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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.

Publié dans : Technologie
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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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