How do I provide an input argument, let's say a str, to an op? I am always getting an error. I have tried the direct approach, using a str parameter, a Config derived parameter, but always the same error: Must pass the output from previous node invocations or inputs to the composition function as inputs when invoking nodes during composition.

When you're constructing a job in Dagster and you want to provide an input argument to an op, you cannot directly pass Python values like strings or integers to ops within the job's composition function. Instead, you must pass the output from previous node invocations or inputs to the composition function as inputs when invoking nodes. Here's an example from the documentation that shows how to provide a value for an input via run config for an op that operates on a built-in Dagster type like string:

python
@op
def my_op(context, input_string: str):
    <http://context.log.info|context.log.info>(f"input string: {input_string}")

@job
def my_job():
    my_op()

my_job.execute_in_process(
    run_config={"ops": {"my_op": {"inputs": {"input_string": {"value": "marmot"}}}}}
)

In this example,

my_op

is an op that takes a string input. When executing the job

my_job

, you provide the input value for

input_string

through the

run_config

parameter of the

execute_in_process

method. If you're still encountering errors, it's important to ensure that the structure of your

run_config

matches the expected input structure of your op. If you're using a custom type, you may need to define a

DagsterTypeLoader

to specify how to load that input via run config. If you continue to face issues, please provide more details about your code and the specific error message you're receiving, and I can help you debug further. Clearer error message when passing Pytho... button Graphs with Inputs button Please help me with ops parameters and c... button How to use AssetCheck within Op of Graph... button Thank you for your feedback!

@Scout How would that work if I have a few ops that I attempt to use within a graph_asset?

When using

graph_asset

in Dagster, you define a graph of ops that collectively produce an asset. If you have multiple ops within a

graph_asset

and you want to provide input arguments to those ops, you should define the inputs at the level of the individual ops and then wire them together within the

graph_asset

. The inputs to the ops within a

graph_asset

are typically the outputs of other ops or the inputs to the

graph_asset

itself. Here's an example based on the documentation:

python from dagster import op, graph_asset  @op def process_data(input_str: str):     # Your processing logic here     return f"Processed {input_str}"  @op def another_op(processed_data: str):     # Further processing or computation     return f"Further processed {processed_data}"  @graph_asset def my_graph_asset(input_str: str):     processed = process_data(input_str)     result = another_op(processed)     return result

In this example,

process_data

and

another_op

are ops that take string inputs. The

my_graph_asset

is a

graph_asset

that takes a string input and wires the ops together. The input to

my_graph_asset

is passed to

process_data

, and the output of

process_data

is passed to

another_op

. When you want to execute this

graph_asset

, you would provide the input argument for

input_str

through the run config, similar to how you would for a single op. Here's an example of how you might execute this

graph_asset

with a specific input:

python result = my_graph_asset.execute_in_process(     run_config={"ops": {"my_graph_asset": {"inputs": {"input_str": "example string"}}}} )

If you're still encountering issues, please ensure that the inputs and outputs are correctly defined and that the run config matches the expected structure. If you need further assistance, please provide more details about the specific error message and the code you're working with.

@Scout the previous solution isn't correct: @graph_asset defines an AssetsDefinition, which do not have "execute_in_process" method

You are correct; my previous response contained an error.

@graph_asset

is used to define an asset, and assets do not have an

execute_in_process

method. Instead, assets are materialized by including them in a job and executing that job. To provide input arguments to the ops within a

graph_asset

, you would typically define the inputs at the level of the individual ops and then wire them together within the

graph_asset

. However, if you need to provide inputs from outside the

graph_asset

, you would do so when you define a job that includes the asset. Here's an example of how you might define a job that includes your

graph_asset

and how to provide inputs to it:

python
from dagster import asset, define_asset_job

@asset
def my_asset(input_str: str):
    # Your processing logic here
    return f"Processed {input_str}"

# Define a job that includes the asset
my_job = define_asset_job(
    name="my_job",
    selection=[my_asset],
    input_values={"input_str": "example string"}
)

# Execute the job
result = my_job.execute_in_process()

In this example,

my_asset

is an asset that takes a string input. The

my_job

is a job that includes

my_asset

and specifies the input value for

input_str

using the

input_values

parameter. When you execute the job using

execute_in_process

, the input values provided in the job definition are used to materialize the assets. If you have a more complex scenario where your asset is defined by a

graph_asset

that composes multiple ops, you would still include that

graph_asset

in a job and provide the necessary inputs when defining the job. If you need further clarification or assistance, please provide more details about your specific use case, including the structure of your

graph_asset

and how you are trying to materialize it.

