What does "dagster._core.errors.DagsterInvalidInvocationError: Attempted to invoke execute_in_process for 'job_name' without specifying an input_value for input 'input_name', but downstream input input_name of op 'op_name' has no other way of being loaded." mean? I'm using ops and graph

The error message you're encountering,

dagster._core.errors.DagsterInvalidInvocationError

, indicates that there is an issue with how you are invoking the

execute_in_process

method for a job or graph in Dagster. Specifically, the error is saying that you have not provided a required input value for an input named

'input_name'

when attempting to execute the job or graph named

'job_name'

. Additionally, the downstream input

'input_name'

of the operation

'op_name'

does not have an alternative way of being loaded, which means that it is expecting this input to be provided explicitly. In Dagster, when you define a job or graph that includes operations (ops), each op can have inputs that need to be provided for it to execute. If an op's input is not connected to the output of another op within the graph, you must provide a value for that input when you invoke the job or graph. This can be done through the

run_config

parameter of the

execute_in_process

method. The documentation provides an example of how to provide a value for an unconnected input using `run_config`:

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, the

my_op

operation has an unconnected input

input_string

, and a value is provided for it in the

run_config

when

execute_in_process

is called. To resolve the error you're seeing, you should ensure that you are providing all required inputs for your ops when invoking

execute_in_process

. If the input is supposed to come from another op, make sure that the connections between ops are correctly defined in your job or graph. If the input is unconnected, you'll need to provide a value for it in the

run_config

.

@Scout I have an op that will receive some inputs from upstream ops (connected in a graph) and some other inputs will come from the run config. How do I configure that?

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@Scout I have an op that will receive some inputs from upstream ops (connected in a graph) and some other inputs will come from the run config. How do I configure that?

In Dagster, when you have an op that requires inputs from both upstream ops and run config, you can define your op with the necessary input parameters and then provide the values for the inputs that are not connected to upstream ops through the

run_config

when you execute the job or graph. Here's a step-by-step guide on how to configure this: 1. Define your op with input parameters for both the upstream data and the configuration data. For example:

python @op def my_combined_op(context, upstream_input, config_input):     # Your op logic here     pass

2. In your graph, connect the upstream ops to

my_combined_op

for the

upstream_input

parameter.

python @op def upstream_op():     # Logic that produces an output     return "upstream_data"  @graph def my_graph():     my_combined_op(upstream_input=upstream_op())

3. When you define your job, you don't need to do anything special for the inputs that are connected within the graph.

python @job def my_job():     my_graph()

4. When you execute the job, provide the configuration input through the

run_config

. The

run_config

should specify the input values for the ops that require them.

python my_job.execute_in_process(     run_config={         "ops": {             "my_combined_op": {  # Use the name of your op here                 "inputs": {                     "config_input": "value_from_config"  # Provide the value for the config input                 }             }         }     } )

In this example,

upstream_op

provides the

upstream_input

to

my_combined_op

, and the

config_input

is provided via the

run_config

when

execute_in_process

is called. Make sure that the keys in the

run_config

dictionary match the names of your ops and inputs. If your op is nested within a graph that is nested within another graph, you may need to specify the config using the names of the graphs as namespaces in the

run_config

. The documentation provides guidance on how to configure ops within nested graphs. Remember that the names used in the

run_config

should match the actual names of the ops and inputs as defined in your code. If you're using aliases or if your ops are within nested graphs, you'll need to adjust the

run_config

accordingly to reflect the structure of your job or graph.

@Scout in your example, what if

upstream_op

itself receives an input from the config? Can I set these inputs at the graph level?

Yes, you can set inputs for

upstream_op

from the config at the graph level. When you have a graph that includes ops with inputs that need to be configured, you can provide the configuration for these ops through the

run_config

when you execute the job that contains the graph. Here's how you can do it: 1. Define your ops, making sure to include any config inputs as parameters:

python @op def upstream_op(context, config_input_for_upstream):     # Logic that uses the config input     return "upstream_data"  @op def my_combined_op(context, upstream_input, config_input_for_combined):     # Logic that uses both the upstream data and its own config input     pass

2. Connect the ops in your graph:

python @graph def my_graph(config_input_for_upstream):     combined_input = upstream_op(config_input_for_upstream)     my_combined_op(upstream_input=combined_input)

3. Define your job:

python @job def my_job():     my_graph()

4. When executing the job, provide the configuration for both

upstream_op

and

my_combined_op

through the `run_config`:

python my_job.execute_in_process(     run_config={         "ops": {             "my_graph": {  # Use the name of your graph here                 "inputs": {                     "config_input_for_upstream": "value_for_upstream"  # Config input for upstream_op                 }             },             "my_combined_op": {  # Use the name of your combined op here                 "inputs": {                     "config_input_for_combined": "value_for_combined"  # Config input for my_combined_op                 }             }         }     } )

