how can I selective load one code location and not another locally?

To selectively load one code location and not another locally, you can use the Dagster CLI with specific arguments to target the desired code location. According to the documentation, you can specify a Python file or module that contains your Dagster definitions using the

-f

or

-m

flags when running the

dagster

command. For example, if you want to load a code location from a Python file, you can use the following command:

shell dagster dev -f my_file.py

This command will load the definitions in

my_file.py

as a code location in the current Python environment. If you want to load a code location from a Python module, you can use the

-m

flag:

shell dagster dev -m my_module

This will load the module

my_module

as a code location. By specifying the file or module for the code location you want to load, you can control which code locations are loaded during local development. If you have multiple code locations, you can choose to load only one by providing the appropriate file or module name for that location.

@Scout can you walk me through the parameter options for

dagster-cloud workspace sync

?

The

dagster-cloud workspace sync

command is used to synchronize your local workspace with Dagster Cloud. This command is part of the

dagster-cloud

CLI, which is designed to work with Dagster Cloud. However, as of my knowledge cutoff date, the specific parameter options for

dagster-cloud workspace sync

are not detailed in the documentation I have access to. Typically, CLI commands come with a help option that provides more information about the available parameters and their usage. You can usually access this help by running the command with the

--help

flag. For the

dagster-cloud

CLI, you would run:

shell dagster-cloud workspace sync --help

This should display a list of all the available options and flags for the

dagster-cloud workspace sync

command, along with a brief description of what each one does. If you need further assistance with the

dagster-cloud

CLI or have specific questions about its commands and parameters, I recommend checking the latest Dagster Cloud documentation or reaching out to the Dagster community for support.

@Scout how would I split out my dagster project from a package to instead load from two different modules? Can I load from two different modules within the same package?

To split your Dagster project from a single package to load from two different modules, you can organize your code into separate Python modules and then configure Dagster to recognize these modules as distinct code locations. Each module can be a separate directory with its own

__init__.py

file, making it a Python module. Here's a high-level overview of how you might structure your project:

my_dagster_project/ ├── module_one/ │   ├── __init__.py │   └── ... (other Python files, assets, jobs, etc.) ├── module_two/ │   ├── __init__.py │   └── ... (other Python files, assets, jobs, etc.) └── workspace.yaml

In the

workspace.yaml

file, you would define both modules as code locations. Here's an example of what the

workspace.yaml

might look like:

yaml load_from:   - python_module: my_dagster_project.module_one   - python_module: my_dagster_project.module_two

This configuration tells Dagster to load definitions from both

module_one

and

module_two

. Regarding your second question, yes, you can load from two different modules within the same package. A package can contain multiple modules, and you can configure Dagster to load from each module separately as shown above. Each module would be a subdirectory within the package, and you would reference them individually in your

workspace.yaml

file. If you want to load multiple modules using the CLI, you can use the

-m

flag multiple times with the

dagster

command. For example:

shell dagster dev -m my_dagster_project.module_one -m my_dagster_project.module_two

This command would start the Dagster development environment and load the definitions from both

module_one

and

module_two

. Remember to ensure that each module has its own

__init__.py

file and that the modules are structured in a way that makes sense for your project's organization and dependency management.

@Scout can i have an asset in one code location depend on an asset in a another code location?

As of the last update I have, assets within one code location can depend on assets in another code location. This is possible because assets are identified by their asset keys, which are globally unique within a Dagster instance. When you define an asset, you can specify its dependencies by asset keys, and Dagster will resolve these dependencies across code locations. However, there are some considerations and limitations to keep in mind: 1. Asset Materialization History: As long as an asset has the same asset key, its materialization history should be unaffected by what code location it's in. You shouldn't need to take any additional actions to migrate an asset's materialization history between code locations if the asset's key doesn't change. 2. Asset Group Namespacing: There has been discussion about the namespacing of asset groups to a repository or location. Ideally, asset groups would be globally scoped, allowing similar assets from different code locations to be nested within the same asset group. This would enable a more organized view in Dagit, Dagster's UI. 3. Cross-Code Location Asset Checks: There is an open issue regarding support for asset checks that target assets in a different code location. Currently, Dagster will try to launch the asset and the check in one job, which can result in an error if the assets are not in the same code location. 4. Asset Dependencies in Jobs: There has been an issue about allowing asset dependencies inside jobs to be satisfied at repository construction time, which would facilitate cross-code location dependencies. Given these points, while it is technically possible to have cross-code location asset dependencies, you may encounter some limitations or need to follow specific patterns to ensure that your assets are correctly recognized and that their dependencies are resolved. Always refer to the latest Dagster documentation or reach out to the Dagster community for the most current information and best practices.