AWS Lambda를 통한 Tensorflow 및 Keras 기반 추론 모델 서비스하기 :: 이준범 :: AWS Summit Seoul 2018

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8. • model = Sequential() model.add(Dense(1, activation='sigmoid', input_dim=100)) from tensorflow.python.keras.models import Sequential from tensorflow.python.keras.layers import Dense import numpy as np model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) data = np.random.random((1000, 100)) labels = np.random.randint(2, size=(1000, 1)) model.fit(data, labels, epochs=10, batch_size=32)

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11. • •   • from tensorflow.python.keras.applications.xception import Xception model = Xception(weights='imagenet')

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14. • from tensorflow.python.keras.applications.xception import Xception from tensorflow.python.keras.preprocessing import image from tensorflow.python.keras.applications.resnet50 import preprocess_input, decode_predictions import numpy as np model = Xception(weights='imagenet') img_path = 'elephant.jpg' img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) preds = model.predict(x) print('Predicted:', decode_predictions(preds, top=3)[0])

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19. • • • from tensorflow.python.keras.applications.xception import Xception model = Xception(include_top=False, weights='imagenet')

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21. def train(train_data_dir, validation_data_dir, model_path): base_model = Xception(weights='imagenet', include_top=False) x = base_model.output x = GlobalAveragePooling2D()(x) predictions = Dense(nb_classes, activation='softmax')(x) model = Model(base_model.input, predictions) transformation_ratio = .05 train_datagen = ImageDataGenerator(rescale=1. / 255, rotation_range=transformation_ratio, shear_range=transformation_ratio, zoom_range=transformation_ratio, cval=transformation_ratio, horizontal_flip=True, vertical_flip=True)

22. def train(train_data_dir, validation_data_dir, model_path): ...( )... train_generator = train_datagen.flow_from_directory(train_data_dir, batch_size=32, class_mode='categorical') validation_generator = validation_datagen.flow_from_directory(validation_data_dir, batch_size=32, class_mode='categorical') model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy']) top_weights_path = os.path.join(os.path.abspath(model_path), 'top_model_weights.h5') callbacks_list = [ ModelCheckpoint(top_weights_path, monitor='val_acc', verbose=1, save_best_only=True), EarlyStopping(monitor='val_acc', patience=5, verbose=0) ] model.fit_generator(train_generator, samples_per_epoch=train_generator.nb_sample, nb_epoch=nb_epoch / 5, validation_data=validation_generator, nb_val_samples=validation_generator.nb_sample, callbacks=callbacks_list)

23. •   •   final_weights_path = os.path.join(os.path.abspath(model_path), 'model_weights.h5') model.save_weights(final_weights_path) model_json = model.to_json() json_file = open(os.path.join(os.path.abspath(model_path), 'model.json'), 'w') json_file.write(model_json)

24. def inference(trained_model_dir, test_data_dir, results_path): # load json and create model json_file = open(os.path.join(trained_model_dir, model_name), 'r') loaded_model_json = json_file.read() json_file.close() model = model_from_json(loaded_model_json) model.load_weights(os.path.join(trained_model_dir, model_weights)) # Read Data test_datagen = ImageDataGenerator(rescale=1. / 255) test_generator = test_datagen.flow_from_directory(test_data_dir, batch_size=batch_size, shuffle=False) # Calculate class posteriors probabilities y_probabilities = model.predict_generator(test_generator, val_samples=test_generator.nb_sample) # Calculate class labels y_classes = probas_to_classes(y_probabilities) filenames = [filename.split('/')[1] for filename in test_generator.filenames] ids = [filename.split('.')[0] for filename in filenames]

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36. # event { 'Records': [ { 'eventVersion': '2.0', 'eventSource': 'aws:s3', 'awsRegion': 'ap-northeast-2', # 'eventTime': '2017-12-13T03:28:13.528Z', # 'eventName': 'ObjectCreated:Put', 'userIdentity': {'principalId': 'AFK2RA1O3ML1F'}, 'requestParameters': {'sourceIPAddress': '123.24.137.5'}, 'responseElements': { 'x-amz-request-id': '1214K424C14C384D', 'x-amz-id-2': 'BOTBfAoB/gKBbn412ITN4t2psTW499iMRKZDK/CQTsjrkeSSzSdsDUMGabcdnvHeYNtbTDHoHKs=' }, 's3': { 's3SchemaVersion': '1.0', 'configurationId': 'b249eeda-3d48-4319-a7e2-853f964c1a25', 'bucket': { 'name': 'aws-summit-kr-2018', # 'ownerIdentity': { 'principalId': 'AFK2RA1O3ML1F' }, 'arn': 'arn:aws:s3:::aws-summit-kr-2018' }, 'object': { 'key': 'img/test_img.png', # 'size': 11733, # 'eTag': 'f2d12d123aebda1cc1fk17479207e838', 'sequencer': '125B119E4D7B2A0A48' } } } ] }

37. • • • • # def handler(event, context): bucket_name = event['Records'][0]['s3']['bucket']['name'] file_path = event['Records'][0]['s3']['object']['key']

