Update: there's a new version of mlflow that supports a built-in artifact server. However, it is currently bugged. When using a local directory, the web ui artifact browser doesn't work and when using a proxy with NGINX, the client sets the artifact URI port to None. It also doesn't create the default experiment.

When it's stable this repository will be updated to use the built-in server.

A docker-compose setup that sets up a backend MLflow server (sqlite) and artifact store (SFTP) on the directories you specify. An nginx proxy is provided which adds HTTP Basic Auth with SSL and the sFTP likewise requires authentication, which makes it safe(ish) to expose them to the clearnet.

A .env.example file is provided which allows you to quickly set up a configuration to your liking. The configuration uses volume binds, so the backend and artifact stores use local system directories (by default ./artifacts and ./backend). As a result, this set up is completely portable.

Moving servers? Just transfer this folder to the new one.

Start by copying .env.example to .env and modifying it to taste. It should work as is.

cp .env.example .env

If you change the UID/GID of docker through .env (so you can modify files without root), you'll have to create the bind directories yourself, because docker will create them using the root user.

mkdir -p artifacts backend cert

If you'd rather use a domain name instead of an IP to make it easier to remember when setting up machines and you don't have one, check out Duck DNS and Freenom. You can get .tk domains from freedom and .duck-dns.org from Duck DNS (which can auto-update), no credit card required. Place your domain in MLFLOW_ARTIFACT_URI and use it when specifying the server in clients.

Cloudflare is a good free DNS service if using Freenom.

We will be exposing ports on the host machine. Before continuing, install ufw if not already installed and enable it.

sudo apt update && sudo apt install ufw
sudo ufw allow ssh
sudo ufw enable
sudo ufw status

Do a trial run by running the following:

docker-compose up

You can exit with ctrl+C, test everything works as expected.

If you make any changes, type the following to scrap the containers before restarting:

docker-compose down

Finally, use the following to launch in the background (detached mode) and monitor using the log command

docker-compose up -d
docker-compose logs -f

You can always stop the containers without removing them with the following:

docker-compose stop

If you wish to test a service on its own you can specify it at launch. Then, only it and its dependencies will be launched.

docker-compose up flow

You can now allow traffic to the secured backend port and the sftp port.

sudo ufw allow 23
sudo ufw allow 80
# or
sudo ufw allow 443

If you are launching a VM behind a modern cloud, you will also have to modify the security group and forward the ports there as well (the ssh port is forwarded by default).

NGINX forwards the backend using HTTP Basic Auth to ports 80 (HTTP) and 443 (HTTPS). Use whichever you prefer. The keys for the SSL connection are placed on the cert folder and you can replace them with your own.

The cert will support the domain names and IPs you specified in .env. Just point MLFLOW_TRACKING_SERVER_CERT_PATH to nginx.crt and the https connection will be secured. However, IP wildcards are not supported, so to use an IP you will either need to use your own certificate or modify ./nginx/gen_key.sh.

By using the HTTP protocol, so it's trivial for a MITM to steal your credentials

Consider using HTTPS with MLFLOW_TRACKING_INSECURE_TLS or MLFLOW_TRACKING_SERVER_CERT_PATH.

First, you need to add the host keys of the artifact server to the known_hosts file (change port and domain to your own):

ssh-keyscan -H -p 23 yourdomain.com >> ~/.ssh/known_hosts

Then, launch your python code while specifying the tracking server using environment variables. Below is an example of a bash file you could use to set the environment variables for your client using SSL.

export MLFLOW_TRACKING_URI=https://127.0.0.1
export MLFLOW_TRACKING_USERNAME=mlflow
export MLFLOW_TRACKING_PASSWORD=12345
export MLFLOW_TRACKING_SERVER_CERT_PATH=./cert/nginx.crt
# export MLFLOW_TRACKING_INSECURE_TLS=true

python flow.py

This repository extends Andy Ganse's aganse/docker_mlflow_db repository by simplifying it and making it ready to use. It also implements some best practices from web development for using docker.

HTTP Basic Auth is now provided by default by nginx and the username and password are set using the .env file. NGINX also forwards an https port and a self signed certificate is generated for you. By placing that certificate in the client and setting the proper environment variable you can use mlflow securely with TLS.

I do not see the reason for using PostgreSQL for a server used by one researcher. So sqlite is used for the backend. As I use MLflow I might change my mind though.

Old credits: Originally based on [Guillaume Androz's 10-Jan-2020 Toward-Data-Science post, "Deploy MLflow with docker compose"] (https://towardsdatascience.com/deploy-mlflow-with-docker-compose-8059f16b6039), with some changes to:

• replace AWS usage with local mapping for artifact store
• replace mysql with postgresql and other options.
• optionally apply htpasswd access control to mlflow website via nginx frontend