Slice a pipeline

Sometimes it is desirable to run a subset, or a ‘slice’ of a pipeline’s nodes. In this page, we illustrate the programmatic options that Kedro provides. You can also use the Kedro CLI to pass parameters to kedro run command and slice a pipeline.

Let’s look again at the example pipeline from the pipeline introduction documentation, which computes the variance of a set of numbers:

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def mean(xs, n):
    return sum(xs) / n


def mean_sos(xs, n):
    return sum(x**2 for x in xs) / n


def variance(m, m2):
    return m2 - m * m


full_pipeline = pipeline(
    [
        node(len, "xs", "n"),
        node(mean, ["xs", "n"], "m", name="mean_node", tags="mean"),
        node(mean_sos, ["xs", "n"], "m2", name="mean_sos", tags=["mean", "variance"]),
        node(variance, ["m", "m2"], "v", name="variance_node", tags="variance"),
    ]
)

The Pipeline.describe() method returns the following output:

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#### Pipeline execution order ####
Name: None
Inputs: xs

len([xs]) -> [n]
mean_node
mean_sos
variance_node

Outputs: v
##################################

Slice a pipeline by providing inputs

One way to slice a pipeline is to provide a set of pre-calculated inputs which should serve as a start of the pipeline. For example, in order to slice the pipeline to run from input m2 downstream you can specify it like this:

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print(full_pipeline.from_inputs("m2").describe())

Output:

#### Pipeline execution order ####
Name: None
Inputs: m, m2

variance_node

Outputs: v
##################################

Slicing the pipeline from inputs m and xs results in the following pipeline:

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print(full_pipeline.from_inputs("m", "xs").describe())

Output:

#### Pipeline execution order ####
Name: None
Inputs: xs

len([xs]) -> [n]
mean_node
mean_sos
variance_node

Outputs: v
##################################

As you can see, adding m in the from_inputs list does not guarantee that it will not be recomputed if another input like xs is specified.

Slice a pipeline by specifying nodes

Another way of slicing a pipeline is to specify the nodes which should be used as a start of the new pipeline. For example:

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print(full_pipeline.from_nodes("mean_node").describe())

Output:

#### Pipeline execution order ####
Name: None
Inputs: m2, n, xs

mean_node
variance_node

Outputs: v
##################################

As you can see, this will slice the pipeline and run it from the specified node to all other nodes downstream.

You can run the resulting pipeline slice by running the following command in your terminal window:

kedro run --from-nodes=mean_node

Slice a pipeline by specifying final nodes

Similarly, you can specify the nodes which should be used to end a pipeline. For example:

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print(full_pipeline.to_nodes("mean_node").describe())

Output:

#### Pipeline execution order ####
Name: None
Inputs: xs

len([xs]) -> [n]
mean_node

Outputs: m
##################################

As you can see, this will slice the pipeline, so it runs from the beginning and ends with the specified node:

kedro run --to-nodes=mean_node

You can also slice a pipeline by specifying the start and finish nodes, and thus the set of nodes to be included in the pipeline slice:

kedro run --from-nodes=A --to-nodes=Z

or, when specifying multiple nodes:

kedro run --from-nodes=A,D --to-nodes=X,Y,Z

Slice a pipeline with tagged nodes

You can also slice a pipeline from the nodes that have specific tags attached to them. For example, for nodes that have both tag mean AND tag variance, you can run the following:

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print(full_pipeline.only_nodes_with_tags("mean", "variance").describe())

Output:

#### Pipeline execution order ####
Inputs: n, xs

mean_sos

Outputs: m2
##################################

To slice a pipeline from nodes that have tag mean OR tag variance:

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sliced_pipeline = full_pipeline.only_nodes_with_tags(
    "mean"
) + full_pipeline.only_nodes_with_tags("variance")
print(sliced_pipeline.describe())

Output:

#### Pipeline execution order ####
Inputs: n, xs

mean
mean_sos
variance

Outputs: v
##################################

Slice a pipeline by running specified nodes

Sometimes you might need to run only some of the nodes in a pipeline, as follows:

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print(full_pipeline.only_nodes("mean_node", "mean_sos").describe())

Output:

#### Pipeline execution order ####
Name: None
Inputs: n, xs

mean_node
mean_sos

Outputs: m, m2
##################################

This will create a sliced pipeline, comprised of the nodes you specify in the method call.

Note

All the inputs required by the specified nodes must exist, i.e. already produced or present in the data catalog.

How to recreate missing outputs

Kedro can automatically generate a sliced pipeline from existing node outputs. This can be helpful if you want to avoid re-running nodes that take a long time:

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print(full_pipeline.describe())

Output:

#### Pipeline execution order ####
Name: None
Inputs: xs

len([xs]) -> [n]
mean_node
mean_sos
variance_node

Outputs: v
##################################

To demonstrate this, let us save the intermediate output n using a JSONDataset.

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from kedro_datasets.pandas import JSONDataset
from kedro.io import DataCatalog, MemoryDataset

n_json = JSONDataset(filepath="./data/07_model_output/len.json")
io = DataCatalog(dict(xs=MemoryDataset([1, 2, 3]), n=n_json))

Because n was not saved previously, checking for its existence returns False:

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io.exists("n")

Output:

Out[15]: False

Running the pipeline calculates n and saves the result to disk:

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SequentialRunner().run(full_pipeline, io)

Output:

Out[16]: {'v': 0.666666666666667}

io.exists("n")

Output:

Out[17]: True

We can avoid re-calculating n (and all other results that have already been saved) by using the Runner.run_only_missing method. Note that the first node of the original pipeline (len([xs]) -> [n]) has not been run:

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SequentialRunner().run_only_missing(full_pipeline, io)

Ouput:

Out[18]: {'v': 0.666666666666667}

try:
    os.remove("./data/07_model_output/len.json")
except FileNotFoundError:
    pass