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import click
from pipelines.deployment_pipeline import (
continuous_deployment_pipeline,
inference_pipeline,
)
from rich import print
from zenml.integrations.mlflow.mlflow_utils import get_tracking_uri
from zenml.integrations.mlflow.model_deployers.mlflow_model_deployer import (
MLFlowModelDeployer,
)
@click.command()
@click.option(
"--stop-service",
is_flag=True,
default=False,
help="Stop the prediction service when done",
)
def run_main(stop_service: bool):
"""Run the prices predictor deployment pipeline"""
model_name = "prices_predictor"
if stop_service:
# Get the MLflow model deployer stack component
model_deployer = MLFlowModelDeployer.get_active_model_deployer()
# Fetch existing services with same pipeline name, step name, and model name
existing_services = model_deployer.find_model_server(
pipeline_name="continuous_deployment_pipeline",
pipeline_step_name="mlflow_model_deployer_step",
model_name=model_name,
running=True,
)
if existing_services:
existing_services[0].stop(timeout=10)
return
# Run the continuous deployment pipeline
continuous_deployment_pipeline()
# Get the active model deployer
model_deployer = MLFlowModelDeployer.get_active_model_deployer()
# Run the inference pipeline
inference_pipeline()
print(
"Now run \n "
f" mlflow ui --backend-store-uri {get_tracking_uri()}\n"
"To inspect your experiment runs within the mlflow UI.\n"
"You can find your runs tracked within the `mlflow_example_pipeline`"
"experiment. Here you'll also be able to compare the two runs."
)
# Fetch existing services with the same pipeline name, step name, and model name
service = model_deployer.find_model_server(
pipeline_name="continuous_deployment_pipeline",
pipeline_step_name="mlflow_model_deployer_step",
)
if service[0]:
print(
f"The MLflow prediction server is running locally as a daemon "
f"process and accepts inference requests at:\n"
f" {service[0].prediction_url}\n"
f"To stop the service, re-run the same command and supply the "
f"`--stop-service` argument."
)
if __name__ == "__main__":
run_main()
# http://127.0.0.1:8000/invocations |