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ADL LRS

Installation tested on Ubuntu 12.10 machine with Python 2.7.3. Should be good with Ubuntu 10.04 LTS - 13.04 releases. Updated to be compliant with the 1.0.1 xAPI spec.

This version is stable, but only intended to support a small amount of users as a proof of concept. While it uses programming best practices, it is not designed to take the place of an enterprise system.

Installation

Software Installation

sudo apt-get install git fabric postgresql-9.1 python-setuptools postgresql-server-dev-9.1 python-dev libxml2-dev libxslt-dev
sudo easy_install pip
sudo pip install virtualenv

Setup Postgres

sudo passwd postgres (set password for postgres system user)
sudo -u postgres createuser -P <db_owner> (create postgres user that will be owner of the db - make superuser)
su postgres
psql template1

Create database inside of postgres shell

CREATE DATABASE lrs OWNER <db_owner>;
\q (exits shell)
exit (logout as system postgres user)

Create ADL LRS system user

sudo useradd -c "ADL Learning Record Store System" -m -s "/bin/bash" adllrs
sudo passwd adllrs (set password)
su adllrs
cd ~

Create desired directory to keep LRS

mkdir <dir_name>
cd <dir_name>

Clone the LRS repository

git clone https://github.com/adlnet/ADL_LRS.git
cd ADL_LRS

Note: Under ADL_LRS/adl_lrs/settings.py, make sure the database USER and PASSWORD are the same as the db_owner created earlier. Also, be sure to replace the current SECRET_KEY flag with a secret string of your own, and be sure not to share it.

Set Site Scheme

Inside of ADL_LRS/adl_lrs/settings.py there is a SITE_SCHEME value you should set (defaults to http but if you're using https set it here)

Setup the environment

fab setup_env
source ../env/bin/activate

Setup the LRS - while still in the activated virtual environment (creates media directories and cache tables, then syncs database)

fab setup_lrs (when prompted make adllrs a Django superuser)

Starting

While still in the ADL_LRS directory, run

supervisord

To verify it's running

supervisorctl

Set your site domain

Visit the admin section of your website (/admin). Click Sites and you'll see the only entry is 'example.com' (The key for this in the DB is 1 and it maps back to the SITE_ID value in settings). Change the domain and name to the domain you're going to use. If running locally it could be localhost:8000, or if production could be lrs.adlnet.gov (DON'T include the scheme here, that should be set in settings.py already). Sync your database again to apply the change

python manage.py syncdb

Whenever you want to exit the virtual environment, just type deactivate

You should see a task named web running. This will host the application using gunicorn with 2 worker processes. If you open a browser and visit http://localhost:8000/xapi you will hit the LRS. Gunicorn does not serve static files so no CSS will be present. This is fine if you're doing testing/development but if you want to host a production-ready LRS, Nginx needs to be setup to serve static files. For more production-like environments, we also recommend using uWSGI instead of Gunicorn. Please read these instructions for including Nginx and using uWSGI intead of Gunicorn. For a more detailed description of the tools being used in general, visit here. Additionally if you're just doing dev, instead of using supervisor you can just run python manage.py runserver and use Django's built-in web server.

Test LRS

fab test_lrs

Helpful Information

License

Copyright 2012 Advanced Distributed Learning

Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.

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ADL's Open Source Learning Record Store (LRS) is used to store learning data collected with the Experience API.

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