In this example,

config_input_for_upstream

is provided to

upstream_op

through the

run_config

at the graph level, and

config_input_for_combined

is provided to

my_combined_op

directly. It's important to note that the

run_config

structure reflects the hierarchy of your job. If you have nested graphs, you would need to specify the configuration for the ops within those graphs using the names of the graphs as namespaces. The documentation provides examples of how to provide configuration to ops inside a sub-graph when launching a run, which is similar to providing configuration at the graph level.

@Scout I've realized this error comes when I call

to_job

on my @graph:

What does "dagster._core.errors.DagsterInvalidInvocationError: Attempted to invoke execute_in_process for 'job_name' without specifying an input_value for input 'input_name', but downstream input input_name of op 'op_name' has no other way of being loaded." mean?

The error message you're encountering indicates that when you converted your

@graph

into a

@job

using the

to_job

method, you attempted to execute the job with the

execute_in_process

function without providing a required input value for an input named

'input_name'

. This input is required by the op

'op_name'

within the job

'job_name'

, and there is no default value or other means specified to load this input. Here's a step-by-step explanation of what might be happening: 1. You have a graph that includes an op that requires an input:

python @op def op_name(context, input_name):     # Your op logic here  @graph def my_graph():     op_name()  # This op requires 'input_name' to be provided

2. You convert this graph into a job using `to_job`:

python job_name = my_graph.to_job()

3. You attempt to execute the job without providing the required input:

python job_name.execute_in_process()

Since

op_name

requires an input called

'input_name'

, and you did not provide it when calling

execute_in_process

, Dagster raises a

DagsterInvalidInvocationError

. To fix this error, you need to provide the required input when executing the job. You can do this by passing a

run_config

that includes the input value:

python job_name.execute_in_process(     run_config={         "ops": {             "op_name": {  # Use the name of your op here                 "inputs": {                     "input_name": "value_for_input"  # Provide the value for the input                 }             }         }     } )

Make sure that the

run_config

correctly specifies the input values for the ops that require them. If your op is nested within a graph that is nested within another graph, you may need to specify the config using the names of the graphs as namespaces in the

run_config

. If you expect the input to be provided by an upstream op, ensure that the graph structure correctly connects the ops so that the required inputs are passed along. If the input is supposed to be provided from outside the job (such as from the

run_config

), then you must provide it when invoking the job as shown above.

@Scout I get this if I don't call `execute_in_process`: Input 'input_name' of op 'op_name' has no way of being resolved. Must provide a resolution to this input via another op/graph, or via a direct input value mapped from the top-level graph

The error message you're seeing indicates that the op

'op_name'

within your graph or job requires an input

'input_name'

that has not been provided or connected to any upstream op or graph output. This means that Dagster cannot determine how to provide a value for this input when constructing the execution plan for the graph or job. When you define a graph in Dagster, each op can have inputs that need to be provided for it to execute. These inputs can be satisfied in one of two ways: 1. Connected from an upstream op: The input is the output of another op within the graph. 2. Provided as a direct input value: The input is provided when the graph is invoked, either at the top-level graph or when the graph is converted to a job. Here's an example of how you might define a graph where an op requires an input:

python @op def op_name(context, input_name):     # Your op logic here  @graph def my_graph(input_name):     op_name(input_name=input_name)

In this case,

my_graph

has a parameter

input_name

that is used to provide the input for

op_name

. When you convert this graph to a job, you need to ensure that the required inputs are provided. If you're not using

execute_in_process

and are instead using the Dagster UI (Dagit) or the CLI to execute the job, you need to provide the inputs in the

run_config

or as part of the job definition. Here's how you might provide the input when converting the graph to a job:

python job_name = my_graph.to_job(     input_values={"input_name": "value_for_input"} )

Alternatively, if you're executing the job without using

to_job

to provide input values, you would specify the inputs in the

run_config

when you launch the job:

yaml # run_config.yaml ops:   op_name:     inputs:       input_name: "value_for_input"

Then you would use this

run_config

when launching the job through Dagit or the CLI:

bash dagster job execute -f my_job_file.py -c run_config.yaml

Make sure that the inputs are correctly provided and that the names used in the

input_values

or

run_config

match the actual names of the ops and inputs as defined in your code. If you're using aliases or if your ops are within nested graphs, you'll need to adjust the input values or

run_config

accordingly to reflect the structure of your job or graph.