[225]yarn 기반의 deep learning application cluster 구축...

YARN, Docker 기반의Deep Learning Application Cluster 구축

김제민

NAVER Search

하고 있고, 해 왔던 것들

Naver Search

• 검색 모델링

• 검색 데이터 정제

• 검색 서비스

• 대용량 데이터 처리

• 분산 데이터 처리

• 실시간 데이터 처리

C3 Hadoop Cluster 소개

• Apache Ambari

• Cluster 배포/관리/모니터

• Hadoop 2.7.1

• Hive, Spark, HBase, Oozie etc ..

Deep Learning• Machine Learning 기법 중의 하나• Artificial Neural Network 인데 Deep함

< AlexNet >

• 60 million parameters and 650,000 neurons

From “ImageNet Classification with Deep Convolutional Neural Networks”

Deep Learning Breakthrough

• 학습의 어려움 극복

• 2006, “A fast learning algorithm for deep belief nets” by

Geoffrey E. Hinton

• Hardware

• GPU 성능의 발전

• NVIDIA CUDA

Deep Learning 은

거의 모든 분야에서

절대적으로 성능이 우월함

이제 Deep Learning 으로 Go, Go !!!

• 다수의 프로젝트• 다수의 연구원/개발자

But

• GPU 자원은 제한적

Server

Server

Server

Server

GPU 자원의 효과적인 공유 필요성

?GPU Cluster

Deep Learning Application 개발 환경

• GPU• NVIDIA CUDA• Deep Learning Frameworks

• Caffe

• TensorFlow

• Torch

• Theano

• Keras

그래서

결론적으로

지금 우리에게 필요한 것은 …

Multi-tenant Deep Learning Application 실행 환경

• 다양한 Deep Learning Framework 지원• GPU Cluster 자원 관리

? GPU Cluster

DL Application

DL Application

DL Application

Hadoop YARN 기반의Multi-tenant Deep Learning Application 실행 환경

?GPU Cluster

Hadoop YARNDL Application

DL Application

DL Application

Why YARN ? • Mesos ?

• YARN의 장점

• Capacity Scheduler : Queue기반의 Resource Scheduling

• Enterprise 환경에서 Resource Planning 용이

• 기타

• 기존 클러스터(C3)와의 호환성 및 통합 가능성

• Cluster 배포 및 관리 시스템 기존재 (Ambari)

• YARN Cluster 운영 Knowhow

목차

1. Technical Issues

2. Deep Learning Application Toolset 개발

3. C3 Deep Learning Cluster 구축

1.Technical Issues

Technical Issues

1) YARN Cluster에서 Shell Script Application 실행

2) 다양한 Deep Learning Framework 환경 제공

3) YARN Cluster에서 GPU Resource Scheduling

1. 1)YARN Cluster 에서Shell Script Application 실행

Shell Script Application on YARN ?

YARN Cluster

CPU Core 1

Memory 1G

Main main.sh

dl_app.tar.gz

data/examples/output/

main.shcreate_mnist.shlenet_solver.prototxtlenet_train_test.prototxttrain_lenet.sh


ü main.shcreate_mnist.shlenet_solver.prototxtlenet_train_test.prototxttrain_lenet.sh

CPU Core 1

Memory 1G

tar

DL Application

Hadoop YARN

• Hadoop YARN ( Hadoop v2)

• Yet Another Resource Negotiator

• MapReduce 이외에 일반적인 Application 실행 가능

• CPU,Memory 기반의 Resource Scheduling

• YARN 구조

• Resource Manager ( Master)

• Node Manager ( Slaves )

• 최소 실행 단위 : Container

YARN 구조

Resource Manager(Master)

Node Manager(Slave)

Server Container

Container

Container

Container

NM

NM

YARN Application

Resource Manager(Master) NM

Application MasterContainer

NM

NM

Container Request

YARN Application

Resource Manager(Master) NM

Application MasterContainer

Container

NM

NM

Container

Container

Shell Script Application on YARN ?

