how can i specify my op config in my call to define_asset_job so that I don't see the error in the launchpad each time?

To specify op configuration when using

define_asset_job

, you need to provide the configuration as part of the

config

argument. This configuration should match the schema expected by the op. If your op has required configuration and you do not provide it, you will encounter errors in the launchpad when trying to launch the job. Here's a step-by-step guide on how to specify op config in your call to `define_asset_job`: 1. Define your op with the required config schema. For example:

python    from dagster import op, Field     @op(config_schema={"my_config": Field(str)})    def my_op(context):        # Your op logic here        pass

2. When defining your asset job, provide the necessary configuration for the op using the

config

argument. The configuration should be a dictionary where the keys correspond to the op names and the values correspond to the config values. For example:

python    from dagster import define_asset_job, AssetSelection     my_job = define_asset_job(        name="my_job",        selection=AssetSelection.assets(my_op),        config={            "ops": {                "my_op": {                    "config": {                        "my_config": "value"                    }                }            }        }    )

By providing the correct configuration schema in the

config

argument of

define_asset_job

, you should not see errors related to missing op configuration in the launchpad. If you continue to encounter errors, make sure that the configuration you provide matches the schema defined in your op and that you are using the correct op names in the configuration dictionary. If the issue persists, you may need to review the error messages for additional clues or check the documentation for any updates or changes to the configuration API.

@Scout do you see anything wrong with my config? config={ "ops": {"example_lan_file_asset": {"config": {"last_run_time": 0}}}, } class LanUploadRunRequestConfig(Config): last_run_time: float # from a datetime.timestamp

Based on the information you've provided, your configuration dictionary seems to be structured correctly, assuming that

example_lan_file_asset

is the correct name of the op that you are configuring and that it expects a config with a

last_run_time

key of type

float

. However, there are a few things to check to ensure that everything is set up correctly: 1. Op Name: Confirm that

"example_lan_file_asset"

is the correct name of the op within the job. The name in the configuration dictionary must match the name used when the op is invoked within the job. 2. Config Schema: Ensure that the op's config schema is expecting a

last_run_time

key and that it is of type

float

. The

LanUploadRunRequestConfig

class you've defined should match the config schema expected by the op. 3. Config Value: Verify that the value you are providing for

last_run_time

is appropriate for the op's config schema. In your case, you are setting it to

0

, which is a valid

float

, so this should be fine. 4. Usage in Job: Make sure that when you define your job, you are referencing the op correctly and that the job is expecting the configuration you are providing. Here's an example of how you might define an op and a job using the

LanUploadRunRequestConfig

class:

python from dagster import op, job, Config  class LanUploadRunRequestConfig(Config):     last_run_time: float  @op(config_schema=LanUploadRunRequestConfig) def example_lan_file_asset(context):     last_run_time = context.op_config["last_run_time"]     # Your op logic here  @job def my_job():     example_lan_file_asset()  # When launching the job, provide the config my_job.execute_in_process(     run_config={         "ops": {             "example_lan_file_asset": {                 "config": {                     "last_run_time": 0                 }             }         }     } )

If you've checked all of the above and the configuration still doesn't work, you may need to provide more context or error messages for further troubleshooting. Additionally, ensure that you are using the latest version of Dagster, as there may have been updates or changes to the configuration API.