CompreFace is delivered as a docker-compose file by default, so you can easily start it with one command. However, CompreFace could be scaled up to distribute computations on different servers and achieve high availability. This section describes the architecture of CompreFace, each of its components, and suggestions on how to scale the system.
Container name in the docker-compose file: compreface-fe
This container runs Nginx that serves CompreFace UI.
In the default config, it's also used as the main gateway - Nginx proxies user requests to admin and API servers.
Container name in the docker-compose file: compreface-admin
Admin server is a Spring Boot application, and it's responsible for all operations that are done on UI. Admin server connects to PostgreSQL database to store the data.
Container name in the docker-compose file: compreface-api
API servers handle all user API calls: face recognition, face detection, and face verification.
It provides API key validation, proxies images to Embedding servers, and classifies the face. For face classification, we use the ND4J library.
By default, the number of API servers in the config is 1, but for production environments to increase possible bandwidth and achieve high availability, there should be at least two such servers, and they should be on different machines. In addition, the data synchronization is implemented via PostgreSQL notifications, so if, for example, you add a new face to a collection, all other servers know about it and can recognize this new face.
Classification is not a very heavy operation as embedding calculation and doesn't require GPU in most cases. API server connects to PostgreSQL database to store the data.
There is a PYTHON_URL environment variable that tells this container where
to send requests to compreface-core containers.
Default value: http://compreface-core:3000.
There is a PYTHON_URL environment variable that tells this container where to send requests to compreface-core containers. Default value: http://compreface-core:3000.
Container name in the docker-compose file: compreface-core
The embedding server is responsible for running neural networks. It calculates embeddings from faces and makes all plugin recognitions like age and gender detection. These servers are stateless, don't have a connection to a database, and don't require any synchronization between them.
By default, the number of embedding servers in the config is 1, but for production environments to increase possible bandwidth and achieve high availability, there should be at least two such servers, and they should be on different machines.
Running neural networks is a very heavy operation. Therefore, the total performance of the system highly depends on these nodes. That is why we recommend using highly performant nodes to run Embedding Servers, ideally with GPU support. To learn more about how to run CompreFace with GPU, see custom-builds documentation.
Default docker-compose configuration includes postgreSQL database.
If you want CompreFace to connect to your database, you need to provide
such environment variables for compreface-admin and compreface-api
containers:
- POSTGRES_PASSWORD
- POSTGRES_URL
- POSTGRES_USER