Hi Everyone, I'm able to pass arguments when invoking an op as per examples.

# pass config as arg
my_op(build_op_context(), 1, 2, MyConfig(z=3))

But how to do this when calling the op from a job? When attempting to do the above in a job I get an error when starting up Dagster:

Must pass the output from previous node invocations or inputs to the composition function as inputs when invoking nodes during composition.

🤔

could you provide an example of what you're trying to do when you get this error? generally, when passing inputs to an op they need to be passed from another op's outputs. in Dagster you don't hard-code arguments in your graph, and the

context

is passed automatically under the hood - you won't need to use

build_op_context

when constructing a graph or calling ops (it's generally used for testing ops that rely on an

OpExecutionContext

. to pass arguments to your job to kick it off you'll generally use run config, which will allow you to pass initial configuration to each op that defines a config schema or pythonic config.

What Zach said is correct-- have you done the Tutorial? Highly recommended to get acquainted with the basics: https://docs.dagster.io/tutorial

thanks for the feedback guys, the code example above is from https://github.com/dagster-io/dagster/pull/13459

Multiple invocations of the same op get aliased automatically (you can also choose the aliases). See here: https://docs.dagster.io/concepts/ops-jobs-graphs/graphs#that-re-use-ops This allows you to set separate config for each call:

from dagster import Config, RunConfig, job, op


class AddNConfig(Config):
    n: int


@op
def return_one(context) -> int:
    return 1


@op
def add_n(context, number, config: AddNConfig) -> int:
    return number + config.n


@op
def adder(context, a: int, b: int) -> int:
    result = a + b
    print(result)
    return result


@job
def inputs_and_outputs():
    value = return_one()
    a = add_n(value)
    b = add_n(value)
    adder(a, b)


inputs_and_outputs.execute_in_process(
    run_config=RunConfig({
        "add_n": AddNConfig(n=1),
        "add_n_2": AddNConfig(n=1)
    })
)

didn't know about the aliasing, I'll give that a go thank you