Fetching Pipelines

How to inspect a finished pipeline run

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Inspecting Pipeline Runs

Once a pipeline run has completed, we can access it using the post-execution utilities.

Each pipeline can have multiple runs associated with it, and for each run there might be several outputs for each step. Thus, to inspect a specific output, we first need to access the respective pipeline, then fetch the respective run, and then choose the step output of that specific run.

The overall hierarchy looks like this:

pipelines -> runs -> steps -> outputs

# where -> implies a 1-many relationship.

Let us investigate how to traverse this hierarchy level by level:

Pipelines

ZenML keeps a collection of all created pipelines with at least one run sorted by the time of their first run from from newest to oldest.

You can either access this collection via the get_pipelines() method or query a specific pipeline by name using get_pipeline(pipeline=...):

from zenml.post_execution import get_pipelines, get_pipeline

# get all pipelines from all stacks
pipelines = get_pipelines()

# now you can get pipelines by index; pipelines are sorted in descending order
# of their first run time (most recent first)
pipeline_with_latest_initial_run_time = pipelines[0]

# or get one pipeline by name
pipeline_x = get_pipeline(pipeline="example_pipeline")

# or even use the pipeline class
pipeline_x = get_pipeline(pipeline=example_pipeline)

Be careful when accessing pipelines by index. Even if you just ran a pipeline it might not be at index -1, due to the fact that the pipelines are sorted by time of first run. Instead, it is recommended to access the pipeline using the pipeline class, an instance of the class or even the name of the pipeline as a string: get_pipeline(pipeline=...).

Using the CLI

You can also access your pipelines through the CLI by executing the following command on the terminal:

zenml pipeline list

Runs

Getting runs from a fetched pipeline

Each pipeline can be executed many times. You can get a list of all runs using the runs attribute of a pipeline.

# get all runs of a pipeline chronologically ordered
runs = pipeline_x.runs 

# get the last run by index, runs are ordered by execution time in ascending order
last_run = runs[0]

# or get a specific run by name
run = pipeline_x.get_run(run_name="my_run_name")

Getting runs from a pipeline instance:

Alternatively, you can also access the runs from the pipeline class/instance itself:

from zenml.pipelines import pipeline

# Definition of pipeline
@pipeline
def example_pipeline(...):
    ...

# Instantiation and execution of pipeline
pipe = example_pipeline(...)
pipe.run()

# get all runs of the defined pipeline chronologically ordered
runs = example_pipeline.get_runs()

# get all runs of the instantiated pipeline chronologically ordered
runs = pipe.get_runs()

# get the last run by index, runs are ordered by execution time in ascending order
last_run = runs[0]

# or get a specific run by name
run = example_pipeline.get_run(run_name=...)

Directly getting a run

Finally, you can also access a run directly with the get_run(run_name=...):

from zenml.post_execution import get_run, get_unlisted_runs

run = get_run(run_name="my_run_name")
run = get_unlisted_runs()[0]  # Get last unlisted run

Using the CLI

You can also access your runs through the CLI by executing the following command on the terminal:

zenml pipeline runs list
zenml pipeline runs list -p <MY_PIPELINE_NAME_OR_ID>

Runs Configuration

Each run has a collection of useful metadata which you can access to ensure all runs are reproducible:

Git SHA

The Git commit SHA that the pipeline run was performed on. This will only be set if the pipeline code is in a git repository and there are no uncommitted files when running the pipeline.

commit = run.git_sha

Status

The status of a pipeline run can also be found here. There are four possible states: failed, completed, running, cached:

status = run.status

Configuration

The pipeline_configuration is an object that contains all configuration of the pipeline and pipeline run, including pipeline-level BaseSettings, which we will learn more about later. You can also access the settings directly via the settings variable.

pipeline_config = run.pipeline_configuration
pipeline_settings = run.settings

Docstring

If you wrote a docstring into your pipeline function, you can retrieve it here as well:

pipeline_docstring = run.docstring

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 metadata attribute:

run_metadata = run.metadata
# The following only works for runs on certain remote orchestrators
orchestrator_url = run_metadata["orchestrator_url"]

Steps

Within a given pipeline run you can now further zoom in on individual steps using the steps attribute or by querying a specific step using the get_step(step=...) method.

# get all steps of a pipeline for a given run
steps = run.steps

# get the step that was executed first
first_step = steps[0]

# or get a specific step by name
step = run.get_step(step="first_step")

The step name refers to the pipeline attribute and not the class name of the steps that implement the step for a pipeline instance.

# Definition of pipeline
@pipeline
def example_pipeline(step_1, step_2):
    ...

# Initialize a new pipeline run
pipe = example_pipeline(step_1=first_step(), step_2=second_step())
pipe.run()

# Get the first step
pipe.get_runs()[0].get_step(step="step_1")

# This won't work:
# pipe.get_runs()[0].get_step(step="first_step")

The steps are ordered by time of execution. Depending on the orchestrator, steps can be run in parallel. Thus, accessing steps by index can be unreliable across different runs, and it is recommended to access steps by the step class, an instance of the class or even the name of the step as a string: get_step(step=...) instead.

Similar to the run, for reproducibility, you can use the step object to access:

• The BaseParameters used to run the step via step.parameters,

• Step-level BaseSettings via step.step_configuration,

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

• Input and output artifacts.

Outputs

Finally, this is how you can inspect the output of a step:

• If there only is a single output, use the output attribute

• If there are multiple outputs, use the outputs attribute, which is a dictionary that can be indexed using the name of an output:

# The outputs of a step
# if there are multiple outputs they are accessible by name
output = step.outputs["output_name"]

# if there is only one output, use the `.output` property instead 
output = step.output 

# read the value into memory
output.read()  

The names of the outputs can be found in the Output typing of your steps:

from zenml.steps import step, Output

@step
def some_step() -> Output(output_name=int):
    ...

Output Artifact Metadata

All output artifacts saved through ZenML will automatically have certain datatype-specific metadata saved with them. NumPy Arrays, for instance, always have their storage size, shape, dtype, and some statistical properties saved with them. You can access such metadata via the metadata attribute of an output, e.g.:

output_metadata = output.metadata
storage_size = output_metadata["storage_size"]

Code Example

Putting it all together, this is how we can access the output of the last step of our example pipeline from the previous sections:

from zenml.post_execution import get_pipeline

pipeline = get_pipeline(pipeline="first_pipeline")
last_run = pipeline.runs[0]
last_step = last_run.steps[-1]
model = last_step.output.read()

or alternatively:

# Definition of pipeline
@pipeline
def example_pipeline(step_1, step_2):
    ...
# Initialize a new pipeline run
pipe = example_pipeline(step_1=first_step(), step_2=second_step())
pipe.run()

# Get the first step
step_1 = pipe.get_runs()[0].get_step(step="step_1")
output = step_1.output.read()

Final note: Fetching older pipeline runs within a step

While most of this document has been focusing on the so called post-execution workflow (i.e. fetching objects after a pipeline has completed), it can also be used within the context of a running pipeline.

This is often desirable in cases where a pipeline is running continously over time and decisions have to be made according to older runs.

E.g. Here, we fetch from within a step the last pipeline run for the same pipeline:

from zenml.post_execution import get_pipeline
from zenml.environment import Environment

@step
def my_step():
    # Fetch the current pipeline
    p = get_pipeline('pipeline_name')

    # Fetch an older run
    older_run = p.runs[-2]  # -1 will be the current run

    # Use the older run to make a decision
    ...

You can get a lot more metadata within a step as well, something we'll learn in more detail in the advanced docs.