def test_service(service: AksWebservice,
container: str,
blob: str,
write_logs: bool = True) -> None:
if write_logs:
logs = service.get_logs()
with open("logs.txt", "w") as fp:
fp.write(logs)
data = {"container": container, "blob": blob}
data_raw = bytes(json.dumps({"data": data}), encoding="utf8")
print("Testing service: {0}".format(service.name))
print("Container: {0}, blob: {1}".format(container, blob))
ping = time.time()
response = service.run(input_data=data_raw)
print("Elapsed time: {0:.5f}".format(time.time() - ping))
print("Response: {0}".format(response))
test_service = Webservice.deploy_from_image(
workspace=ws,
name=aks_service_settings["name"],
image=image,
deployment_config=aks_config,
deployment_target=aks_test_cluster)
# Show output of the deployment on stdout
test_service.wait_for_deployment(show_output=True)
print(test_service.state)
# Checking status of test web service
print("Checking status of AKS Test Deployment")
if test_service.state != "Healthy":
raise Exception(
"Test Deployment on AKS failed with the following status: {} and logs: \n{}"
.format(test_service.state, test_service.get_logs()))
# Testing AKS web service
print("Testing AKS test web service")
test_sample = test_functions.get_test_data_sample()
print("Test Sample: ", test_sample)
test_sample_encoded = bytes(test_sample, encoding='utf8')
try:
prediction = test_service.run(input_data=test_sample)
print(prediction)
except Exception as e:
result = str(e)
logs = test_service.get_logs()
test_service.delete()
raise Exception(
"AKS Test web service is not working as expected: \n{} \nLogs: \n{}".
# Print the predicted class for each case.
for i in range(len(x_new)):
print (x_new[i]), predictions[i] )
#check service state
from azureml.core.webservice import AksWebservice
# Get the deployed service
service = AksWebservice(name='classifier-service', workspace=ws)
# Check its state
print(service.state)
#review service logs
print(service.get_logs())
#deploy to local container to diagnose probs
from azureml.core.webservice import LocalWebservice
deployment_config = LocalWebservice.deploy_configuration(port=8890)
service = Model.deploy(ws, 'test-svc', [model], inference_config, deployment_config)
#test deployed service
print(service.run(input_data = json_data))
#troubleshoot issues by changing scoring file
#then reload wo having to redeploy
service.reload()
print(service.run(input_data = json_data))
# Deploying prod web service from image
prod_service = Webservice.deploy_from_image(workspace=ws,
name=aks_service_settings["name"],
image=image,
deployment_config=aks_config,
deployment_target=aks_prod_cluster)
# Show output of the deployment on stdout
prod_service.wait_for_deployment(show_output=True)
print(prod_service.state)
# Checking status of prod web service
print("Checking status of AKS Prod Deployment")
if prod_service.state != "Healthy":
raise Exception(
"Prod Deployment on AKS failed with the following status: {} and logs: \n{}".format(
prod_service.state, prod_service.get_logs()
)
)
# Testing AKS web service
print("Testing AKS prod web service")
test_sample = test_functions.get_test_data_sample()
print("Test Sample: ", test_sample)
test_sample_encoded = bytes(test_sample, encoding='utf8')
try:
prediction = prod_service.run(input_data=test_sample)
print(prediction)
except Exception as e:
result = str(e)
logs = prod_service.get_logs()
raise Exception("AKS Prod web service is not working as expected: \n{} \nLogs: \n{}".format(result, logs))
prod_service = Webservice.deploy_from_image(
workspace=ws,
name=aks_service_settings["name"],
image=image,
deployment_config=aks_config,
deployment_target=aks_prod_cluster)
# Show output of the deployment on stdout
prod_service.wait_for_deployment(show_output=True)
print(prod_service.state)
# Checking status of prod web service
print("Checking status of AKS Prod Deployment")
if prod_service.state != "Healthy":
raise Exception(
"Prod Deployment on AKS failed with the following status: {} and logs: \n{}"
.format(prod_service.state, prod_service.get_logs()))
# Testing AKS web service
print("Testing AKS prod web service")
test_sample = test_functions.get_test_data_sample()
print("Test Sample: ", test_sample)
test_sample_encoded = bytes(test_sample, encoding='utf8')
try:
prediction = prod_service.run(input_data=test_sample)
print(prediction)
except Exception as e:
result = str(e)
logs = prod_service.get_logs()
raise Exception(
"AKS Prod web service is not working as expected: \n{} \nLogs: \n{}".
format(result, logs))
import json
import pickle
import numpy as np
import pandas as pd
from azureml.core.workspace import Workspace
import azureml.train.automl
from sklearn.externals import joblib
from azureml.core.model import Model
ws = Workspace.from_config('./config.json')
from azureml.core.webservice import Webservice, AciWebservice, AksWebservice
# service = AciWebservice(ws, "sentiment-scorer-korean")
# service = AksWebservice(ws, "sentiment-scorer-korean-aks")
service = AksWebservice(ws, "sentiment-scorer-korean-aks-prob")
service.get_logs()