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Deep Learning을 위한 AWS 기반 인공 지능(AI) 서비스 (윤석찬)

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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.

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Deep Learning을 위한 AWS 기반 인공 지능(AI) 서비스 (윤석찬)

  1. 1. © 2017, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
  2. 2. http://hunkim.github.io/ml/
  3. 3. 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
  4. 4. 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
  5. 5. 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
  6. 6. Spot Instances (80% ↓) = $30 per hour
  7. 7. $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 ...
  8. 8. 1GiB GPU Memory 2 GiB 4 GiB 8 GiB
  9. 9. 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
  10. 10. https://aws.amazon.com/ko/batch/
  11. 11. • • • • • http://bit.ly/deepami http://bit.ly/deepubuntu
  12. 12. • • • •
  13. 13. 기반 예제 • • • • • • • • • • http://mxnet.io/ https://github.com/dmlc/mxnet http://incubator.apache.org/projects/mxnet.html
  14. 14. 3
  15. 15. http://bit.ly/deepcfn • • • • •
  16. 16. • • 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
  17. 17. ../../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
  18. 18. • • • • • • • • • • •
  19. 19. https://www.youtube.com/ watch?v=q6gx9yk0nQo https://www.slideshare.net/AIFrontie rs/scaling-deep-learning-with-mxnet
  20. 20. http://j.mp/2hne9IL
  21. 21. 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) • •
  22. 22. $ 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
  23. 23. • •
  24. 24. 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!
  25. 25. • • • •

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