AWS는 클라우드 기반의 기계 학습 및 딥러닝 기술을 제공하는 인공 지능 서비스 개발 플랫폼을 제공합니다. AWS Deep Learning AMI를 사용하면 심도 깊은 학습을 실행할 수 있습니다. 정교한 맞춤형 AI 모델을 개발하며, 새로운 알고리즘을 실험하기 위한 오픈 소스 심층 학습 엔진(Apache MXNet 등) AMI를 GPU 기반 인스턴스와 클러스터를 스팟 인스턴스를 통해 비용 효율적으로 구성하여 운영하는 방법을 안내합니다.

1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.

4. http://hunkim.github.io/ml/

12. More compute
Accuracy
Scale (data size, model size)
neural
networks
other
approaches
Now
© Jeff Dean, Trends and Developments in Deep Learning Research
http://www.slideshare.net/AIFrontiers/jeff-dean-trends-and-developments-in-deep-learning-research

14. BlindTool by Joseph Paul Cohen on Nexus 4
Mobile Application
• https://github.com/dmlc/mxnet.js/
• http://rupeshs.github.io/machineye
MXNetJS in Web Browser
Web Applications
• https://www.youtube.com/watch
?v=UHUC4ueEiwM
• https://play.google.com/store/ap
ps/details?id=the.blindtool

15. Deep Drone: Object Detection and Tracking
for Smart Drones on Embedded System
TX1 with
customized
board
Drone
• https://web.stanford.edu/class/cs231a/prev_projects_20
16/deep-drone-object__2_.pdf
Deep RL | Playing Flappy Birds
• https://github.com/li-haoran/DRL-FlappyBird
• https://github.com/devsisters/DQN-tensorflow
Human-Level Control
through Deep Reinforcement Learning

24. Spot Instances (80% ↓)
= $30 per hour

25. $aws ec2-run-instances ami-b232d0db
--instance-count 20
--instance-type p2.8xlarge
--region us-east-1
$aws ec2-stop-instances
i-10a64379 i-10a64280 ...

26. 1GiB
GPU Memory
2 GiB
4 GiB
8 GiB

27. NVIDIA Tesla
GPU Card
P2: GPU-accelerated computing
§ Enabling a high degree of parallelism – each
GPU has thousands of cores
§ Consistent, well documented set of APIs
(CUDA, OpenACC, OpenCL)
§ Supported by a wide variety of ISVs and open
source frameworks
Xilinx UltraScale+
FPGA
F1: FPGA-accelerated computing
§ Massively parallel – each FPGA includes millions
of parallel system logic cells
§ Flexible – no fixed instruction set, can implement
wide or narrow datapaths
§ Programmable using available, cloud-based FPGA
development tools

28. https://aws.amazon.com/ko/batch/

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http://bit.ly/deepami
http://bit.ly/deepubuntu

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34. 기반 예제
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http://mxnet.io/
https://github.com/dmlc/mxnet
http://incubator.apache.org/projects/mxnet.html

35. 3

37. http://bit.ly/deepcfn
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1
4.75
8.5
12.25
16
1 4.75 8.5 12.25 16
Speedup(x)
# GPUs
Resnet 152 Inceptin V3
Alexnet Ideal
91% Efficiency
88% Efficiency
# GPUs
• EC2 16x P2.16xlarge by AWS CloudFormation
• Mounted on Amazon EFS

39. ../../tools/launch.py -n
$DEEPLEARNING_WORKERS_COUNT -H
$DEEPLEARNING_WORKERS_PATH
python train_mnist.py
--gpus $(seq -s , 0 1 $
(($DEEPLEARNING_WORKER_GPU_COUNT - 1)))
--network lenet --kv-store dist_sync
parameter
server
network
choice
update
policy

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42. https://www.youtube.com/
watch?v=q6gx9yk0nQo
https://www.slideshare.net/AIFrontie
rs/scaling-deep-learning-with-mxnet

46. http://j.mp/2hne9IL

47. import mxnet as mx
bucket = 'my-model-bucket’
s3_client = boto3.client('s3')
f_params = 'resnet-18-0000.params'
f_symbol = 'resnet-18-symbol.json'
#params
f_params_file = tempfile.NamedTemporaryFile()
s3_client.download_file(bucket, f_params, f_params_file.name)
#symbol
f_symbol_file = tempfile.NamedTemporaryFile()
s3_client.download_file(bucket, f_symbol, f_symbol_file.name)
def lambda_handler(event, context):
model = load_model(f_params_file, f_symbol_file)
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48. $ wrk -t40 -c120 -d60s https://48l7awor09.execute-api.us-
east-1.amazonaws.com/dev/predict?url=img_url
Thread Stats Avg Stdev Max +/- Stdev
Latency 1.18s 96.82ms 1.98s 81.23%
Req/Sec 2.39 2.87 10.00 87.91%
4520 requests in 1.00m, 3.16MB read
Socket errors: connect 0, read 0, write 0, timeout 0
Requests/sec: 75.23
Transfer/sec: 53.85KB

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52. 딥러닝 개발자를 위한 AWS 크레딧 제공!
http://bit.ly/awskr-feedback
AWS Activate 패키지
100달러 무료 크레딧 + 80 달러 Qwiklab Credit
600달러 온라인 강좌 수강권+ 100달러 1개월 비지니스 서포트
등록하시면 패키지를 받으실 수 있는 URL 및 AWS 학습 정보를 이메일로 보내드립니다!

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