tenant_id=auth_config["tenant_id"],
service_principal_id=auth_config["service_principal_id"],
service_principal_password=os.environ["SP_SECRET"],
)
ws = Workspace(
subscription_id=auth_config["subscription_id"],
resource_group=auth_config["resource_group"],
workspace_name=auth_config["workspace_name"],
auth=auth,
)
# Usually, the cluster already exists, so we just fetch
compute_target = next(
(m for m in ComputeTarget.list(ws) if m.name == compute["name"]), None
)
# Specify the compute environment and register it for use in scoring
env = Environment("component-condition")
env.docker.enabled = True
cd = CondaDependencies.create(
conda_packages=[
"tensorflow=2.0.0",
"pandas",
"numpy",
"matplotlib"
],
pip_packages=[
"azureml-mlflow==1.5.0",
"azureml-defaults==1.5.0"
print("Azure ML SDK Version: ", azureml.core.VERSION)
ws = Workspace.from_config()
print("Resource group: ", ws.resource_group)
print("Location: ", ws.location)
print("Workspace name: ", ws.name)
from azureml.core.webservice import Webservice
for web_svc in Webservice.list(ws):
print("Deleting web service", web_svc.name, "...")
web_svc.delete()
from azureml.core import ComputeTarget
for target in ComputeTarget.list(ws):
print("Deleting compute target", target.name, "...")
target.delete()
from azureml.core import Image
for img in Image.list(ws):
print("Deleting image", img.id, "...")
img.delete()
from azureml.core.model import Model
for model in Model.list(ws):
print("Deleting model", model.id, "...")
model.delete()