mlflow

name	mlflow
description	MLflow 3.13 (tracking + AI Gateway) on this stack. Load when logging experiments from Spark/Python jobs, registering models, or wiring AI Gateway routes to Anthropic / Ollama for LLM-in-pipeline patterns.

MLflow 3.13

Tracking server: http://localhost:5000. AI Gateway: http://localhost:5001. Both in docker-compose-mlflow.yml. Backend: PostgreSQL (mlflow database). Artifact store: SeaweedFS (s3://mlflow/). Image base is ghcr.io/mlflow/mlflow:v3.13.0-full (the -full variant bundles psycopg2-binary + boto3, so Postgres and S3 work without a custom pip layer).

Tracking client

import mlflow

mlflow.set_tracking_uri("http://localhost:5000")
mlflow.set_experiment("my-experiment")

with mlflow.start_run() as run:
    mlflow.log_param("lr", 0.01)
    mlflow.log_metric("accuracy", 0.94)
    mlflow.log_artifact("plot.png")
    print(run.info.run_id)

Set MLFLOW_TRACKING_URI=http://localhost:5000 in .env and clients pick it up without explicit set_tracking_uri.

Spark autologging

import mlflow.spark
mlflow.spark.autolog()    # logs Spark ML pipelines, datasets, params

# or specifically for sklearn-style estimators
import mlflow.sklearn
mlflow.sklearn.autolog()

Autolog requires the run to be active (with mlflow.start_run():).

Model registry

mlflow.register_model(
    model_uri=f"runs:/{run_id}/model",
    name="orders-classifier",
)

Stage transitions (Staging → Production) are managed via the UI or mlflow.MlflowClient().transition_model_version_stage(...).

AI Gateway (port 5001)

The Gateway is an LLM proxy with route-based config in config/mlflow/gateway-config.yml. Pre-configured routes (verify the file in this repo, but typically):

• chat-anthropic — Anthropic Claude (requires ANTHROPIC_API_KEY in .env)
• chat-ollama — local Ollama models (requires Ollama running on the host)

Call from Python:

from mlflow.deployments import get_deploy_client
client = get_deploy_client("http://localhost:5001")

response = client.predict(
    endpoint="chat-anthropic",
    inputs={"messages": [{"role": "user", "content": "Summarize this Iceberg snapshot."}]},
)

Use cases on this stack:

• LLM-as-evaluator inside a Spark UDF
• Generating documentation strings for new tables in a Iceberg maintenance DAG
• Routing demo queries to a local Ollama model to avoid API costs

Common pitfalls

• Connection refused: localhost:5000 — MLflow container isn't up. ./lakehouse start mlflow.
• Artifact upload failed: S3 access denied — Mlflow's S3 creds (in docker-compose-mlflow.yml env) don't match the SeaweedFS keys in .env. They must match.
• Autolog logs to the wrong tracking server — MLFLOW_TRACKING_URI env var beats set_tracking_uri() call order. Set the env var explicitly.
• Spark autologging duplicate runs — mlflow.spark.autolog() + manual start_run() can create nested runs. Choose one.

Performance / cleanup

MLflow artifacts in SeaweedFS accumulate quickly. Cleanup:

# Delete runs older than 30 days via MLflow API
mlflow gc --backend-store-uri postgresql://... --older-than 30

For demos, just docker compose -f docker-compose-mlflow.yml down -v to wipe everything.