mlflow-tutorial

I walk through this tutorial and others here on GitHub and on my Medium blog. Here is a friend link for open access to the article on Towards Data Science: Machine learning model serving for newbies with MLflow. I'll always add friend links on my GitHub tutorials for free Medium access if you don't have a paid Medium membership (referral link).

If you find any of this useful, I always appreciate contributions to my Saturday morning fancy coffee fund!

This GitHub repo walks through an example of training a classifier model with sklearn and serving the model with mlflow. The first section saves the mlflow model locally to disk, and the second section shows how to use the mlflow registry for model tracking and versioning.

TLDR

To skip through and run all components with Docker Compose you can run this whole tutorial with the registry:

docker compose -f docker-compose.yml up --build

or without the registry:

docker compose -f docker-compose-no-registry.yml up --build

with the model served on port 1234.

To run only the mlflow server with Docker, port forwarding to localhost:5000, you can use a compose file with the command below:

docker compose -f compose-server.yml up --build

Serving models with mlflow (no registry)

Train a model

The clf-train.py script uses the sklearn breast cancer dataset, trains a simple random forest classifier, and saves the model to local disk with mlflow. Adding the optional flag for writing output test data will split the training data first to add an example test data file.

python clf-train.py clf-model --outputTestData test.csv

Serve model to port 1234

Serve your trained clf-model to port 1234.

mlflow models serve -m clf-model -p 1234 -h 0.0.0.0

Serving models with mlflow (with registry)

Start an mlflow server for UI on port 5000

This uses a sqlite database backend and stores model artifacts at the local specified location.

mlflow server \
--backend-store-uri sqlite:///mlflow.db \
--default-artifact-root ./mlflow-artifact-root \
--host 0.0.0.0

Train a model

The clf-train-registry.py script uses the sklearn breast cancer dataset, trains a simple random forest classifier, overwrites the model predict method to return probabilities instead of classes, and saves and registers the model with mlflow. The newest model is moved to the mlflow Staging version. Edit for your own models or preferred stage or versions. Adding the optional flag for writing output test data will split the training data first to add an example test data file.

python clf-train-registry.py clf-model "http://localhost:5000" --outputTestData test.csv

The jupyter notebook also works through model training and serving with mlflow.

Serve model to port 1234

Serve your latest Staging version of the trained clf-model to port 1234.

export MLFLOW_TRACKING_URI=http://localhost:5000
mlflow models serve -m models:/clf-model/Staging -p 1234 -h 0.0.0.0

Make predictions

For inference data in a file called test.csv, run the following:

curl http://localhost:1234/invocations  -H 'Content-Type: text/csv' --data-binary @test.csv

or just run the script (after making executable):

predict.sh test.csv

This returns an array of predicted probabilities.

Cleaning up

If you're using Compose, when finished, shut down all containers with the following command:

docker compose down

Note well that the compose files mount volumes and write to the local directory.