学习任务，并支持各种 ML 框架。KFP 包括一个用于构建 ML 管道的 SDK，以及用于运行这些管道的运行时环境。通过 KFP，用户可以轻松地将机器学习模型从实验转移到生产环境，同时保持模型的可追溯性和版本控制。

KFP 的建议版本 1.8.19 或 1.8.22：

Name: kfp
Version: 1.8.22
Summary: KubeFlow Pipelines SDK
Home-page: https://github.com/kubeflow/pipelines
Author: The Kubeflow Authors
Author-email: 
License: 
Location: /Users/wang/venv_ml_p3/lib/python3.9/site-packages
Requires: absl-py, click, cloudpickle, Deprecated, docstring-parser, fire, google-api-core, google-api-python-client, google-auth, google-cloud-storage, jsonschema, kfp-pipeline-spec, kfp-server-api, kubernetes, protobuf, pydantic, PyYAML, requests-toolbelt, strip-hints, tabulate, typer, uritemplate, urllib3
Required-by: 


Name: PyYAML
Version: 6.0.1
Summary: YAML parser and emitter for Python
Home-page: https://pyyaml.org/
Author: Kirill Simonov
Author-email: xi@resolvent.net
License: MIT
Location: /Users/wang/venv_ml_p3/lib/python3.9/site-packages
Requires: 
Required-by: kfp, kubernetes

注意：不要安装最新的版本，否则会报错在component_factory.py中，Missing type annotation for argument

在安装 kfp 过程中，如果遇到 AttributeError: cython_sources 问题，即错误：

running egg_info
writing lib3/PyYAML.egg-info/PKG-INFO
writing dependency_links to lib3/PyYAML.egg-info/dependency_links.txt
writing top-level names to lib3/PyYAML.egg-info/top_level.txt
      AttributeError: cython_sources
      [end of output]
# ...  
  note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error

参考： GitHub - AttributeError: cython_sources when installing KFP SDK + running Python components

Seeing this issue on GCP Vertex that uses Kubeflow internally. Forcing pyyaml==5.3.1 with non-working kfp==1.8.21 fixes the issue (following already mentioned comment)

即预先安装 pyyaml==5.3.1，即可：

pip uninstall cython
 
pip install pyyaml==5.3.1
pip install kfp==1.8.19

pip install pyyaml==6.0.1
pip install kfp==1.8.22

构建生成脚本 afm_pipeline.py，包括 2 个Jobs，Job1 在 Job2 之后运行，即：

from string import Template

import kfp
import kfp.dsl as dsl
import yaml

msa_job_yaml = Template("""
apiVersion: batch/v1
kind: Job
metadata:
  name: afm-msa-search # 训练名字
spec:
  completions: 2    # 总pod数量
  parallelism: 2    # 并行运行的pod数量
  backoffLimit: 0   # 重试次数，这里失败后不需要重试
  template:
    metadata:
        annotations:
            sidecar.istio.io/inject: "false"
        labels:
            file-mount: "true"      # 这两个label必须加，kubeflow帮你自动配置一些基本环境
            user-mount: "true"
    spec:
      nodeSelector:
          service: "[your service name]"   # device是gpu类型，比如a10,a100
      tolerations:
        - key: role
          operator:  Equal
          value: [your group name]
          effect: NoSchedule
      containers:
        - name: sp
          image: "af2:v1.04"   # 统一用这个镜像，提供一个基础conda和cuda环境
          imagePullPolicy: Always
          resources:
            limits:
              cpu: "62"
              memory: 124G
            requests:
              cpu: "62"
              memory: 124G
          command: [
            "/bin/sh",
            "-cl",
            "python -u [msa_script]"]     # 执行的命令
          workingDir: "af2/"        # 默认的工作目录，就是你启动脚本的所在目录
          env:                     # 这是把每个pod的名字注入环境变量，以便能够在程序里区分当前是在哪一个pod中
            - name: PODNAME
              valueFrom:
                fieldRef:
                  fieldPath: metadata.name
      restartPolicy: Never
""")

