Starting the MLflow Tracking Server

Before diving into MLflow’s rich features, let’s set up the foundational components: the MLflow Tracking Server and the MLflow UI. This guide will walk you through the steps to get both up and running.

Setting Up MLflow

The first thing that we need to do is to get MLflow.

Step 1: Install MLflow from PyPI

MLflow is conveniently available on PyPI. Installing it is as simple as running a pip command.

pip install mlflow

Step 2 (Optional): Launch the MLflow Tracking Server

If you would like to use a simpler solution by leveraging a managed instance of the MLflow Tracking Server, please see the details about options here.

To begin, you’ll need to initiate the MLflow Tracking Server. Remember to keep the command prompt running during the tutorial, as closing it will shut down the server.

mlflow server --host 127.0.0.1 --port 8080

Once the server starts running, you should see the following output:

[2023-11-01 10:28:12 +0900] [28550] [INFO] Starting gunicorn 20.1.0
[2023-11-01 10:28:12 +0900] [28550] [INFO] Listening at: http://127.0.0.1:8080 (28550)
[2023-11-01 10:28:12 +0900] [28550] [INFO] Using worker: sync
[2023-11-01 10:28:12 +0900] [28552] [INFO] Booting worker with pid: 28552
[2023-11-01 10:28:12 +0900] [28553] [INFO] Booting worker with pid: 28553
[2023-11-01 10:28:12 +0900] [28555] [INFO] Booting worker with pid: 28555
[2023-11-01 10:28:12 +0900] [28558] [INFO] Booting worker with pid: 28558
...

Note

Remember the host and port name that your MLflow tracking server is assigned. You will need this information in the next section of this tutorial!

Congratulations! Your MLflow environment is now set up and ready to go. As you progress, you’ll explore the myriad of functionalities MLflow has to offer, streamlining and enhancing your machine learning workflows.