I am attempting to deploy a model to an Endpoint in MS Azure Machine Learning studio but I get some errror.
2 Questions
1) How can we get the logs for a deployed model (See my attempts below)
2) How do models get loaded into the docker images as I suspect for some reason it didn't get copied into the docker image.
Step 1) Upload the model: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-existing-model
Which is successful. I can see and download that my model is correct.
Step 2) Deploy to Azure Container Instances: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-azure-container-instance
I use this code snippet to deploy my code
from azureml.core.model import InferenceConfig
from azureml.core.webservice import AciWebservice
from azureml.core.webservice import Webservice
from azureml.core.model import Model
from azureml.core.environment import Environment
script_file_name = 'inference/score.py'
inference_config = InferenceConfig(entry_script=script_file_name, environment=tf_env)
aciconfig = AciWebservice.deploy_configuration(cpu_cores = 1,
memory_gb = 1,
tags = {'iris': "rh1832", 'type': "sklearn"},
description = 'sample service for iris')
aci_service_name = 'rh1832-iris-demo'
print(aci_service_name)
aci_service = Model.deploy(ws, aci_service_name, [model], inference_config, aciconfig)
aci_service.wait_for_deployment(True)
print(aci_service.state)
And my score.py inference script looks like
# ---------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# ---------------------------------------------------------
import json
import logging
import os
import pickle
import numpy as np
import pandas as pd
import joblib
try:
logger = logging.getLogger('azureml.automl.core.scoring_script')
except:
pass
def init():
global model
model_path = os.path.join(os.getenv('AZUREML_MODEL_DIR'), 'sklearn_model')
path = os.path.normpath(model_path)
path_split = path.split(os.sep)
logger.info("Loading model from path.")
model = joblib.load(model_path)
logger.info("Loading successful.")
def run(data):
try:
target_names = ['database', 'network', 'resource']
result = model.predict([data])
return json.dumps(target_names[result[0]])
except Exception as e:
print("error: " + str(e))
result = str(e)
return json.dumps({"error": result})
After submitting the webservice to be deployed i get an error that just says its in crashloopbackoff state with no details for which part of the script failed. 
Step 3) I attempted to debug the logs by follow this guide: https://docs.microsoft.com/en-us/azure/machine-learning/how-to-troubleshoot-deployment?tabs=azcli#dockerlog
1) Running script locally which I was able to successfully run score.py
2) Running docker image locally I didn't see the model in the folder azureml-environment-setup in the docker container:
root@09e7230839e3:/azureml-environment-setup# ls
environment_context.json log4j.properties mutated_conda_dependencies.yml send_conda_dependencies.py spark_cache.py
and also i didn't see the entrypoint for running the docker container. Is there any guide for running the docker image locally?
3) Finally I attempted to get the logs using Azure CLI and it only return an Null list it appeared