[225]yarn 기반의 deep learning application cluster 구축 김제민

YARN, Docker 기반의
Deep 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 “ImageNetClassification 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

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-tenantDeep Learning Application 실행 환경
• 다양한 Deep Learning Framework 지원
• GPU Cluster 자원 관리
? GPU Cluster
DL Application
DL Application
DL Application

Hadoop YARN 기반의
Multi-tenantDeep Learning Application 실행 환경
?
GPU Cluster
Hadoop YARN
DL Application
DL Application
DL Application

Why YARN ?
• Mesos ?
• YARN의 장점
• Capacity Scheduler : Queue기반의 ResourceScheduling
• Enterprise환경에서 ResourcePlanning 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

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 에서
Shell 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.sh
create_mnist.sh
lenet_solver.prototxt
lenet_train_test.prototxt
train_lenet.sh
data/
examples/
output/
ü main.sh
create_mnist.sh
train_lenet.sh
CPU Core 1
Memory 1G
tar
DL Application

Hadoop YARN
• Hadoop YARN ( Hadoop v2)
• Yet Another ResourceNegotiator
• MapReduce이외에 일반적인 Application 실행 가능
• CPU,Memory 기반의 ResourceScheduling
• YARN 구조
• ResourceManager ( 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 Master
Container
NM
NM
Container Request

YARN Application
Resource
Manager
(Master) NM
Application Master
Container
Container
NM
NM
Container
Container

YARN Application 개발하기
• ApplicationMaster ResourceManager
• org.apache.hadoop.yarn.client.api.async.AMRMClientAsync
• InterfaceAMRMClientAsync.CallbackHandler
• ApplicationMaster NodeManager
• org.apache.hadoop.yarn.client.api.async.NMClientAsync
• InterfaceNMClientAsync.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 Resource요청
• 특정 Server Node 에 Resource요청
• Server Node Blacklist설정
• YARN-4703

Technical Issues
ü YARN Cluster에서 Shell Script Application 실행
2) 다양한 Deep Learning Framework 환경 제공
è C3 Distributed Shell

1. 2)
다양한
Deep 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..

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 Module
361.48
User Level NVIDIA Libs
361.48
NVIDIA Driver Version
Major 361
Minor Docker Container(Image)
361.48
GPU Application
CUDA Libs
/dev/nvidiactl
/dev/nvidia-uvm
/dev/nvidia

그렇지만
Docker Image에
Host 의존성이 …

Nvidia-Docker
• Docker 상에서 GPU Device를 사용시 발생하는 Host 의존성을
해결
• GPU Resource Isolation 지원 !!

Nvidia-Docker
Host Server
NVIDIA Kernel Module
361.48
361.48
Docker Container(Image)
GPU Application
CUDA Libs
volume
361.48

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 Cluster에서
GPU 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
message ResourceProto { 
optional int32 memory = 1; 
optional int32 virtual_cores = 2; 
optional int32 gpu_cores = 3; 
}
enum SchedulerResourceTypes { 
MEMORY = 0; 
CPU = 1; 
GPU = 2; 
}
message 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
• org.apache.hadoop.yarn.conf.YarnConfiguration
public 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
• 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 변화
UI Changes
* 테스트용 클러스터 화면 캡쳐

YARN Cluster
CPU Core 1
Memory 1G
GPU 3
Main main.sh
tar dl_app.tar.gz
data/
examples/
output/
main.sh
create_mnist.sh
train_lenet.sh
data/
examples/
output/
ü main.sh
create_mnist.sh
train_lenet.sh
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 Application
Launcher 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 Zookeeper
를 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
Source
Code

YARN Cluster
CPU Core 1
Memory 1G
GPU 3
Main main.sh
tar dl_app.tar.gz
data/
examples/
output/
main.sh
create_mnist.sh
train_lenet.sh
data/
examples/
output/
ü main.sh
create_mnist.sh
train_lenet.sh
CPU Core 1
Memory 1G
GPU 3
User Program ( Source Code)
Input Data ?

• Input/Output Data
• Data Repository Tech.
• NFS
• Ceph, GlusterFS, Lustre
ü 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/OutputData 들이 위치함
• Host Server상의 Directory이며 , Docker Container에 volumemount
되는 영역

/User_Dev_Workspace
/DL_App_Workspace
Deep Learning App
Input
Output
User
Container

DL App 실행 과정
YARN Container
Yarn Container 시작

YARN Container
GPU Device ID 할당
GPU Device ID
Manager
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 Code
Docker
Volume
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
DL App Docker 실행
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
OutputData 복사
GPU 0,3

YARN Container
GPU Device ID 해제
GPU Device ID
Manager
GPU 0,3
Zookeeper

YARN Container
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
C3
Distributed
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
Application
생성

dl_app.properties
• Docker Image
• 실행 Src Code Dir Path
• main 실행 script
• Resource : CPU,Mem,GPU
• Input Data 경로
• Output Data 경로
C3
Distributed
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
Application
생성

• 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=tony
appname='c3 deeplearning app caffe mnist example'
docker_image=naver/c3_dl-caffe
user_dev_workspace_path=./user_dev_workspace
user_shell_script=dl_app_start.sh
user_shell_args='-gpu all'
[from_hdfs]
hdfs=/user/tony/caffe_test/*
input_path=data
[to_hdfs]
output_path=output
hdfs=/user/tony/caffe_mnist_example_output
overwrite=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 dl_app_caffe.properties
C3 DL Cluster Version check : ok
CheckDLAppProperties : ok
user_dev_workspace_path = /path/to/user_dev_workspace
user_dev_workspace_exclude_list = []
user_dev_workspace dir size = 4 kb
DL App Submission : OK
DL 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_bbbb
DL App application_aaaaaaaaaaa_bbbb is FINISHED
Username : tony
DL App ID : application_aaaaaaaaaaa_bbbb
DL App Name : 'c3 deeplearning app caffe mnist example'
startTime : 2016-09-26 22:24:11.575000
finishTime : 2016-09-26 22:24:57.703000
elapsedTime : 0:00:46.13
stdout
http://XXXXX/applicationhistory/logs/XXXXX/container_aaaaaaaaaaa_bbbb_01_000004/container_aaaaaaaaaaa_bbbb
_01_000004/tony/stdout/?start=-4096
stderr
http://XXXXX/applicationhistory/logs/XXXXX/container_aaaaaaaaaaa_bbbb_01_000004/container_aaaaaaaaaaa_bbbb
_01_000004/tony/stderr/?start=-4096

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/outputData등의 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

DL App 개발 서버
DL App Shell
( NVIDIA Docker ) : Bash Shell
Source Code
GPU 0,3

dlapp-shell 실행 예
dl_app_dev_server
dl_app_dev_server
dl_app_dev_server
dl_app_dev_server
dl_app_dev_server
dl_app_dev_server
user
user
user
user
yarn_resource_manager
yarn_timeline_server
aaaaaaaaa_bbbb
aaaaaaaaa_bbbb
aaaaaaaaa_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]

DL
Application
C3 Deep Learning Cluster
Zookeeper
Dev. Environment
Dev. Server
Resource
Manager
YARN
Node
Manager
YARN
Docker
Registry C3 HDFS Storage
YARN
with
GPU
Scheduling
Node
Manager
YARN
Node
Manager
YARN
. . .
NVIDIA Docker
GPU Device ID
Manager Input
Output
DL App
Toolset
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 ResourcePlanning ( 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

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[225]yarn 기반의 deep learning application cluster 구축 김제민