class AzureMLService():
def __init__(self, ws:Workspace, service_name: str):
self.__ws = ws
self.__azure_service = Webservice(ws, service_name)
def make_request(self, inference_dataset_name):
inference_dataset = Dataset.get_by_name(self.__ws,inference_dataset_name)
df = inference_dataset.to_pandas_dataframe()
body = json.dumps({'data': json.loads(df.to_json(orient='values'))})
result = self.__azure_service.run(body)
print(result)
def get_result(players):
import os
import azureml
from azureml.core import Workspace
from azureml.core.webservice import Webservice
from azureml.core.authentication import ServicePrincipalAuthentication
print("getting results")
filename = os.path.join(app.static_folder, 'champion.json')
with open(filename) as json_file:
champions = json.load(json_file)['data']
X = [create_feature_row(players, champions)]
# Check core SDK version number
print("SDK version:", azureml.core.VERSION)
workspace = "league-ws-deploy"
subscription_id = "79451499-b2c0-4513-8dea-ef7f37173fbb"
resource_grp = "league"
svc_pr = ServicePrincipalAuthentication(
tenant_id="1f0113ce-bee6-43b0-9e26-61617eced2e4",
service_principal_id="4c9cfeac-dda9-4298-af3c-d51003c7438b",
service_principal_password="******")
ws = Workspace(workspace_name=workspace,
subscription_id=subscription_id,
resource_group=resource_grp,
auth=svc_pr)
ws.get_details()
print('Workspace name: ' + ws.name,
'Azure region: ' + ws.location,
'Subscription id: ' + ws.subscription_id,
'Resource group: ' + ws.resource_group,
sep='\n')
print("Send to server to predict")
sample = json.dumps({"data": X})
sample = bytes(sample, encoding='utf8')
service = Webservice(workspace=ws, name='lrmpredictfinal6')
# predict using the deployed model
result = service.run(input_data=sample)
return result[0][1]
#model = Model(myws, 'sklearn-mnist')
#web = 'https://mlworkspace.azure.ai/portal/subscriptions/bcbc4e01-e5d6-42b0-95af-06286341e6ca/resourceGroups/mnist3/providers/Microsoft.MachineLearningServices/workspaces/mnist1/deployments/mnist'
#print(Webservice.list(myws)[0].name)
service = Webservice(myws, 'mnist')
#print(type(model))
data_folder = os.path.join(os.getcwd(), 'data')
X_test = load_data(os.path.join(data_folder, 'test-images.gz'), False) / 255.0
y_test = load_data(os.path.join(data_folder, 'test-labels.gz'),
True).reshape(-1)
sample_indices = np.random.permutation(X_test.shape[0])[0:n]
test_samples = json.dumps({"data": X_test[sample_indices].tolist()})
test_samples = bytes(test_samples, encoding='utf8')
#result = service.run(input_data=test_samples)
# predict using the deployed model
result = service.run(input_data=test_samples)
# compare actual value vs. the predicted values:
i = 0
plt.figure(figsize=(20, 1))
for s in sample_indices:
plt.subplot(1, n, i + 1)
plt.axhline('')
plt.axvline('')
# use different color for misclassified sample
font_color = 'red' if y_test[s] != result[i] else 'black'
clr_map = plt.cm.gray if y_test[s] != result[i] else plt.cm.Greys
plt.text(x=10, y=-10, s=result[i], fontsize=18, color=font_color)
# Get the hosted web service
service = Webservice(workspace=ws, name=service_name)
# Input for Model with all features
input_j = [[
1.62168882e+02, 4.82427351e+02, 1.09748253e+02, 4.32529303e+01,
3.52377597e+01, 4.37307613e+01, 1.15729573e+01, 4.27624778e+00,
1.68042813e+02, 4.61654301e+02, 1.03138200e+02, 4.08555785e+01,
1.80809993e+01, 4.85402042e+01, 1.09373285e+01, 4.18269355e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
3.07200000e+03, 5.64000000e+02, 2.22900000e+03, 9.84000000e+02,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00,
3.03000000e+02, 6.63000000e+02, 3.18300000e+03, 3.03000000e+02,
5.34300000e+03, 4.26300000e+03, 6.88200000e+03, 1.02300000e+03,
1.80000000e+01
]]
print(input_j)
test_sample = json.dumps({'data': input_j})
test_sample = bytes(test_sample, encoding='utf8')
try:
prediction = service.run(input_data=test_sample)
print(prediction)
except Exception as e:
result = str(e)
print(result)
raise Exception('AKS service is not working as expected')
# Delete aks after test
#service.delete()
image=image,
deployment_config=aks_config,
deployment_target=aks_target)
service.wait_for_deployment(show_output=True)
print(service.state)
api_key, _ = service.get_keys()
print(
"Deployed AKS Webservice: {} \nWebservice Uri: {} \nWebservice API Key: {}"
.format(service.name, service.scoring_uri, api_key))
aks_webservice = {}
aks_webservice["aks_service_name"] = service.name
aks_webservice["aks_service_url"] = service.scoring_uri
aks_webservice["aks_service_api_key"] = api_key
print("AKS Webservice Info")
print(aks_webservice)
print("Saving aks_webservice.json...")
aks_webservice_filepath = os.path.join('./outputs', 'aks_webservice.json')
with open(aks_webservice_filepath, "w") as f:
json.dump(aks_webservice, f)
print("Done saving aks_webservice.json!")
# Single test data
test_data = [['manufactured in 2016 made of plastic in good condition']]
# Call the webservice to make predictions on the test data
prediction = service.run(json.dumps(test_data))
print('Test data prediction: ', prediction)
# Prepare input for forecasting
time_column_name = 'dtime'
freq = granularity[0].upper()
X_test = pd.date_range(
start=from_datetime, periods=horizon,
freq=freq).to_frame(index=False).rename(columns={0: time_column_name})
y_test = np.full(len(X_test), np.nan, dtype=np.float)
test_sample = json.dumps({
'X': X_test.to_json(date_format='iso'),
'y': y_test.tolist()
})
print('input json:{}'.format(test_sample))
# Find the web service
service = Webservice(ws, model_name)
# Call the web service
response = service.run(test_sample)
print('output json:{}'.format(response))
# Parse results
res_dict = json.loads(response)
y_fcst_all = pd.read_json(res_dict['index'])
y_fcst_all[time_column_name] = pd.to_datetime(y_fcst_all[time_column_name],
unit='ms')
y_fcst_all['forecast'] = res_dict['forecast']
y_fcst_all.index = y_fcst_all[time_column_name]
y_fcst_all.drop(time_column_name, axis=1, inplace=True)
y_fcst_all.sort_index(inplace=True)
print(y_fcst_all)
deployment_config=aci_config, models = [registered_model], image_config=image_config, ) # # Step 8 - Test the ACI deployed webservice # #%% import json age = 60 km = 40000 test_data = json.dumps([[age,km]]) print(test_data) webservice = Webservice(workspace=ws, name=service_name) # If the service is not ready, run this cell again... result = webservice.run(input_data=test_data) print(result) # # Step 9 - Provision an AKS cluster # #%% from azureml.core.compute import AksCompute, ComputeTarget from azureml.core.webservice import Webservice, AksWebservice # Use the default configuration, overriding the default location to a known region that supports AKS prov_config = AksCompute.provisioning_configuration(location='westus2') aks_name = 'aks-cluster01' # Create the cluster aks_target = ComputeTarget.create(workspace = ws,