YARN Cluster

CPU Core 1

Memory 1G

Main main.sh

dl_app.tar.gz





CPU Core 1

Memory 1G

tar

DL Application

YARN Application 개발하기

• ApplicationMaster ResourceManager

• org.apache.hadoop.yarn.client.api.async.AMRMClientAsync

• Interface AMRMClientAsync.CallbackHandler

• ApplicationMaster NodeManager

• org.apache.hadoop.yarn.client.api.async.NMClientAsync

• Interface NMClientAsync.CallbackHandler

YARN Distributed Shell (Example)

• https://github.com/apache/hadoop-common/tree/trunk/hadoop-yarn-project/hadoop-yarn/hadoop-yarn-

applications/hadoop-yarn-applications-

distributedshell/src/main/java/org/apache/hadoop/yarn/applications/distributedshell

• ApplicationMaster.java

• Client.java

• DSConstants.java

• Log4jPropertyHelper.java

C3 Distributed Shell

• Yarn Distributed Shell 기반• 추가 기능

• CONTAINTER_INDEX, NUM_CONTAINERS 환경 변수

• YARN-5696

• Server Node 에 따른 Resource 요청

• 특정 Server Node 에 Resource 요청

• Server Node Blacklist 설정

• YARN-4703

Technical Issues

ü YARN Cluster에서 Shell Script Application 실행

2) 다양한 Deep Learning Framework 환경 제공


è C3 Distributed Shell

1. 2)다양한Deep Learning Framework 환경 제공

Classical Approach• 모든 DL Framework를 모든 서버에 설치

• Installation, Upgrade, Update 등 유지 보수의 어려움

• 같은 DL Framework 에서 Multi Version 지원 이슈

• 서로 상이한 DL Framework간의 Library 의존성 충돌

• DevOps Tools :

• Dimensions

• ( DL Framworks ) * ( Versions ) * ( 의존 libs ) * ( Server 환경)

각 Deep Learning Framework 환경들이

깔끔히 Isolation되어

관리되면 좋을텐데..

Docker ?

그렇지만 GPU는 ?

CUDA는 ?

Docker 에서 가능한가?

Docker 에서 CUDA 사용하기• Docker Image에 NVIDIA User Level Libraries 와 CUDA

Library 를 설치

• 제약 : Host에 설치된 NVIDIA Driver Major,Minor Version과 일치 해

야함

• Docker 실행시 NVIDIA Device File 들을 Docker에 노출

$ docker run …--device=/dev/nvidiactl \--device=/dev/nvidia-uvm \--device=/dev/nvidia \

Docker 에서 CUDA 사용하기

Host Server

NVIDIA Kernel Module361.48

User Level NVIDIA Libs361.48

NVIDIA Driver Version Major 361Minor Docker Container(Image)


GPU Application

CUDA Libs/dev/nvidiactl/dev/nvidia-uvm/dev/nvidia

그렇지만

Docker Image에

Host 의존성이 …

Nvidia-Docker !!

Nvidia-Docker

• Docker 상에서 GPU Device를 사용시 발생하는 Host 의존성을해결

• GPU Resource Isolation 지원 !!

Nvidia-Docker

Host Server

NVIDIA Kernel Module361.48


Docker Container(Image)

GPU Application

CUDA Libs

volume User Level NVIDIA Libs361.48

Nvidia-Docker 실행• 기본 실행

• nvidia-docker <docker-options> <docker-command> <docker-

args>

• GPU isolation

• NV_GPU 환경 변수에 GPU Device ID 목록을 설정

• NV_GPU='0,1' nvidia-docker <docker-options> <docker-command> …

• Docker Image 제약

• Nvidia-docker 용으로 미리 build된 image 사용

Nvidia-docker 기반으로

Deep Learning Framework 환경을

Docker Image로 제공

Technical Issues


ü 다양한 Deep Learning Framework 환경 제공



è Nvidia-Docker

1. 3)YARN Cluster에서GPU Resource Scheduling

YARN GPU Scheduling 현상황

• 현재 YARN 은 CPU와 Memory 만으로 스케쥴링

• GPU Scheduling은 지원하지 않음

• 새로운 자원 형태를 Scheduling 하기 위해서는 부가적인 개발이

필요함

YARN GPU Scheduling 개발

주요 Source Code 변경 대상

• YARN Protocol

• YARN Configuration

• Resource 추상화 객체

• Dominant Resource Calculator

*YARN JIRA : YARN-5517

YARN Protocol

• yarn_protos.proto

• yarn_service_protos.proto

Implementation Details• yarn_protos.protomessage ResourceProto { optional int32 memory = 1; optional int32 virtual_cores = 2; optional int32 gpu_cores = 3; }

enum SchedulerResourceTypes { MEMORY = 0; CPU = 1; GPU = 2; }


Implementation Details• yarn_protos.protomessage ResourceProto { optional int32 memory = 1; optional int32 virtual_cores = 2; optional int32 gpu_cores = 3; }

