https://github.com/artefactory/one-click-mlflow

A tool to deploy a mostly serverless MLflow tracking server on a GCP project with one command
https://github.com/artefactory/one-click-mlflow

docker gcp mlflow serverless terraform

Last synced: about 2 months ago
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A tool to deploy a mostly serverless MLflow tracking server on a GCP project with one command

• Host: GitHub
• URL: https://github.com/artefactory/one-click-mlflow
• Owner: artefactory
• License: lgpl-3.0
• Created: 2020-09-01T09:12:17.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2021-11-17T13:35:44.000Z (about 3 years ago)
• Last Synced: 2023-03-06T23:16:34.916Z (almost 2 years ago)
• Topics: docker, gcp, mlflow, serverless, terraform
• Language: HCL
• Homepage:
• Size: 225 KB
• Stars: 61
• Watchers: 4
• Forks: 17
• Open Issues: 13
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

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README

        
# 1. one-click-mlflow

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

- [1. one-click-mlflow](#1-one-click-mlflow)

  - [1.1. How to use](#11-how-to-use)

    - [1.1.1. Pre-requesites](#111-pre-requesites)

    - [1.1.2. Deploying](#112-deploying)

    - [1.1.3. What it does](#113-what-it-does)

    - [1.1.4. Other available make commands](#114-other-available-make-commands)

    - [1.1.5. Pushing logs and artifacts](#115-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

![Architecture](/doc/archi.svg)

### 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.

```bash

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.