Nodes and pipelines

Note: This documentation is based on Kedro 0.15.1, if you spot anything that is incorrect then please create an issue or pull request. In this section we introduce pipelines and nodes.

Relevant API documentation:

To run the code snippets demonstrated below for yourself, you will first need to import some Kedro and standard libraries.

from kedro.pipeline import *
from kedro.io import *
from kedro.runner import *

import pickle
import os

Nodes

Nodes are used to coordinate complex, dependent tasks. Pipelines are flexible and are used to combine nodes reproducibly to build simple machine learning workflows or even build entire end-to-end production workflows.

Creating a pipeline node

A node is created by specifying a function, input variable names and output variable names. Let’s consider a simple function that adds two numbers:

def add(x, y):
    return x + y

The add function has two inputs x and y and a single output. A new node can now be created with this function:

adder_node = node(func=add, inputs=['a', 'b'], outputs='sum')
adder_node

Output:

Out[1]: Node(add, ['a', 'b'], 'sum', None)

You can also add labels to nodes which will be used to describe them in logs:

adder_node = node(func=add, inputs=['a', 'b'], outputs='sum')
print(str(adder_node))

adder_node = node(func=add, inputs=['a', 'b'], outputs='sum', name='adding_a_and_b')
print(str(adder_node))

Output:

add([a,b]) -> [sum]
adding_a_and_b: add([a,b]) -> [sum]

Let’s break down the node definition:

  • add is our function that will execute when running the node
  • ['a', 'b'] specify our input variable names. Note that in this case, they are different from x and y
  • sum specifies the name of our return variable. The value returned by add will be bound in this variable
  • name is an optional label, which can be used to provide description of the business logic of the node

Node definition syntax

There is a special syntax for describing function inputs and outputs. This allows different Python functions to be reused in nodes and supports dependency resolution in pipelines.

Syntax for input variables

Input syntax Meaning Example function parameters How function is called when node runs
None No input def f() f()
'a' Single input def f(arg1) f(a)
['a', 'b'] Multiple inputs def f(arg1, arg2) f(a, b)
dict(arg1='x', arg2='y') Keyword inputs def f(arg1, arg2) f(arg1='x', arg2='y')

Syntax for output variables

Output syntax Meaning Example return statement
None No output Does not return
'a' Single output return a
['a', 'b'] List output return [a, b]
dict(key1='a', key2='b') Dictionary output return dict(key1=a, key2=b)

Any combinations of the above are possible, except nodes of the form node(f, None, None) (at least a single input or output needs to be provided).

Tagging nodes

To tag a node, you can simply specify the tag argument, as follows:

node(func=add, inputs=["a", "b"], outputs="sum", name="adding_a_and_b", tag="node_tag")

Moreover, you can tag all nodes in a Pipeline.

Running nodes

To run a node, you need to instantiate its inputs. In this case, the node expects two inputs:

adder_node.run(dict(a=2, b=3))

Output:

Out[2]: {'sum': 5}

Applying decorators to nodes

For computations that need to run before and after node execution, Kedro is compatible with Python decorators. Below are example decorators that modify the first string argument of a given function:

from functools import wraps
from typing import Callable


def apply_f(func: Callable) -> Callable:
    @wraps(func)
    def with_f(*args, **kwargs):
        return func(*["f({})".format(a) for a in args], **kwargs)
    return with_f


def apply_g(func: Callable) -> Callable:
    @wraps(func)
    def with_g(*args, **kwargs):
        return func(*["g({})".format(a) for a in args], **kwargs)
    return with_g


def apply_h(func: Callable) -> Callable:
    @wraps(func)
    def with_h(*args, **kwargs):
        return func(*["h({})".format(a) for a in args], **kwargs)
    return with_h

So if you want to create a function and make sure that apply_f is applied to every call of your function, including in Kedro nodes, you can do as follows:

@apply_f
def say_hello(name):
    print("Hello {}!".format(name))


hello_node = node(say_hello, 'name', None)
hello_node.run(dict(name="Kedro"))

Output:

In [3]: hello_node.run(dict(name="Kedro"))
Hello f(Kedro)!
Out[3]: {}

If you want to apply an additional decorator to the same function, but just for another node:

hello_node_wrapped = node(apply_g(say_hello), 'name', None)

hello_node.run(dict(name="Kedro"))
hello_node_wrapped.run(dict(name="Kedro"))

Output:

Hello f(Kedro)!
Hello f(g(Kedro))!
Out[4]: {}

Applying multiple decorators to nodes

You can also provide a list of decorators as shown here:

hello_wrapped = node(apply_g(apply_h(say_hello)), 'name', None)
hello_decorated = hello_node.decorate(apply_g, apply_h)

hello_wrapped.run(dict(name="Kedro"))
hello_decorated.run(dict(name="Kedro"))

Output:

Hello f(h(g(Kedro)))!
Hello f(h(g(Kedro)))!

