def get_cluster():
ws = get_workspace()
aks_name = AKS_NAME
cts = ws.compute_targets
if aks_name in cts and cts[aks_name].type == 'AKS':
print('Found existing AKS cluster, will use it!')
aks_target = cts[aks_name]
return aks_target
else:
print('AKS cluster not found, sorry')
return None
def attach_cluster():
# Set the resource group that contains the AKS cluster and the cluster name
resource_group = RESOURCE_GROUP
cluster_name = AKS_CLUSTER_NAME
# Attach the cluster to your workgroup. If the cluster has less than 12 virtual CPUs, use the following instead:
attach_config = AksCompute.attach_configuration(
resource_group=resource_group,
cluster_name=cluster_name,
cluster_purpose=AksCompute.ClusterPurpose.DEV_TEST)
ws = get_workspace()
aks_target = ComputeTarget.attach(ws, AKS_NAME, attach_config)
return aks_target
def deploy_image():
ws = get_workspace()
azure_image = get_image()
aci_config = AciWebservice.deploy_configuration(cpu_cores=1,
memory_gb=1,
tags={'method': 'sklearn'},
description='Worst model',
location=LOCATION)
webservice = Webservice.deploy_from_image(image=azure_image,
workspace=ws,
name=MODEL_NAME,
deployment_config=aci_config)
webservice.wait_for_deployment(show_output=True)
def create_cluster():
ws = get_workspace()
# Use the default configuration (can also provide parameters to customize)
prov_config = AksCompute.provisioning_configuration()
aks_name = AKS_CLUSTER_NAME
# Create the cluster
aks_target = ComputeTarget.create(workspace=ws,
name=aks_name,
provisioning_configuration=prov_config)
aks_target.wait_for_completion(show_output=True)
print(aks_target.provisioning_state)
print(aks_target.provisioning_errors)
return aks_target
def deploy_image():
ws = get_workspace()
azure_image = get_image()
# Set the web service configuration (using default here with app insights)
aks_config = AksWebservice.deploy_configuration(enable_app_insights=True)
# Unique service name
service_name = AKS_NAME
aks_target = get_cluster()
# Webservice creation using single command
aks_service = Webservice.deploy_from_image(workspace=ws,
name=service_name,
deployment_config=aks_config,
image=azure_image,
deployment_target=aks_target)
aks_service.wait_for_deployment(show_output=True)
def get_service():
ws = get_workspace()
services = Webservice.list(workspace=ws, compute_type='AKS')
return services[0]
def model_register():
ws = get_workspace()
model = Model.register(workspace=ws,
model_path="../artifacts/worst.pickle",
model_name="worst-model")
return model
def get_model_path():
ws = get_workspace()
model_path = Model.get_model_path('worst-model', _workspace=ws)
return model_path
def get_experiment():
ws = get_workspace()
experiment_name = EXPERIMENT_NAME
exp = ws.experiments[experiment_name]
return exp
def get_model(runId=get_last_run_id(), model_save_path=MODEL_PATH):
ws = get_workspace()
mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())
model_uri = get_model_uri(runId, model_save_path)
model = mlflow.sklearn.load_model(model_uri)
return model
def get_model_uri(runId=get_last_run_id(), model_save_path=MODEL_PATH):
ws = get_workspace()
mlflow.set_tracking_uri(ws.get_mlflow_tracking_uri())
model_uri = 'runs:/{}/{}'.format(runId, model_save_path)
return model_uri