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Update func of computing resources recycling (#3043)

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* Upload examples of kfp v2

* Upload run experiment example.

* Upload batch prediction job sample.

* Update recycling of computing resources

Recycling computing resources after predictions.

* Upload missing file

Add delete endpoint func to recycle resources.
This commit is contained in:
Aiden010200
2024-06-07 12:39:33 +00:00
committed by GitHub
parent bc577d9f79
commit c5be708d45
1 changed files with 51 additions and 37 deletions
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community-content/vertex_pipeline_kfpv2/deploy-model.py
+51 -37
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@@ -1,37 +1,51 @@
from kfp.v2 import dsl
@dsl.component(base_image='python:3.8',packages_to_install=['google-cloud-aiplatform==1.36.0'])
def deploy_model(
model_id: str,
endpoint_id: str,
machine_type: str,
min_replica_count: int,
max_replica_count: int,
):
import json
from google.cloud import aiplatform
model = aiplatform.Model(model_id)
endpoint = aiplatform.Endpoint(endpoint_id)
endpoint = model.deploy(
endpoint=endpoint,
machine_type=machine_type,
min_replica_count=min_replica_count,
max_replica_count=max_replica_count,
)
@dsl.pipeline(name='deploy-model')
def pipeline_deploy_model():
project = "projects/990000000009/locations/us-west1"
model_id = project + "/models/1100000000000000001"
endpoint_id = project + "/endpoints/2200000000000000002"
deploy_model(model_id, endpoint_id, "n1-standard-2", 1, 1)
if __name__ == "__main__":
from kfp.v2 import compiler
compiler.Compiler().compile(
pipeline_func=pipeline_deploy_model,
package_path='deploy_model.json')
from kfp.v2 import dsl
@dsl.component(base_image='python:3.8',packages_to_install=['google-cloud-aiplatform==1.36.0'])
def deploy_model(
model_id: str,
endpoint_id: str,
machine_type: str,
min_replica_count: int,
max_replica_count: int,
):
import json
from google.cloud import aiplatform
model = aiplatform.Model(model_id)
endpoint = aiplatform.Endpoint(endpoint_id)
endpoint = model.deploy(
endpoint=endpoint,
machine_type=machine_type,
min_replica_count=min_replica_count,
max_replica_count=max_replica_count,
)
@dsl.component(base_image='python:3.8',packages_to_install=['google-cloud-aiplatform==1.36.0'])
def delete_endpoint(
endpoint_id: str,
):
from google.cloud import aiplatform
endpoint = aiplatform.Endpoint(endpoint_id)
endpoint.undeploy_all()
endpoint.delete()
@dsl.pipeline(name='deploy-model')
def pipeline_deploy_model():
project = "projects/990000000009/locations/us-west1"
model_id = project + "/models/1100000000000000001"
endpoint_id = project + "/endpoints/2200000000000000002"
deploy_model(model_id, endpoint_id, "n1-standard-2", 1, 1)
# After serving predictions, recycling computing resources
delete_endpoint(endpoint_id)
if __name__ == "__main__":
from kfp.v2 import compiler
compiler.Compiler().compile(
pipeline_func=pipeline_deploy_model,
package_path='deploy_model.json')