enum SchedulerResourceTypes { MEMORY = 0; CPU = 1; GPU = 2; }


YARN Configuration

• org.apache.hadoop.yarn.conf.YarnConfiguration

Implementation Details• org.apache.hadoop.yarn.conf.YarnConfigurationpublic static final String RM_SCHEDULER_MINIMUM_ALLOCATION_GCORES = YARN_PREFIX + "scheduler.minimum-allocation-gcores"; public static final int DEFAULT_RM_SCHEDULER_MINIMUM_ALLOCATION_GCORES = 0;

public static final String RM_SCHEDULER_MAXIMUM_ALLOCATION_GCORES = YARN_PREFIX + "scheduler.maximum-allocation-gcores"; public static final int DEFAULT_RM_SCHEDULER_MAXIMUM_ALLOCATION_GCORES = 8;

public static final String NM_GCORES = NM_PREFIX + "resource.gcores"; public static final int DEFAULT_NM_GCORES = 0;

Resource 객체• org.apache.hadoop.yarn.util.resource.Resource

Implementation Details• org.apache.hadoop.yarn.util.resource.Resources

public static Resource createResource(int memory) { return createResource(memory, (memory > 0) ? 1 : 0, 0); } public static Resource createResource(int memory, int cores) { return createResource(memory, cores, 0); } public static Resource createResource(int memory, int cores, int gcores) { Resource resource = Records.newRecord(Resource.class); resource.setMemory(memory); resource.setVirtualCores(cores); resource.setGpuCores(gcores); return resource; } public int getGpuCores() { return 0; } public void setGpuCores(int gcores) { throw new RuntimeException("NONE cannot be modified!"); } public int compareTo(Resource o) { int diff = 0 - o.getMemory(); if (diff == 0) { diff = 0 - o.getVirtualCores(); if (diff == 0) { diff = 0 - o.getGpuCores(); } } return diff; }

Dominant Resource Calculator• org.apache.hadoop.yarn.util.resource.DominantResourceCalculator

YARN UI 변화UI Changes

* 테스트용 클러스터 화면 캡쳐

YARN Cluster

CPU Core 1

Memory 1G

GPU 3

Main main.sh

tar dl_app.tar.gz





CPU Core 1

Memory 1G

GPU 3

C3 Distributed Shell : GPU 추가

Technical Issues


ü 다양한 Deep Learning Framework 환경 제공

ü YARN Cluster에서 GPU Resource Scheduling


è Nvidia-Docker

è YARN with GPU Scheduling 개발

2.Deep Learning ApplicationLauncher 및 Toolset 개발

Deep Learning Application Toolset 개발

• GPU Device ID Manager

• DL(Deep Learning) App Concept

• DL App Launcher

• DL App Tools

• DL App Log

• DL App 개발 환경

GPU Device ID 할당 문제점

• 문제점 : YARN은 GPU 갯수만 관리함

• Deep Learning Application에 명시적으로 GPU Device ID를

할당하는 로직 필요

GPU Device ID Manager

• Cluster 전체의 GPU Device ID Allocation 정보를 Zookeeper를 통해 관리

• GPU Device ID Manager

• python kazoo lib

• 분산 Lock

• GPU Device ID 할당/해제

• Garbage Collection

{”host01": {

"gpu0": "application_id_01","gpu1": “application_id_01”,"gpu2": “application_id_02”,"gpu3": “application_id_03”

},“host02": {

"gpu0": “application_id_04",…

},…

}

DL(Deep Learning) App 구현 Concept

• DL App 구성 요소

• Deep Learning Framework 환경 (Caffe, TensorFlow …)

• User Program ( Source Code )

• Input Data

• Output Data Input Output

DL Framework 환경

SourceCode

YARN Cluster

CPU Core 1

Memory 1G

GPU 3

Main main.sh

tar dl_app.tar.gz





CPU Core 1

Memory 1G

GPU 3



User Program ( Source Code)

Input Data ?


• DL App 구성 요소

• Deep Learning Framework 환경 (Caffe, TensorFlow …)

• User Program ( Source Code )

• Input Data

• Output Data Input Output

DL Framework 환경

SourceCode


• Input/Output Data• Data Repository Tech.• NFS• Ceph, GlusterFS, Lustreü HDFS

• C3 HDFS Storage를 저장소로 사용


• Docker Container의 데이터는 volatile함• Docker Container안에 permanent한 작업 영역(dir)이 필요함

• DL App Workspace (Directory) • DL App 실행을 위한 File/Dir들이 존재하는 영역• User Program Src Code , Input/Output Data 들이 위치함• Host Server상의 Directory이며 , Docker Container에 volume mount

