def predict(self, deployment_name, df):
"""
Predict on the specified deployment using the provided dataframe.
Compute predictions on the pandas DataFrame ``df`` using the specified deployment.
Note that the input/output types of this method matches that of `mlflow pyfunc predict`
(we accept a pandas DataFrame as input and return either a pandas DataFrame,
pandas Series, or numpy array as output).
:param deployment_name: Name of deployment to predict against
:param df: Pandas DataFrame to use for inference
:return: A pandas DataFrame, pandas Series, or numpy array
"""
try:
service = Webservice(self.workspace, deployment_name)
except Exception as e:
raise MlflowException(
'Failure retrieving deployment to predict against') from e
# Take in DF, parse to json using split orient
input_data = _get_jsonable_obj(df, pandas_orient='split')
if not service.scoring_uri:
raise MlflowException(
'Error attempting to call webservice, scoring_uri unavailable. '
'This could be due to a failed deployment, or the service is not ready yet.\n'
'Current State: {}\n'
'Errors: {}'.format(service.state, service.error))
# Pass split orient json to webservice
# Take records orient json from webservice
resp = ClientBase._execute_func(service._webservice_session.post,
service.scoring_uri,
data=json.dumps(
{'input_data': input_data}))
if resp.status_code == 401:
if service.auth_enabled:
service_keys = service.get_keys()
service._session.headers.update(
{'Authorization': 'Bearer ' + service_keys[0]})
elif service.token_auth_enabled:
service_token, refresh_token_time = service.get_access_token()
service._refresh_token_time = refresh_token_time
service._session.headers.update(
{'Authorization': 'Bearer ' + service_token})
resp = ClientBase._execute_func(service._webservice_session.post,
service.scoring_uri,
data=input_data)
if resp.status_code == 200:
# Parse records orient json to df
return parse_json_input(json.dumps(resp.json()), orient='records')
else:
raise MlflowException('Failure during prediction:\n'
'Response Code: {}\n'
'Headers: {}\n'
'Content: {}'.format(resp.status_code,
resp.headers,
resp.content))
def test_deployed_model_service():
service = Webservice(ws, deployment_name)
assert service is not None
key1, key2 = service.get_keys()
uri = service.scoring_uri
assert key1 is not None
assert uri.startswith('http')
headers = {
'Content-Type': 'application/json',
'Authorization': f'Bearer {key1}'
}
response = requests.post(uri, test_sample, headers=headers)
assert response.status_code is 200
assert abs(1 - sum(response.json()['predict_proba'][0])) < 0.01
from azureml.core import Workspace from azureml.core import Webservice ws = Workspace.from_config() service = Webservice(ws, 'lpr') scoring_uri = service.scoring_ui primary, secondary = service.get_keys() print(primary) print(service.get_logs())
