Advanced model serving graph - notebook example#

This example demonstrates how to use MLRun serving graphs and their advanced functionality including:

  • Use of flow, task, model, and ensemble router states

  • Build tasks from custom handlers, classes and storey components

  • Use custom error handlers

  • Test graphs locally

  • Deploy the graph as a real-time serverless functions

In this example

Define functions and classes used in the graph#

from cloudpickle import load
from typing import List
from sklearn.datasets import load_iris
import numpy as np

# model serving class example
class ClassifierModel(mlrun.serving.V2ModelServer):
    def load(self):
        """load and initialize the model and/or other elements"""
        model_file, extra_data = self.get_model(".pkl")
        self.model = load(open(model_file, "rb"))

    def predict(self, body: dict) -> List:
        """Generate model predictions from sample."""
        feats = np.asarray(body["inputs"])
        result: np.ndarray = self.model.predict(feats)
        return result.tolist()

# echo class, custom class example
class Echo:
    def __init__(self, context, name=None, **kw):
        self.context = context = name = kw

    def do(self, x):
        print("Echo:",, x)
        return x

# error echo function, demo catching error and using custom function
def error_catcher(x):
    x.body = {"body": x.body, "origin_state": x.origin_state, "error": x.error}
    print("EchoError:", x)
    return None
# mark the end of the code section, DO NOT REMOVE !
# mlrun: end-code

Create a new serving function and graph#

Use code_to_function to convert the above code into a serving function object and initialize a graph with async flow topology.

function = mlrun.code_to_function(
    "advanced", kind="serving", image="mlrun/mlrun", requirements=["storey"]
graph = function.set_topology("flow", engine="async")
# function.verbose = True

Specify the sklearn models that are used in the ensemble.

models_path = ""
path1 = models_path
path2 = models_path

Build and connect the graph (DAG) using the custom function and classes and plot the result. Add states using the method (adds a new state after the current one), or using the graph.add_step() method.

Use the graph error_handler if you want an error from the graph or a step to be fed into a specific state (catcher). See the full description in Error handling.

You can specify which state is the responder (returns the HTTP response) using the state.respond() method. If you don't specify the responder, the graph is non-blocking.

# use built-in storey class or our custom Echo class to create and link Task steps. Add an error handling step that runs if the grah step fails"storey.Extend", name="enrich", _fn='({"tag": "something"})').to(
    class_name="Echo", name="pre-process", some_arg="abc"
).error_handler(name="catcher", handler="handle_error", full_event=True)

# add an Ensemble router with two child models (routes). The "*" prefix mark it is a router class
router = graph.add_step(
    "*mlrun.serving.VotingEnsemble", name="ensemble", after="pre-process"
router.add_route("m1", class_name="ClassifierModel", model_path=path1)
router.add_route("m2", class_name="ClassifierModel", model_path=path2)

# add the final step (after the router) that handles post processing and responds to the client
graph.add_step(class_name="Echo", name="final", after="ensemble").respond()

# plot the graph (using Graphviz) and run a test

Test the function locally#

Create a test set.

import random

iris = load_iris()
x = random.sample(iris["data"].tolist(), 5)

Create a mock server (simulator) and test the graph with the test data.

Note: The model and router objects support a common serving protocol API, see the protocol and API section.

server = function.to_mock_server()
resp = server.test("/v2/models/infer", body={"inputs": x})
> 2021-01-09 22:49:26,365 [info] model m1 was loaded
> 2021-01-09 22:49:26,493 [info] model m2 was loaded
> 2021-01-09 22:49:26,494 [info] Loaded ['m1', 'm2']
Echo: pre-process {'inputs': [[6.9, 3.2, 5.7, 2.3], [6.4, 2.7, 5.3, 1.9], [4.9, 3.1, 1.5, 0.1], [7.3, 2.9, 6.3, 1.8], [5.4, 3.7, 1.5, 0.2]], 'tag': 'something'}
Echo: final {'model_name': 'ensemble', 'outputs': [2, 2, 0, 2, 0], 'id': '0ebcc5f6f4c24d4d83eb36391eaefb98'}
{'model_name': 'ensemble',
 'outputs': [2, 2, 0, 2, 0],
 'id': '0ebcc5f6f4c24d4d83eb36391eaefb98'}

Deploy the graph as a real-time serverless function#

> 2021-01-09 22:49:40,088 [info] Starting remote function deploy
2021-01-09 22:49:40  (info) Deploying function
2021-01-09 22:49:40  (info) Building
2021-01-09 22:49:40  (info) Staging files and preparing base images
2021-01-09 22:49:40  (info) Building processor image
2021-01-09 22:49:41  (info) Build complete
2021-01-09 22:49:47  (info) Function deploy complete
> 2021-01-09 22:49:48,422 [info] function deployed,

Invoke the remote function using the test data

function.invoke("/v2/models/infer", body={"inputs": x})
{'model_name': 'ensemble',
 'outputs': [1, 2, 0, 0, 0],
 'id': '0ebcc5f6f4c24d4d83eb36391eaefb98'}