되는 영역


/User_Dev_Workspace

/DL_App_Workspace

Deep Learning App

Input

Output

User

Container

DL App 실행 과정

DL App 실행 과정YARN Container

Yarn Container 시작


GPU Device ID 할당


GPU 0,3

Zookeeper

YARN Container


DL App Workspace

Source Code

User Workspace 복사

GPU 0,3

YARN Container


DL App Workspace

Input

Source Code

Input Data 복사

GPU 0,3

YARN Container


DL App Workspace( NVIDIA Docker )

Input

Source Code

NVIDIA Docker Cotainer 실행

GPU 0,3

YARN Container


DL App Workspace( NVIDIA Docker )

Input

Source CodeDockerVolume

DL App Workspace Mount

GPU 0,3

YARN Container


( NVIDIA Docker )

Input

Source Code

DL App Docker 실행

GPU 0,3

YARN Container


( NVIDIA Docker )

Input

Source Code

Output


GPU 0,3

YARN Container


Input

Source Code

Output

DL App Workspace

DL App Docker 종료

GPU 0,3

YARN Container


Input

Source Code

Output

DL App Workspace

Output Data 복사

GPU 0,3


GPU Device ID 해제


GPU 0,3

Zookeeper


Yarn Container 종료

DL App 실행 과정 Yarn Container 종료

원하는 DL App Properties

• Docker Image

• 실행 Src Code Dir Path

• main 실행 script

• Resource : CPU,Mem,GPU

• Input Data 경로

• Output Data 경로

DL App 생성 및 실행하기

C3Distributed

Shell


2) GPU ID 할당

3) User Workspace 복사

4) Input Data 복사

5) DL App Workspace 구성 완료

6) DL App NVIDIA Docker 실행

7) Output Data 복사

8) GPU ID 해제

YARN

Cluster

Shell Script Application

Shell Script

Application생성

dl_app.properties

• Docker Image

• 실행 Src Code Dir Path

• main 실행 script

• Resource : CPU,Mem,GPU

• Input Data 경로

• Output Data 경로

C3Distributed

Shell


2) GPU ID 할당

3) User Workspace 복사

4) Input Data 복사

5) DL App Workspace 구성 완료

6) DL App NVIDIA Docker 실행

7) Output Data 복사

8) GPU ID 해제

YARN

Cluster

dlapp-launcher dl_app.properties

DL App Launcher

Shell Script Application

Shell Script

Application생성

• Caffe LeNet MNIST Example• caffe/examples/mnist 기반• .

├── dl_app_caffe.properties└── user_dev_workspace

├── create_mnist.sh├── data/├── dl_app_start.sh├── examples/├── lenet_solver.prototxt├── lenet_train_test.prototxt├── output/└── train_lenet.sh

dlapp-launcher 실행 예

• dl_app_caffe.properties[application]username=tonyappname='c3 deeplearning app caffe mnist example'

docker_image=naver/c3_dl-caffeuser_dev_workspace_path=./user_dev_workspaceuser_shell_script=dl_app_start.shuser_shell_args='-gpu all'

[from_hdfs]hdfs=/user/tony/caffe_test/*input_path=data

[to_hdfs]output_path=outputhdfs=/user/tony/caffe_mnist_example_outputoverwrite=true

• dl_app_start.sh

#!/usr/bin/env bash

./create_mnist.sh

./train_lenet.sh $@


.├── dl_app_caffe.properties└── user_dev_workspace

├── create_mnist.sh├── data/├── dl_app_start.sh├── examples/├── lenet_solver.prototxt├── lenet_train_test.prototxt├── output/└── train_lenet.sh

dlapp-launcher 실행 예$ dlapp-launcher dl_app_caffe.propertiesC3 DL Cluster Version check : okCheckDLAppProperties : okuser_dev_workspace_path = /path/to/user_dev_workspaceuser_dev_workspace_exclude_list = []user_dev_workspace dir size = 4 kb

DL App Submission : OKDL App ID = application_aaaaaaaaaaa_bbbb

DL App Dashboard* While app is running , check : http://XXXXXXXXX/cluster/app/application_aaaaaaaaaaa_bbbb* After app is completed, check : http://XXXXXXXXX/applicationhistory/app/application_aaaaaaaaaaa_bbbb

DL App Status and stdout/stderr Log URLs* dlapp-status application_aaaaaaaaaaa_bbbb

DL App Tools

• dlapp-status [ DL App ID ] • dlapp-list [Username] • dlapp-softkill [Username] [ DL App ID ] • dlapp-kill [Username] [ DL App ID ]

DL App Log 확인

• YARN History,Timeline Server 기능 활용• YARN Application stdout/stderr Log 는 Web 또는 yarn 명령어을 통

