MLflow

Overview

MLflow is the de‑facto open‑source platform for logging experiments, tracking metrics, storing artifacts and registering models. The MLFlow Core app on Apolo gives you a production‑ready tracking server with:

• A lightweight web UI

• Flexible metadata backend — SQLite or Postgres

• Built‑in artifact storage on Apolo Files

• Optional HTTPS ingress secured by platform auth

Key Features

Feature	How the Apolo App Helps
Experiment Tracking	Logs parameters, metrics & code revisions via REST API.
Artifact Storage	Push model binaries & notebook outputs to an S3‑compatible bucket (storage: URI).
Model Registry	Register & promote models with the same endpoint.
Pluggable Metadata	Choose embedded SQLite (PVC) or external Postgres DSN.
One‑click Scaling	Resize resources by switching the Resource Preset.
Secure Ingress	Auto‑generated HTTPS domain with platform auth.

Installing

There are two ways to deploy the app:

1. Web Console UI – click‑through wizard (no YAML).

2. Apolo CLI – declarative app install command that fits CI/CD. Refer to Apolo CLI MLFlow page.

Installing via Apolo Console

To find more information about how to manage your installable apps using Apolo Console, refer to Managing Apps.

Step 1 — Navigate to the Apps page, find MLFlow from the list and click the corresponding "Install" button. This will redirect you to the installation page

Step 2 — Configure the application by filling the required fields.

Section	Field	Example	Notes
Resource Preset	cpu-large	4 vCPU / 8 GiB	Pick any preset.
Enable HTTP Ingress	auth = true	—	Creates https://mlflow‑<id>.apps.<cluster>.apolo.us.
Metadata Storage	SQLite – PVC mlflow-sqlite-storage Postgres – postgresql://user:pwd@host:5432/mlflow	Choose one backend.
Artifact Store	storage:mlflow-artifacts	Path in Apolo Files.

Step 3 — Click "Install" to initiate deployment. You will be redirected to the application details page where you can monitor the installation progress and view application outputs. For instructions on how to access the application, please refer to the Usage section.

Usage

Use the CLI to set the MLFLOW_TRACKING_TOKEN with the token from your session (only if you enabled platform auth)

export MLFLOW_TRACKING_TOKEN=$(apolo config show-token)

The only requirement is to expose MLFLOW_TRACKING_URI and a basic‑auth header.

import mlflow
from mlflow.tracking import MlflowClient

TRACKING_URI = "https://apolo-taddeus-mlflow-core-d660e438.apps.apolo.us"
mlflow.set_tracking_uri(TRACKING_URI)
mlflow.set_registry_uri(TRACKING_URI)  # optional

client = MlflowClient()
print("Current experiments:")
for exp in client.list_experiments():
    print(exp.name)

References

• Mananing Apps via Apolo Console

• Managing Apps via Apolo CLI

• Installing MLFlow via Apolo CLI

• MLflow Official Documentation – Comprehensive guides covering Tracking, Projects, Models, and Registry

• MLflow GitHub Repository – Source code, issue tracker, and community discussions