Amazon SageMaker 推論エンドポイントを利用したアプリケーション開発

© 2018, Amazon Web Services, Inc. or its Affiliates. All rights reserved.
Masaki Samejima
Solutions Architect, Amazon Web Services Japan.
2018.10.28
AWS

: http://bit.ly/sagemaker_api
1. API Gateway Lambda
SageMaker
2. S3 SageMaker
1. EC2 ( )

•
•

IAM
• IAM ( )
• AWSCloud9Administrator
• AmazonSageMakerFullAccess
• AWSLambdaFullAccess
• AmazonAPIGatewayAdministrator
• AmazonAPIGatewayInvokeFullAccess
• AmazonEC2FullAccess
• CloudFormation (Fullaccess
• IAM (AWS 1 )
• AWSServiceRoleForAWSCloud9 ( )
• Amazon SageMaker : AmazonSageMakerFullAccess ( ARN )
• AWS Lambda : AWSLambdaFullAccess AmazonSageMakerFullAccess
( )
IAM Administrator
Lambda

AWS Lambda

AamzonSageMakerFullAccess

•
• Lambda

•
•
(Entry point)
Amazon
SageMaker
S3
•
• Amazon SageMaker

https://console.aws.amazon.com/sagemaker

•
•
ml.t2.medium
• IAM

IAM
S3
S3

• VPC,
•

Jupyter Notebook
• InService
Jupyter Notebook

•
•
•

Upload

deploy.ipynb

Run
---

https://ap-northeast-1.console.aws.amazon.com/sagemaker/home?region=ap-
northeast-1#/endpoints

API Gateway Lambda

1
API Gateway, Lambda SageMaker
Endpoint
Amazon
API Gateway
AWS
Lambda
Amazon
SageMaker

2
S3 API Gateway Lambda
Amazon
API Gateway
AWS
Lambda
Amazon
SageMaker
S3

1
Cloud9 AWS Lambda

Cloud9
AWS
Lambda
Amazon API
Gateway
• Cloud9
• GUI Lambda API Gateway Lambda

Cloud9
https://aws.amazon.com/jp/cloud9/

•
• AWS

•
•
create environment
• Cloud9

Python3
Python 2 → Python 3

python 3
alias python=python36
alias pip=pip-3.6

• Lambda
• SageMaker

• AWS Resources
λ+ Lambda
• Lambda

Blueprint (
)
empty-python

• API Gateway
• Resource path: /
• Security: NONE

• Role Choose an existing role
• Existing role AWSLambdaFullAccess
AmazonSageMakerFullAccess role

•
• (ResourceNotFoundException)
Lambda, API Gateway, CloudFormation
⋯

AWS Lambda
https://console.aws.amazon.com/lambda/
Cloud9

Lambda
• Lambda
python
• lambda.py
•

import boto3
import json
from io import BytesIO
from PIL import Image
import base64
import numpy as np
import logging
logger = logging.getLogger()
logger.setLevel(logging.INFO)
def lambda_handler(event, context):
labels = ['aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor ]
image = base64.b64decode(event['base64Image'])
image = Image.open(BytesIO(image))
transposed = np.array(image).transpose(2,0,1)
npy = BytesIO()
np.save(npy, transposed)
ByteArr = npy.getvalue()
runtime= boto3.client('sagemaker-runtime )
response = runtime.invoke_endpoint(EndpointName=“ ”,
ContentType='application/x-npy',
Accept = 'application/json',
Body=ByteArr)
predictions = np.array(json.load(response['Body']))
results =[]
for i in range(predictions.shape[1]):
bbox, label_index, score = predictions[:, i]
result = {
'class_name' : labels[label_index],
'y_min': int(bbox[0]),
'x_min': int(bbox[1]),
'y_max': int(bbox[2]),
'x_max': int(bbox[3]),
'score': score
}
results.append(result)
return {
"statusCode": 200,
"body": json.dumps(results)
}
npy
Endpoint

• Lambda Lambda
• Lambda
Cloud9 Lambda
•
cd (Lambda )
pip install numpy -t .
pip install pillow -t .

Cloud9 API Gateway

SageMaker Role

API Gateway
API Gateway

1
API Gateway

API
Lambda

• POST method
•
• ANY
POST
• POST

Lambda Cloud9 ( )

•
• image/jpeg

• image/jpeg
Lambda
Lambda
image =
base64.b64decode(event['base64Image'])
{
"operation": "inference",
"base64Image": "$input.body"
}

• json
• Lambda json Empty
: Empty
: application/json
: {}

•
• POST
•

•
CORS
•
CORS CORS

API URL

1

• API
(test_images cats.jpg )
• curl
• Status code 200
{"statusCode": 200, "body": "[{¥"class_name¥": ¥"cat¥", ¥"y_min¥": 170,
¥"x_min¥": 443, ¥"y_max¥": 558, ¥"x_max¥": 812, ¥"score¥":
0.9977871179580688}, {¥"class_name¥": ¥"cat¥", ¥"y_min¥": 82, ¥"x_min¥":
249, ¥"y_max¥": 583, ¥"x_max¥": 465, ¥"score¥": 0.9967633485794067}]"}
curl --request POST -H “Content-Type: image/jpeg” --data-
binary ”@test_images/cats.jpg“ http://(API URL)

Javascript
• object_detect.html API URL
const apiurl = 'https://…..execute-api.ap-northeast-1.amazonaws.com/Prod’
• object_detect.html test_images

