This page was generated from examples/models/mlflow_v2_protocol_end_to_end/README.ipynb.

MLflow v2 protocol elasticnet wine example¶

In this example we are going to build a model using mlflow, pack and deploy it on seldon-core on a local kind cluster

Prerequisites before running this notebook:

install and configure mc, follow the relevant section in this link
run this jupyter notebook in conda environment

$ conda create --name python3.8-mlflow-example python=3.8 -y
$ conda activate python3.8-mlflow-example
$ pip install jupyter
$ jupyter notebook

Setup `seldon-core` and `minio`¶

Setup Seldon Core¶

Use the setup notebook to Setup Cluster with Ambassador Ingress and Install Seldon Core. Instructions also online.

Setup MinIO¶

Use the provided notebook to install Minio in your cluster and configure mc CLI tool. Instructions also online.

Train elasticnet wine model using `mlflow`¶

Install `mlflow` and required dependencies to train the model¶

[ ]:

!pip install mlflow scikit-learn==0.23.2 pandas

Define where the model artifacts will be saved¶

[ ]:

import os
import shutil
from pathlib import Path
MODEL_DIR = Path(os.getcwd()) / "elasticnet_wine_model"
shutil.rmtree(MODEL_DIR, ignore_errors=True)

Define training function¶

[ ]:

# Wine Quality Sample a copy from:
# https://github.com/mlflow/mlflow/blob/master/examples/sklearn_elasticnet_wine/train.ipynb

def train(in_alpha, in_l1_ratio):
    import os
    import warnings
    import sys

    import pandas as pd
    import numpy as np
    from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
    from sklearn.model_selection import train_test_split
    from sklearn.linear_model import ElasticNet

    import mlflow
    import mlflow.sklearn

    import logging
    logging.basicConfig(level=logging.WARN)
    logger = logging.getLogger(__name__)

    def eval_metrics(actual, pred):
        rmse = np.sqrt(mean_squared_error(actual, pred))
        mae = mean_absolute_error(actual, pred)
        r2 = r2_score(actual, pred)
        return rmse, mae, r2


    warnings.filterwarnings("ignore")
    np.random.seed(40)

    # Read the wine-quality csv file from the URL
    csv_url =\
        'http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv'
    try:
        data = pd.read_csv(csv_url, sep=';')
    except Exception as e:
        logger.exception(
            "Unable to download training & test CSV, check your internet connection. Error: %s", e)

    # Split the data into training and test sets. (0.75, 0.25) split.
    train, test = train_test_split(data)

    # The predicted column is "quality" which is a scalar from [3, 9]
    train_x = train.drop(["quality"], axis=1)
    test_x = test.drop(["quality"], axis=1)
    train_y = train[["quality"]]
    test_y = test[["quality"]]

    # Set default values if no alpha is provided
    if float(in_alpha) is None:
        alpha = 0.5
    else:
        alpha = float(in_alpha)

    # Set default values if no l1_ratio is provided
    if float(in_l1_ratio) is None:
        l1_ratio = 0.5
    else:
        l1_ratio = float(in_l1_ratio)

    # Useful for multiple runs (only doing one run in this sample notebook)
    with mlflow.start_run():
        # Execute ElasticNet
        lr = ElasticNet(alpha=alpha, l1_ratio=l1_ratio, random_state=42)
        lr.fit(train_x, train_y)

        # Evaluate Metrics
        predicted_qualities = lr.predict(test_x)
        (rmse, mae, r2) = eval_metrics(test_y, predicted_qualities)

        # Print out metrics
        print("Elasticnet model (alpha=%f, l1_ratio=%f):" % (alpha, l1_ratio))
        print("  RMSE: %s" % rmse)
        print("  MAE: %s" % mae)
        print("  R2: %s" % r2)

        # Log parameter, metrics, and model to MLflow
        mlflow.log_param("alpha", alpha)
        mlflow.log_param("l1_ratio", l1_ratio)
        mlflow.log_metric("rmse", rmse)
        mlflow.log_metric("r2", r2)
        mlflow.log_metric("mae", mae)

        mlflow.sklearn.save_model(lr, MODEL_DIR)
        print(f" Model saved to {MODEL_DIR}")

Train the elasticnet_wine model¶

[ ]:

train(0.5, 0.5)

Install dependencies to be able to pack and deploy the model on `seldon_core`¶

We are going to use `conda-pack <https://conda.github.io/conda-pack/>`__ to pack the python enviornment. We also need mlserver dependencies. We are planning to simplify this workflow in future releases.

