g, and cached.

status = run.status

ConfigurationThe pipeline_configuration is an object that contains all configurations of the pipeline and pipeline run, including the pipeline-level settings, which we will learn more about later:

pipeline_config = run.config

pipeline_settings = run.config.settings

Component-Specific metadata

Depending on the stack components you use, you might have additional component-specific metadata associated with your run, such as the URL to the UI of a remote orchestrator. You can access this component-specific metadata via the run_metadata attribute:

run_metadata = run.run_metadata

# The following only works for runs on certain remote orchestrators

orchestrator_url = run_metadata["orchestrator_url"].value

## Steps

Within a given pipeline run you can now further zoom in on individual steps using the `steps` attribute:

```python

# get all steps of a pipeline for a given run

steps = run.steps

# get a specific step by its invocation ID

step = run.steps["first_step"]

If you're only calling each step once inside your pipeline, the invocation ID will be the same as the name of your step. For more complex pipelines, check out this page to learn more about the invocation ID.

Inspect pipeline runs with our VS Code extension

If you are using our VS Code extension, you can easily view your pipeline runs by opening the sidebar (click on the ZenML icon). You can then click on any particular pipeline run to see its status and some other metadata. If you want to delete a run, you can also do so from the same sidebar view.

Step information

Similar to the run, you can use the step object to access a variety of useful information:

The parameters used to run the step via step.config.parameters,

The step-level settings via step.config.settings,

Component-specific step metadata, such as the URL of an experiment tracker or model deployer, via step.run_metadata

See the StepRunResponse definition for a comprehensive list of available information.

Artifacts