@Scout: I am setting up a cloud based template to utilize on multiple projects based on the deploy_dagster example project from the dagster repo and docs. How can I modify the deploy_dagster repo to work with hot reloading and dagster dev? : https://docs.dagster.io/deployment/guides/docker#example

To set up a cloud-based template that utilizes hot reloading and is suitable for development with Dagster, you can start by looking at the

deploy_dagster

example project. This project provides a Docker-based deployment example that you can use as a starting point. For hot reloading, you'll want to ensure that your development environment is set up in such a way that changes to your code are automatically picked up without needing to rebuild your Docker images or restart your services manually. This typically involves mounting your code as a volume in your Docker container so that changes on your host machine are reflected inside the container. Here are some steps you can follow to modify the

deploy_dagster

example to work with hot reloading: 1. Mount Code as a Volume: Modify the

docker-compose.yml

file to mount your Dagster project code as a volume inside the container. This allows the running services to see changes made to the code on the host filesystem. 2. Use Development Servers: Ensure that you are using development servers that support hot reloading. For example, Dagit, the web interface for Dagster, automatically reloads when it detects changes to your code. 3. Configure

dagster.yaml

: The

dagster.yaml

file is used to configure your Dagster instance. For development purposes, you may want to set up different run storage, event log storage, or compute log storage that makes it easier to iterate on your code. 4. Use the

watch

Command: Dagster provides a

dagster-daemon

service that can be run in watch mode during development. This service will automatically pick up changes to your repository and workspace definitions. 5. Set Up Development Tools: If you're using Dagster Cloud, you might want to use the

dagster dev

command, which is designed to streamline local development. It can be used to start up all the necessary components for local development, including Dagit and the Dagster daemon. 6. Test Your Setup: Before using this setup in a production environment, thoroughly test it to ensure that hot reloading works as expected and that there are no issues with the development workflow. Remember that the exact steps may vary depending on the specifics of your project and the cloud environment you are using. The documentation provides guidance on configuring your Dagster instance and setting up your development environment, but it may not cover all scenarios for hot reloading or development-specific configurations. For more detailed instructions and best practices, you should refer to the Dagster documentation, particularly the sections on [Dagster instance configuration](https://docs.dagster.io/deployment/dagster-instance) and [development workflows](https://docs.dagster.io/development/workflows). I

Can I use gRPC server with module definitions? If so how? @Scout

Yes, you can use a gRPC server with module definitions in Dagster. The gRPC server allows you to load your Dagster repository or job definitions from a Python module and serve them over gRPC. This is particularly useful when you have a distributed setup where Dagit and user code execution are happening on different machines. To use a gRPC server with module definitions, you can follow these steps: 1. Organize Your Code: Ensure that your repository or job definitions are organized in a Python module. A module in Python is simply a file containing Python definitions and statements. The file name is the module name with the suffix

.py

added. 2. Start the gRPC Server: Use the

dagster api grpc

command to start a gRPC server that loads your repository or job from the specified module. You can use the

--module-name

or

-m

flag to specify the module where your definitions reside. Here's an example command to start a gRPC server with a module named `my_module`:

shell
dagster api grpc -m my_module

This command assumes that

my_module

is a Python module accessible in your Python environment and contains your repository or job definitions as top-level symbols. 3. Configure Your Workspace: In your

workspace.yaml

file, you will need to configure the gRPC server as a code location. Here's an example of what that might look like:

yaml
load_from:
  - grpc_server:
      host: localhost
      port: 4000
      location_name: "my_repository_location"

This configuration tells Dagit where to find the gRPC server hosting your repository or job definitions. 4. Ensure Network Accessibility: Make sure that the gRPC server is accessible from wherever Dagit is running. If they are running on different machines, you will need to ensure that the network is configured to allow traffic between them on the specified port. 5. Use Environment Variables (Optional): If you prefer, you can also use environment variables to provide defaults for the gRPC server options. For example, you can set

