1. one-click-mlflow

A tool to deploy a mostly serverless MLflow on a GCP project with one command

• 1. one-click-mlflow
  • 1.1. How to use
  • 1.1.1. Pre-requesites
  • 1.1.2. Deploying
  • 1.1.3. What it does
  • 1.1.4. Other available make commands
  • 1.1.5. Pushing logs and artifacts

1.1. How to use

1.1.1. Pre-requisites

• A GCP project on which you are owner
• Terraform, make, and jq installed
• Initialized gcloud SDK with your owner account

1.1.2. Deploying

Clone the repo

Run make one-click-mlflow and let the wizard guide you.

If you want to see the innards, you can run it in debug mode: DEBUG=true make one-click-mlflow

1.1.3. What it does

• Enables the necessary services
• Builds and deploys the MLFlow docker image
• Creates a private IP CloudSQL (MySQL) database for the tracking server
• Creates an AppEngine Flex on the default service for the web UI, secured by IAP
• Manages all the network magic
• Creates the mlflow-log-pusher service account

1.1.4. Other available make commands

• make deploy: builds and pushes the application image and (re)deploys the infrastructure
• make docker: builds and pushes the application image
• make apply: (re)deploys the infrastructure
• make destroy: destroys the infrastructure. Will not delete the OAuth consent screen, and the app engine application.

1.1.5. Pushing your first parameters, logs, artifacts

Once the deployment successful, you can start pushing to your MLFlow instance.

cd examples
python3 -m venv venv 
source venv/bin/activate
pip install -r requirements.txt
python track_experiment.py

You can than adapt examples/track_experiment.py and examples/mlflow_config.py to suit your application's needs.