/MLFlow-Basic-Operation

Automating machine learning experiment tracking with MLFlow on AWS and Dagshub.

Primary LanguagePythonMIT LicenseMIT

🚀 MLFlow-Basic-Operation

Welcome to MLFlow-Basic-Operation! Dive into the world of MLFlow, your go-to platform for tracking and managing machine learning experiments.

📚 Documentation & Tutorials

🛠️ Getting Started

Ready to embark on your MLFlow journey? Launch the MLflow UI with ease:

mlflow ui

🌐 Integration with Dagshub

Seamlessly integrate MLFlow with Dagshub for collaborative tracking.

export MLFLOW_TRACKING_URI=https://dagshub.com/send2manoo/MLFlow-Basic-Operation.mlflow
export MLFLOW_TRACKING_USERNAME=send2manoo
export MLFLOW_TRACKING_PASSWORD=0ea83aeca6cad84965aa3308c523881447297583

Execute these commands to configure your environment.

☁️ MLFlow on AWS

Elevate your MLFlow experience on AWS:

🚀 Setup

  • AWS Console: Log in and navigate to glory.
  • IAM User: Craft with AdministratorAccess.
  • AWS CLI: Set sail with bash aws configure .
  • S3 Bucket: Create your data treasure chest.
  • EC2 Instance: Launch Ubuntu and fortify with Security Groups for port 5000.

🛠️ Installation & Configuration

sudo apt update
sudo apt install python3-pip
sudo pip3 install pipenv virtualenv

mkdir mlflow && cd mlflow
pipenv install mlflow awscli boto3
pipenv shell

Arm yourself with AWS credentials using `bash aws configure `.

🚀 Running MLflow Server

mlflow server -h 0.0.0.0 --default-artifact-root s3://mlflow-buc23

Unveil your server at your EC2's Public IPv4 DNS on port 5000.

🌐 Setting MLFLOW_TRACKING_URI

Craft your MLFlow tracking URI:

export MLFLOW_TRACKING_URI=http://ec2-3-80-202-174.compute-1.amazonaws.com:5000/

🌟 Feel free to contribute! 🌟

Happy tracking and coding! 🚀