mlflow_demo/readme.md

Mlflow experiments

MLFLOW_TRACKING_URI=https://dagshub.com/priyatoshanand/mlflow_demo.mlflow MLFLOW_TRACKING_USERNAME=priyatoshanand MLFLOW_TRACKING_PASSWORD=6e371efbbbe275457498b60ef3c17d17176dcf3b python script.py

export MLFLOW_TRACKING_URI=https://dagshub.com/priyatoshanand/mlflow_demo.mlflow

export MLFLOW_TRACKING_USERNAME=priyatoshanand

export MLFLOW_TRACKING_PASSWORD=6e371efbbbe275457498b60ef3c17d17176dcf3b

MLflow on AWS

MLflow on AWS Setup:

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-06

#open Public IPv4 DNS to the port 5000

#set uri in your local terminal and in your code export MLFLOW_TRACKING_URI=http://ec2-52-207-218-138.compute-1.amazonaws.com:5000/