This example demonstrates how Dagster can be used with Celery for distributed processing. It includes:

• Distributed pipeline processing using Celery with RabbitMQ broker and Postgres backend.
• Worker selection through multiple Celery queues.
• Minio as Dagster intermediate store.
• Flower for Celery monitoring.
• All services dockerized (docker compose).

Note that this example currently relies on shared volume mounts to make the pipeline definition available to all parties (workers and dagit).

• RabbitMQ is used as message queue to pass Celery tasks and status messages.
• Postgres is used as Celery result store and Dagster DB.
• Minio is used as intermediate store for Dagster.

docker-compose up rabbit postgres minio flower

The workers rely on a custom image, this needs to be built first:

docker-compose build

Runs two workers, a generic one and a (fake-) GPU-accelerated one.

docker-compose up worker-generic 
docker-compose up worker-gpu 

Run a Dagit instance where tasks can be triggered manually through the Dagit UI.

docker-compose up dagit

• Point your browser at http:https://localhost:3000
• Go to Playground
• Use the following config as example:

solids: 
  basic_solid:
    inputs:
      in1: one, two, test!
execution:
  celery:
    config:
      broker: pyamqp:https://rabbit:rabbit@rabbit:5672//
      backend: db+postgresql:https://postgres:postgres@postgres:5432/dagster_celery
intermediate_storage:
  s3:
    config:
      s3_bucket: dagster-intermediates
resources:
  s3:
    config:
      endpoint_url: http:https://minio:9000/
      access_key: minio
      secret_key: miniosecret