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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,7 +1,305 @@ | ||
| --- | ||
| title: dagster_cloud.yaml reference | ||
| sidebar_position: 200 | ||
| --- | ||
|
|
||
| :::note | ||
| This reference is applicable to Dagster+. | ||
| ::: | ||
|
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| <table | ||
| className="table" | ||
| style={{ | ||
| width: "100%", | ||
| }} | ||
| > | ||
| <tbody> | ||
| <tr> | ||
| <td | ||
| style={{ | ||
| width: "15%", | ||
| }} | ||
| > | ||
| <strong>Name</strong> | ||
| </td> | ||
| <td>dagster_cloud.yaml</td> | ||
| </tr> | ||
| <tr> | ||
| <td | ||
| style={{ | ||
| width: "15%", | ||
| }} | ||
| > | ||
| <strong>Status</strong> | ||
| </td> | ||
| <td>Active</td> | ||
| </tr> | ||
| <tr> | ||
| <td | ||
| style={{ | ||
| width: "15%", | ||
| }} | ||
| > | ||
| <strong>Required</strong> | ||
| </td> | ||
| <td>Required for Dagster+</td> | ||
| </tr> | ||
| <tr> | ||
| <td | ||
| style={{ | ||
| width: "15%", | ||
| }} | ||
| > | ||
| <strong>Description</strong> | ||
| </td> | ||
| <td> | ||
| {" "} | ||
| Similar to the <code>workspace.yaml</code> in open source to define code | ||
| locations for Dagster+. | ||
| </td> | ||
| </tr> | ||
| <tr> | ||
| <td | ||
| style={{ | ||
| width: "15%", | ||
| }} | ||
| > | ||
| <strong>Uses</strong> | ||
| </td> | ||
| <td> | ||
| Defines multiple code locations for Dagster+. For Hybrid deployments, this file can be used | ||
| <a href="/dagster-plus/managing-deployments/setting-environment-variables-agents"> to manage | ||
|
Check warning on line 71 in docs/docs-beta/docs/dagster-plus/deployment/code-locations/dagster-cloud-yaml.md
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| environment variables/secrets.</a> | ||
| <ul></ul> | ||
| </td> | ||
| </tr> | ||
| </tbody> | ||
| </table> | ||
|
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| ## File location | ||
|
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| The `dagster_cloud.yaml` file should be placed in the root of your Dagster project. Below is an example of a file structure modified from the [Dagster+ ETL quickstart](https://github.com/dagster-io/dagster/tree/master/examples/quickstart_etl). | ||
|
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| ```shell | ||
| quickstart_etl | ||
| ├── README.md | ||
| ├── quickstart_etl | ||
| │ ├── __init__.py | ||
| │ ├── assets | ||
| │ ├── docker_image | ||
| ├── ml_project | ||
| │ ├── quickstart_ml | ||
| │ ├── __init__.py | ||
| │ ├── ml_assets | ||
| ├── random_assets.py | ||
| ├── quickstart_etl_tests | ||
| ├── dagster_cloud.yaml | ||
| ├── pyproject.toml | ||
| ├── setup.cfg | ||
| └── setup.py | ||
| ``` | ||
|
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| If your repository contains multiple Dagster projects in subdirectories - otherwise known as a monorepository - add the `dagster_cloud.yaml` file to the root of where the Dagster projects are stored. | ||
|
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| ## File structure | ||
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| Settings are formatted using YAML. For example, using the file structure above as an example: | ||
|
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| ```yaml | ||
| # dagster_cloud.yaml | ||
|
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| locations: | ||
| - location_name: data-eng-pipeline | ||
| code_source: | ||
| package_name: quickstart_etl | ||
| build: | ||
| directory: ./quickstart_etl | ||
| registry: localhost:5000/docker_image | ||
| - location_name: ml-pipeline | ||
| code_source: | ||
| package_name: quickstart_ml | ||
| working_directory: ./ml_project | ||
| executable_path: venvs/path/to/ml_tensorflow/bin/python | ||
| - location_name: my_random_assets | ||
| code_source: | ||
| python_file: random_assets.py | ||
| container_context: | ||
| k8s: | ||
| env_vars: | ||
| - database_name | ||
| - database_username=hooli_testing | ||
| env_secrets: | ||
| - database_password | ||
| ``` | ||
| ## Settings | ||
| The `dagster_cloud.yaml` file contains a single top-level key, `locations`. This key accepts a list of code locations; for each code location, you can configure the following: | ||
|
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| - [Location name](#location-name) | ||
| - [Code source](#code-source) | ||
| - [Working directory](#working-directory) | ||
| - [Build](#build) | ||
| - [Python executable](#python-executable) | ||
| - [Container context](#container-context) | ||
|
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| ### Location name | ||
|
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| **This key is required.** The `location_name` key specifies the name of the code location. The location name will always be paired with a [code source](#code-source). | ||
|
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| ```yaml | ||
| # dagster_cloud.yaml | ||
| locations: | ||
| - location_name: data-eng-pipeline | ||
| code_source: | ||
| package_name: quickstart_etl | ||
| ``` | ||
|
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| | Property | Description | Format | | ||
| |-----------------|----------------------------------------------------------------------------------------|----------| | ||
| | `location_name` | The name of your code location that will appear in the Dagster UI Code locations page. | `string` | | ||
|
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| ### Code source | ||
|
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| **This section is required.** The `code_source` defines how a code location is sourced. | ||
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| A `code_source` key must contain either a `module_name`, `package_name`, or `file_name` parameter that specifies where to find the definitions in the code location. | ||
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| <Tabs> | ||
| <TabItem value="Single code location"> | ||
|
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| ```yaml | ||
| # dagster_cloud.yaml | ||
| locations: | ||
| - location_name: data-eng-pipeline | ||
| code_source: | ||
| package_name: quickstart_etl | ||
| ``` | ||
|
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| </TabItem> | ||
| <TabItem value="Multiple code locations"> | ||
|
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| ```yaml | ||
| # dagster_cloud.yaml | ||
| locations: | ||
| - location_name: data-eng-pipeline | ||
| code_source: | ||
| package_name: quickstart_etl | ||
| - location_name: machine_learning | ||
| code_source: | ||
| python_file: ml/ml_model.py | ||
| ``` | ||
|
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| </TabItem> | ||
| </Tabs> | ||
|
