AI 클라우드로 완전 정복하기 - 데이터 분석부터 딥러닝까지 (윤석찬, AWS테크에반젤리스트)
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This document discusses how Amazon SageMaker can be used to train machine learning models on large datasets using hosted Jupyter notebooks. It notes that DigitalGlobe plans to use SageMaker to train models on petabytes of Earth observation imagery so that users can create and deploy models within one scalable environment. The document also quotes the CTO of Maxar Technologies saying they will use SageMaker to build and deploy novel AI algorithms at scale to solve complex problems.
![sagemaker = boto3.client(service_name='sagemaker')
sagemaker.create_training_job(**training_params)
create_model_response = sage.create_model(
ModelName = model_name,
ExecutionRoleArn = role,
PrimaryContainer = primary_container)
endpoint_config_response = sage.create_endpoint_config(
EndpointConfigName = endpoint_config_name,
ProductionVariants=[{
'InstanceType':'ml.m4.xlarge',
'InitialInstanceCount':1,
'ModelName':model_name,
'VariantName':'AllTraffic'}])
endpoint_response = sagemaker.create_endpoint(
'EndpointName': endpoint_name,
'EndpointConfigName': endpoint_config_name
2
.
1 3
.](https://image.slidesharecdn.com/awsbigdataamazonsagemakeraicon2018-180707100405/85/AI-AWS-32-320.jpg)
![## train data
num_gpus = 4
gpus = [mx.gpu(i) for i in range(num_gpus)]
model = mx.model.FeedForward(
ctx = gpus,
symbol = softmax,
num_round = 20,
learning_rate = 0.01,
momentum = 0.9,
wd = 0.00001)
model.fit(X = train, eval_data = val,
batch_end_callback =
mx.callback.Speedometer(batch_size=batch_size))](https://image.slidesharecdn.com/awsbigdataamazonsagemakeraicon2018-180707100405/85/AI-AWS-35-320.jpg)
AI 클라우드로 완전 정복하기 - 데이터 분석부터 딥러닝까지 (윤석찬, AWS테크에반젤리스트)
- 1. © 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved. ,
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- 6. 데이터베이스 관Dd 부담이 많습니다. 관계형 DB 는 확장성이 T지 않아요. Had,,p 배o 및 관Dp기. 힘?니다. 기존 D)는 L잡p고 비싸고 느립니다. 상a DB는 고비a에 관D, 확장이 어B워요. 실W/ 데이터는 수집p고 분석p기 힘?니다. 데이터 클E징(E(L)을 좀더 T게 할 수 없을까요? 딥러닝 데이터 H델/배o를 좀 더 T게 p고 싶어요. ü Amazon RDS ü Amazon DynamoDB ü Amazon EMR ü Amazon Redshift ü Amazon Aurora ü Amazon Kinesis ü AWS Glue ü Amazon SageMaker
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- 9. Transactions ERP Data analysts 1 4 0 9 5 Amazon Quicksight
- 10. 2 45 ) B e M 01) 3 W D cKa cG CS : ) R D ( :4 ) : E D https://aws.amazon.com/ko/solutions/case-studies/supercell/
- 11. Transactions ERP Data Lake expdp Data Data analysts Data Warehouse Amazon Redshift Direct Query Amazon Athena Data Storage Amazon S3
- 12. Data Lake Business users Transactions ERP Social media Data Stream Capture Amazon Kinesis Events Amazon QuickSight Data Warehouse Amazon Redshift Stream Data Amazon ElasticSearch Data Storage Amazon S3
- 13. Raw Data Amazon S3 ETL (Hadoop) Amazon EMR Triggered Code Amazon Lambda Staged Data (Data Lake) Amazon S3 ETL & Catalog Management AWS Glue Data Warehouse Amazon Redshift Triggered Code Amazon Lambda
- 14. Transactions Data scientists Business users Connected devices Data Event Insights Data Lake ML / Analytics / DLWeb logs / clickstream Amazon SageMaker
- 15. A QUIET OFFICE Amazon SageMaker Image Classification Amazon Rekognition Image CHAIR LAPTOP LAMP DESK 97% 95% 88% 82% Object Identification WORKING! <HISTORY>
- 16. Modern data architecture Real-time engagement and interactive customer experiences Transactions ERP Data analysts Data scientists Business users Engagement platformsConnected devices Automation / events Data Event Action Insights Data Lake ML / Analytics Predict / Recommend AI Services Social media Web logs / clickstream
- 17. .