1.4.5

raises an error when contexts ar... button AssetsDefinition.from_graph is missing n... button How to provide configuration to the Op i... button asset definitions that are computed with... button Thank you for your feedback!

@Scout what is "result" in that case? Won't that create a job that will run each time the code location is loaded?

The

result

in the example provided is the return value from the

execute_in_process

method, which is an instance of

JobExecutionResult

. This object contains information about the execution of the job, such as whether it succeeded, the events that occurred during execution, and the outputs produced. Defining a job using

define_asset_job

does not automatically cause it to run each time the code location is loaded. The job will only run when you explicitly execute it, either through a script using

execute_in_process

, through the Dagster CLI, or through the Dagit UI. In Dagster, a code location is a collection of definitions (such as assets, jobs, resources, schedules, and sensors) that are loadable and accessible by Dagster's tools. The code location itself does not dictate when jobs run; it simply makes the definitions available for execution. To execute a job, you would typically use one of the following methods: 1. Dagit UI: You can trigger job execution manually through the Dagit UI by selecting the job and clicking on the "Launchpad" to configure and launch a run. 2. Dagster CLI: You can use the Dagster command-line interface to execute a job. For example, you can use the

dagster job execute

command to run a job defined in your code location. 3. Programmatic Execution: You can execute a job programmatically using the

execute_in_process

method, as shown in the example. This is often used for testing or running jobs from within a Python script. The job execution is an explicit action, and the job will not run automatically upon loading the code location unless you have set up schedules or sensors that trigger the job based on certain conditions or time intervals. These schedules or sensors would need to be defined and enabled for the job to run automatically. runs executed from the CLI don't show up... button Add a way to reload definitions from wit... button Code server taking a long time to load i... button How to see results in the UI for runs th... button Thank you for your feedback!

@Scout previous solution is as bad then: I do not want the job to run each time the code location is loaded. You use "execute_in_process" to provide an argument to underlying ops of an graph_asset, which isn't what I want. I want to define a graph_asset that is calling, within it, several ops. Some of those ops depend upon a str parameter

I apologize for the confusion. To define a

graph_asset

that calls several ops with some of those ops depending on a

str

parameter, you would define the

graph_asset

with the necessary inputs and then provide the values for those inputs when you materialize the asset through a job. Here's a step-by-step guide on how to do this: 1. Define your ops, making sure to specify any inputs they require:

python
from dagster import op

@op
def op_with_input(my_input: str):
    # Your logic here
    pass

@op
def another_op():
    # Your logic here
    pass

2. Define your

graph_asset

and wire up the ops, passing the necessary inputs:

python
from dagster import graph_asset

@graph_asset
def my_graph_asset(my_input: str):
    # Pass the input to the op that requires it
    op_with_input(my_input)
    # Call other ops that do not require the input
    another_op()

3. When you want to materialize this asset, you would include it in a job and provide the input values. You can use

define_asset_job

to create a job that includes your `graph_asset`:

python
from dagster import define_asset_job

# Define a job that includes the asset and provides the input values
my_job = define_asset_job(
    name="my_job",
    selection=[my_graph_asset],
    input_values={"my_input": "the string value"}
)

4. Materialize the asset by executing the job:

python
# Execute the job to materialize the asset
result = my_job.execute_in_process()

In this setup, the job will not run each time the code location is loaded. It will only run when you explicitly call

execute_in_process

or trigger the job through Dagit or the Dagster CLI. By using

input_values

when defining the job, you can provide the necessary string parameters to the ops within your

graph_asset

. This allows you to pass parameters to ops without having to execute the job upon loading the code location. The job execution is an explicit action that you control. executor_def argument to define_asset_jo... button How to execute a graph definition? button Allow

Graph-backed assets

to run in a ... button Dagster Docs button Thank you for your feedback!

