def add_endpoint(ws: Workspace, pipeline: PublishedPipeline,
endpoint_name: str) -> PipelineEndpoint:
endpoint_list = [p.name for p in PipelineEndpoint.list(ws)]
endpoint = None
# endpoint does not exist so add
if endpoint_name in endpoint_list:
endpoint = PipelineEndpoint.get(workspace=ws, name=endpoint_name)
endpoint.add_default(published_pipeline)
else:
endpoint = PipelineEndpoint.publish(
workspace=ws,
name=endpoint_name,
pipeline=published_pipeline,
description="Seer Pipeline Endpoint")
return endpoint
def clean_azml_workspace(ctx):
"""
[WARNING] Only use in test-only workspace. Remove or disable all compute clusters, published pipelines, published pipeline endpoints and schedules from Azure ML workspace.
"""
ws = Workspace.from_config()
# remove compute clusters
for _, compute in ws.compute_targets.items():
if not compute.provisioning_state == "Deleting":
compute.delete()
# deactivate schedules
for s in Schedule.list(ws):
s.disable()
# remove pipeline endpoints
for pe in PipelineEndpoint.list(ws):
pe.disable()
# remove pipelines
for p in PublishedPipeline.list(ws):
p.disable()
from azureml.pipeline.core import Pipeline, PublishedPipeline, PipelineEndpoint
print(f'Azure ML SDK version: {azureml.core.VERSION}')
endpoint_name = "training-pipeline-endpoint"
pipeline_id = os.getenv('PIPELINE_ID')
# Connect to the workspace
ws = Workspace.from_config()
print(f'WS name: {ws.name}')
print(f'Region: {ws.location}')
print(f'Subscription id: {ws.subscription_id}')
print(f'Resource group: {ws.resource_group}')
print(f'Pipeline ID: {pipeline_id}')
published_pipeline = PublishedPipeline.get(workspace=ws, id=pipeline_id)
print(f'Published Pipeline: {published_pipeline}')
# Check if PipelineEndpoint already exists
if any(pe.name == endpoint_name for pe in PipelineEndpoint.list(ws)):
print(f'Pipeline Endpoint with name {endpoint_name} already exists, will add pipeline to it')
pipeline_endpoint = PipelineEndpoint.get(workspace=ws, name=endpoint_name)
pipeline_endpoint.add(published_pipeline)
# Set it to default, as we already tested it beforehand!
pipeline_endpoint.set_default(published_pipeline)
else:
print(f'Will create Pipeline Endpoint with name {endpoint_name}')
pipeline_endpoint = PipelineEndpoint.publish(workspace=ws,
name=endpoint_name,
pipeline=published_pipeline,
description="New Training Pipeline Endpoint")