Python virtual environement manager

Orchestra is a collection of tools for managing the backend part of AMDA's machine learning pipeline. Orchestra uses Flask to expose a REST API used by AMDA's internal components to retrieve information about the modules that are installed, create new prediction or training tasks, and more.

Each machine learning model is implemented as a python module that is installed with all its requirements in a dedictated virtual environement.

Installation

Docker

Follow instructions at [https://docs.docker.com/engine/install] to install the docker engine.

Add your user to the docker user group :

sudo groupadd docker
sudo usermod -aG docker <username>
newgrp docker

Test the docker installation :

docker run hello-world

If you still get a permission denied error try the following :

sudo chmod 666 /var/run/docker.sock

Dependencies

Make sure you have git installed as well as python with the venv module installed.

Then create directory to hold orchestra's source code.

mkdir orchestra
cd orchestra

Create a virtual environement and activate it :

python3 -m venv venv
. venv/bin/activate

Clone and move into the git repository :

git clone https://github.com/cdppirap/orchestra.git
cd orchestra

Install the requirements :

python -m pip install -r requirements.txt

Start the REST server :

python -m orchestra.rest

Crontab

Here should be instructions on how to lauch orchestra. Use the docker-compose file to launch all the processes.

Testing

Run tests to check that everything works :

python -m orchestra.test

REST API endpoints

List of endpoints exposed by the REST API :

/modules : retrieve a list of modules and assiciated metadata
/modules/<int:module_id> : specific module metadata
/modules/<int:module_id>/run [args] : request executing specified model with arguments supplied by user
/tasks : retrieve list of tasks and associated metadata
/tasks/<int:task_id> : specific task metadata (status, errors, output, ...)
/tasks/<int:task_id>/output : download task output
/tasks/<int:task_id>/kill : kill task

Installing a new module

Modules are installed by passing the --register parameter to orchestra with a path pointing to either :

a folder containing a python module
a github repository containing a python module

In both cases the root of the target folder must contain a metadata.json file. This file contains all metadata describing the model as well as a list of files required to create a virtual environement (requirements, files, etc...).

From folder

The module is located in /path/to/module/Model. The /path/to/module looks like :

metadata.json
requirements.txt
Model
- init.py
- ...

The module is installed with :

python -m orchestra --register /path/to/module

From GitHub

The module is stored in a GitHub repository, install it with :

python -m orchestra --register https://github.com/module_repository.git

List of modules

The user can list the modules that are installed with :

python -m orchestra --list

Deleting a module

You can delete a module by passing its id with the --remove option :

python -m orchestra --remove <module_id>

Dolgalad/orchestra