Start all the services

Test web server

In a shell/command window, move into the openrem folder:

  • Ubuntu linux: /usr/local/lib/python2.7/dist-packages/openrem/
  • Other linux: /usr/lib/python2.7/site-packages/openrem/
  • Linux virtualenv: vitualenvfolder/lib/python2.7/site-packages/openrem/ (remember to activate the virtualenv)
  • Windows: C:\Python27\Lib\site-packages\openrem\
  • Windows virtualenv: virtualenvfolder\Lib\site-packages\openrem\ (remember to activate the virtualenv)

Web access on OpenREM server only

Run the built in web server:

python manage.py runserver --insecure

In a web browser on the same computer, go to http://localhost:8000/ - you should now see the message about creating users. For full functionality start the Celery task queue before moving on to Configure the settings.

Web access on other computers

The built-in webserver only provides a service on the computer OpenREM is installed on by default (it’s only there really for testing). To view the OpenREM interface on another computer, you need to modify the runserver command:

python manage.py runserver --insecure 0.0.0.0:8000

This will enable the web service to be available from other computers on the network. If your server has several network cards and you want to restrict it to one, then you can use a real address rather than 0.0.0.0. Likewise you can specify the port (here it is 8000).

In a web browser on a different computer on the same network, go to http://192.168.1.10:8000/ (changing the IP address to the one you are running the server on) and you should see the OpenREM interface and the message about creating users. For full functionality start the Celery task queue before moving on to Configure the settings.

Note

Why are we using the --insecure option? With DEBUG mode set to True the test web server would serve up the static files. In this release, DEBUG mode is set to False, which prevents the test web server serving those files. The --insecure option allows them to be served again.

Celery task queue

Celery will have been automatically installed with OpenREM, and along with RabbitMQ allows for asynchronous task processing for imports, exports and DICOM networking tasks.

Note

Celery needs to be able to write to the place where the Celery logs and pid file are to be stored, so make sure:

  • the folder exists (the suggestion below is to create a folder in the MEDIA_ROOT location)
  • the user that starts Celery can write to that folder

You can put the folder wherever you like, for example you might like to create a /var/log/openrem/ folder on a linux system.

If you are using the built-in Test web server then Celery and the webserver will be running as your user. If you are running a production webserver, such as Apache or nginx on linux, then the user that runs those daemons will need to be able to write to the MEDIA_ROOT and the Celery log files folder. In this case, you need to change the ownership of the folders and change to the right user before running Celery. On Ubuntu:

mkdir /path/to/media/celery  # change as appropriate
sudo chown www-data /path/to/media  # change as appropriate
sudo su -p www-data

Now start celery…

Move into the openrem folder:

  • Ubuntu linux: /usr/local/lib/python2.7/dist-packages/openrem/
  • Other linux: /usr/lib/python2.7/site-packages/openrem/
  • Linux virtualenv: vitualenvfolder/lib/python2.7/site-packages/openrem/ (remember to activate the virtualenv)
  • Windows: C:\Python27\Lib\site-packages\openrem\
  • Windows virtualenv: virtualenvfolder\Lib\site-packages\openrem\ (remember to activate the virtualenv)

Linux - \ is the line continuation character:

celery multi start default -Ofair -A openremproject -c 4 -Q default \
--pidfile=/path/to/media/celery/%N.pid --logfile=/path/to/media/celery/%N.log

Windows - celery multi doesn’t work on Windows, and ^ is the continuation character:

celery worker -n default -Ofair -A openremproject -c 4 -Q default ^
--pidfile=C:\path\to\media\celery\default.pid --logfile=C:\path\to\media\celery\default.log

Celery concurrency

Set the number of workers (concurrency, -c) according to how many processor cores you have available. The more you have, the more processes (imports, exports, query-retrieve operations etc) can take place simultaneously. However, each extra worker uses extra memory and if you have too many they will be competing for CPU resources too.

Problems with Celery 4 on Windows

Full support for Celery on Windows was dropped with version 4 due to lack of Windows based developers. Therefore for Windows the instructions fix Celery at version 3.1.25 to retain full functionality.

To stop the celery queues in Linux:

celery multi stop default --pidfile=/path/to/media/celery/%N.pid

For Windows, just press Ctrl+c

You will need to do this twice if there are running tasks you wish to kill.

For production use, see Daemonising Celery below.

Celery task management: Flower

Flower will have been automatically installed with OpenREM and enables monitoring and management of Celery tasks.

You should start Flower with the same user that you started Celery with, and put the log file in the same place too.

