How to run authentication on a mlFlow server?

If you just want MLFlow installed with some basic authentication you can use mlflow-easyauth to get a Docker container with HTTP basic auth (username/password) setup integrated. It uses Nginx under the hood. Authentication details are configured using environment variables.

Disclaimer: I am the maintainer of that project

the problem here is that both mlflow and nginx are trying to run on the same port...

1. first lets deal with nginx:

   1.1 in /etc/nginx/sites-enable make a new file sudo nano mlflow and delete the exist default.

   1.2 in mlflow file:

server {
    listen YOUR_PORT;
    server_name YOUR_IP_OR_DOMAIN;
    auth_basic           “Administrator’s Area”;
    auth_basic_user_file /etc/apache2/.htpasswd; #read the link below how to set username and pwd in nginx

    location / {
        proxy_pass http://localhost:8000;
        include /etc/nginx/proxy_params;
        proxy_redirect off;
    }
}

1.3. restart nginx sudo systemctl restart nginx

2. on your server run mlflow mlflow server --host localhost --port 8000

Now if you try access the YOUR_IP_OR_DOMAIN:YOUR_PORT within your browser an auth popup should appear, enter your host and pass and now you in mlflow

3. now there are 2 options to tell the mlflow server about it:

   3.1 set username and pwd as environment variable export MLFLOW_TRACKING_USERNAME=user export MLFLOW_TRACKING_PASSWORD=pwd

   3.2 edit in your /venv/lib/python3.6/site-packages/mlflowpackages/mlflow/tracking/_tracking_service/utils.py the function

def _get_rest_store(store_uri, **_):
    def get_default_host_creds():
        return rest_utils.MlflowHostCreds(
            host=store_uri,
            username=replace with nginx user
            password=replace with nginx pwd
            token=os.environ.get(_TRACKING_TOKEN_ENV_VAR),
            ignore_tls_verification=os.environ.get(_TRACKING_INSECURE_TLS_ENV_VAR) == 'true',
        )

in your .py file where you work with mlflow:

import mlflow
remote_server_uri = "YOUR_IP_OR_DOMAIN:YOUR_PORT" # set to your server URI
mlflow.set_tracking_uri(remote_server_uri)
mlflow.set_experiment("/my-experiment")
with mlflow.start_run():
    mlflow.log_param("a", 1)
    mlflow.log_metric("b", 2)

A link to nginx authentication doc https://docs.nginx.com/nginx/admin-guide/security-controls/configuring-http-basic-authentication/