Kedro comes with a few built-in decorators which are very useful when building your pipeline. You can learn more how to apply decorators to whole pipelines and the list of built-in decorators in a separate section below.

Building pipelines

To benefit from Kedro’s automatic dependency resolution, nodes can be chained in a pipeline. A pipeline is a list of nodes that use a shared set of variables.

In the following example, we construct a simple pipeline that computes the variance of a set of numbers. In practice, pipelines can use more complicated node definitions and variables usually correspond to entire datasets:

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


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


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


pipeline = Pipeline([
    node(len, 'xs', 'n'),
    node(mean, ['xs', 'n'], 'm', name='mean node'),
    node(mean_sos, ['xs', 'n'], 'm2', name='mean sos'),
    node(variance, ['m', 'm2'], 'v', name='variance node')
])

describe can be used to understand what nodes are part of the pipeline:

print(pipeline.describe())

Output:

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

len([xs]) -> [n]
mean node
mean sos
variance node

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

Tagging pipeline nodes

You can specify a name for your Pipeline, which will be used to tag all of the pipeline’s nodes.

pipeline = Pipeline([node(..., name="node1"), node(..., name="node2", tag="node_tag")], name="pipeline_tag")

Node node1 will only be tagged with pipeline_tag, while node2 will have both node_tag and pipeline_tag.

Merging pipelines

You can merge multiple pipelines as shown below. Note that, in this case, pipeline_de and pipeline_ds are expanded to a list of their underlying nodes of nodes which are simply merged together:

pipeline_de = Pipeline([
    node(len, 'xs', 'n'),
    node(mean, ['xs', 'n'], 'm')
])

pipeline_ds = Pipeline([
    node(mean_sos, ['xs', 'n'], 'm2'),
    node(variance, ['m', 'm2'], 'v')
])

last_node = node(print, 'v', None)

pipeline_all = Pipeline([
    pipeline_de,
    pipeline_ds,
    last_node
])
print(pipeline_all.describe())

Output:

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

len([xs]) -> [n]
mean([n,xs]) -> [m]
mean_sos([n,xs]) -> [m2]
variance([m,m2]) -> [v]
print([v]) -> None

Outputs: None
##################################

Fetching pipeline nodes

Pipelines provide access to their nodes in a topological order for enabling custom functionality, e.g. custom visualisation of pipelines. Each node has information about its inputs and outputs:

nodes = pipeline.nodes
nodes

Output:

Out[5]:
[Node(len, 'xs', 'n', None),
 Node(mean, ['xs', 'n'], 'm', 'mean node'),
 Node(mean_sos, ['xs', 'n'], 'm2', 'mean sos'),
 Node(variance, ['m', 'm2'], 'v', 'variance node')]
nodes[0].inputs

Output:

Out[6]: ['xs']

Bad pipelines

As you notice, pipelines can usually readily resolve their dependencies. In some cases, resolution is not possible and pipelines are not well-formed.

Pipeline with bad nodes

In this case we have a pipeline consisting of a single node with no input and output:

try:
    Pipeline([
        node(lambda: print('!'), None, None)
    ])
except Exception as e:
    print(e)

Output:

Invalid Node definition: it must have some `inputs` or `outputs`.
Format should be: node(function, inputs, outputs)

Pipeline with circular dependencies

For every two variables where the first depends on the second, there must not be a way in which the second also depends on the first, otherwise, a circular dependency will prevent us from compiling the pipeline.

The first node captures the relationship of how to calculate y from x and the second captures the relationship of how to calculate x knowing y. Both cannot coexist in the same pipeline:

try:
    Pipeline([
        node(lambda x: x+1, 'x', 'y', name='first node'),
        node(lambda y: y-1, 'y', 'x', name='second node')
    ])
except Exception as e:
    print(e)

Output:

Circular dependencies exist among these items: ['first node: <lambda>([x]) -> [y]', 'second node: <lambda>([y]) -> [x]']

Running pipelines

When running pipelines, it can be useful to check the inputs and outputs of a pipeline:

pipeline.inputs()

Output:

Out[7]: {'xs'}
pipeline.outputs()

Output:

Out[8]: {'v'}

Runners

Runners are different execution mechanisms for running pipelines. They all inherit from AbstractRunner. You can use SequentialRunner to execute pipeline nodes one-by-one based on their dependencies.

We recommend using SequentialRunner in cases where:

  • the pipeline has limited branching
  • the pipeline is fast
  • the resource consuming steps require most of a scarce resource (e.g., significant RAM, disk memory or CPU)
  • PySpark is being used

Now we can execute the pipeline by providing a runner and values for each of the inputs.