model_job_yaml = Template("""
apiVersion: batch/v1
kind: Job
metadata:
  name: afm-model-infer # 训练名字
spec:
  completions: 2    # 总pod数量
  parallelism: 2    # 并行运行的pod数量
  backoffLimit: 0   # 重试次数，这里失败后不需要重试
  template:
    metadata:
        annotations:
            sidecar.istio.io/inject: "false"
        labels:
            file-mount: "true"      # 这两个label必须加，kubeflow帮你自动配置一些基本环境
            user-mount: "true"
    spec:
      nodeSelector:
          gpu.device: "a100"
      tolerations:
        - key: role
          operator:  Equal
          value: [your group name]
          effect: NoSchedule
      containers:
        - name: sp
          image: "af2:v1.04"   # 统一用这个镜像，提供一个基础conda和cuda环境
          imagePullPolicy: Always
          resources:
            limits:
              cpu: 4
              memory: "64G"
              nvidia.com/gpu: 1
          command: [
            "/bin/sh",
            "-cl",
            "python -u [model_script]"]     # 执行的命令
          workingDir: "af2/"        # 默认的工作目录，就是你启动脚本的所在目录
          env:                      # 这是把每个pod的名字注入环境变量，以便能够在程序里区分当前是在哪一个pod中
            - name: PODNAME
              valueFrom:
                fieldRef:
                  fieldPath: metadata.name
      restartPolicy: Never
""")


@dsl.pipeline(
    name="psp-multimer-ag2-afm",
)
def job_pipeline(input_fasta_path, output_dir):
    job_msa = yaml.safe_load(msa_job_yaml.substitute(
        {
            'input_fasta_path': input_fasta_path,
            'output_dir': output_dir,
        }
    ))
    job_model = yaml.safe_load(model_job_yaml.substitute(
        {
            'input_fasta_path': input_fasta_path,
            'output_dir': output_dir,
        }
    ))
    print(f"[Info]: job_msa: {job_msa}")
    print(f"[Info]: job_model: {job_model}")
    msa = dsl.ResourceOp(name='psp-multimer-ag2-afm-msa', k8s_resource=job_msa,
                         set_owner_reference=True,
                         success_condition='status.conditions.0.type==Complete',
                         failure_condition='status.failed >0')

    model = dsl.ResourceOp(name='psp-multimer-ag2-afm-model', k8s_resource=job_model,
                           set_owner_reference=True,
                           success_condition='status.conditions.0.type==Complete',
                           failure_condition='status.failed >0')
    model.after(msa)


if __name__ == '__main__':
    kfp.compiler.Compiler().compile(job_pipeline, 'psp-multimer-ag2-afm.yaml')

运行脚本，生成 YAML 文件 psp-multimer-ag2-afm.yaml，即：

python afm_pipeline.py

运行日志：

[Info]: job_msa: {'apiVersion': 'batch/v1', 'kind': 'Job', 'metadata': {'name': 'afm-msa-search'}, 'spec': {'completions': 2, 'parallelism': 2, 'backoffLimit': 0, 'template': {'metadata': {'annotations': {'sidecar.istio.io/inject': 'false'}, 'labels': {'file-mount': 'true', 'user-mount': 'true'}}, 'spec': {'nodeSelector': {'service': 'ai2-msa-only'}, 'tolerations': [{'key': 'role', 'operator': 'Equal', 'value': 'ai2-msa-only', 'effect': 'NoSchedule'}], 'containers': [{'name': 'sp', 'image': 'af2:v1.04', 'imagePullPolicy': 'Always', 'resources': {'limits': {'cpu': '62', 'memory': '124G'}, 'requests': {'cpu': '62', 'memory': '124G'}}, 'command': ['/bin/sh', '-cl', 'python -u [msa_script]'], 'workingDir': 'af2/', 'env': [{'name': 'PODNAME', 'valueFrom': {'fieldRef': {'fieldPath': 'metadata.name'}}}]}], 'restartPolicy': 'Never'}}}}
[Info]: job_model: {'apiVersion': 'batch/v1', 'kind': 'Job', 'metadata': {'name': 'afm-model-infer'}, 'spec': {'completions': 2, 'parallelism': 2, 'backoffLimit': 0, 'template': {'metadata': {'annotations': {'sidecar.istio.io/inject': 'false'}, 'labels': {'file-mount': 'true', 'user-mount': 'true'}}, 'spec': {'nodeSelector': {'gpu.device': 'a100'}, 'tolerations': [{'key': 'role', 'operator': 'Equal', 'value': 'algo2', 'effect': 'NoSchedule'}], 'containers': [{'name': 'sp', 'image': 'af2:v1.04', 'imagePullPolicy': 'Always', 'resources': {'limits': {'cpu': 4, 'memory': '64G', 'nvidia.com/gpu': 1}}, 'command': ['/bin/sh', '-cl', 'python -u [model_script]'], 'workingDir': 'af2/', 'env': [{'name': 'PODNAME', 'valueFrom': {'fieldRef': {'fieldPath': 'metadata.name'}}}]}], 'restartPolicy': 'Never'}}}}