38. • ACCESS_KEY = os.environ.get('ACCESS_KEY') SECRET_KEY = os.environ.get('SECRET_KEY') def downloadFromS3(strBucket, s3_path, local_path): s3_client = boto3.client('s3', aws_access_key_id=ACCESS_KEY, aws_secret_access_key=SECRET_KEY) s3_client.download_file(strBucket, s3_path, local_path) def uploadToS3(bucket, s3_path, local_path): s3_client = boto3.client('s3', aws_access_key_id=ACCESS_KEY, aws_secret_access_key=SECRET_KEY) s3_client.upload_file(local_path, bucket, s3_path)

39. • •   def handler(event, context): bucket_name = event['Records'][0]['s3']['bucket']['name'] file_path = event['Records'][0]['s3']['object']['key'] file_name = file_path.split('/')[-1] downloadFromS3(bucket_name, file_path, '/tmp/'+file_name)  

40. • def handler(event, context): bucket_name = event['Records'][0]['s3']['bucket']['name'] file_path = event['Records'][0]['s3']['object']['key'] file_name = file_path.split('/')[-1] downloadFromS3(bucket_name, file_path, '/tmp/'+file_name) downloadFromS3( 'aws-summit-kr-2018', 'xception_weights_tf_dim_ordering_tf_kernels.h5', '/tmp/.keras/xception_weights_tf_dim_ordering_tf_kernels.h5' )

41. • from tensorflow.python.keras.applications.xception import Xception from tensorflow.python.keras.preprocessing import image from tensorflow.python.keras.applications.resnet50 import preprocess_input, decode_predictions import numpy as np def predict(img_path): model = Xception(weights='imagenet') img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) preds = model.predict(x) return decode_predictions(preds, top=3)[0]

42. • def handler(event, context): bucket_name = event['Records'][0]['s3']['bucket']['name'] file_path = event['Records'][0]['s3']['object']['key'] file_name = file_path.split('/')[-1] downloadFromS3(bucket_name, file_path, '/tmp/'+file_name) downloadFromS3( 'aws-summit-kr-2018', 'xception_weights_tf_dim_ordering_tf_kernels.h5', '/tmp/.keras/xception_weights_tf_dim_ordering_tf_kernels.h5' ) result = predict('/tmp/'+file_name) return result

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56. •   •   •   docker run -v $(pwd):/outputs --name lambdapack -d amazonlinux:latest tail -f /dev/null

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58. dev_install() { yum -y update yum -y upgrade yum install -y wget gcc gcc-c++ cmake python36-devel python36-virtualenv python36-pip findutils zlib-devel zip unzip blas-devel lapack-devel atlas-devel }

59. mkvirtualenv() { cd /home/ rm -rf env python3 -m virtualenv env --python=python3 source env/bin/activate } pip_install() { source /home/env/bin/activate pip install -U pip wheel pip install --use-wheel tensorflow==1.7.0 --no-deps pip install protobuf html5lib bleach --no-deps pip install --use-wheel pillow==4.0.0 pip install h5py }

60. gather_pack() { cd /home/ && rm -rf pack && mkdir pack && cd pack cp -R /home/env/lib/python3.6/site-packages/* . cp -R /home/env/lib64/python3.6/site-packages/* . cp /outputs/index.py /home/pack/index.py find . -type d -name "test" -exec rm -rf {} + find -name "*.so" | xargs strip find -name "*.so.*" | xargs strip rm -r pip && rm -r pip-* && rm -r wheel && rm -r wheel-* find . | grep -E "(__pycache__|.pyc$)" | xargs rm -rf echo "stripped size $(du -sh /home/pack | cut -f1)" zip -FS -r1 /outputs/pack.zip * > /dev/null echo "compressed size $(du -sh /outputs/pack.zip | cut -f1)" }

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63. import boto3 def upload_to_s3(bucket, s3_path, local_path): client = boto3.client('s3', aws_access_key_id=ACCESS_KEY,aws_secret_access_key=SECRET_KEY) client.upload_file(local_path, bucket, s3_path) def update_lambda(function_name, bucket, s3_path): client = boto3.client('lambda', aws_access_key_id=ACCESS_KEY,aws_secret_access_key=SECRET_KEY) client.update_function_code( FunctionName=function_name, S3Bucket=bucket, S3Key=s3_path, ) uploadToS3(' ', ' /pack.zip', './pack.zip') update_lambda(' ', ' ', ' /pack.zip')

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66. def download_s3_object(strBucket, s3_path): import io file_obj = io.BytesIO() s3_client = boto3.client( 's3', aws_access_key_id=ACCESS_KEY, aws_secret_access_key=SECRET_KEY ) s3_client.download_fileobj(strBucket, s3_path, file_obj) return file_obj pack2 = download_s3_object('bucket-name', 'pack2.zip') import zipfile zip_ref = zipfile.ZipFile(pack2) zip_ref.extractall('/tmp') zip_ref.close() import sys sys.path.append("/tmp")

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77. © 2018, Amazon Web Services, Inc. or Its Affiliates. All rights reserved. docker run --rm -v "$PWD":/var/task lambci/lambda:python3.6 my_module.my_handler docker run --rm -v "$PWD":/var/task lambci/lambda:python3.6 my_module.my_handler '{"some": "event"}'

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AWS Lambda를 통한 Tensorflow 및 Keras 기반 추론 모델 서비스하기 :: 이준범 :: AWS Summit Seoul 2018

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AWS Lambda를 통한 Tensorflow 및 Keras 기반 추론 모델 서비스하기 :: 이준범 :: AWS Summit Seoul 2018