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Amazon SageMaker
?
AWS
AWS Deep Learning AMIs
EC2 GPUs EC2 CPUs IoT Edge
Amazon SageMaker Amazon Mechanical Turk
R E K O G N I T I O N R E K O G N I T I O N
V I D E O
P O L L Y T R A N S C R I B E T R A N S L A T E C O M P R E H E N D L E X
KERAS
SageMaker
•
•
•
• IoT
•
•
SageMaker
SageMaker
SageMaker
SageMaker
Agenda
SageMaker
SageMaker
•
• 1
1
• 1
•
•
SageMaker
SageMaker
8
SageMaker
•
•
•
•
•
•
•
•
•
Jupyter Notebook
•
•
• 4
⎼ ml.t2
⎼ ml.m4
⎼ ml.p2
⎼ ml.p3
• VPC
ENI VPC
CreateTrainingJob API Docker
•
• 2
• m4, m5, c4, c5,
p2, p3
•
S3
https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateTrainingJob.html
CreateEndpoint API Docker
• AB
•
•
https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpointConfig.html
https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreateEndpoint.html
https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/API_UpdateEndpoint.html
https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/API_UpdateEndpointWeightsAndCapacities.html
SageMaker
SageMaker 2
AWS SDK
•
•
SageMaker SDK
•
• AWS SDK SageMaker SDK AWS SDK
scikit-learn
• Python Spark
• Jupyter Notebook
https://github.com/aws/sagemaker-python-sdk
https://github.com/aws/sagemaker-spark
AWS SDK SageMaker SDK
• SageMaker SDK
Jupyter Notebook
• AWS SDK
SageMaker SDK AWS SDK
create-endpoint
create-notebook-instance
create-training-job
delete-endpoint
delete-notebook-instance
describe-endpoint
describe-notebook-instance
…
estimator = TensorFlow(…)
estimator.set_hyperparameters(…)
estimator.fit(…)
predictor = estimator.deploy(…)
Predictor.predict(…)
SageMaker
1. SageMaker
2. Tensorflow/Chainer/PyTorch/MXNet
3.
SageMaker
1. SageMaker
→
2. Tensorflow/Chainer/PyTorch/MXNet
→
3.
→
SageMaker
Linear Learner
XGBoost (eXtreme Gradient
Boosting)
PCA
k-means
k-NN
Factorization Machines
Random Cut Forest (Amazon)
LDA (Latent Dirichlet Allocation)
SageMaker
SageMaker
Image classification
Object Detection
seq2seq
Neural Topic Model
Blazing text
(Amazon)
DeepAR Forecasting
(Amazon)
Image Classification
ResNet
• ResNet CNN
• ILSVRC 2015 1
• ImageNet
• use_pretrained_model
1 0
https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/imageclassification_caltech/Image-classification-transfer-learning.ipynb
https://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/He_Deep_Residual_Learning_CVPR_2016_paper.pdf
https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/image-classification.html
https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/IC-Hyperparameter.html
Object Detection
SSD
•
• VGG or ResNet
• ImageNet
• use_pretrained_model
1 0
https://github.com/awslabs/amazon-sagemaker-examples/blob/master/introduction_to_amazon_algorithms/object_detection_pascalvoc_coco/object_detection_image_json_format.ipynb
https://arxiv.org/pdf/1512.02325.pdf
https://docs.aws.amazon.com/sagemaker/latest/dg/object-detection.html
https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/IC-Hyperparameter.html
SageMaker
S3
estimator.fit()
SageMaker
S3
estimator.deploy()
SageMaker
S3
https://docs.aws.amazon.com/sagemaker/latest/dg/sagemaker-algo-docker-registry-paths.html
SageMaker
Tensorflow/Chainer/PyTorch
/MXNet
*
S3
SageMaker
S3
estimator.fit() .
AWS
SageMaker
S3
estimator.deploy()
AWS
SageMaker
S3
Tensorflow
• model_fn
• estimator_fn tensorflow.estimator
• keras_model_fn tf.keras
• train_input_fn
• eval_input_fn
• serving_input_fn
• input_fn
• output_fn
https://docs.aws.amazon.com/sagemaker/latest/dg/tf-training-inference-code-template.html
5
https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/API_CreateTrainingJob.html
https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/tf-example1-train.html
CreateTrainingJob
Hyperparameters
Chainer
• __main__
• model_fn:
•
https://docs.aws.amazon.com/sagemaker/latest/dg/chainer.html
https://github.com/awslabs/amazon-sagemaker-examples/blob/master/sagemaker-python-
sdk/chainer_mnist/chainer_mnist_single_machine.py
PyTorch
• __main__
• model_fn:
•
https://docs.aws.amazon.com/sagemaker/latest/dg/pytorch.html
https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/pytorch/README.rst
MXNet
• train
• save
• model_fn:
• transform_fn:
•
https://docs.aws.amazon.com/sagemaker/latest/dg/mxnet-training-inference-code-template.html
docker run IMAGE_ID train
• train
• estimator.fit() docker run train
docker run IMAGE_ID serve
• serve
• estimator.fit() serve
• predictor.predict() /invocations
•
https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-training-algo.html
https://docs.aws.amazon.com/sagemaker/latest/dg/your-algorithms-inference-code.html
ECR
push
SageMaker
S3
S3ECR
SageMaker
estimator.fit()
S3ECR
SageMaker
