mlflow-heroku

by yennanliu

GitHub Readme.md

mlflow-heroku-dev

Demo how to deploy a Mlflow app to Heroku cloud. Main point : Show how to train/manage ML tasks as experiments with various parameter collections, save the model config, train results and visualize them via Mlflow framework. For more data infra/ETL development ideas, please visit :

Build Status PRs

Deploy to Heroku

Deploy

Quick start

Quick-Start-docker
$ cd ~ && git clone https://github.com/yennanliu/mlflow-heroku-dev.git
$ cd ~ && cd mlflow-heroku-dev 
$ docker build -t mlflow . && docker run -p 5000:5000 mlflow
# visit the MLflow UI via http://0.0.0.0:5000
Quick-Start-deploy-to-heroku
$ cd ~ && git clone https://github.com/yennanliu/mlflow-heroku-dev.git
$ cd ~ && cd mlflow-heroku-dev 
$ heroku create mlflow-heroku 
$ git add . && git commit -m 'update for heroku deploy' && git push origin 
# Set the stack of your app to container
$ heroku stack:set container
# login to heroku container
$ heroku login
$ heroku container:login
# push dockrized app to heroku
#$ git push heroku master
# push to heroku container
$ heroku container:push web -a mlflow-heroku

Ref