MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/MLflow-Basic-Demo.mlflow
MLFLOW_TRACKING_USERNAME=entbappy
MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0
python script.py
export MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/MLflow-Basic-Demo.mlflow
export MLFLOW_TRACKING_USERNAME=entbappy
export MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0
- Login to AWS console.
- Create IAM user with AdministratorAccess
- Export the credentials in your AWS CLI by running "aws configure"
- Create a s3 bucket
- Create EC2 machine (Ubuntu) & add Security groups 5000 port
Run the following command on EC2 machine
sudo apt update
sudo apt install python3-pip
sudo pip3 install pipenv
sudo pip3 install virtualenv
mkdir mlflow
cd mlflow
pipenv install mlflow
pipenv install awscli
pipenv install boto3
pipenv shell
## Then set aws credentials
aws configure
#Finally
mlflow server -h 0.0.0.0 --default-artifact-root s3://mlflow-test-23
#open Public IPv4 DNS to the port 5000
#set uri in your local terminal and in your code
export MLFLOW_TRACKING_URI=http://ec2-54-147-36-34.compute-1.amazonaws.com:5000/