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

For more information about MLFlow, please visit the main MLFlow app page.

Installing

MLFlow can be installed on Apolo either via the CLI or the Web Console. Below are the detailed instructions for installing MLFlow using Apolo CLI.

Installing via Apolo CLI

The CLI flow mirrors the UI fields but lets you automate everything.

# 1. place this in mlflow.yaml

template_name: "mlflow-core"
template_version: "v25.5.0"
input:
  preset:
    name: "cpu-small"
  ingress_http:
    auth: true          # basic‑auth on
  metadata_storage:
    postgres_uri:
      uri: "postgresql://mlflow:Pa55w0rd@postgres-svc:5432/mlflow"
  artifact_store:
    path: "storage:mlflow-artifacts"

# 2. deploy it
apolo app install -f mlflow.yaml \
  --cluster <CLUSTER> --org <ORG> --project <PROJECT>

Explanation

• preset.name=cpu-small – 2 vCPU / 4 GiB (adjust as needed).

• postgres_uri.uri – Points to an existing Postgres service; omit this block to fall back to SQLite.

• ingress_http.auth=true – Adds basic‑auth to the public URL.

---

Inputs / Outputs (schema v1)

Inputs

JSON Path	Default	Description
preset.name	–	Resource preset.
ingress_http.auth	true	Basic‑auth on external URL.
metadata_storage.pvc_name	mlflow-sqlite-storage	For SQLite backend.
metadata_storage.postgres_uri.uri	null	Full Postgres DSN.
artifact_store.path	storage:mlflow-artifacts	Artifact bucket path.

Outputs (seen in Details ▸ Output)

Key	Purpose
external_web_app_url.*	Public UI & REST endpoint.
external_server_url.*	Tracking server URL for clients.

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

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

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