How to Deploy MLflow on the Vultr Kubernetes Engine

12 Nov, 2023

Originally written for Vultr

Introduction

MLflow is a versatile open-source Machine Learning (ML) platform for model lifecycle management. It supports experimentation, duplicability, deployment, and a central model registry. MLflow is used to track machine learning workload experiments, packaging and deployment of code, and collaborative management. Frequently used by data scientists and by MLOps professionals.

This article explains how to deploy MLflow on a Vultr Kubernetes Engine (VKE) cluster with a working environment and demo application.

Prerequisites

Before you begin:

Deploy a Vultr Kubernetes Engine (VKE) cluster
Deploy a Vultr Ubuntu instance to use as your management server
Using SSH, access the server
Install and configure Kubectl to access the cluster
Install the Helm package manager
```
  $ sudo snap install helm --classic
```

Deploy Persistent Volume and Persistent Volume Claims (PVCs)

MLflow requires persistent storage to store artifacts and experiment data. In this section, deploy a PV and PVC to the cluster as described in the steps below.

Create a new file mlflow-pv-pvc.yaml
```
$ touch mlflow-pv-pvc.yaml
```
Using a text editor such as nano, edit the file
```
$ nano mlflow-pv-pvc.yaml
```

Add the following contents to the file

 apiVersion: v1

 kind: PersistentVolume

 metadata:

 name: mlflow-pv

 labels:

     type: local

 spec:

 storageClassName: manual

 capacity:

     storage: 10Gi

 accessModes:

     - ReadWriteOnce

 hostPath:

     path: "/mnt/data"

 ---

 apiVersion: v1

 kind: PersistentVolumeClaim

 metadata:

 name: mlflow-pvc

 spec:

 storageClassName: manual

 accessModes:

     - ReadWriteOnce

 resources:

     requests:

     storage: 10Gi

Save the file.

Apply the configuration to your cluster
```
 $ kubectl apply -f mlflow-pv-pvc.yaml
```

Verify the deployed Cluster PVs

 $ kubectl get pv

Output:

 NAME        CAPACITY   ACCESS MODES   RECLAIM POLICY   STATUS   CLAIM                STORAGECLASS   REASON   AGE

 mlflow-pv   10Gi       RWO            Retain           Bound    default/mlflow-pvc   manual

Install Mlflow

Using Helm, add community-charts to your repositories

 $ helm repo add community-charts https://community-charts.github.io/helm-charts

Update the Helm repository index
```
 $ helm repo update
```
Install MLflow. Replace `vultr`` with your desired MLflow label
```
 $ helm install vultr community-charts/mlflow
```

View the cluster deployment to verify that MLflow is ready and available

 $ kubectl get deployments

Your output should look like the one below:

 NAME           READY   UP-TO-DATE   AVAILABLE   AGE

 vultr-mlflow    1/1     1            1           19h

Expose the MLflow Forwarding Service For External Access

To access your MLflow deployment over the Internet, set up a forwarding service to expose the application for external access as described in the steps below.

Create a new file mlflow-service.yaml
```
 $ touch mlflow-service.yaml
```
Edit the file
```
 $ nano mlflow-service.yaml
```

Add the following contents to the file

 apiVersion: v1

 kind: Service

 metadata:

     name: mlflow-service

 spec:

     selector:

         app: mlflow

     ports:

        - protocol: TCP

         port: 80

         targetPort: 5000

 type: LoadBalancer

Apply the service to your cluster

 $ kubectl apply -f mlflow-service.yaml

Wait for at least 3 minutes, view the cluster services, and verify the MLflow External IP value

    $ kubectl get services

Your output should look like the one below:

    NAME             TYPE           CLUSTER-IP      EXTERNAL-IP   PORT(S)        AGE

    kubernetes       ClusterIP      10.96.0.1       <none>        443/TCP        40m

    mlflow-service   LoadBalancer   10.110.234.98   192.0.2.100   80:31545/TCP   64s

    vultr-mlflow     ClusterIP      10.102.93.29    <none>        5000/TCP       101s

Create a sample Mlflow Experiment

To test the Mlflow functionalities, create a sample linear regression experiment as described in the steps below.

Create a new directory Models
```
 $ mkdir Models
```
Switch to the directory
```
 $ cd Models
```
Using `pip``, install the necessary experiment dependencies
```
 $ pip install mlflow scikit-learn shap matplotlib
```
When using Conda, install the dependencies in your environment:
```
 $ conda install mlflow scikit-learn shap matplotlib
```
Create a new environment variable with your desired experiment name
```
 $ export MLFLOW_EXPERIMENT_NAME='my-sample-experiment'
```
Export a new MLFLOW_TRACKING_URI with your MLflow service external IP HTTP URL as the value. For example 192.0.2.100
```
 $ export MLFLOW_TRACKING_URI='http://192.0.2.100'
```
Create a new file main.py
```
 $ touch main.py
```
Edit the file
```
 $ nano main.py
```

Add the following contents to the file

 # Import Libraries



 import os



 import numpy as np

 import shap

 from sklearn.datasets import load_diabetes

 from sklearn.linear_model import LinearRegression



 import mlflow

 from mlflow.artifacts import download_artifacts

 from mlflow.tracking import MlflowClient



 # Prepare the Training Data



 X, y = load_diabetes(return_X_y=True, as_frame=True)

 X = X.iloc[:50, :4]

 y = y.iloc[:50]



 # Train a model



 model = LinearRegression()

 model.fit(X, y)



 # Log an explanation



 with mlflow.start_run() as run:

     mlflow.shap.log_explanation(model.predict, X)



 # List Artifacts



 client = MlflowClient()

 artifact_path = "model_explanations_shap"

 artifacts = [x.path for x in client.list_artifacts(run.info.run_id, artifact_path)]

 print("# artifacts:")

 print(artifacts)



 # Load the logged explanation



 dst_path = download_artifacts(run_id=run.info.run_id, artifact_path=artifact_path)

 base_values = np.load(os.path.join(dst_path, "base_values.npy"))

 shap_values = np.load(os.path.join(dst_path, "shap_values.npy"))



 # Show a Force Plot



 shap.force_plot(float(base_values), shap_values[0, :], X.iloc[0, :], matplotlib=True)

Save and close the file.

The above Python application imports all necessary libraries and uses the diabetes dataset for training data. The dataset contains a total of 442 samples. Then, the model is trained and an explanation is logged to MLflow using SHAP (Shapley Additive Explanations) that plots the output for a visual representation of the data.

Test the Mlflow Experiement

Run the Python application
```
 $ python3 main.py
```
Using a web browser such as Chrome, visit your Mlflow external IP address
```
 http://192.0.2.100
```

Verify that the application is included in your MLflow experiments. It uses a linear regression statistical approach to model a relationship between a scalar response and one or more variables.

Mlflow UI showing the diabetes linear regression experiment

Conclusion

You have deployed MLflow on a Vultr Kubernetes Engine (VKE) cluster. To test the service operations, implement more examples from the MLflow repository. For more information about MLflow, visit the following documentation resources:

Next Steps

To implement more solutions, visit the following resources:

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#kubernetes #ml #vultr