How do I deploy locally using multiple containerized code locations (each code location has its own environment, defined by a Dockerfile)?

To deploy locally using multiple containerized code locations, where each code location has its own environment defined by a Dockerfile, you can follow these general steps based on the documentation: 1. Define Your Code Locations: Each code location should be defined with its own Dockerfile that specifies the environment, including the base image, dependencies, and any other necessary setup. For example, you might have one Dockerfile for a data engineering team using Spark and another for an ML team using TensorFlow. 2. Build Your Docker Images: Build Docker images for each of your code locations. You can do this by running

docker build

with the appropriate context and tag for each Dockerfile. 3. Configure Your Workspace: In your

workspace.yaml

file, you can specify multiple code locations by using the

load_from

key. For each code location, you would specify the Docker image that should be used. Here's an example snippet from the documentation:

yaml     load_from:       - python_file:           relative_path: path/to/dataengineering_spark_team.py           location_name: dataengineering_spark_team_py_38_virtual_env           executable_path: venvs/path/to/dataengineering_spark_team/bin/python       - python_file:           relative_path: path/to/team_code_location.py           location_name: ml_team_py_36_virtual_env           executable_path: venvs/path/to/ml_tensorflow/bin/python

In a containerized environment, you would replace the

executable_path

with the Docker image you built for each code location. 4. Run Your gRPC Servers: Each code location will need to run its own gRPC server. You can start these servers in their respective Docker containers. Ensure that the

DAGSTER_CURRENT_IMAGE

environment variable is set to the name of the Docker image before starting the server, as this will inform the webserver about the Docker image to use for runs launched from that code location. 5. Launch Runs: When you launch runs from these code locations, the system will use the Docker images specified in the

workspace.yaml

file. If you're using run launchers or executors that expect to use Docker images, such as

DockerRunLauncher

or

docker_executor

, they will use the images you've specified. Please note that the above steps are a high-level guide. The specifics of how you configure your workspace and run your gRPC servers may vary depending on your local setup and the version of Dagster you are using. Always refer to the latest Dagster documentation for the most accurate and detailed instructions.

@Scout given I follow the setup described, what is the

dagster dev

command I would use to deploy locally? And if deploying to k8s, how do I apply changes to workspace.yaml?

For local development and deployment using Dagster, you would typically use the

dagster dev

command to run your Dagster instance, which includes Dagit (the web interface) and the Dagster daemon. This command is designed for local development and testing, and it's not suitable for production deployments. When using

dagster dev

, you don't explicitly "deploy" in the same sense as you would in a Kubernetes environment. Instead, you run your Dagster instance locally with the configuration specified in your

workspace.yaml

file. The

dagster dev

command automatically picks up changes in your

workspace.yaml

file when you start or restart the command. Here's how you would use

dagster dev

for local development:

shell dagster dev

This command will start the development environment using the configuration specified in your

workspace.yaml

file. If you have multiple containerized code locations, you would ensure that each gRPC server for your code locations is running in its respective Docker container, and the

workspace.yaml

file would reference these gRPC servers. For deploying to Kubernetes, you would typically use Helm charts to manage your deployments. If you make changes to your

workspace.yaml

file and want to apply these changes to your Kubernetes deployment, you would need to rebuild your Docker images if the changes affect the Dockerfile or the code within the images. Then, you would update your Helm chart values to reference the new Docker images and any other configuration changes. Here's a high-level overview of the steps to apply changes to a Kubernetes deployment: 1. Rebuild Docker Images: If necessary, rebuild your Docker images with the updated code or dependencies. 2. Push Docker Images: Push the updated Docker images to your container registry. 3. Update Helm Chart: Modify your Helm chart's

values.yaml

file to reference the new Docker images and any other changes you've made to your

workspace.yaml

or other configuration files. 4. Deploy with Helm: Apply the changes to your Kubernetes cluster using Helm:

shell helm upgrade [RELEASE_NAME] [CHART] --values [VALUES_FILE]

Replace

[RELEASE_NAME]

with the name of your Helm release,

[CHART]

with the path to your Helm chart, and

[VALUES_FILE]

with the path to your

values.yaml

file. Please note that the specifics of how you apply these changes can vary depending on your Kubernetes setup and the Helm chart you are using. Always refer to the latest Dagster documentation and your Helm chart documentation for the most accurate and detailed instructions.

@Scout how do I run the grpc server in each docker container?

To run the gRPC server in each Docker container, you need to ensure that each container has the necessary Dagster packages installed and that you have a command in the Dockerfile to start the gRPC server. Here's a step-by-step guide based on the documentation: 1. Prepare Your Dockerfile: Each Dockerfile should set up the environment for the code location, install Dagster and any other dependencies, and specify the command to run the gRPC server. Here's an example Dockerfile snippet:

Dockerfile     FROM python:3.8-slim      # Install Dagster     RUN pip install dagster dagster-graphql      # Set the DAGSTER_HOME environment variable     ENV DAGSTER_HOME=/opt/dagster/dagster_home      # Create the DAGSTER_HOME directory     RUN mkdir -p $DAGSTER_HOME      # Copy your Dagster repository code and other necessary files     WORKDIR /opt/dagster/app     COPY . /opt/dagster/app      # Expose the port that the gRPC server will run on     EXPOSE 4000      # Start the Dagster gRPC server     CMD ["dagster", "api", "grpc", "-h", "0.0.0.0", "-p", "4000", "-f", "repo.py"]

Replace

repo.py

with the path to your Dagster repository file within the container. 2. Build Your Docker Images: Build the Docker image for each code location using the Dockerfile.

shell     docker build -t my-dagster-repo-image .

3. Run the Docker Containers: Start a Docker container from each image, which will run the gRPC server.

shell     docker run -d -p 4000:4000 my-dagster-repo-image

The

-d

flag runs the container in detached mode, and

-p

maps the container's port to a port on the host machine. Make sure to use different host ports if you're running multiple containers on the same host. 4. Configure Your

workspace.yaml

: In your

workspace.yaml

file, you'll need to specify the gRPC servers for each code location. Here's an example configuration:

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

If you have multiple containers, you would add additional entries under

load_from

with the appropriate host and port for each gRPC server. 5. Start Your Local Dagster Instance: Use the

dagster dev

command to start your local Dagster instance, which will now connect to the gRPC servers running in your Docker containers. By following these steps, you can run the gRPC server in each Docker container, allowing your local Dagster instance to load code from multiple containerized environments.

@Scout can you show me an example of a workspace.yaml file that specifies 2 code locations that both refer to docker images running grpc servers locally?

Certainly! Below is an example of a

workspace.yaml

file that specifies two code locations, each referring to a Docker image running a gRPC server locally. Each gRPC server is running on a different port to avoid conflicts.

yaml load_from:   - grpc_server:       host: localhost       port: 4000       location_name: "code_location_one"   - grpc_server:       host: localhost       port: 4001       location_name: "code_location_two"

In this example: - The first code location is named

code_location_one

and is served by a gRPC server running on

localhost

at port

4000

. - The second code location is named

code_location_two

and is served by a gRPC server running on

localhost

at port

4001

. Each

grpc_server

entry corresponds to a Docker container running a gRPC server that serves a Dagster repository. The

host

should be set to

localhost

if you're running the Docker containers on your local machine. The

port

should match the port you've exposed and mapped when you started the Docker container. Make sure that the Docker containers for these gRPC servers are running and that the ports specified in the

workspace.yaml

file are correctly mapped to the ports exposed by the containers.