Setup MLflow in Neevcloud Instance

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 set the MLflow in Neevcloud

Set Up Your Environment:

1. Ubuntu 22.04

2. Python3

3. Mlflow

Create a ubuntu-22 instance.

Update && Upgrade all the packages

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

Install python and set the environment

sudo apt-get install -y python3-pip 

The pip is the package installer for Python, which allows you to easily install and manage libraries and packages for your Python projects

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.

Set up a packagesVirtual environment

sudo python3 -m venv myenv

source myenv/bin/activate

Install necessary packages

Upgrade and install packages within the environment.

pip install --upgrade pip

pip install --upgrade setuptools
pip install mlflow scikit-learn

MLFLOW_TRACKING_URI=http://0.0.0.0:5000 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

The command runs the MLflow server in the background, ensuring it continues running after logout, logs output to mlflow.log, and sets up the tracking server with SQLite backend

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

After running the command open your browser and log in with the IP (Your server IP) and Port(5000)

http://your_server_ip:5000