2
Cloud9 AWS Lambda

Cloud9 Lambda
• AWS Resources
λ+ Lambda
• Lambda

Lambda API Gateway
• Function Name/Application Name:
( : Batchinference)
• Blueprint: empty-python
• Trigger:
• Function Trigger: API Gateway
• Resource Path: /
• Security: AWS_IAM
• Lambda
• Endpoint Role

Lambda
• lambda_batch.py
•
• lambda

lambda_batch.py
import boto3
import json
import time
import logging
def lambda_handler(event, context):
model_name = event['model']
timestamp = time.strftime("%Y%m%d-%H%M%S")
job_name = 'transformjob' + timestamp
indir = event['input']
outdir = event['output'] + timestamp
instance_type = event['instance_type']
instance_count = event['instance_count']
runtime= boto3.client('sagemaker')
response = runtime.create_transform_job(
TransformJobName=job_name,
ModelName=model_name,
TransformInput={
'DataSource': {
'S3DataSource': {
'S3DataType': 'S3Prefix',
'S3Uri': indir
}
},
'ContentType': 'image/jpeg',
},
TransformOutput={
'S3OutputPath': outdir,
'Accept': 'application/json'
},
TransformResources={
'InstanceType': instance_type,
'InstanceCount': instance_count,
}
)
return {
"statusCode": 200,
"body": response
}

AWS Lambda

API Gateway

2
API Gateway

• Cloud9 Lambda ( )
• 1 Lambda

•
• multipart/form-data

•
• multipart/form-data
multipart/form-data

2

Javascript
• batch_detect.html apiurl
API Gateway URL
• multipart/form-data (API Gateway)

S3
• SageMaker S3
test_images
• S3 input

Model Name
S3 input:
S3 output: S3 input
Instance type: (ml.m4.xlarge)
Instance Count: (1)

S3

[[[170.97262573242188, 443.8755798339844,
558.6778564453125, 812.5823364257812],
[82.46746063232422, 249.7886505126953,
583.5474853515625, 465.04547119140625]],
[7, 7],
[0.9977871179580688, 0.9967633485794067]]
2
2 (7= )

• SageMaker
• SageMaker
• Cloud9 (EC2)
• S3
• Lambda
• API Gateway
• CloudFormation

https://ap-northeast-1.console.aws.amazon.com/sagemaker/home?region=ap-
northeast-1#/notebook-instances
Stopped

SageMaker
https://ap-northeast-
1.console.aws.amazon.com/sagemaker/home?region=ap-
northeast-1#/endpoints

Cloud9
https://us-west-
2.console.aws.amazon.com/cloud9/home?region=us-
west-2
Delete

S3
https://s3.console.aws.amazon.com/s3/
SageMaker

Lambda
1.console.aws.amazon.com/lambda/home?region=ap-
northeast-1#/functions
• Cloud9
•

API Gateway
1.console.aws.amazon.com/apigateway/home?region=ap-
northeast-1
• API
•
• 1 1

CloudFormation
1.console.aws.amazon.com/cloudformation/home?region
=ap-northeast-1#/stacks?filter=active
•
•

Option:
1
EC2 Web application

Amazon
SageMaker
Amazon
EC2
Lambda, API Gateway EC2

Amazon
EC2

• Cloud9 Cloud9
us-west2 ( ) SageMaker

setting.py ALLOWED_HOSTS=[‘*’]

cd MyTestApp
python3 manage.py runserver 0.0.0.0:8080
URL

from django.http.response import HttpResponse
import boto3
import json
from PIL import Image
import base64
import numpy as np
import logging
import os
# Create your views here.
def helloworld(request):
labels = ['aeroplane', 'bicycle', 'bird', 'boat',
'bottle', 'bus', 'car', 'cat', 'chair',
'cow', 'diningtable', 'dog', 'horse',
'motorbike', 'person', 'pottedplant',
'sheep', 'sofa', 'train', 'tvmonitor']
#return HttpResponse(os.listdir("MyTestApp"))
image = Image.open("./helloworld/dogs.jpg")
transposed = np.array(image).transpose(2,0,1)
npy = BytesIO()
np.save(npy, transposed)
ByteArr = npy.getvalue()
runtime= boto3.client('sagemaker-runtime')
response = runtime.invoke_endpoint(EndpointName=' ',
ContentType='application/x-npy',
Accept = 'application/json',
Body=ByteArr)
predictions = np.array(json.load(response['Body']))
results =[]
for i in range(predictions.shape[1]):
bbox, label_index, score = predictions[:, i]
result = {
'class_name' : labels[label_index],
'y_min': int(bbox[0]),
'x_min': int(bbox[1]),
'y_max': int(bbox[2]),
'x_max': int(bbox[3]),
'score': score
}
results.append(result)
http_res = {
"statusCode": 200,
"body": json.dumps(results)
}
return HttpResponse(http_res[“body”])

INSTALLED_APPS ’helloworld’

from django.conf.urls import url
from . import views
urlpatterns = [
url(r'^$', views.helloworld, name='helloworld'),
]

from django.contrib import admin
from django.urls import path
from django.conf.urls import include, url
urlpatterns = [
path('admin/', admin.site.urls),
url('helloworld/', include(('helloworld.urls','helloworld'),)),
]

Amazon SageMaker 推論エンドポイントを利用したアプリケーション開発

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