MLflow-Basic-Demo

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MLflow-Basic-Demo

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MLflow-Basic-Demo 최신버젼 다운로드

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# MLflow-Basic-Demo

## For Dagshub:

MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/MLflow-Basic-Demo.mlflow \
MLFLOW_TRACKING_USERNAME=entbappy \
MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0 \
python script.py

```bash

export MLFLOW_TRACKING_URI=https://dagshub.com/entbappy/MLflow-Basic-Demo.mlflow

export MLFLOW_TRACKING_USERNAME=entbappy

export MLFLOW_TRACKING_PASSWORD=6824692c47a369aa6f9eac5b10041d5c8edbcef0

```

# 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"
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 apt install pipenv

sudo apt 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-tracking-buc25 --allowed-hosts *

#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/
```

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