Translation service

HTTP service that uses bergamot-translator and compressed neural machine translation models for fast inference on CPU.

Running locally

1. Install Git LFS https://git-lfs.github.com/
2. git clone this repo
3. make setup-models
4. make build-docker
5. make run
6. make call

Calling the service

$ curl --header "Content-Type: application/json" \
      --request POST \
      --data '{"from":"es", "to":"en", "text": "Hola Mundo"}' \
      http://0.0.0.0:8080/v1/translate
> {"result": "Hello World"}

Service configuration

Directory that contains models ('esen', 'ende' etc.) should be mounted to /models in Docker container.

Environment variables to set in container:

PORT - service port (default is 8000)

LOGGING_LEVEL - ERROR, WARNING, INFO or DEBUG (default is INFO)

WORKERS - number of bergamot-translator workers (default is 1). 0 - automatically set as number of available CPUs. It is recommended to minimize workers and scale horizontaly with k8s means.

Testing

make python-env - install pip packages

make test - to run integration API tests

make load-test - to run a stress test (requires more models to download that unit tests)