Deploy ML Dashboards on Kubernetes with Bodywork

This repository contains a Bodywork project that demonstrates how to run a ML workflow on Kubernetes, with Bodywork. The example ML workflow has two stages:

1. Run a batch job to train a model that is stored in a AWS S3 bucket.
2. Deploy a Plotly dashboard to present model performance information.

To run this project, follow the steps below.

Get Access to a Kubernetes Cluster

Use our Quickstart Guide to Kubernetes for MLOps to spin-up a local Minikube cluster in minutes.

Install the Bodywork Python Package

$ pip install bodywork

Load Dashboard Credentials into Cluster

The dashboard uses basic authentication, which requires a username and password to be passed to it via environment variables. These can be securely injected into the containers running the app, using Bodywork's secret management capabilities,

$ bodywork create secret plotly-dash-credentials \
    --group dev \
    --data DASH_USERNAME=bodywork \
    --data DASH_PASSWORD=bodywork123

Run the ML Pipeline

$ bodywork create deployment https://github.com/bodywork-ml/bodywork-ml-dashboard-project.git

The orchestrator logs will be streamed to your terminal until the job has been successfully completed.

Accessing the Dashboard

Once the deployment has completed, the dashboard server be ready for testing. Bodywork will create ingress routes to your endpoints using the following scheme:

/PIPELINE_NAME/STAGE_NAME/

To open an access route to the cluster for testing, start a new terminal and run,

$ kubectl -n ingress-nginx port-forward service/ingress-nginx-controller 8080:80

Then browse the dashboard at,

http://localhost:8080/bodywork-ml-dashboard-project/stage-2-dashboard-app/dash/

You should see something that looks like,

Running the Workflow on a Schedule

If you're happy with the test results, you can schedule the workflow-controller to operate remotely on the cluster on a pre-defined schedule. For example, to setup the the workflow to run every hour, use the following command,

$ bodywork create cronjob https://github.com/bodywork-ml/bodywork-ml-dashboard-project \
    --name=train-with-metrics-dashboard \
    --schedule="0 * * * *" \

Each scheduled workflow will attempt to re-run the batch-job, as defined by the state of this repository's master branch at the time of execution.

To get the execution history for all train-with-metrics-dashboard jobs use,

$ bodywork get cronjob train-with-metrics-dashboard --history

Cleaning Up

To delete the deployment in its entirety,

$ bodywork delete deployment train-with-metrics-dashboard

Make this Project Your Own

This repository is a GitHub template repository that can be automatically copied into your own GitHub account by clicking the Use this template button above.

After you've cloned the template project, use official Bodywork documentation to help modify the project to meet your own requirements.