4. Putting everything together¶
In the code below, we put together the TrainWork, the MLServer and the Locust components in an app.py file.
from locust_component import Locust
from model_server import MLServer
from train import TrainModel
from lightning import LightningApp, LightningFlow
class TrainAndServe(LightningFlow):
def __init__(self):
super().__init__()
self.train_model = TrainModel()
self.model_server = MLServer(
name="mnist-svm",
implementation="mlserver_sklearn.SKLearnModel",
workers=8,
)
self.performance_tester = Locust(num_users=100)
def run(self):
self.train_model.run()
self.model_server.run(self.train_model.best_model_path)
if self.model_server.alive():
# The performance tester needs the model server to be up
# and running to be started, so the URL is added in the UI.
self.performance_tester.run(self.model_server.url)
def configure_layout(self):
return [
{"name": "Server", "content": self.model_server.url + "/docs"},
{"name": "Server Testing", "content": self.performance_tester},
]
app = LightningApp(TrainAndServe())
Run the App¶
To run the app, simply open a terminal and execute this command:
lightning run app docs/source/examples/model_deploy_app/app.py
Here is a gif of the UI.
Congrats, you have finished the Build a Model Server example !
Find more examples¶
Develop a DAG
Develop a DAG pipeline
Intermediate
