kubeflow系列(二):kubeflow组件介绍

为了对kubeflow有个更直观深入的了解,对kubeflow的各组件进行简单的介绍,先从机器学习任务来看kubeflow的的实现。

机器学习任务工程化实现流程

一个建模任务下来主要可以分为四大块任务

  • 业务理解(Business Understanding)
  • 数据获取及数据理解(Data Acquistition)
  • 特征处理(Feature Engineering)、建模与模型训练(Model Training)、模型评估(Model Evaluation)
  • 模型部署(Deployment),提供模型服务

datascience-lifecycle

一个机器学习任务从开始到结束主要分为了四大任务,Kubeflow的各项功能可以说就是围绕这四项任务构建的。

kubeflow

kubeflow 最开始基于tf-operator,后来随着项目发展最后变成一个基于云原生构建的机器学习任务工具大集合。从数据采集,验证,到模型训练和服务发布,几乎所有步骤的小组件 Kubeflow 都提供解决方案的组件:

kubeflow

kubeflow特点:

  • 基于k8s,具有云原生的特性:弹性伸缩、高可用、DevOps等
  • 集成大量机器学习所用到的工具

结构

kubeflow的完整结构可以看他的kustomize安装文件:

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kustomize/
├── ambassador.yaml
├── api-service.yaml
├── argo.yaml
├── centraldashboard.yaml
├── jupyter-web-app.yaml
├── katib.yaml
├── metacontroller.yaml
├── minio.yaml
├── mysql.yaml
├── notebook-controller.yaml
├── persistent-agent.yaml
├── pipelines-runner.yaml
├── pipelines-ui.yaml
├── pipelines-viewer.yaml
├── pytorch-operator.yaml
├── scheduledworkflow.yaml
├── tensorboard.yaml
└── tf-job-operator.yaml

ambassador 微服务网关
argo 用于任务工作流编排
centraldashboard kubeflow的dashboard看板页面
tf-job-operator 深度学习框架引擎,一个基于tensorflow构建的CRD,资源类型kind为TFJob
tensorboard tensorflow的训练可视化UI界面
katib 超参数服务器
pipeline 一个机器学习的工作流组件
jupyter 一个交互式业务IDE编码环境

TFJob

TFJob 是将 tensorflow 的分布式架构基于 k8s 构建的一种CRD:

  • Chief 负责协调训练任务
  • Ps 参数服务器,为模型的参数提供分布式的数据存储
  • Worker 负责实际训练模型的任务. 在某些情况下 worker 0 可以充当Chief的责任。
  • Evaluator 负责在训练过程中进行性能评估
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apiVersion: kubeflow.org/v1beta2
kind: TFJob
metadata:
name: mnist-train
namespace: kubeflow
spec:
tfReplicaSpecs:
Chief: # 调度器
replicas: 1
template:
spec:
containers:
- command:
- /usr/bin/python
- /opt/model.py
env:
- name: modelDir
value: /mnt
- name: exportDir
value: /mnt/export
image: mnist-test:v0.1
name: tensorflow
volumeMounts:
- mountPath: /mnt
name: local-storage
workingDir: /opt
restartPolicy: OnFailure
volumes:
- name: local-storage
persistentVolumeClaim:
claimName: local-path-pvc
Ps: # 参数服务器
replicas: 1
template:
spec:
containers:
- command:
- /usr/bin/python
- /opt/model.py
env:
- name: modelDir
value: /mnt
- name: exportDir
value: /mnt/export
image: mnist-test:v0.1
name: tensorflow
volumeMounts:
- mountPath: /mnt
name: local-storage
workingDir: /opt
restartPolicy: OnFailure
volumes:
- name: local-storage
persistentVolumeClaim:
claimName: local-path-pvc
Worker: # 计算节点
replicas: 2
template:
spec:
containers:
- command:
- /usr/bin/python
- /opt/model.py
env:
- name: modelDir
value: /mnt
- name: exportDir
value: /mnt/export
image: mnist-test:v0.1
name: tensorflow
volumeMounts:
- mountPath: /mnt
name: local-storage
workingDir: /opt
restartPolicy: OnFailure
volumes:
- name: local-storage
persistentVolumeClaim:
claimName: local-path-pvc

