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The DagsterTriggeredRun class represents a Metaflow run that was triggered through a Dagster deployment. It provides access to run status, the Metaflow Run object, and links to the Dagster UI.

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Properties

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pathspec
str
The Metaflow pathspec for this run in the format FlowName/dagster-<run_id>.The run ID portion corresponds to the Dagster run UUID, which can be used to locate the run in the Dagster UI.
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status
str | None
A simple status string based on the underlying Metaflow run.Returns:
  • "PENDING" - The run has not yet appeared in Metaflow’s metadata
  • "RUNNING" - The run is currently executing
  • "SUCCEEDED" - The run completed successfully
  • "FAILED" - The run finished but was not successful
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run
metaflow.Run | None
The Metaflow Run object for this triggered run.This property polls Metaflow until the run with the given pathspec appears. It applies deployer environment variables so local metadata works correctly. Returns None if the run has not yet appeared in the metadata.Once the Run object is available, you can access all standard Metaflow Run properties and methods such as:
  • run.data - Access artifacts from the run
  • run.successful - Check if the run completed successfully
  • run.finished - Check if the run has finished
  • run.steps - Iterate through run steps
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dagster_ui
str | None
URL to the Dagster UI for this run, if available.Returns a link to the local Dagster UI (http://localhost:3000) by default. The run ID embedded in the Metaflow pathspec is extracted and used to construct the URL.Returns None if the pathspec format is unexpected.

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Usage Examples

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Basic Usage

Trigger a run and monitor its status: 
from metaflow import Runner

# Deploy and trigger a run
with Runner("my_flow.py").deployer("dagster") as deployer:
    deployed_flow = deployer.create()
    triggered_run = deployed_flow.run(alpha=0.5, max_iterations=100)

# Check the status
print(f"Pathspec: {triggered_run.pathspec}")
print(f"Status: {triggered_run.status}")
print(f"Dagster UI: {triggered_run.dagster_ui}")

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Accessing Run Data

Wait for the run to complete and access its data: 
import time
from metaflow import Runner

with Runner("my_flow.py").deployer("dagster") as deployer:
    deployed_flow = deployer.create()
    triggered_run = deployed_flow.run(input_data="example")

# Poll until the run completes
while triggered_run.status in ["PENDING", "RUNNING"]:
    print(f"Current status: {triggered_run.status}")
    time.sleep(5)

# Access the run object
if triggered_run.status == "SUCCEEDED":
    run = triggered_run.run
    if run:
        # Access artifacts from the final step
        for step in run.steps():
            print(f"Step: {step.id}")
            if step.finished_at:
                for task in step:
                    print(f"  Task: {task.pathspec}")
                    # Access task data
                    print(f"  Data: {task.data}")
else:
    print(f"Run failed with status: {triggered_run.status}")

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Monitoring Multiple Runs

Trigger multiple runs and monitor them: 
from metaflow import Runner
import time

with Runner("my_flow.py").deployer("dagster") as deployer:
    deployed_flow = deployer.create()
    
    # Trigger multiple runs with different parameters
    runs = [
        deployed_flow.run(experiment_id=1, learning_rate=0.01),
        deployed_flow.run(experiment_id=2, learning_rate=0.001),
        deployed_flow.run(experiment_id=3, learning_rate=0.0001),
    ]

# Monitor all runs
while any(r.status in ["PENDING", "RUNNING"] for r in runs):
    for i, triggered_run in enumerate(runs):
        print(f"Run {i+1}: {triggered_run.status} - {triggered_run.dagster_ui}")
    time.sleep(10)

# Check final results
for i, triggered_run in enumerate(runs):
    print(f"Run {i+1} final status: {triggered_run.status}")
    if triggered_run.status == "SUCCEEDED" and triggered_run.run:
        # Access results
        print(f"  Pathspec: {triggered_run.run.pathspec}")

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Opening Dagster UI

Get the Dagster UI link for a triggered run: 
from metaflow import Runner

with Runner("my_flow.py").deployer("dagster") as deployer:
    deployed_flow = deployer.create()
    triggered_run = deployed_flow.run()

# Print the Dagster UI URL
if triggered_run.dagster_ui:
    print(f"View run in Dagster: {triggered_run.dagster_ui}")
    # You can open this URL in a browser to see the run details

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Inheritance

DagsterTriggeredRun inherits from metaflow.runner.deployer.TriggeredRun, which provides the base run property that polls Metaflow until the run with the given pathspec appears. The Dagster implementation adds the status and dagster_ui properties for convenience.