[ ]:

!pip install conda-pack mlserver==0.4.0 mlserver-mlflow==0.4.0

Pack the conda enviornment¶

[ ]:

import conda_pack
env_file_path = MODEL_DIR / "environment.tar.gz"
conda_pack.pack(
    output=str(env_file_path),
    force=True,
    verbose=True,
    ignore_editable_packages=False,
    ignore_missing_files=True,
)

Configure `mc` to access the minio service in the local kind cluster¶

note: make sure that minio ip is reflected properly below, run kubectl get service -n minio-system

[ ]:

!mc config host add minio-seldon http://172.18.255.3:9000 minioadmin minioadmin

Copy the model artifacts to minio¶

[ ]:

import os
target_bucket = "minio-seldon/models"
os.system(f"mc rb --force {target_bucket}")
os.system(f"mc mb {target_bucket}")
os.system(f"mc cp --recursive {MODEL_DIR} {target_bucket}")

Create model deployment configuration¶

[ ]:

%%writefile mlflow_elasticnet_wine_v2.yaml
apiVersion: machinelearning.seldon.io/v1alpha2
kind: SeldonDeployment
metadata:
  name: mlflow
spec:
  protocol: kfserving  # Activate v2 protocol
  name: wines
  predictors:
    - graph:
        children: []
        implementation: MLFLOW_SERVER
        modelUri: s3://models/elasticnet_wine_model  # note: s3 points to minio-seldon in the local kind cluster
        envSecretRefName: seldon-rclone-secret
        name: classifier
      name: default
      replicas: 1

Deploy the model on the local kind cluster¶

[ ]:

!kubectl apply -f mlflow_elasticnet_wine_v2.yaml

[ ]:

!kubectl rollout status deploy/$(kubectl get deploy -l seldon-deployment-id=mlflow -o jsonpath='{.items[0].metadata.name}')

Get prediction from the service using REST¶

[ ]:

import json

import requests

inference_request = {
    "parameters": {
        "content_type": "pd"
    },
    "inputs": [
        {
          "name": "fixed acidity",
          "shape": [1],
          "datatype": "FP32",
          "data": [7.4],
          "parameters": {
              "content_type": "np"
          }
        },
        {
          "name": "volatile acidity",
          "shape": [1],
          "datatype": "FP32",
          "data": [0.7000],
          "parameters": {
              "content_type": "np"
          }
        },
        {
          "name": "citric acidity",
          "shape": [1],
          "datatype": "FP32",
          "data": [0],
          "parameters": {
              "content_type": "np"
          }
        },
        {
          "name": "residual sugar",
          "shape": [1],
          "datatype": "FP32",
          "data": [1.9],
          "parameters": {
              "content_type": "np"
          }
        },
        {
          "name": "chlorides",
          "shape": [1],
          "datatype": "FP32",
          "data": [0.076],
          "parameters": {
              "content_type": "np"
          }
        },
        {
          "name": "free sulfur dioxide",
          "shape": [1],
          "datatype": "FP32",
          "data": [11],
          "parameters": {
              "content_type": "np"
          }
        },
        {
          "name": "total sulfur dioxide",
          "shape": [1],
          "datatype": "FP32",
          "data": [34],
          "parameters": {
              "content_type": "np"
          }
        },
        {
          "name": "density",
          "shape": [1],
          "datatype": "FP32",
          "data": [0.9978],
          "parameters": {
              "content_type": "np"
          }
        },
        {
          "name": "pH",
          "shape": [1],
          "datatype": "FP32",
          "data": [3.51],
          "parameters": {
              "content_type": "np"
          }
        },
        {
          "name": "sulphates",
          "shape": [1],
          "datatype": "FP32",
          "data": [0.56],
          "parameters": {
              "content_type": "np"
          }
        },
        {
          "name": "alcohol",
          "shape": [1],
          "datatype": "FP32",
          "data": [9.4],
          "parameters": {
              "content_type": "np"
          }
        },
    ]
}

# note is the local balancer for istion, make sure that the ip is reflected in the setup,
# run kubectl get service -n istio-system
endpoint = "http://172.18.255.1/seldon/seldon/mlflow/v2/models/infer"
response = requests.post(endpoint, json=inference_request)

print(json.dumps(response.json(), indent=2))
assert response.ok

Delete the model deployment¶

[ ]:

!kubectl delete -f mlflow_elasticnet_wine_v2.yaml

[ ]:

MLflow v2 protocol elasticnet wine example¶

Setup seldon-core and minio¶

Setup Seldon Core¶

Setup MinIO¶

Train elasticnet wine model using mlflow¶

Install mlflow and required dependencies to train the model¶

Define where the model artifacts will be saved¶

Define training function¶

Train the elasticnet_wine model¶

Install dependencies to be able to pack and deploy the model on seldon_core¶

Pack the conda enviornment¶

Configure mc to access the minio service in the local kind cluster¶

Copy the model artifacts to minio¶

Create model deployment configuration¶

Deploy the model on the local kind cluster¶

Get prediction from the service using REST¶

Delete the model deployment¶

Setup `seldon-core` and `minio`¶

Train elasticnet wine model using `mlflow`¶

Install `mlflow` and required dependencies to train the model¶

Install dependencies to be able to pack and deploy the model on `seldon_core`¶

Configure `mc` to access the minio service in the local kind cluster¶