Welcome to MLFlow-Basic-Operation! Dive into the world of MLFlow, your go-to platform for tracking and managing machine learning experiments.
Ready to embark on your MLFlow journey? Launch the MLflow UI with ease:
mlflow ui
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.
Elevate your MLFlow experience on AWS:
- 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.
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 `.
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.
Craft your MLFlow tracking URI:
export MLFLOW_TRACKING_URI=http://ec2-3-80-202-174.compute-1.amazonaws.com:5000/
π Feel free to contribute! π
Happy tracking and coding! π