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Long-running tasks need to be delegated to workers. Celery is used to manage these.


Creates segmentations/annotations at the end of GPU worker task.

GPU worker

GPU support in docker-compose is very experimental, not working currently - see docker-compose_gpu.yml - docker-compose override gives errors, that's why one .yml file is needed - NVIDIA driver still isn't visible then, waiting for stable support

Current workaround - expose rabbitMQ queue in docker-compose to host - run celery worker on host without virtualization

cd gpu_worker

# install environment for neuralnets celery worker
conda env update -f celery_all_environment.yaml
conda activate celery_neuralnets

# On Linux

# On Windows
set ROOT_DATA_FOLDER=X:/biosegment/data
start_worker.bat   # On Windows

If force stopping the auto-reloading watchdog for workers (x2 Ctrl-C), some workers may linger. This will show up as warning when a new worker with the same name is started.

View all host celery workers

ps aux|grep 'celery worker'

Kill them all

ps auxww | grep 'celery worker' | awk '{print $2}' | xargs kill -9