alexeygrigorev / serverless-deep-learning

Example from my "Serverless Deep Learning" talk

Serverless Deep Learning

Presentation:

• https://www.slideshare.net/AlexeyGrigorev/serverless-deep-learning

AWS Lambda limits

Imagine you want to use AWS Lambda for serving TenorFlow models

TensorFlow 2.2 is quite big

1,5G tensorflow

Even packing doesn't help:

573M build.zip

The limits for AWS Lambda is 50 mb packed / 250 mb unpacked - so it goes well above the limits

TensorFlow Lite

Alternative: Use TF Lite

1. Compile it for the AWS Lambda environment
2. Convert your Keras model to TF-Lite format
3. Build a zip file with the lambda code
4. Test it
5. Deploy the code to AWS Lambda

Compile TF-Lite

• Clone TF
• Compile in inside docker using amazonlinux (here)
• Extract the compiled wheel from the image

Compile:

TF_LITE_BUILDER_IMAGE_NAME=tflite_amazonlinux
docker build -f tflite-compile.dockerfile -t ${TF_LITE_BUILDER_IMAGE_NAME} .

Extract the wheel:

docker run --rm \
    -v $(pwd)/tflite:/tflite/results \
    ${TF_LITE_BUILDER_IMAGE_NAME}

The result (for python 3.7) is already in the tflite folder:

• tflite/tflite_runtime-2.2.0-cp37-cp37m-linux_x86_64.whl

Source:

• https://github.com/tpaul1611/python_tflite_for_amazonlinux

Convert Keras to TF-Lite

• Save to saved_model format
• Load saved_model with TF-Lite converter
• Save it in TF-lite format

Saving the model:

tf.saved_model.save(
    model, 
    output_folder,
)

Loading with TF-Lite converter and saving it:

converter = tf.lite.TFLiteConverter.from_saved_model(output_folder)

tflite_model = converter.convert()

with tf.io.gfile.GFile('resnet50.tflite', 'wb') as f:
    f.write(tflite_model)

See the keras_to_tflite.ipynb notebook for the full example

Upload the model to S3

Building a Zip File

• Build a zip file in docker
• Extract the file from the image

Building the zip file:

BUILDER_IMAGE_NAME=tflite_build_lambda
docker build -t ${BUILDER_IMAGE_NAME} -f build.dockerfile .

Extracting the file

docker run --rm \
    -v $(pwd):/app/results \
    ${BUILDER_IMAGE_NAME}

The result is only 20M:

20M build.zip

Test it

• Unpack the zip file inside docker (use amazonlinux)
• Run the inference to make sure it works

Build the test container

TEST_IMAGE_NAME=tflite_test_lambda
docker build -t ${TEST_IMAGE_NAME} -f test.dockerfile .

Run the test

docker run --rm \
    -v $(pwd)/resnet50.tflite:/tmp/resnet50.tflite \
    ${TEST_IMAGE_NAME}

• Request: {'url': 'https://upload.wikimedia.org/wikipedia/commons/9/9a/Pug_600.jpg'}
• Response: {'pug': 0.99937063, 'Norwegian_elkhound': 0.0005375595, 'chow': 3.780921e-05}

AWS Lambda function

• Create a lambda function (e.g. "DeepLearningLambda")
• Upload the zip archive to S3
• Update the lambda function with AWS Cli

ZIP_FILE="build.zip"
S3_BUCKET="data-science-temporary"
S3_KEY="lambdas/resnet50.zip"
FUNCTION_NAME="DeepLearningLambda"

aws s3 cp "${ZIP_FILE}" "s3://${S3_BUCKET}/${S3_KEY}"


aws lambda update-function-code \
    --function-name ${FUNCTION_NAME} \
    --s3-bucket ${S3_BUCKET} \
    --s3-key ${S3_KEY}

That's all!