- 18. AWS DEEP LEARNING AMI Apache MXNet TensorFlowCaffe2 Torch KerasCNTK PyTorch GluonTheano VISION AWS DeepLensAmazon SageMaker LANGUAGE Amazon Rekognition Amazon Polly Amazon Lex Amazon Rekognition Video Amazon Transcribe Amazon Comprehend Alexa for Business VR/AR Amazon Sumerian Amazon Machine Learning Amazon EMR & SparkMechanical Turk INSTANCES GPU(G2/P2/P3) CPU (C5) FPGA (F1) Amazon Translate ( )
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- 21. - J N ) ( (( K-Means Clustering Principal Component Analysis Neural Topic Modelling Factorization Machines Linear Learner - Regression XGBoost Latent Dirichlet Allocation Image Classification Seq2Seq Linear Learner - Classification ALGORITHMS Apache MXNet TensorFlow Caffe2, CNTK, PyTorch, Torch FRAMEWORKS
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- 24. K-Means Clustering Principal Component Analysis Neural Topic Modelling Factorization Machines Linear Learner - Regression XGBoost Latent Dirichlet Allocation Image Classification Seq2Seq Linear Learner - Classification BUILT ALGORITHMS Caffe2, CNTK, PyTorch, Torch IM Estimators in Spark DEEP LEARNING FRAMEWORKS Bring Your Own Script (IM builds the Container) BRING YOUR OWN MODEL ML Training code Fetch Training data Save Model Artifacts Amazon ECR Save Inference Image Amazon S3
- 25. https://nucleusresearch.com/research/single/guidebook-tensorflow-aws/ In analyzing the experiences of researchers supporting more than 388unique projects, Nucleus found that 88 percent of cloud-based TensorFlow projects are running on Amazon Web Services (AWS). “
- 26. from sagemaker.tensorflow import TensorFlow tf_estimator = TensorFlow( entry_point="tf-train.py", role='SageMakerRole', training_steps=10000, evaluation_steps=100, train_instance_count=1, train_instance_type='ml.p2.xlarge’) tf_estimator.fit('s3://bucket/path/to/training/data’) from sagemaker.pytorch import Pytorch pytorch_estimator = Pytorch(entry_point="pt-train.py", framework_version=”0.4.0”, role='SageMakerRole', train_instance_type="ml.p2.xlarge", train_instance_count=2, hyperparameters={ 'epochs': 6, 'backend': 'gloo’ }) pytorch_estimator.fit("s3://my_bucket/my_training_data/")
- 27. - H J N
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- 29. predictor = tf_estimator.deploy( initial_instance_count=1, instance_type='ml.c4.xlarge') predictor = mxnet_estimator.deploy( deploy_instance_type="ml.p2.xlarge", min_instances=1, https://runtime.sagemaker.us-east-1.amazonaws.com/ endpoints/model-name/invocations • BK A ID A • A I
- 30. SageMaker Notebooks Training Algorithm SageMaker Training Amazon ECR Code Commit Code Pipeline SageMaker Hosting Coco dataset AWS Lambda API Gateway Build Train Deploy static website hosted on S3 Inference requests Amazon S3 Amazon Cloudfront Web assets on Cloudfront
- 31. - • • w I n l 9 l NF l l T • C x N S • , C e , • 03 2 x oMs r C • N S • C 8
- 32. sagemaker = boto3.client(service_name='sagemaker') sagemaker.create_training_job(**training_params) create_model_response = sage.create_model( ModelName = model_name, ExecutionRoleArn = role, PrimaryContainer = primary_container) endpoint_config_response = sage.create_endpoint_config( EndpointConfigName = endpoint_config_name, ProductionVariants=[{ 'InstanceType':'ml.m4.xlarge', 'InitialInstanceCount':1, 'ModelName':model_name, 'VariantName':'AllTraffic'}]) endpoint_response = sagemaker.create_endpoint( 'EndpointName': endpoint_name, 'EndpointConfigName': endpoint_config_name 2 . 1 3 .
- 33. • • • •
- 34. 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 P2.16xlarge (8 Nvidia Tesla K80 - 16 GPUs) Synchronous SGD (Stochastic Gradient Descent) 91% Efficiency 88% Efficiency 16x P2.16xlarge by AWS CloudFormation Mounted on Amazon EFS # GPUs
- 35. ## train data num_gpus = 4 gpus = [mx.gpu(i) for i in range(num_gpus)] model = mx.model.FeedForward( ctx = gpus, symbol = softmax, num_round = 20, learning_rate = 0.01, momentum = 0.9, wd = 0.00001) model.fit(X = train, eval_data = val, batch_end_callback = mx.callback.Speedometer(batch_size=batch_size))
- 36. 기반 예제 B : A I A AA • ( A B • . DD A DD A B A • A A IBD A AD D AD • -A D AD D : D • BB A • -. D A: D : • /D BD D A • - AD C D : • A D • D A D )A B D A A ).. http://mxnet.io/ https://github.com/dmlc/mxnet http://incubator.apache.org/projects/mxnet.html
- 37. http://gluon.mxnet.io - H • ,X P b fd S • ( C X g NT MI ce • ) A ) A A A K a W • A ,C C a
- 38. We plan to use Amazon SageMaker to train models against petabytes of Earth observation imagery datasets using hosted Jupyter notebooks, so DigitalGlobe's Geospatial Big Data Platform (GBDX) users can just push a button, create a model, and deploy it all within one scalable distributed environment at scale. - Dr. Walter Scott, CTO of Maxar Technologies and founder of DigitalGlobe
- 39. EC : A C “With Amazon SageMaker, we can accelerate our Artificial Intelligence initiatives at scale by building and deploying our algorithms on the platform. We will create novel large-scale machine learning and AI algorithms and deploy them on this platform to solve complex problems that can power prosperity for our customers." - Ashok Srivastava, Chief Data Officer, Intuit
- 40. $$$$ $$$ $$ $ Minutes Hours Days Weeks Months Single Machine Distributed, with Strong Machines
- 41. $$$$ $$$ $$ $ Minutes Hours Days Weeks Months EC2 + AMI Amazon SageMaker On-premise
- 42. ! AWS Only http://bit.ly/awskr-ml-credits - e - o n S Ug m M m R . 21, 31 : r L A : k a) .( m :