estimator.deploy()
SageMaker
SageMaker 3
1:
• SageMaker
2: GPU
• AWS
SageMaker API
CreateTrainingJob
CreateEndpointConfig
UpdateEndpoint
UpdateEndpointWeightsAndCapacities
• →
•
VPC
EMR
SageMaker
EMR
EMR VPC
EMR Livy
https://aws.amazon.com/jp/blogs/news/build-amazon-sagemaker-notebooks-backed-by-spark-in-amazon-emr/
AWS
SageMaker
Presigned URL API
• CreatePresignedNotebookInstanceUrl
• AWS AWS
https://docs.aws.amazon.com/sagemaker/latest/dg/API_CreatePresignedNotebookInstanceUrl.html
CreatePresignedNotebookInstanceUrl
CreateNotebookInstance
Presigned Instance URL Returned
Presigned Instance URL
Notebook Instance Request
Instance Created
SageMaker
→ instance_count 2
Tensorflow/Chainer/PyTorch/MXNet
→ instance_count
→
Docker SageMaker
/opt/ml/input/config/resourceConfig.json
Estimater hyperparameters
Tensorflow/Chainer/PyTorch/MXNet BYOA
https://github.com/awslabs/amazon-sagemaker-examples/tree/master/hyperparameter_tuning
https://github.com/aws/sagemaker-python-sdk#sagemaker-automatic-model-tuning
CloudWatch Logs
CloudWatch Logs
Tensorflow
AWS Tensorflow Docker
model_fn
source_dir
Tensorflow 1.4-1.9 Keras
https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/tf-examples.html
SageMaker Tensorflow/Chainer/PyTorch/MXNet
github
• SageMaker pull /
•
instance_type ‘local’
https://github.com/aws/sagemaker-python-sdk#local-mode
https://github.com/aws/sagemaker-tensorflow-containers
https://github.com/aws/sagemaker-mxnet-containers
https://github.com/aws/sagemaker-chainer-container
https://github.com/aws/sagemaker-pytorch-container
PIPE
2
• FILE:
• PIPE: S3 API
PIPE
• Tensorflow TFRecord
• MXNet RecordIO
Chainer PyTorch
PIPE
=
2
• SageMakerVariantInvocationsPerInstance
⎼ 1 1
•
https://docs.aws.amazon.com/sagemaker/latest/dg/endpoint-auto-scaling.html#endpoint-auto-scaling-add-policy
https://docs.aws.amazon.com/ja_jp/autoscaling/application/userguide/application-auto-scaling-target-tracking.html
Tensorflow
AWS Tensorflow Docker
Tensorflow Serving
input_fn, output_fn
https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/tf-examples.html
A/B
•
•
•
https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/API_runtime_InvokeEndpoint.html
Transform Job
Transform Job
•
•
S3
:
KMS key ID
SSE-KMS
• CreateTrainingJob /
• CreateEndpointConfig
•
•
Cloudtrail
PCI DSS HIPPA
https://aws.amazon.com/about-aws/whats-new/2018/01/aws-kms-based-encryption-is-now-available-in-amazon-sagemaker-training-and-hosting/
https://aws.amazon.com/about-aws/whats-new/2018/01/aws-cloudtrail-integration-is-now-available-in-amazon-sagemaker/
https://aws.amazon.com/about-aws/whats-new/2018/01/amazon-sagemaker-achieves-pci-dss-compliance/
https://aws.amazon.com/about-aws/whats-new/2018/04/access-amazon-vpc-resources-for-training-and-hosting-with-amazon-sageMaker/
https://aws.amazon.com/about-aws/whats-new/2018/05/Amazon-SageMaker-Achieves-HIPAA-Eligibility/
https://aws.amazon.com/jp/about-aws/whats-new/2018/06/amazon-sagemaker-inference-calls-are-supported-on-aws-privatelink/
:
SageMaker S3 S3 VPC
• S3
• S3
SageMaker API PrivateLink
• SageMaker Service API
• SageMaker Runtime API
https://aws.amazon.com/about-aws/whats-new/2018/04/access-amazon-vpc-resources-for-training-and-hosting-with-amazon-sageMaker/
https://aws.amazon.com/jp/about-aws/whats-new/2018/06/amazon-sagemaker-inference-calls-are-supported-on-aws-privatelink/
https://aws.amazon.com/about-aws/whats-new/2018/08/amazon-sagemaker-apis-supported-on-aws-privatelink/
ML
• SageMaker
1
ML
•
• 0.14 USD/GB/
•
• 0.016 USD/GB
https://aws.amazon.com/jp/sagemaker/pricing/
3
SageMaker Example Notebooks
• https://github.com/awslabs/amazon-sagemaker-examples
SageMaker SDK
• https://github.com/aws/sagemaker-python-sdk
(Doc : https://readthedocs.org/projects/sagemaker/)
SageMaker
• https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/whatis.html
https://github.com/awslabs/amazon-sagemaker-examples/tree/master/introduction_to_amazon_algorithms
https://docs.aws.amazon.com/ja_jp/sagemaker/latest/dg/algos.html
•
•
•
•
•
•
•
•
•
•
•
•
•
•
© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Amazon Web Services Japan, K. K.
Amazon SageMaker
SageMaker
https://aws.amazon.com/jp/console/
(Chrome, Firefox IE, Safari )
SageMaker
SageMaker
•
•
ml.t2.medium
• IAM
IAM
S3
S3
• VPC,
•
Jupyter Notebook
• InService
Jupyter Notebook
• New → Terminal
Terminal
cd SageMaker/
wget http://bit.ly/sm-handson -O handson.zip
unzip handson.zip + (O)
URL DL
Jupyter Notebook
handson
•
• XGBoost MNIST
• Chainer on SageMaker
• Chainer MLP MNIST
• MNIST Factorization Machines
@awscloud_jp
http://on.fb.me/1vR8yWm
Twitter/Facebook
AWS
Amazon SageMaker で始める機械学習

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