Python ML AZ Func

__init__.py

import azure.functions as func
import json
from . import predict
from ..shared import logger

# Azure Function Main Method
async def main(classifyBatch: func.QueueMessage, res: func.Out[func.QueueMessage]) -> None:
    await queue_classification_results(classifyBatch, res)
    
# Method to obtain and enqueue classification results
async def queue_classification_results(classifyBatch: func.QueueMessage, res: func.Out[func.QueueMessage]) -> None:
    """Queue Trigger Azure Function that obtains and enqueus classification results
    Args:
    classifyBatch: meta-data queue message for a batch images to be classified
    res: resulting queue message with classification results
    Returns:
    void
    """
    # Get Message Args
    infer_batch_message = classifyBatch.get_json()
    jwt = infer_batch_message.get('Jwt')
    image_uris = infer_batch_message.get('BlobUris')
    activity_id = infer_batch_message.get('ActivityId')
    image_metas = infer_batch_message.get('ImageMetas')
    image_ids = list(map(lambda _ : _['FileId'], image_metas))
    user_id = infer_batch_message.get('UserId')
    job_id = infer_batch_message.get('JobId')
    cv_model = infer_batch_message.get('CvModel')
    # Init Dependencies
    log = logger.Logger(activity_id)
    predictor = predict.Predictor(log)
    log.log_info('Python Classificaion Queue Triggered')

    # Initialize model
    log.log_info("Initializing model...")
    predictor.initialize()
       
    # Prediction step
    log.log_info('Classifying images: {}'.format(image_uris))
    results = predictor.predict_uris(image_uris)
    inference_results = []
    for i, result in enumerate(results):
        inference_result = {
            "ImageId": image_ids[i],
            "Results": [result],
        }
        inference_results.append(inference_result)
    
    inference_results = {
        "InferenceResults": inference_results,
        "UserId": user_id,
        "JobId": job_id,
        "CvModel": cv_model
    }
    # Push Inference Results back onto Azure Message Queue
    result_json = json.dumps(inference_results)
    log.log_info('Obtained results: {}'.format(result_json))
    log.dispatch_logs(jwt)
    res.set(result_json)

Dockerfile

FROM mcr.microsoft.com/azure-functions/python:2.0

COPY . /home/site/wwwroot

RUN cd /home/site/wwwroot && \
    pip install -r requirements.txt

requitements.txt

azure-functions==1.0.0b5
azure-functions-worker==1.0.0b10
grpcio==1.20.1
grpcio-tools==1.20.1
keras==2.2.4
keras-applications==1.0.7
numpy==1.15.4
pillow==5.4.1
protobuf==3.6.1
requests==2.20.1
tensorflow==1.12.0
azure-storage-blob==2.1.0
urllib3==1.21.1