DAGSTER_GRPC_PORT

to specify the default port for the gRPC server. By following these steps, you can set up a gRPC server that serves your Dagster repository or job definitions from a Python module. This setup is useful for development and production environments where you want to separate the execution of pipelines from the Dagit interface. For more detailed instructions and best practices, refer to the Dagster documentation, particularly the sections on [gRPC servers](https://docs.dagster.io/deployment/grpc) and [workspace configuration](https://docs.dagster.io/deployment/workspaces). If you encounter any issues or have specific requirements, you may also find it helpful to look at discussions and issues in the Dagster GitHub repository for

@Scout Currently I have an entrypoint script like this that is starting my Dockerfile_user_code container :

## start_user_code.sh
#!/bin/bash
cd /code
# check if venv already exists
if [ -d "/environments/pipelines" ]; then
  echo "pipelines venv already exists"
else
  echo "pipelines venv does not exist, creating"
  python3 -m venv /environments/pipelines
fi
source /environments/pipelines/bin/activate
pip install -e /code/pipelines
dagster code-server start -h 0.0.0.0 -p 4000 -m pipelines.definitions

It is able to load fine, but when I get to a job and launch it in the ui (indicating it is definitely loaded somewhere) I get this error : Could not load job definition. dagster._core.errors.DagsterImportError: Encountered ImportError:

No module named 'pipelines'

while importing module pipelines.definitions. Local modules were resolved using the working directory

/code

. If another working directory should be used, please explicitly specify the appropriate path using the

-d

or

--working-directory

for CLI based targets or the

working_directory

configuration option for workspace targets.

The error message you're encountering suggests that the Python environment within your Docker container cannot find the

pipelines

module when attempting to load the job definition. This could be due to a few reasons, such as the module not being installed correctly, the Python environment not being activated, or the working directory not being set correctly. Here are some steps to troubleshoot and resolve the issue: 1. Check Module Installation: Ensure that the

pipelines

module is installed correctly in the virtual environment. Since you're using the

-e

flag with

pip install

, it should install the module in editable mode, which means changes to the code will immediately affect the installed package without needing a reinstall. Verify that the installation process completes without errors. 2. Activate Virtual Environment: It looks like you're correctly activating the virtual environment with

source /environments/pipelines/bin/activate

. Make sure that this step is successful and that the subsequent

pip install

command is running within the activated environment. 3. Working Directory: The error message suggests that the working directory might be incorrect. You're already using

cd /code

to set the working directory to

/code

. Ensure that the

pipelines

module is indeed located in the

/code/pipelines

directory and that it is structured as a Python package (i.e., it contains an

__init__.py

file). 4. Use the Working Directory Flag: If the

pipelines

module is in a different directory or if there's any ambiguity about the working directory, you can explicitly set it using the

-d

or

--working-directory

flag when starting the code server. For example:

shell
dagster code-server start -h 0.0.0.0 -p 4000 -m pipelines.definitions -d /path/to/module

Replace

/path/to/module

with the actual path to the directory containing your

pipelines

module. 5. Check Python Path: Ensure that the

PYTHONPATH

environment variable includes the directory where your

pipelines

module is located. If it's not included, you can set it in your

start_user_code.sh

script before starting the code server:

shell
export PYTHONPATH="/code:$PYTHONPATH"

6. Check Container Logs: Look at the logs of your Docker container to see if there are any other error messages that might give you clues about what's going wrong. 7. Workspace Configuration: Double-check your

workspace.yaml

file to ensure that it's correctly pointing to the gRPC server and that the

location_name

matches the name you expect. If you've gone through these steps and the issue persists, you may want to verify the entire setup step by step, ensuring that each part of the system is configured correctly. If necessary, consult the Dagster documentat