tensorboard 训练可视化界面

挂载日志文件,创建 tensorboard 可视化服务

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apiVersion: v1
kind: Service
metadata:
name: tensorboard-tb
namespace: kubeflow
spec:
ports:
- name: http
port: 8080
targetPort: 80
selector:
app: tensorboard
tb-job: tensorboard
---
apiVersion: apps/v1beta1
kind: Deployment
metadata:
name: tensorboard-tb
namespace: kubeflow
spec:
replicas: 1
template:
metadata:
labels:
app: tensorboard
tb-job: tensorboard
name: tensorboard
namespace: kubeflow
spec:
containers:
- command:
- /usr/local/bin/tensorboard
- --logdir=/mnt
- --port=80
env:
- name: logDir
value: /mnt
image: tensorflow/tensorflow:1.11.0
name: tensorboard
ports:
- containerPort: 80
volumeMounts:
- mountPath: /mnt
name: local-storage
serviceAccount: default-editor
volumes:
- name: local-storage
persistentVolumeClaim:
claimName: mnist-test-pvc

tf-serving

tenserflow serving 提供一个稳定的接口,供用户调用,来应用该模型,serving 通过模型文件直接创建模型即服务(Model as a service)

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apiVersion: v1
kind: Service
metadata:
labels:
app: mnist
name: mnist-service-local
namespace: kubeflow
spec:
ports:
- name: grpc-tf-serving
port: 9000
targetPort: 9000
- name: http-tf-serving
port: 8500
targetPort: 8500
selector:
app: mnist
type: ClusterIP
---
apiVersion: extensions/v1beta1
kind: Deployment
metadata:
labels:
app: mnist
name: mnist-service-local
namespace: kubeflow
spec:
template:
metadata:
labels:
app: mnist
version: v1
spec:
containers:
- args:
- --port=9000
- --rest_api_port=8500
- --model_name=mnist
- --model_base_path=/mnt/export
command:
- /usr/bin/tensorflow_model_server
env:
- name: modelBasePath
value: /mnt/export
image: tensorflow/serving:1.11.1
imagePullPolicy: IfNotPresent
livenessProbe:
initialDelaySeconds: 30
periodSeconds: 30
tcpSocket:
port: 9000
name: mnist
ports:
- containerPort: 9000
- containerPort: 8500
resources:
limits:
cpu: "4"
memory: 4Gi
requests:
cpu: "1"
memory: 1Gi
volumeMounts:
- mountPath: /mnt
name: local-storage

pipeline

pipeline 是一个可视化的kubeflow任务工作流(Workflow),定义了一个有向无环图描述的流水线,流水线中每一步流程是由容器定义组成的组件。

运行步骤:

  • 先要定义一个Experiment实验
  • 然后发起任务,定义一个Pipeline
  • 运行Pipeline实例

结构介绍

pipeline主要分为八部分:

  • Python SDK: 用于创建kubeflow pipeline 的DSL
  • DSL compiler: 将Python代码转换成YAML静态配置文件
  • Pipeline web server: pipeline的前端服务
  • Pipeline Service: pipeline的后端服务
  • Kubernetes resources: 创建CRDs运行pipeline
  • Machine learning metadata service: 用于存储任务流容器之间的数据交互(input/output)
  • Artifact storage: 用于存储 Metadata 和 Pipeline packages, views
  • Orchestration controllers:任务编排,比如Argo Workflow.

案例

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import kfp
from kfp import dsl

def gcs_download_op(url):
return dsl.ContainerOp(
name='GCS - Download',
image='google/cloud-sdk:272.0.0',
command=['sh', '-c'],
arguments=['gsutil cat $0 | tee $1', url, '/tmp/results.txt'],
file_outputs={
'data': '/tmp/results.txt',
}
)


def echo2_op(text1, text2):
return dsl.ContainerOp(
name='echo',
image='library/bash:4.4.23',
command=['sh', '-c'],
arguments=['echo "Text 1: $0"; echo "Text 2: $1"', text1, text2]
)


@dsl.pipeline(
name='Parallel pipeline',
description='Download two messages in parallel and prints the concatenated result.'
)
def download_and_join(
url1='gs://ml-pipeline-playground/shakespeare1.txt',
url2='gs://ml-pipeline-playground/shakespeare2.txt'
):
"""A three-step pipeline with first two running in parallel."""

download1_task = gcs_download_op(url1)
download2_task = gcs_download_op(url2)

echo_task = echo2_op(download1_task.output, download2_task.output)

if __name__ == '__main__':
kfp.compiler.Compiler().compile(download_and_join, __file__ + '.yaml')

jupyter-notebook

jupyter 是最大限度的利用交互式的工作,他的主要工作体现利用交互式的操作帮助用户快速理解数据和测试评估模型。

主要包括两个模块jupyter-web-appnotebook-controller, jupyter 架构:

也可以用 jupyterhub 代替jupyter, jupyterhub提供了更多功能, jupyterhub 结构:

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