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To ensure that the default 
io_manager
shows up in the Dagster UI, you need to make sure that it is properly configured and recognized by the system. The default 
io_manager
is typically the filesystem IO manager, which is used to handle the input and output of assets unless another IO manager is explicitly specified. Here are some steps you can follow based on the documentation: 1. Define the IO Manager: If you haven't already defined a custom IO manager, Dagster will use the default filesystem IO manager. If you have defined a custom IO manager and want it to be the default, you can use the 
@io_manager
decorator to define it and ensure it is available in your repository definition. 2. Attach the IO Manager to your Repository: Make sure that your IO manager is included in your repository definition. This can be done by including it in the 
resources
dictionary when defining your repository or job. 3. Ensure Proper Configuration: If your IO manager requires configuration, ensure that the configuration is provided in your workspace YAML file under the 
resources
section for the relevant jobs or pipelines. 4. Check the UI: Once your IO manager is properly defined and configured, it should show up in the Dagster UI under the relevant job or pipeline's configuration. If you are still having trouble seeing the default 
io_manager
in the UI, you may want to check the following: - Version Compatibility: Ensure that you are using a version of Dagster that supports displaying IO managers in the UI. - UI Access: Make sure that you have access to the Dagster UI and that it is properly set up to connect to your Dagster instance. If you've followed these steps and the 
io_manager
is still not showing up in the UI, you may want to consult the documentation for any additional configuration steps or reach out to the community for support.
If you've passed your custom IO manager as "io_manager" and it's not showing up under resources in the Dagster UI, there are a few things you can check: 1. Resource Definition: Ensure that your custom IO manager is correctly defined with the 
@io_manager
decorator and that it is returning an instance of 
IOManager
. 2. Pipeline/Job Configuration: Verify that your pipeline or job is configured to use your custom IO manager. This involves specifying your custom IO manager in the 
resource_defs
argument of your pipeline/job definition.
Copy code
python    @pipeline(        mode_defs=[ModeDefinition(resource_defs={"io_manager": my_custom_io_manager})]    )    def my_pipeline():        ...
3. Run Configuration: If your custom IO manager requires specific configuration, make sure that the run configuration you're providing when launching a run includes the necessary configuration for your custom IO manager under the 
resources
section. 4. Repository Definition: If you're using a repository to organize your pipelines/jobs, ensure that the repository definition includes your custom IO manager. 5. Workspace Configuration: Check your workspace YAML file to ensure that it's correctly pointing to the repository where your custom IO manager is defined. 6. Dagster Version: Make sure you're using a version of Dagster that supports the features you're using. If you're using features from a newer version, you may need to upgrade. 7. UI Refresh: Sometimes, the UI may not immediately reflect changes. Try refreshing the UI or restarting the Dagster daemon if you're using it. 8. Error Messages: Look for any error messages in the Dagster UI or logs that might indicate why the custom IO manager is not being recognized. If you've checked all of these and the issue persists, it could be a bug or a configuration issue that's not immediately apparent. In such cases, it might be helpful to reach out to the Dagster community with details about your setup, the version of Dagster you're using, and the steps you've taken. They may be able to provide more targeted assistance.
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