Install gcloud and gsutil command-line tools.

MLFlow uses a database in order to store logged parameters, and metrics. This repository assumes that you are going to use a serverless PostgreSQL database that is running on GCP. If you would like to use this repository, you need to create a new serverles PostgreSQL instance on GCP. Please follow these steps:

1. Create a serverless PostgreSQL instance on GCP,
2. Create a new user: Users -> ADD USER ACCOUNT, and save its password to GCP Secret Manager,
3. Create a new database: Databases -> CREATE DATABASE,

MLFlow needs a large storage to store your artifacts. So you need to create a GCS Bucket for MLFlow:

1. Create Bucket

Next, you need create 2 files in order to set some environment variables. Please create these files:

1. .envs/.gcp
2. .envs/.tracking-server

There is already example of how these files should look like in .envs directory:

1. .envs/.gcp-example
2. .envs/.tracking-server-example

After everything is ready you can call:

make deploy IMAGE_TAG=<docker-image-tag>

As IMAGE_TAG, you can specify anything you like. A docker image will be built and pushed to GCP Docker Registery, and the given IMAGE_TAG will be its tag.

Since this setup doesn't use expernal IP, you need ssh tunneling in order to view the web API.

1. Check VM Instances, to see if your VM was created,
2. Run gcloud compute ssh <vm-instance-name> --zone <vm-instance-zone> --tunnel-through-iap -- -N -L 610 0:localhost:6100
3. Check http://localhost:6100 if your MLFlow instance is running.
4. Run python ./examples/connecting-to-tracking-server.py to see if everything is working.

Happy experimentation! :)