@Scout Another bad solution. "define_asset_job" do not have a parameter named "input_values" and I am getting an error because of that: define_asset_job() got an unexpected keyword argument 'input_values'

I apologize for the oversight. You are correct that

define_asset_job

does not have an

input_values

parameter. Instead, you should define the inputs to your assets using the

ins

parameter on the

@asset

decorator to specify the inputs that your asset requires. Here's how you can define a

graph_asset

that includes several assets, some of which depend on a

str

parameter: 1. Define your assets using the

@asset

decorator and specify any inputs using the

ins

parameter:

python from dagster import asset, AssetIn  @asset def asset_with_input(input_str: str):     # Your logic here that uses input_str     pass  @asset def another_asset():     # Your logic here that does not require input_str     pass

2. Define a

graph_asset

that composes these assets:

python from dagster import graph_asset  @graph_asset def my_graph_asset():     # Wire up your assets here     result = asset_with_input()     another_asset()     return result

3. When you want to materialize the assets defined in your

graph_asset

, you would include them in a job. You can use

define_asset_job

to create a job that includes your `graph_asset`:

python from dagster import define_asset_job  # Define a job that includes the assets my_job = define_asset_job(     name="my_job",     selection=[my_graph_asset], )

4. To provide the input values for the assets when executing the job, you would use the

run_config

parameter:

python # Execute the job to materialize the assets, providing the necessary input values result = my_job.execute_in_process(     run_config={         "ops": {             "asset_with_input": {                 "inputs": {                     "input_str": "the string value"                 }             }         }     } )

In this example,

asset_with_input

is the name of the asset that requires the

input_str

parameter. The

run_config

is used to provide the value for this input when the job is executed. This approach allows you to define assets that depend on input parameters and to provide those parameters at execution time without the job running automatically when the code location is loaded. The job will only run when you explicitly execute it.

@Scout Again, I do not want to use "execute_in_process", as it will run the job when the code location is loaded. I want to be able to use the job in a schedule later on

I understand now; you want to define a job that includes a

graph_asset

with assets that depend on a string parameter, and you want to be able to schedule this job without executing it immediately. To achieve this, you can define your assets and

graph_asset

as before, but when it comes to providing the string parameter, you will do so through the job's

run_config

when you set up the schedule. Here's how you can do it: 1. Define your assets using the

@asset

decorator:

python from dagster import asset  @asset def asset_with_input(input_str: str):     # Your logic here that uses input_str     pass  @asset def another_asset():     # Your logic here that does not require input_str     pass

2. Define a

graph_asset

that composes these assets:

python from dagster import graph_asset  @graph_asset def my_graph_asset():     # Wire up your assets here     result = asset_with_input()     another_asset()     return result

3. Define a job that includes your `graph_asset`:

python from dagster import define_asset_job  # Define a job that includes the assets my_job = define_asset_job(     name="my_job",     selection=[my_graph_asset], )

4. Set up a schedule for the job. When defining the schedule, you will provide the

run_config

that includes the string parameter for the asset:

python from dagster import ScheduleDefinition  my_schedule = ScheduleDefinition(     job=my_job,     cron_schedule="0 * * * *",  # Every hour, as an example     run_config={         "ops": {             "asset_with_input": {                 "inputs": {                     "input_str": "the string value"                 }             }         }     } )

In this setup, the

run_config

provided in the

ScheduleDefinition

will be used whenever the schedule triggers a run of the job. The job will not execute immediately; it will only run according to the schedule you define. This way, you can define a job with a

graph_asset

that includes assets with parameters, and you can schedule the job to run at specific intervals with the necessary parameters provided in the schedule's

run_config

.

