SKLearn Sample

Requirements

• Authenticated to gcloud (gcloud auth application-default login)

This notebook demonstrate how to deploy iris classifier based on Scikit Learn model using MLP

!pip install --upgrade -r requirements.txt > /dev/null

import merlin
import warnings
import os
from sklearn import svm
from sklearn import datasets
from joblib import dump
from sklearn.datasets import load_iris
from merlin.model import ModelType
warnings.filterwarnings('ignore')

1. Initialize MLP Resources

1.1 Set MLP Server

# Set MLP Server
merlin.set_url("localhost:3000/api/merlin")

1.2 Set Active Project

project represent a project in real life. You may have multiple model within a project.

merlin.set_project(<project_name>) will set the active project into the name matched by argument. You can only set it to an existing project. If you would like to create a new project, please do so from the MLP console at http://localhost:3000/projects/create.

merlin.set_project("sample")

1.3 Set Active Model

model represents an abstract ML model. Conceptually, model in MLP is similar to a class in programming language. To instantiate a model you’ll have to create a model_version.

Each model has a type, currently model type supported by MLP are: sklearn, xgboost, tensorflow, pytorch, and user defined model (i.e. pyfunc model).

model_version represents a snapshot of particular model iteration. You’ll be able to attach information such as metrics and tag to a given model_version as well as deploy it as a model service.

merlin.set_model(<model_name>, <model_type>) will set the active model to the name given by parameter, if the model with given name is not found, a new model will be created.

merlin.set_model("sklearn-sample", ModelType.SKLEARN)

2. Train and Deploy

2.1 Train and Upload Model

merlin.new_model_version() is a convenient method to create a model version and start its development process. It is equal to following codes:

v = model.new_model_version()
v.start()
v.log_model(model_dir=model_dir)
v.finish()

model_dir = "sklearn-model"
MODEL_FILE = "model.joblib"

url = ""

# Create new version of the model
with merlin.new_model_version() as v:
    clf = svm.SVC(gamma='scale')
    iris = datasets.load_iris()
    X, y = iris.data, iris.target
    clf.fit(X, y)
    dump(clf, os.path.join(model_dir, MODEL_FILE))

    # Upload the serialized model to MLP
    merlin.log_model(model_dir=model_dir)

2.2 Deploy Model

endpoint = merlin.deploy(v)

2.3 Send Test Request

%%bash -s "$endpoint.url"
curl -v -X POST $1 -d '{
  "instances": [
    [2.8,  1.0,  6.8,  0.4],
    [3.1,  1.4,  4.5,  1.6]
  ]
}'

3.4 Delete Deployment

merlin.undeploy(v)