You can deploy VisARTM locally on your computer. It supports any OS: Windows, Linux or MacOS. Please follow next steps.

  1. Make sure you have installed python 3. We recommend to use Anaconda.
  2. Make sure you have installed BigARTM.
  3. Install PostgreSQL and pgAdmin. You can use any database management system with django, but we recommend PostgreSQL.
  4. Open pgAdmin and create new database. Please remember username and password to this database. Default username in PostgreSQL is "postgres".
  5. Make sure you have installed git.
  6. Get VisARTM from repository.
    git clone
  7. Install all required dependencies, including django:
    cd visartm
    pip install -r requirements.txt
  8. Now link database created in step 4 with VisARTM. For that, open file visartm/ and find lines like these:
    	'default': {
    		'ENGINE': 'django.db.backends.postgresql_psycopg2',
    		'NAME': 'artmonlinedb',
    		'USER': 'postgres',
    		'PASSWORD': '******',
    		'HOST': '',
    		'PORT': '5432',
    Modify values as follows. NAME is name of database. USER is name of user of database. PASSWORD is his password. If you use PostgreSQL, you don't need to change ENGINE. If you are running database server locally, you don't need to modify HOST value.
  9. Now you need django to create tables. For that, go to folder visartm and run:
    python makemigrations
    python migrate
  10. Create superuser for the service. Django will ask you for username and passwrod. Please remember them, you will need them to use service.
    python createsuperuser
  11. Run the server:
    python runserver
  12. Open web browser (Google Chrome is recommended) and navigate to
Now you can use VisARTM. But additional actions are required to enable advanced options (topic spectrum building and some visualizations).
  1. Install gcc.
  2. Install SciPy and scikit-learn.
    pip install scipy
    pip install scikit-learn
    If you use Anaconda, you have those already installed.
    WARNING! SciPy installation fails under virtualenv.
  3. Install Lin-Kernighan-Helsgaun algorithm for Travelling Salesman Problem. You can find instructions, how to do that in /algo/lkh/readme.txt.
    WARNING! LKH code is distributed for research use. Don't use it, if you use VisARTM for any commercial purposes. Author of VisARTM doesn't distribute LKH algorithm, hence he isn't responsible for any kind of inappropriate usage of LKH algorithm.