Deploying Dagster to Docker#

If you are running on AWS ECS or another container-based orchestration system, you'll likely want to package the Dagster webserver using a Docker image.

A minimal skeleton Dockerfile that will run the webserver is shown below:

FROM python:3.10-slim

RUN mkdir -p /opt/dagster/dagster_home /opt/dagster/app

RUN pip install dagster-webserver dagster-postgres dagster-aws

# Copy your code and workspace to /opt/dagster/app
COPY repo.py workspace.yaml /opt/dagster/app/

ENV DAGSTER_HOME=/opt/dagster/dagster_home/

# Copy dagster instance YAML to $DAGSTER_HOME
COPY dagster.yaml /opt/dagster/dagster_home/

WORKDIR /opt/dagster/app

EXPOSE 3000

ENTRYPOINT ["dagster-webserver", "-h", "0.0.0.0", "-p", "3000"]

You'll also need to include a workspace file (workspace.yaml) file in the same directory as the Dockerfile to configure your code location server:

load_from:
  # References the file copied into your Dockerfile
  - python_file: repo.py

As well as a dagster.yaml file to configure your Dagster instance:

storage:
  postgres:
    postgres_db:
      username:
        env: DAGSTER_PG_USERNAME
      password:
        env: DAGSTER_PG_PASSWORD
      hostname:
        env: DAGSTER_PG_HOST
      db_name:
        env: DAGSTER_PG_DB
      port: 5432

compute_logs:
  module: dagster_aws.s3.compute_log_manager
  class: S3ComputeLogManager
  config:
    bucket: "mycorp-dagster-compute-logs"
    prefix: "dagster-test-"

local_artifact_storage:
  module: dagster.core.storage.root
  class: LocalArtifactStorage
  config:
    base_dir: "/opt/dagster/local/"

In cases where you're using environment variables to configure the instance, you should ensure these environment variables are exposed in the container running dagster-webserver.

The webserver exposes a health check endpoint at /server_info, which returns a JSON response like:

{
  "dagster_webserver_version": "0.12.0",
  "dagster_graphql_version": "0.12.0",
  "dagster_version": "0.12.0"
}

Multi-container Docker deployment#

More advanced Dagster deployments will require deploying more than one container. For example, if you are using the Dagster daemon to run schedules and sensors or manage a queue of runs, you'll likely want a separate container running the dagster-daemon service.

This service must have access to your dagster.yaml and workspace.yaml files, just like the webserver container. You can also configure your code location server (gRPC server) so that your code can be updated and deployed separately in its own container, without needing to redeploy the other Dagster services. To enable this setup, include a container exposing a gRPC server at a port and add that port in your workspace.yaml file.

For example, your user code container might have the following Dockerfile:

FROM python:3.10-slim

# Checkout and install dagster libraries needed to run the gRPC server by exposing
# your code location to dagster-webserver and dagster-daemon, and loading the
# DagsterInstance.

RUN pip install \
    dagster \
    dagster-postgres \
    dagster-docker

# Set $DAGSTER_HOME and copy dagster instance there

ENV DAGSTER_HOME=/opt/dagster/dagster_home

RUN mkdir -p $DAGSTER_HOME

COPY dagster.yaml $DAGSTER_HOME

# Add repository code

WORKDIR /opt/dagster/app

COPY repo.py /opt/dagster/app

# Run dagster gRPC server on port 4000

EXPOSE 4000

# Using CMD rather than ENTRYPOINT allows the command to be overridden in
# run launchers or executors to run other commands using this image
CMD ["dagster", "api", "grpc", "-h", "0.0.0.0", "-p", "4000", "-f", "repo.py"]

And your code location server might look like:

load_from:
  # Each entry here corresponds to a container that exposes a gRPC server.
  - grpc_server:
      host: docker_example_user_code
      port: 4000
      location_name: "example_user_code"

When you update your code, you can rebuild and restart your user code container without needing to redeploy other parts of the system. The Dagster UI will automatically notice that a new server has been redeployed and prompt you to refresh.

When you add or remove a user code container, you can also remove the corresponding entry from your workspace.yaml file. If this file is mounted on the webserver Dagster daemon containers as a volume, you can pick up the changes in the UI by reloading from the Deployment tab. The Dagster daemon container will automatically pick up the changes by periodically reloading the workspace.yaml file.

Launching runs in containers#

To launch each run in its own container, you can add the DockerRunLauncher to your dagster.yaml file:

run_launcher:
  module: dagster_docker
  class: DockerRunLauncher
  config:
    env_vars:
      - DAGSTER_POSTGRES_USER
      - DAGSTER_POSTGRES_PASSWORD
      - DAGSTER_POSTGRES_DB

This launcher will start each run in a new container, using whatever image set in the DAGSTER_CURRENT_IMAGE environment variable in your user code container. This will usually be the same image as the user code container itself.

Any container that launches runs - usually the dagster-daemon container if you're maintaining a run queue or launching runs from schedules or sensors - must have permissions to create Docker containers in order to use this run launcher. Mounting /var/run/docker.sock as a volume is one way to give it these permissions.

Mounting volumes#

You can mount your code in your user code container so that you don't have to rebuild your container whenever your code changes. Even if you're using volume mounts, you still need to restart the container whenever your code changes.

If you're mounting your code as a volume in your user code container and using DockerRunLauncher to launch each run in a new container, you must specify your volume mounts in the DockerRunLauncher config as well. For example:

run_launcher:
  module: dagster_docker
  class: DockerRunLauncher
  config:
    env_vars:
      - DAGSTER_POSTGRES_USER
      - DAGSTER_POSTGRES_PASSWORD
      - DAGSTER_POSTGRES_DB
    container_kwargs:
      volumes:
        - /absolute/path/to/local/repo.py:/opt/dagster/app/

Docker compose example#

This example demonstrates a Dagster deployment using docker-compose that includes:

• A webserver container for loading and launching jobs
• A dagster-daemon container for managing a run queue and submitting runs from schedules and sensors
• A Postgres container for persistent storage
• A container with user code. The Dagster instance uses DockerRunLauncher to launch each run in its own container.

To start the deployment, run:

docker-compose up