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MLflow Docker - MLflow Tracking Server and MySQL

Overview

Launches a full-fledged MLflow server environment consisting of two containers:

  • mlflow_server - MLflow tracking server
  • mlflow_mysql - MySQL database server

Two types of mlflow_server containers can be built depending on where the artifact repository lives:

  • local - mounted shared volume between (laptop) host and container
  • S3 - artifacts are stored on S3

See MLflow Tracking Servers and Referencing Artifacts.

Run

The required environment variables are specified in the standard docker compose .env file. Copy one of the two .env template files to .env, make appropriate changes and then run docker-compose.

To launch a local MLflow server:

cp .env-local-template .env
vi .env # make changes 
docker-compose -f docker-compose.yaml -f docker-compose-local.yaml  up -d

To launch an S3 MLflow server:

cp .env-s3-template .env
vi .env # make changes 
docker-compose -f docker-compose.yaml -f docker-compose-s3.yaml up -d

You will then see two containers:

CONTAINER ID  IMAGE                  COMMAND                  PORTS                     NAMES
7a4be1019858  mlflow_server:latest   "/bin/sh -c 'cd /hom…"   0.0.0.0:5005->5000/tcp    mlflow_server
3b4eb5a2026e  mlflow_mysql:5.7.31    "docker-entrypoint.s…"   0.0.0.0:33306->3306/tcp   mlflow_mysql

If you don't see the mlflow_server container, just run the docker-compose command again. It failed to start because mlflow_mysql wasn't up yet. It's a TODO to add a wait-until-alive feature.

Environment variables

Env Var Description Default
MySQL
MYSQL_ROOT_PASSWORD MySQL root password efcodd
HOST_MYSQL_PORT Port exposed on host 5306
HOST_MYSQL_DATA_DIR Host mounted volume path
MLflow
MLFLOW_ARTIFACT_URI Base URI for artifacts - either S3 or local path
HOST_MLFLOW_PORT Port exposed on host for tracking server 5005
MLflow S3
AWS_ACCESS_KEY_ID
AWS_SECRET_ACCESS_KEY

Sample local .env

# MySQL 
MYSQL_ROOT_PASSWORD=efcodd
HOST_MYSQL_PORT=5306
HOST_MYSQL_DATA_DIR=/opt/mlflow_docker/mysql

# MLflow tracking server
MLFLOW_ARTIFACT_URI=/opt/mlflow_docker/mlflow_server
HOST_MLFLOW_PORT=5005

Sample S3 .env

# MySQL 
MYSQL_ROOT_PASSWORD=efcodd
HOST_MYSQL_PORT=5306
HOST_MYSQL_DATA_DIR=/opt/mlflow_docker/mysql

# MLflow tracking server
MLFLOW_ARTIFACT_URI=s3://my-bucket/mlflow
HOST_MLFLOW_PORT=5005

# AWS 
AWS_ACCESS_KEY_ID=my_access_key_id
AWS_SECRET_ACCESS_KEY=my_secret_access_key

Check server health

Database

docker exec -it mlflow_mysql mysql \
  -u root --password=efcodd --port=5306 \
  -e "use mlflow ; select * from experiments"

+---------------+---------+-------------------------------------+-----------------+
| experiment_id | name    | artifact_location                   | lifecycle_stage |
+---------------+---------+-------------------------------------+-----------------+
|             0 | Default | /opt/mlflow_docker/mlflow_server/0  | active          |
+---------------+---------+-------------------+-----------------+-----------------+

Tracking server

curl http://localhost:5005/api/2.0/mlflow/experiments/list

{
  "experiments": [
    {
      "experiment_id": "0",
      "name": "Default",
      "artifact_location": "/opt/mlflow_docker/mlflow_server/0",
      "lifecycle_stage": "active"
    }
  ]

Login to containers

You can check things out inside the containers.

docker exec -i -t mlflow_server /bin/bash

docker exec -i -t mlflow_mysql /bin/bash

Files

Dockerfile Compose Note
General docker-compose.yaml Basic config for MySQL and MLflow server
MySQL Dockerfile-mysql MySQL-specific configs
MLflow Local Dockerfile-mlflow-local docker-compose-local.yaml Local-specific configs
MLflow S3 Dockerfile-mlflow-s3 docker-compose-s3.yaml S3-specific configs