[Python] Cannot Run Azure ML Model Locally, Help on Entry script

I am working on a project where a model will be created using Azure ML (ml.azure.com),

I plan to utilize Azure ML Designer eventually, so I picked up a sample pipeline (Regression - Automobile Price Prediction (Basic)). I then created a realtime inference pipeline and created a realtime endpoint.

Post this, I am able to fetch predictions from this model from a .Net client over internet as expected.

As a part of the same project, I eventually want to fully eliminate the internet dependency/Affiliated latency with Predictions. I understand this can be achieved by packaging the model and downloading a docker image that runs on a local computer and exposes the model via a scoring web service.

To package the model and download it as a docker image, I utilized the following python code, this would execute on my local PC:

import azureml.core
print(azureml.core.VERSION)


from azureml.core import Workspace
ws = Workspace.get(name="TestMLWorkSpace", subscription_id='XXXXXX-a9c4-4426-XXXX-XXXXXXX', resource_group='MyResourceGroup')

from azureml.core.experiment import Experiment
experiment = Experiment(workspace=ws, name='TestAutomobileSampleExp')

list_runs = experiment.get_runs()
for run in list_runs:
print(run.id)

list_experiments = Experiment.list(ws)


from azureml.core.model import Model
import os

model = Model(workspace=ws, name="amlstudio-regrealtimeendpoint")
model.download(target_dir=os.getcwd(), exist_ok=1 )

from azureml.core import Environment
from azureml.core.model import InferenceConfig

env = Environment.get(workspace=ws, name="AzureML-Minimal")
inference_config = InferenceConfig(
environment=env,
source_directory="./",
entry_script="./score.py",
)



package = Model.package(ws, [model], inference_config)
package.wait_for_creation(show_output=True)

package.pull()
#checked image id using ---> docker images
# replace image id from the result above in the following command
# docker run -p 6789:5001 --name mycontainer c262eaa7bab9

# http://localhost:6789/score


Now when I run this script, a docker image is downloaded on my computer and available to be run using docker.

What is the problem?

As a part of creating the inference config, I need to specify an entry script called score.py It seems I cannot run the docker image because the entry script is likely not correct.

Here is how my score.py currently looks like:

import os
import json


from azureml.core.model import Model
import logging


def init():
global model
logging.basicConfig(level=logging.DEBUG)
model_path = Model.get_model_path(model_name='amlstudio-regrealtimeendpoint')
print(Model.get_model_path(model_name='amlstudio-regrealtimeendpoint'))
#How do I load the model here??


def run(input_data):
try:
data = json.loads(input_data)['data']
data = np.array(data)
result = model.predict(data)
return json.dumps({"result": result.tolist()})
except Exception as e:
result = str(e)
# return error message back to the client
return json.dumps({"error": result})


I built this entry script from Microsoft materials but it does not work and Docker image fails to initiate.

My questions are:

1. Is my source Dir in inference config correct? what should be my source Directory given the fact that I just want the model to run locally be able to provide predictions locally?
2. what changes should I make to this entry script to get the scoring to work locally on my computer?

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