Test mlflow dagshub
dagshub dagshub
MLFLOW_TRACKING_URI=https://dagshub.com/larakim/Mlflowtest.mlflow MLFLOW_TRACKING_USERNAME=larakim MLFLOW_TRACKING_PASSWORD=6f0caff147881a6dd8d7169da2dc21273f023c6c python script.py
Run this to export as env variables:
set MLFLOW_TRACKING_URI=https://dagshub.com/larakim/Mlflowtest.mlflow
set MLFLOW_TRACKING_USERNAME=larakim
set MLFLOW_TRACKING_PASSWORD=6f0caff147881a6dd8d7169da2dc21273f023c6c - Login to AWS console
- Create IAM user with AdminAccess
- 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
set MLFLOW_TRACKING_URI=http://ec2-54-147-36-34.compute-1.amazonaws.com:5000/