The LOST Ecosystem¶
The LOST Container Landscape¶
LOST is a composition of different docker containers. The main ingredients for LOST are a MySQL database, a RabbitMQ message broker, the FLASK framework, a NGINX web server, the LOST framework itself and the LOST data folder where all LOST related data is stored.
Figure 1 shows a schematic illustration of the LOST container landscape. Starting on the left side of the illustration we see the LOST data folder that is used to store all data of LOST on the host machine. This folder is mounted inside the most containers of LOST. On the right side of Figure 1 you can see all containers that are started together with the help of Docker Compose. We see the containers called rabbitmqlost, db-lost, lost, lost-cv and phpmyadmin, while the numbers indicate the ports where the applications can be accessed.
The most important container to understand here is the container called lost. This container will serve the LOST web application with NGINX on port 80 and is used as default Worker to execute scripts. It is connected to the rabbitmqlost container to use Celery for script execution scheduling and to the db-lost container in order to access the MySQL database that contains the current application state. The container called lost-cv is connected analog to lost. The pypmyadmin container is used for easy database monitoring during development and serves a graphical user interface to the MySQL database on port 8081.
Pipeline Engine and Workers¶
The PipeEngine will bring your annotation process to live by executing PipelineElements in the specified order. Therefore it will start AnnotationTasks and assigns Scripts to Workers that will execute these Scripts.
A Worker is a specific docker container that is able to execute LOST Script elements inside an annotation pipeline. In each Worker a set of python libraries is installed inside an Anaconda environment.
A LOST application may have multiple Workers with different Environments installed, since some scripts can have dependencies on specific libraries. For example, as you can see in Figure 1 LOST is shipped with two workers by default. One is called lost and the other one lost-cv. The lost worker can execute scripts that just rely on the lost python api. The lost-cv worker has also installed libraries like Keras, Tensorflow and OpenCV that are used for computer vision and machine learning.
Celery as Scheduler¶
Since each worker may have a specific software environment installed the PipeEngine will take care that scripts are only executed by Workers that have the correct Environment. This is achieved by creating one message queue per Environment. Workers that have this Environment installed will listen to this message queue. Once the PipeEngine finds a Script for execution in a specific Environment it will send it to the related message queue. Now the Script will be assigned by the round robin approach to one of the Workers that listen to the message queue related to the Environment.