This experimental CLI allows you to manage user code deployments for a Dagster instance deployed on Kubernetes. It packages your code branch into a Docker container, uploads it to your container registry, and updates your existing Dagster instance to enable your user code deployment.

• Kubectl with a valid config
• Helm3
• Podman
• Python3.10+
• AZ CLI (if you are on azure)

• pip install dagster-uc
• Create a configuration file named .config_user_code_deployments.yaml in the root of your repository or your home directory. You can also create one by running dagster-uc init-config -f '.config_user_code_deployments.yaml'.

dev:
  cicd: false
  code_path: dagster_pipelines/repo.py
  image_prefix: 'team-alpha'
  container_registry: myacr.azurecr.io
  dagster_gui_url: null
  dagster_version: 1.8.4
  docker_root: .
  dockerfile: ./Dockerfile
  environment: dev
  kubernetes_context: "my-kubernetes-context"
  namespace: dagster-dev
  limits:
    cpu: '2'
    memory: 2Gi
  node: small
  repository_root: .
  requests:
    cpu: '1'
    memory: 1Gi
  use_project_name: true
  use_az_login: false
  user_code_deployment_env:
    - name: ON_K8S
      value: 'True'
    - name: ENVIRONMENT
      value: dev
  user_code_deployment_env_secrets:
    - name: my-env-secret
  user_code_deployments_configmap_name: dagster-user-deployments-values-yaml
  dagster_workspace_yaml_configmap_name: dagster-workspace-yaml
  uc_deployment_semaphore_name: dagster-uc-semaphore
  verbose: false

• To deploy the currently checked out Git branch, run dagster-uc deployment deploy.
• To see all possible commands, run dagster-uc --help

Dagster-uc allows you to have specific user-code deployement configurations per environment. This enables different configurations for your Kubernetes cluster, container registry, resource usage, etc.

The default environment used is dev, so you need to have dev in your configuration file. Other environment names are up to you. An example structure:

dev:
  container_registry: dev-project.azurecr.io
  ...
acc:
  container_registry: acc-project.azurecr.io.
  ...
prd:
  container_registry: prd-project.azurecr.io
  ...

Specify the environment with dagster-uc --environment prd deployment deploy, or dagster-uc -e prd deployment deploy to use the prd config for the deployment.

It's possible to dynamically set different values for fields in one of the environment configurations, while loading the config. This can be achieved through environment variables, examples:

• export CICD=TRUE
• export VERBOSE=TRUE

Dagster-uc deploys a Git branch as a code location to Dagster. When cicd: true is set in the config_user_code_deployments.yaml, the deployment name of the code location is derived from the environment config variable.

If cicd: false the deployment name is derived from the Git branch. The branch name is transformed by replacing non-alphanumeric characters with hyphens and removing any leading or trailing hyphens.

Example: Git branch feat: my amazing feature becomes deployment feat-my-amazing-feature

During deployment, you can provide a --deployment-name-suffix to add a suffix to your deployment name. This is useful for testing by deploying the same branch twice with different configurations.

With the use_project_name flag in the dagster-uc configuration file, you can prefix the project name to the user-code deployment. The project name is taken from the pyproject.toml, so you need to call dagster-uc in the same directory as the .toml file.

For example, if the project name in pyproject.toml is my_dummy_project, the deployment name will be my-dummy-project--feat-my-amazing-feature.

Important: Internally the deployment name will use -- to seperate the project and branch, which is visible on Kubernetes and the container registry. However in the dagster UI it appears as project:branch

Dagster-uc creates a container image from your existing codebase during deployment.

An example Dockerfile:

FROM python:3.11-slim
ARG BRANCH_NAME
ARG DIR="APP"
WORKDIR $DIR
COPY my_project my_project # Contains all code
COPY pyproject.toml uv.lock README.md ./
RUN --mount=type=cache,target=/root/.cache/uv \
    uv sync --no-dev --link-mode=copy
ENV PATH="/$DIR/.venv/bin:$PATH"
ENV BRANCH_NAME=${BRANCH_NAME}

Set code_path in the configuration to the path of the Python executable containing the Dagster definitions. This is used to start the gRPC server. For more details, see the Dagster K8S docs.

Set image_prefix to prefix all the build images. Useful for grouping images under a prefix.

Dagster-uc deploys each image with a version number as a tag. The versioning is done by checking the latest version of that image in the container registry, and then increment by one.

Without this, using the same image tag would cause Dagster to pull the latest image of that tag during existing jobs, potentially causing data inconsistencies.

Important: Use a custom garbage collection policy to remove old branches or keep only the last X tag versions to prevent your container registry from growing too large in size.

Dagster-uc passes a build-arg=BRANCH_NAME to the image building step.This is useful because you can script the use of the BRANCH_NAME environment variable in your Dagster project code to perform different tasks, such as using a custom IO manager or different secrets. The branch name is either the Git branch or the environment when cicd is true.

Instruct dagster-uc to use the correct kubernetes_context that can access your namespace. Additionally, configure the pod to use specific compute resource requests and limits, and set secrets as environment variables using user_code_deployment_env_secrets or plain environment variables using user_code_deployment_env.