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Setup MLflow in RackBank Instance

In this guide, we will explain how to set up MLflow in an Ubuntu instance, enabling you to manage your machine learning lifecycle effectively and efficiently.

Setup MLflow in RackBank​

What is MLflow?​

MLflow is an open-source platform designed to manage the machine learning lifecycle. It includes features to:

  • Track experiments and runs.
  • Package and share models.
  • Deploy models to various environments.
  • Monitor model performance.

Now, let's set up MLflow in a RackBank instance.

Setup Environment​

  • Ubuntu 22.04
  • Python3
  • MLflow

Step 1: Create a Ubuntu-22 Instance​

Start by creating an Ubuntu 22.04 instance on RackBank.

Step 2: Update & Upgrade All Packages​

Run the following commands to update and upgrade the package lists:

sudo apt-get update -y  
sudo apt-get upgrade -y

Step 3: Install Python and Set Up the Environment​

Install Python3 and pip:

sudo apt-get install -y python3-pip

pip is the package installer for Python, allowing you to easily install and manage libraries and packages for your Python projects.

Install the venv module:

sudo apt-get install -y python3-venv

The venv module in Python creates isolated environments for projects, preventing dependency conflicts, ensuring reproducibility, and allowing safe experimentation without affecting the system-wide Python installation.

Step 4: Set Up a Virtual Environment​

Create a virtual environment:

sudo python3 -m venv myenv

Activate the virtual environment:

source myenv/bin/activate

Step 5: Install Necessary Packages​

Upgrade pip and setuptools:

pip install --upgrade pip  

pip install --upgrade setuptools

Install MLflow and Scikit-learn:

pip install mlflow scikit-learn

Step 6: Set the MLflow Tracking Server URL​

The following command sets the MLflow Tracking Server URL, enabling centralized logging and tracking of machine learning experiments for collaboration and consistency:

export MLFLOW_TRACKING_URI=http://0.0.0.0:5000

Step 7: Run the MLflow Server​

Run the MLflow server in the background using the following command. This ensures it continues running after logout, logs output to mlflow.log, and sets up the tracking server with an SQLite backend:

nohup mlflow server --backend-store-uri sqlite:///mlflow.db --default-artifact-root ./mlruns --host 0.0.0.0 --port 5000 &> mlflow.log &

Step 8: Access MLflow​

After running the command, open your browser and navigate to the following URL using your server's IP and port (5000):

http://your_server_ip:5000