How can I run multiple jobs with different python dependencies on the same project? Note that I am running dagster on a kubernetes cluster.

To run multiple jobs with different Python dependencies on the same project within a Kubernetes cluster, we recommend structuring your project to use multiple code locations. Each code location can have its own set of dependencies, potentially even different Python versions, and can be configured with its own

executable_path

to use independent Python environments. This is particularly useful when different teams or jobs have conflicting dependencies. Here's a step-by-step guide based on the documentation: 1. Structure Your Project: Organize your Dagster project to include multiple code locations. Each code location can correspond to a different team or job with its own

setup.py

for package requirements and potentially its own Dockerfile for deployment. 2. Configure Code Locations: In your

workspace.yaml

file, specify each code location with the

executable_path

key to point to the Python environment that contains the necessary dependencies for that location. For example:

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

3. Build and Push Docker Images: For each code location, build a Docker image that includes the necessary dependencies and push it to a container registry accessible from your Kubernetes cluster. 4. Deploy to Kubernetes: Use Helm charts or Kubernetes manifests to deploy your Dagster instance and user code deployments. Each user code deployment can reference the appropriate Docker image for the job it will execute. 5. Run Jobs: When you trigger jobs on your Kubernetes cluster, Dagster will use the specified Docker images with the correct dependencies for each job. By following these steps, you can manage and run multiple jobs with different Python dependencies within the same Dagster project on a Kubernetes cluster. This approach allows for greater flexibility and isolation between different jobs and their dependencies. Run multiple agents in different cluster... button How do I run Dagster jobs consisting of ... button Structuring your Dagster project | Dagst... button Dagster Helm, run a job in the same code... button Thank you for your feedback!

And how would you define each job?