해 확인 가능• YARN Container 의 Attempt ID 및 Container ID 를 통해 확인

• dlapp-status tool을 통해 stdout/stderr Log URL 제공

dlapp-status 실행 예$ dlapp-status application_aaaaaaaaaaa_bbbbDL App application_aaaaaaaaaaa_bbbb is FINISHED

Username : tonyDL App ID : application_aaaaaaaaaaa_bbbbDL App Name : 'c3 deeplearning app caffe mnist example'

startTime : 2016-09-26 22:24:11.575000finishTime : 2016-09-26 22:24:57.703000elapsedTime : 0:00:46.13

stdouthttp://XXXXX/applicationhistory/logs/XXXXX/container_aaaaaaaaaaa_bbbb_01_000004/container_aaaaaaaaaaa_bbbb_01_000004/tony/stdout/?start=-4096

stderrhttp://XXXXX/applicationhistory/logs/XXXXX/container_aaaaaaaaaaa_bbbb_01_000004/container_aaaaaaaaaaa_bbbb_01_000004/tony/stderr/?start=-4096

Example

dlapp-status 실행 예

dlapp-list 실행 예

dlapp-kill 실행 예

DL App Development 환경의 필요성

• DL App을 개발하려면 GPU 장비가 필요함

• 초기 DL App개발 시점에서 기본적인 검증 용도로 DL Cluster를

이용하는 것은 매우 번거로움

• 코드의 사소한 실수를 수정하는 과정의 반복

• Input/output Data등의 Directory 구조 확인

DL App Development 환경

• DL App 개발 전용 GPU 서버 구축• dlapp-shell

• dlapp-launcher 와 동일한 Docker Image 및 Workspace로 실행되는

docker bash 환경 제공

• dlapp-run : dlapp-shell 환경에서 C3 DL Cluster 에 dl app 을 실행

YARN Container


( NVIDIA Docker )

Input

Source Code


GPU 0,3

DL App 개발 서버

DL App Shell

( NVIDIA Docker ) : Bash Shell

Source Code

GPU 0,3

dlapp-shell 실행 예dl_app_dev_serverdl_app_dev_server

dl_app_dev_server

dl_app_dev_serverdl_app_dev_serverdl_app_dev_server

user

useruser

user

yarn_resource_manageryarn_timeline_server

aaaaaaaaa_bbbb

aaaaaaaaa_bbbbaaaaaaaaa_bbbb

aaaaaaaaa_bbbb

docker shell

DL App Toolset

• dlapp-launcher dl_app.properties• dlapp-shell dl_app.properties• dlapp-run

• dlapp-status [DL App ID] • dlapp-list [Username] • dlapp-softkill [Username] [DL App ID] • dlapp-kill [Username] [DL App ID]

3.C3 Deep Learning Cluster

Hadoop YARN 기반의Multi-tenant Deep Learning Application 실행 환경

?GPU Cluster

Hadoop YARNDL Application

DL Application

DL Application

DL Application

C3 Deep Learning Cluster

Zookeeper

Dev. Environment

Dev. Server

Resource Manager

YARN

Node Manager

YARN

DockerRegistry C3 HDFS Storage

YARN with

GPU Scheduling

Node Manager

YARN

Node Manager

YARN

. . .


NVIDIA Docker

GPU Device ID Manager Input

Output

DL AppToolset

dlapp-launcher

dlapp-shell dlapp-run

DL Framework Docker Images

DL Solution Docker Image

Caffe naver/c3_dl-caffe

Torch naver/c3_dl-torch

Theano naver/c3_dl-theano

TensorFlow naver/c3_dl-tensorflow

Theano+Keras naver/c3_dl-theano-keras

TensorFlow+Keras naver/c3_dl-tensorflow-keras

Base (CUDA/cuDNN) naver/c3_dl-base

• Resource 관리• GPU 자원 Utilization 증가• GPU Resource Planning ( YARN Queue)

• Administration• 작업 이력 관리• 프로젝트 관리• 사용자 관리

• DevOps• 규격화된 개발 환경 제공• 개발 환경 공유

개선점

• 검색 품질 향상을 위한 seq2seq 기반의 검색 키워드 변환

• word2vec,CNN 기반의 문서 주제 분류

• CNN 기반의 Image Tagger

• 이외 다수의 프로젝트가 실행중

현재 C3 DL Cluster에서는 …

* CNN : Convolutional Neural Network

To Do

• GPU Resource Scheduling 고도화

• 대용량 Input Data

• Input Data Caching

• Input Data Feeder

• Distributed Deep Learning

Thank You

[225]yarn 기반의 deep learning application cluster 구축...

Technology

Transcript of [225]yarn 기반의 deep learning application cluster 구축...