Deploying an MLFlow Remote Server with Docker, S3 and SQL

4 min readJun 14, 2021

MLFlow is an open-source platform for managing your machine learning lifecycle. You can either run MLFlow locally on your system, or host an MLFlow Tracking server, which allows for mutiple people to log models and store them remotely in a model repository for quick deployment/reuse.

In this article, I’ll tell you how to deploy MLFlow on a remote server using Docker, an S3 storage container of your choice Minio or Ceph and SQL SQLite or MySQL.

Setting up the Server

Login to your remote server. It should have docker installed. For docker installation, check their official guide.
Create a new folder for your Mlflow server

mkdir mlflow-server
cd mlflow-server

Open a new terminal on your server, go to your mlflow-server directory and create a virtual environment for MLFlow installation

python3 -m venv env
source env/bin/activate

Install necessary packages:

 pip3 install mlflow

Setting up the Backend Store

MLFlow model parameters and run stats are all stored in a backend store which you can specify. By default, MLFlow uses the filesystem, but that is not an ideal setup for a remote tracking server. Here, I’ll tell you how to set it up using either SQLite3 or MySQL — whichever you may prefer. I would recommend using MySQL in a production environment.

Using SQLite3

Install sqlite3

sudo apt install sqlite3

Setup an SQLite database for your MLFlow server’s backend-uri. Make sure that this file is in your mlflow-server folder.

sqlite3 store.db
# press cntrl+D to exit sqlite 
# nothing else needs to be done

Note that yuor backend-store-uri would now be — sqlite:///store.db.

Using MySQL

Install mySQL
Install PyMySQL in your python envirionment which has MLFlow.

pip3 install pymysql

Sign in to you mysql shell and create a new database for MLFlow. Also create a new user/use an existing user and assign access to the MLFLow database.

CREATE USER ‘mlflow-user’ IDENTIFIED BY ‘password’;
CREATE DATABASE ‘mlflowruns’;
GRANT ALL PRIVILEGES ON mlflowruns.* TO ‘mlflow-user’;

Note that your backend-store-uri would now be — mysql+pymysql://'mlflow-user':'password'@localhost:3306/mlflowruns

Setting up the Artifact Store

The artifact store is where your model’s files are stored. This includes your environment and other files that you can use to recreate and deploy your model instantly. By default your model’s artifacts are also stored on the filesystem in a fodler named mlruns, but when deploying a remote tracking server this is not an option as your client needs to have read and write permissions to the artifact — which is not possible for a filesystem path storage. Here, we’ll use S3 buckets to store model artifacts — the two options which we’ll look at are Minio and Ceph. Minio is comparatively lightweight, though Ceph is much more powerful and easier to start with, thanks to Ceph Nano.

Using Minio

Get Minio’s latest docker image

sudo docker pull minio/minio

Run a Minio server and bind it to a server port that is exposed. The Minio server needs to run on a port that is exposed so that a remote client can upload model artifacts to it. Minio will serve as your default artifact root. You can run the following command. Note that you need to fill in an exposed port for minio, and any access/secret key pair of your choice.

docker run -p <exposed_port_for_minio>:9000 — name minio1 \
-e MINIO_ACCESS_KEY=<your_access_key_id> \
-e MINIO_SECRET_KEY=<your_secret_key> \
-v /mnt/data:/data \
-v /mnt/config:/root/.minio \
minio/minio server /data

Install necessary packages in your created environment.

pip3 install minio
pip3 install boto3

Setup local environment variables:

export MLFLOW_S3_ENDPOINT_URL=http://127.0.0.1:<exposed_port_for_minio>
export AWS_ACCESS_KEY_ID=<your_access_key_id>
export AWS_SECRET_ACCESS_KEY=<your_secret_key>

Create a Minio bucket — in your mlflow-server folder, run the following:

wget https://gist.githubusercontent.com/kaushalvivek/9f1905e25a28526dfeaaecf80ef5c361/raw/3bbb30954d8ee7144fe2ec183b78999518226040/create_bucket.py
python3 create_bucket.py
rm create_bucket.py

Note that your default-artifact-root should now be s3://mlflow/artifacts

Using Ceph

Install ceph nano locally. Based on which OS you are using, run the command listed here.
Create a Ceph nano cluster

./cn cluster start -d /tmp mlflow-cluster -f huge

Note down the ceph endpoint URL, AWS access key id and AWS secret key that the cluster auto-generates and prints to shell.
Cretae an S3 bucket

./cn s3 mb mlflow-cluster mlflow-buc

Setup local environment variables:

export MLFLOW_S3_ENDPOINT_URL=http://127.0.0.1:<ceph-endpoint-url>
export AWS_ACCESS_KEY_ID=<your_access_key_id>
export AWS_SECRET_ACCESS_KEY=<your_secret_key>

Note that your default-artifact-root should now be s3://mlflow-buc

Starting the Server

Start the MLFlow server — run the following from you mlflow-server folder. You need to enter an exposed port that would be the MLFlow Server’s port, for UI and logging. This port needs to be different from your Artifacts endpoint port.

mlflow server -p <exposed_port_for_mlflow> \
 — host 0.0.0.0 \
 — backend-store-uri <enter_backend_store_uri> \
 — default-artifact-root <enter_deafult_artifact_root>

And that’s it! your server is up and running. Note that while you’re specifying the endpoint URL, do not use localhost. Stick to http://127.0.0.1 with the http. Ceph is specific about the endpoint URL.

Setting up the Client

On your local system, create a new folder where you’ll work with mlflow and create a local virtual environment

mkdir mlflow-work
cd mlflow-workpython3 -m venv env
source env/bin/activatepip3 install mlflow

Setting up local environment variables — you need to setup your environment for accessing MLFlow’s remote tracking server and logging data to your remote minio artifact server

export MLFLOW_TRACKING_URI=http://<remote_server_ip>:<exposed_port_for_mlflow>
export MLFLOW_S3_ENDPOINT_URL=http://<remote_server_ip>:<exposed_port_for_artifact_root>export AWS_ACCESS_KEY_ID=<enter_aws_access_key_id>
export AWS_SECRET_ACCESS_KEY=<enter_aws_secret_access_key>

And you’re all set! You can download example code from MLFlow examples, and start executing them directly in your mlflow-work directory. For more information about MLFlow checkout MLFlow Documentation.