Move into the openrem folder:

  • Ubuntu linux: /usr/local/lib/python2.7/dist-packages/openrem/
  • Other linux: /usr/lib/python2.7/site-packages/openrem/
  • Linux virtualenv: vitualenvfolder/lib/python2.7/site-packages/openrem/ (remember to activate the virtualenv)
  • Windows: C:\Python27\Lib\site-packages\openrem\
  • Windows virtualenv: virtualenvfolder\Lib\site-packages\openrem\ (remember to activate the virtualenv)

If you need to change the default port from 5555 then you need to make the same change in openremproject\local_settings.py to add/modify the line FLOWER_PORT = 5555

If you wish to be able to use the Flower management interface independently of OpenREM, then omit the --address part of the command. Flower will then be available from any PC on the network at http://yourdoseservernameorIP:5555/

Linux - \ is the line continuation character:

celery flower -A openremproject --port=5555 --address=127.0.0.1  --loglevel=INFO \
---log-file-prefix=/path/to/media/celery/flower.log

Windows - ^ is the line continuation character:

celery flower -A openremproject --port=5555 --address=127.0.0.1  --loglevel=INFO ^
---log-file-prefix=C:\path\to\media\celery\flower.log

For production use, see Daemonising Celery below.

Celery periodic tasks: beat

Note

Celery beat is only required if you are using the Native DICOM store node with direct import. Please read the warnings there before deciding if you need to run Celery beat. At the current time, using a third party DICOM store service is recommended for most users. See the Third-party DICOM Stores documentation for more details

Celery beat is a scheduler. If it is running, then every 60 seconds a task is run to check if any of the DICOM Store SCP nodes are set to keep_alive, and if they are, it tries to verify they are running with a DICOM echo. If this is not successful, then the Store SCP is started.

To run celery beat, open a new shell and move into the openrem folder:

  • Ubuntu linux: /usr/local/lib/python2.7/dist-packages/openrem/
  • Other linux: /usr/lib/python2.7/site-packages/openrem/
  • Linux virtualenv: vitualenvfolder/lib/python2.7/site-packages/openrem/ (remember to activate the virtualenv)
  • Windows: C:\Python27\Lib\site-packages\openrem\
  • Windows virtualenv: virtualenvfolder\Lib\site-packages\openrem\ (remember to activate the virtualenv)

Linux:

celery -A openremproject beat -s /path/to/media/celery/celerybeat-schedule \
-f /path/to/media/celery/celerybeat.log \
--pidfile=/path/to/media/celery/celerybeat.pid

Windows:

celery -A openremproject beat -s C:\path\to\media\celery\celerybeat-schedule ^
-f C:\path\to\media\celery\celerybeat.log ^
--pidfile=C:\path\to\media\celery\celerybeat.pid

For production use, see Daemonising Celery below

As with starting the Celery workers, the folder that the pid, log and for beat, schedule files are to be written must already exist and the user starting Celery beat must be able write to that folder.

To stop Celery beat, just press Ctrl+c

Configure the settings

  • Follow the link presented on the front page to get to the user and group administration.
Initial home page with no users in groups
Configuration menu

  • After the first users are configured, this link will no longer be presented and instead you can go to Config -> Users.

  • You will need the superuser username and password you created just after creating the database. The groups are

    • viewgroup can browse the data only
    • importsizegroup can use the csv import facility to add patient height and weight information
    • importqrgroup can use the DICOM query-retrieve facility to pull in studies, as long as they are pre-configured
    • exportgroup can view and export data to a spreadsheet
    • pidgroup can search using patient names and IDs depending on settings, and export with patient names and IDs if they are also a member of the exportgroup
    • admingroup can delete studies, configure DICOM Store/QR settings, configure DICOM keep or delete settings, configure patient ID settings, and abort and delete patient size import jobs. Members of the admingroup no longer inherit the other groups permissions.
Selecting groups in Django user admin
  • In addition to adding users to these groups, you may like to grant a second user superuser and staff status so that there are at least two people who can manage the users
  • Return to the OpenREM interface (click on View site at the top right)
Link from Django user admin back to OpenREM
  • Follow the link to see more information about how you want OpenREM to identify non-patient exposures, such as QA. See Not-patient indicator settings.
  • Go to Config -> DICOM object delete settings and configure appropriately (see Delete objects configuration)
  • Go to Config -> Patient ID settings and configure appropriately (see Patient identifiable data)
  • If you want to use OpenREM as a DICOM store, or to use OpenREM to query remote systems, go to Config -> Dicom network configuration. For more information go to Importing data to OpenREM.
  • With data in the system, you will want to go to Config -> View and edit display names and customise the display names. An established system will have several entries for each device, from each time the software version, station name or other elements changes. See Display names and user-defined modalities for more information

Start using it - add some data!

See Importing data to OpenREM

Further instructions

Daemonising Celery

In a production environment, Celery will need to start automatically and not depend on a particular user being logged in. Therefore, much like the webserver, it will need to be daemonised.