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| | Property | Description | Format | | ||
| |----------------------------|-----------------------------------------------------------------------------------|--------------------------| | ||
| | `code_source.package_name` | The name of a package containing Dagster code | `string` (folder name) | | ||
| | `code_source.python_file` | The name of a Python file containing Dagster code (e.g. `analytics_pipeline.py` ) | `string` (.py file name) | | ||
| | `code_source.module_name` | The name of a Python module containing Dagster code (e.g. `analytics_etl`) | `string` (module name) | | ||
|
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| ### Working directory | ||
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| Use the `working_directory` setting to load Dagster code from a different directory than the root of your code repository. This setting allows you to specify the directory you want to load your code from. | ||
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| Consider the following project: | ||
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| ```shell | ||
| quickstart_etl | ||
| ├── README.md | ||
| ├── project_directory | ||
| │ ├── quickstart_etl | ||
| │ ├── __init__.py | ||
| │ ├── assets | ||
| │ ├── quickstart_etl_tests | ||
| ├── dagster_cloud.yaml | ||
| ├── pyproject.toml | ||
| ├── setup.cfg | ||
| └── setup.py | ||
| ``` | ||
|
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| To load from `/project_directory`, the `dagster_cloud.yaml` code location would look like this: | ||
|
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| ```yaml | ||
| # dagster_cloud.yaml | ||
| locations: | ||
| - location_name: data-eng-pipeline | ||
| code_source: | ||
| package_name: quickstart_etl | ||
| working_directory: ./project_directory | ||
| ``` | ||
|
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| | Property | Description | Format | | ||
| |---------------------|-------------------------------------------------------------------------|-----------------| | ||
| | `working_directory` | The path of the directory that Dagster should load the code source from | `string` (path) | | ||
|
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| ### Build | ||
|
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| The `build` section contains two parameters: | ||
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| - `directory` - Setting a build directory is useful if your `setup.py` or `requirements.txt` is in a subdirectory instead of the project root. This is common if you have multiple Python modules within a single Dagster project. | ||
| - `registry` - **Applicable only to Hybrid deployments.** Specifies the Docker registry to push the code location to. | ||
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| In the example below, the Docker image for the code location is in the root directory and the registry and image defined: | ||
|
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| ```yaml | ||
| # dagster_cloud.yaml | ||
| locations: | ||
| - location_name: data-eng-pipeline | ||
| code_source: | ||
| package_name: quickstart_etl | ||
| build: | ||
| directory: ./ | ||
| registry: your-docker-image-registry/image-name # e.g. localhost:5000/myimage | ||
| ``` | ||
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|
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| | Property | Description | Format | Default | | ||
| |-------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------|----------------------------|---------| | ||
| | `build.directory` | The path to the directory in your project that you want to deploy. If there are subdirectories, you can specify the path to only deploy a specific project directory. | `string` (path) | `.` | | ||
| | `build.registry` | **Applicable to Hybrid deployments.** The Docker registry to push your code location to | `string` (docker registry) | | | ||
|
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|
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| ### Python executable | ||
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| For Dagster+ Hybrid deployments, the Python executable that is installed globally in the image, or the default Python executable on the local system if you use the local agent, will be used. To use a different Python executable, specify it using the `executable_path` setting. It can be useful to have different Python executables for different code locations. | ||
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| {/* For Dagster+ Serverless deployments, you can specify a different Python version by [following these instructions](/dagster-plus/deployment/deployment-types/serverless/runtime-environment#python-version). */} | ||
| For Dagster+ Serverless deployments, you can specify a different Python version by [following these instructions](/todo). | ||
|
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| ```yaml | ||
| # dagster_cloud.yaml | ||
| locations: | ||
| - location_name: data-eng-pipeline | ||
| code_source: | ||
| package_name: quickstart_etl | ||
| executable_path: venvs/path/to/dataengineering_spark_team/bin/python | ||
| - location_name: machine_learning | ||
| code_source: | ||
| python_file: ml_model.py | ||
| executable_path: venvs/path/to/ml_tensorflow/bin/python | ||
| ``` | ||
|
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| | Property | Description | Format | | ||
| |-------------------|-----------------------------------------------|-----------------| | ||
| | `executable_path` | The file path of the Python executable to use | `string` (path) | | ||
|
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| ### Container context | ||
|
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| If using Hybrid deployment, you can define additional configuration options for code locations using the `container_context` parameter. Depending on the Hybrid agent you're using, the configuration settings under `container_context` will vary. | ||
|
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| Refer to the configuration reference for your agent for more info: | ||
|
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| {/* - [Docker agent configuration reference](/dagster-plus/deployment/agents/docker/configuration-reference) */} | ||
| - [Docker agent configuration reference](/todo) | ||
| {/* - [Amazon ECS agent configuration reference](/dagster-plus/deployment/agents/amazon-ecs/configuration-reference) */} | ||
| - [Amazon ECS agent configuration reference](/todo) | ||
| {/* - [Kubernetes agent configuration reference](/dagster-plus/deployment/agents/kubernetes/configuration-reference) */} | ||
| - [Kubernetes agent configuration reference](/todo) | ||
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