def get_auth():
"""Authentication to access workspace"""
try:
auth = AzureCliAuthentication()
auth.get_authentication_header()
except AuthenticationException:
logger.info("Authentication Error Occured")
return auth
def get_auth():
logger = logging.getLogger(__name__)
logger.debug("Trying to create Workspace with CLI Authentication")
try:
auth = AzureCliAuthentication()
auth.get_authentication_header()
except AuthenticationException:
logger.debug("Trying to create Workspace with Interactive login")
auth = InteractiveLoginAuthentication()
return auth
def get_auth():
'''
Authentication to access workspace
'''
try:
auth = AzureCliAuthentication()
auth.get_authentication_header()
except AuthenticationException:
print("Authentication Error Occured")
return auth
def get_auth():
"""
Method to get the correct Azure ML Authentication type
Always start with CLI Authentication and if it fails, fall back
to interactive login
"""
try:
auth_type = AzureCliAuthentication()
auth_type.get_authentication_header()
except AuthenticationException:
auth_type = InteractiveLoginAuthentication()
return auth_type
def get_auth():
'''
Retreive the user authentication. If they aren't logged in this will
prompt the standard interactive login method.
PARAMS: None
RETURNS: Authentication object
'''
auth = None
print("Get auth...")
try:
auth = AzureCliAuthentication()
auth.get_authentication_header()
except AuthenticationException:
auth = InteractiveLoginAuthentication()
return auth
from azureml.pipeline.core import PublishedPipeline
from azureml.core.authentication import AzureCliAuthentication
cli_auth = AzureCliAuthentication()
##------------- Get Workspace
subscriptionId = "<your subscription id>" # make this a parameter
resourceGroup = "<your resource group>" # make this a parameter
workspaceName = "<your ml workspace name>" # make this a parameter
ws = Workspace(subscriptionId, resourceGroup, workspaceName, auth=cli_auth)
##------------- Run Published pipeline using REST endpoint
aad_token = cli_auth.get_authentication_header()
published_pipeline_id = "ab0691a9-438f-416b-a146-5c7660d1be11" # Replace this with the published pipeline id
published_pipeline = PublishedPipeline.get(ws, published_pipeline_id)
rest_endpoint = published_pipeline.endpoint
print("Rest endpoint: " + rest_endpoint)
response = requests.post(rest_endpoint,
headers=aad_token,
json={
"ExperimentName": "quality_prediction_gb",
"RunSource": "SDK",
"ParameterAssignments": {
"modelName": "quality_gbm_model.pkl",
"datasetName": "qualitydataset",
"datasetStorePath": "/inputdata/train.csv"
}