@Scout: In this dockerfile which of the services is actually throwing that error? : version: "3.7" services: # This service runs the postgres DB used by dagster for run storage, schedule storage, # and event log storage. docker_example_postgresql: image: postgres:latest container_name: docker_example_postgresql ports: - "5432:5432" environment: POSTGRES_USER: "postgres_user" POSTGRES_PASSWORD: "postgres_password" POSTGRES_DB: "postgres_db" networks: - docker_example_network pgadmin: image: dpage/pgadmin4 container_name: pgadmin ports: - "5050:80" environment: PGADMIN_DEFAULT_EMAIL: your@email.com PGADMIN_DEFAULT_PASSWORD: your_password networks: - docker_example_network # This service runs the gRPC server that loads your user code, in both dagster-webserver # and dagster-daemon. By setting DAGSTER_CURRENT_IMAGE to its own image, we tell the # run launcher to use this same image when launching runs in a new container as well. # Multiple containers like this can be deployed separately - each just needs to run on # its own port, and have its own entry in the workspace.yaml file that's loaded by the # webserver. docker_example_user_code: build: context: . dockerfile: ./docker/Dockerfile_user_code container_name: docker_example_user_code image: docker_example_user_code_image restart: always environment: DAGSTER_POSTGRES_USER: "postgres_user" DAGSTER_POSTGRES_PASSWORD: "postgres_password" DAGSTER_POSTGRES_DB: "postgres_db" DAGSTER_CURRENT_IMAGE: "docker_example_user_code_image" networks: - docker_example_network volumes: - ./coderw - ./environments/environmentsrwx # This service runs dagster-webserver, which loads your user code from the user code container. # Since our instance uses the QueuedRunCoordinator, any runs submitted from the webserver will be put on # a queue and later dequeued and launched by dagster-daemon. docker_example_webserver: build: context: . dockerfile: ./docker/Dockerfile_dagster entrypoint: - dagster-webserver - -h - "0.0.0.0" - -p - "3000" - -w - workspace.yaml container_name: docker_example_webserver expose: - "3000" ports: - "3000:3000" environment: DAGSTER_POSTGRES_USER: "postgres_user" DAGSTER_POSTGRES_PASSWORD: "postgres_password" DAGSTER_POSTGRES_DB: "postgres_db" volumes: # Make docker client accessible so we can terminate containers from the webserver - /var/run/docker.sock:/var/run/docker.sock - /tmp/io_manager_storage:/tmp/io_manager_storage - ./coderw networks: - docker_example_network depends_on: - docker_example_postgresql - docker_example_user_code # This service runs the dagster-daemon process, which is responsible for taking runs # off of the queue and launching them, as well as creating runs from schedules or sensors. docker_example_daemon: build: context: . dockerfile: ./docker/Dockerfile_dagster entrypoint: - dagster-daemon - run container_name: docker_example_daemon restart: on-failure environment: DAGSTER_POSTGRES_USER: "postgres_user" DAGSTER_POSTGRES_PASSWORD: "postgres_password" DAGSTER_POSTGRES_DB: "postgres_db" volumes: # Make docker client accessible so we can launch containers using host docker - /var/run/docker.sock:/var/run/docker.sock - /tmp/io_manager_storage:/tmp/io_manager_storage networks: - docker_example_network depends_on: - docker_example_postgresql - docker_example_user_code networks: docker_example_network: driver: bridge name: docker_example_network

The error message `Could not load job definition. dagster._core.errors.DagsterImportError: Encountered ImportError:

No module named 'pipelines'

while importing module pipelines.definitions.` indicates an issue with loading the user code, specifically the

pipelines

module. In the provided

docker-compose.yml

file, the service responsible for loading and running user code is

docker_example_user_code

. This service builds an image from

Dockerfile_user_code

and is likely where the

dagster code-server

command is executed to start the gRPC server that serves the user code to the Dagster instance. The

docker_example_webserver

service runs the Dagit web interface (

dagster-webserver

), which allows you to view and interact with your Dagster instance, including launching jobs. However, the actual loading of the user code definitions happens in the

docker_example_user_code

service. The

docker_example_daemon

service runs the

dagster-daemon

process, which is responsible for scheduling and executing runs, but it also relies on the user code being accessible and correctly loaded by the gRPC server. To resolve the issue, you should check the

docker_example_user_code

service's build context and

Dockerfile_user_code

to ensure that the

pipelines

module is correctly installed and accessible. Make sure that the

pipelines

module is structured as a Python package and that the working directory is set correctly when starting the gRPC server. If necessary, you may need to adjust the volume mounts or the

PYTHONPATH

environment variable to ensure that the

pipelines

module can be found by Python.