@Scout Also, I mentioned ops, not assets, as part of the graph_asset

I apologize for the confusion. To clarify, if you want to define a

graph_asset

that calls several ops, and some of those ops depend on a

str

parameter, you can define the

graph_asset

with the necessary inputs and then provide the values for those inputs when you materialize the asset through a job. Here's an example of how you can define a

graph_asset

with ops that depend on inputs: 1. Define your ops using the

@op

decorator:

python
from dagster import op

@op
def my_op(input_str: str):
    # Your logic here that uses input_str
    pass

@op
def another_op():
    # Your logic here that does not require input_str
    pass

2. Define a

graph_asset

that composes these ops and specify any inputs using the

ins

parameter:

python
from dagster import graph_asset, AssetIn

@graph_asset(ins={"input_str": AssetIn()})
def my_graph_asset(input_str):
    # Wire up your ops here
    my_op(input_str)
    another_op()

3. When you want to materialize the assets defined in your

graph_asset

, you would include them in a job. You can use

define_asset_job

to create a job that includes your `graph_asset`:

python
from dagster import define_asset_job

# Define a job that includes the graph_asset
my_job = define_asset_job(
    name="my_job",
    selection=[my_graph_asset],
)

4. To provide the input values for the ops when executing the job, you would use the

run_config

parameter:

python
# Execute the job to materialize the assets, providing the necessary input values
result = my_job.execute_in_process(
    run_config={
        "solids": {
            "my_graph_asset": {
                "inputs": {
                    "input_str": "the string value"
                }
            }
        }
    }
)

In this example,

my_graph_asset

is the name of the

graph_asset

that includes the ops, and

input_str

is the input parameter that you want to provide to the ops within the

graph_asset

. The

run_config

is used to provide the value for this input when the job is executed. This approach allows you to define a

graph_asset

with ops that depend on input parameters and to provide those parameters at execution time. The job will only run when you explicitly execute it, and it will not run automatically when the code location is loaded. assets built from ops and graphs don't g... button Allow users to toggle between assets and... button Assets vs Ops button Wonky op graph displays when creating As... button Thank you for your feedback!

@Scout Doesn't work neither

I apologize for the confusion earlier. Let's clarify how to provide inputs to ops within a

graph

in Dagster, which can then be used to create assets. Here's how you can define a

graph

with ops that depend on inputs: 1. Define your ops using the

@op

decorator:

python
from dagster import op

@op
def my_op(input_str: str):
    # Your logic here that uses input_str
    pass

@op
def another_op():
    # Your logic here that does not require input_str
    pass

2. Define a

graph

that composes these ops:

python
from dagster import graph

@graph
def my_graph(input_str: str):
    # Wire up your ops here
    my_op(input_str)
    another_op()

3. Create a job from the graph:

python
my_job = my_graph.to_job()

4. When you want to execute the job, you can provide the input values for the ops using the `run_config`:

python
result = my_job.execute_in_process(
    run_config={
        "ops": {
            "my_op": {
                "inputs": {
                    "input_str": "the string value"
                }
            }
        }
    }
)

In this example,

my_op

is the name of the op that requires the

input_str

parameter. The

run_config

is used to provide the value for this input when the job is executed. This approach allows you to define a graph with ops that depend on input parameters and to provide those parameters at execution time. The job will only run when you explicitly execute it, and it will not run automatically when the code location is loaded. If you want to create assets from this graph, you can define assets that depend on the outputs of these ops and include them in the job definition. The inputs to the ops will still be provided through the

run_config

when executing the job. Search (CTRL+K) does not work with --pat... button Zoom on double-click doesn't work for as... button dagster tells you to use "dagster dev -h... button System tags aren't populated on failed r... button Thank you for your feedback!

@Scout this is also not what I want and I stated it a couple of time. I won't need anymore help, it is a waste of my time

I apologize for not meeting your expectations and for any frustration this has caused. If you decide to seek assistance again in the future, please feel free to reach out. We're here to help, and we value your time and your experience with Dagster. Thank you for your patience, and I wish you the best with your project. Thank you for your feedback!