rorolite is an open-source command-line tool to deploy Machine Learning applications to your own server. It provides simple interface to provision the server to install all the required dependencies and deploy the ML application as an API.

This is a lite version of the rorodata platform.


Install rorolite using pip

$ pip install rorolite

System Requirements

The target server should be running Ubuntu 16.04.

How to use

Write a rorolite.yml specifying the host ipaddress and the services.

runtime: python3

# IP address/hostname of the target server

# username on the target server
user: alice

    # run the predict function in credit_risk_service module as an API on port 8000
    - name: api
      function: credit_risk_service.predict
      port: 8000

    # run gunicorn process port 8080
    - name: webapp
      command: gunicorn webapp:app -b
      port: 8080

Either a function or a command can be specified as a service. When a function is specified as a service, rorolite used the firefly to deploy it as a service.

The server needs to provisioned once to install all the necessary system software and base dependencies specified by the runtime mentioned in the rorolite.yml file. All the application dependencies are installed on every deploy.

The currently available runtimes are:

  • python3
  • python3-keras

To provision the server, run:

$ rorolite provision

To deploy your project, run:

$ rorolite deploy
Deploying project version 7...
Services are live at:
  api --
  webapp --

The deploy command pushes your code to the server, sets up a virtual env, installs all the dependencies from your requirements.txt file and starts the specified services.

Inspect the running services using the ps command.

$ rorolite ps
api                              RUNNING   pid 23796, uptime 0:02:07

The logs command allows inspecting logs of any service.

$ rorolite logs api
2017-10-25 04:13:12 firefly [INFO] Starting Firefly...
2017-10-25 04:15:12 predict function called

The run command allows running any command on the remote server.

$ rorolite run python
starting the training...
reading the input files...
building the model...
saving the model...

Or you can even start a jupyter notebook server.

$ rorolite run:notebook
Copy/paste this URL into your browser when you connect for the first time,
to login with a token:

Copying files to/from remote server can be done using put/get commands. A directory /volumes/data is created during provisioning for storing data files, models etc.

$ rorolite put data/loans.csv /volumes/data/
[] put: data/loans.csv -> /volumes/data/loans.csv

$ rorolite get /volumes/data/model.pkl models/model.pkl
[] download: models/model.pkl <- /volumes/data/model.pkl


rorolite is licensed under Apache 2 license. Please see LICENSE file for more details.