This repo contains all the material required to understand how to track your experiments using MLflow
MLFLOW_TRACKING_URI=https://dagshub.com/spcCodes/mlflow.mlflow
MLFLOW_TRACKING_USERNAME=spcCodes
MLFLOW_TRACKING_PASSWORD=3f7a9c79d5525e15df189302054536f641f31cfa
python script.py
export MLFLOW_TRACKING_URI=https://dagshub.com/spcCodes/mlflow.mlflow
export MLFLOW_TRACKING_USERNAME=spcCodes
export MLFLOW_TRACKING_PASSWORD=3f7a9c79d5525e15df189302054536f641f31cfa
# MLflow on AWS
## MLflow on AWS Setup:
1. Login to AWS console.
2. Create IAM user with AdministratorAccess
3. Export the credentials in your AWS CLI by running "aws configure"
Also install aws cli in your local machine
4. Create a s3 bucket
5. Create EC2 machine (Ubuntu) & add Security groups 5000 port
Run the following command on EC2 machine
```bash
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-test12
#open Public IPv4 DNS to the port 5000
#set uri in your local terminal and in your code
export MLFLOW_TRACKING_URI=http://ec2-18-206-186-164.compute-1.amazonaws.com:5000/