From the command line, you can run the pipeline as follows:

kedro run

Output:

2019-04-26 17:19:01,341 - root - INFO - ** Kedro project new-kedro-project
2019-04-26 17:19:01,360 - kedro.io.data_catalog - INFO - Loading data from `example_iris_data` (CSVLocalDataSet)...
2019-04-26 17:19:01,387 - kedro.io.data_catalog - INFO - Loading data from `parameters` (MemoryDataSet)...
2019-04-26 17:19:01,387 - kedro.pipeline.node - INFO - Running node: split_data([example_iris_data,params:example_test_data_ratio]) -> [example_test_x,example_test_y,example_train_x,example_train_y]
2019-04-26 17:19:01,437 - kedro.io.data_catalog - INFO - Saving data to `example_test_x` (MemoryDataSet)...
2019-04-26 17:19:01,439 - kedro.io.data_catalog - INFO - Saving data to `example_train_y` (MemoryDataSet)...
2019-04-26 17:19:01,443 - kedro.io.data_catalog - INFO - Saving data to `example_train_x` (MemoryDataSet)...
2019-04-26 17:19:01,447 - kedro.io.data_catalog - INFO - Saving data to `example_test_y` (MemoryDataSet)...
2019-04-26 17:19:01,447 - kedro.runner.sequential_runner - INFO - Completed 1 out of 4 tasks
2019-04-26 17:19:01,448 - kedro.io.data_catalog - INFO - Loading data from `example_train_x` (MemoryDataSet)...
2019-04-26 17:19:01,454 - kedro.io.data_catalog - INFO - Loading data from `example_train_y` (MemoryDataSet)...
2019-04-26 17:19:01,461 - kedro.io.data_catalog - INFO - Loading data from `parameters` (MemoryDataSet)...
2019-04-26 17:19:01,461 - kedro.pipeline.node - INFO - Running node: train_model([example_train_x,example_train_y,parameters]) -> [example_model]
2019-04-26 17:19:01,887 - kedro.io.data_catalog - INFO - Saving data to `example_model` (MemoryDataSet)...
2019-04-26 17:19:01,887 - kedro.runner.sequential_runner - INFO - Completed 2 out of 4 tasks
2019-04-26 17:19:01,888 - kedro.io.data_catalog - INFO - Loading data from `example_test_x` (MemoryDataSet)...
2019-04-26 17:19:01,888 - kedro.io.data_catalog - INFO - Loading data from `example_model` (MemoryDataSet)...
2019-04-26 17:19:01,888 - kedro.pipeline.node - INFO - Running node: predict([example_model,example_test_x]) -> [example_predictions]
2019-04-26 17:19:01,890 - kedro.io.data_catalog - INFO - Saving data to `example_predictions` (MemoryDataSet)...
2019-04-26 17:19:01,891 - kedro.runner.sequential_runner - INFO - Completed 3 out of 4 tasks
2019-04-26 17:19:01,891 - kedro.io.data_catalog - INFO - Loading data from `example_predictions` (MemoryDataSet)...
2019-04-26 17:19:01,891 - kedro.io.data_catalog - INFO - Loading data from `example_test_y` (MemoryDataSet)...
2019-04-26 17:19:01,891 - kedro.pipeline.node - INFO - Running node: report_accuracy([example_predictions,example_test_y]) -> None
2019-04-26 17:19:01,892 - new_kedro_project.nodes.example - INFO - Model accuracy on test set: 96.67%
2019-04-26 17:19:01,892 - kedro.runner.sequential_runner - INFO - Completed 4 out of 4 tasks
2019-04-26 17:19:01,892 - kedro.runner.sequential_runner - INFO - Pipeline execution completed successfully.

which will run the pipeline using SequentialRunner by default. You can also explicitly use SequentialRunner as follows:

kedro run --runner=SequentialRunner

In case you want to run the pipeline using ParallelRunner, add a flag as follows:

kedro run --parallel

or

kedro run --runner=ParallelRunner

Output:

2019-04-26 17:20:45,012 - root - INFO - ** Kedro project new-kedro-project
2019-04-26 17:20:45,081 - kedro.io.data_catalog - INFO - Loading data from `example_iris_data` (CSVLocalDataSet)...
2019-04-26 17:20:45,099 - kedro.io.data_catalog - INFO - Loading data from `parameters` (MemoryDataSet)...
2019-04-26 17:20:45,099 - kedro.pipeline.node - INFO - Running node: split_data([example_iris_data,params:example_test_data_ratio]) -> [example_test_x,example_test_y,example_train_x,example_train_y]
2019-04-26 17:20:45,115 - kedro.io.data_catalog - INFO - Saving data to `example_test_x` (AutoProxy[MemoryDataSet])...
2019-04-26 17:20:45,121 - kedro.io.data_catalog - INFO - Saving data to `example_test_y` (AutoProxy[MemoryDataSet])...
2019-04-26 17:20:45,123 - kedro.io.data_catalog - INFO - Saving data to `example_train_x` (AutoProxy[MemoryDataSet])...
2019-04-26 17:20:45,125 - kedro.io.data_catalog - INFO - Saving data to `example_train_y` (AutoProxy[MemoryDataSet])...
2019-04-26 17:20:45,135 - kedro.io.data_catalog - INFO - Loading data from `example_train_x` (AutoProxy[MemoryDataSet])...
2019-04-26 17:20:45,140 - kedro.io.data_catalog - INFO - Loading data from `example_train_y` (AutoProxy[MemoryDataSet])...
2019-04-26 17:20:45,142 - kedro.io.data_catalog - INFO - Loading data from `parameters` (MemoryDataSet)...
2019-04-26 17:20:45,142 - kedro.pipeline.node - INFO - Running node: train_model([example_train_x,example_train_y,parameters]) -> [example_model]
2019-04-26 17:20:45,437 - kedro.io.data_catalog - INFO - Saving data to `example_model` (AutoProxy[MemoryDataSet])...
2019-04-26 17:20:45,444 - kedro.io.data_catalog - INFO - Loading data from `example_test_x` (AutoProxy[MemoryDataSet])...
2019-04-26 17:20:45,449 - kedro.io.data_catalog - INFO - Loading data from `example_model` (AutoProxy[MemoryDataSet])...
2019-04-26 17:20:45,449 - kedro.pipeline.node - INFO - Running node: predict([example_model,example_test_x]) -> [example_predictions]
2019-04-26 17:20:45,451 - kedro.io.data_catalog - INFO - Saving data to `example_predictions` (AutoProxy[MemoryDataSet])...
2019-04-26 17:20:45,457 - kedro.io.data_catalog - INFO - Loading data from `example_predictions` (AutoProxy[MemoryDataSet])...
2019-04-26 17:20:45,461 - kedro.io.data_catalog - INFO - Loading data from `example_test_y` (AutoProxy[MemoryDataSet])...
2019-04-26 17:20:45,461 - kedro.pipeline.node - INFO - Running node: report_accuracy([example_predictions,example_test_y]) -> None
2019-04-26 17:20:45,466 - new_kedro_project.nodes.example - INFO - Model accuracy on test set: 100.00%
2019-04-26 17:20:45,494 - kedro.runner.parallel_runner - INFO - Pipeline execution completed successfully.
Note: You cannot use both --parallel and --runner flags at the same time (e.g. kedro run --parallel --runner=SequentialRunner raises an exception).

Applying decorators on pipelines

You can apply decorators on whole pipelines, the same way you apply decorators on single nodes. For example, if you want to apply the decorators defined in the earlier section to all pipeline nodes simultaneously, you can do so as follows:

hello_pipeline = Pipeline([
    node(say_hello, 'name1', None),
    node(say_hello, 'name2', None)
]).decorate(apply_g, apply_h)

SequentialRunner().run(hello_pipeline, DataCatalog({}, dict(name1="Kedro", name2="Python")))

Output:

Hello f(h(g(Kedro)))!
Hello f(h(g(Python)))!
Out[9]: {}

Kedro has a couple of built-in decorators, which can be useful for monitoring your pipeline. You can find the built-in decorators in kedro.pipeline.decorators:

  • log_time will log the time taken for executing your node
  • mem_profile will log the max memory usage of your node. mem_profile needs to perform at least 4 memory snapshots of your node and it will do them every 100ms, therefore it can be used only for nodes which take more than half a second to run.

Running pipelines with IO

The above definition of pipelines only applies for non-stateful or “pure” pipelines that do not interact with the outside world. In practice, we would like to interact with APIs, databases, files and other sources of data. By combining IO and pipelines, we can tackle these more complex use cases.

By using DataCatalog from the IO module we are still able to write pure functions that work with our data and outsource file saving and loading to DataCatalog.

Through DataCatalog, we can control where inputs are loaded from, where intermediate variables get persisted and ultimately the location to which output variables are written:

io = DataCatalog(dict(
    xs=MemoryDataSet()
))
io.list()

Output:

Out[10]: ['xs']
io.save('xs', [1, 2, 3])
SequentialRunner().run(pipeline, catalog=io)

Output:

Out[11]: {'v': 0.666666666666667}

In this simple example, we defined a MemoryDataSet called xs to store our inputs, saved our input list [1, 2, 3] into xs, then instantiated SequentialRunner and called its run method with the pipeline and data catalog instances.

Outputting to a file

We can also use IO to save outputs to a file. In this example, we define a custom LambdaDataSet that would serialise the output to a file locally:

def save(value):
    with open("./data/07_model_output/variance.pickle", "wb") as f:
        pickle.dump(value, f)

def load():
    with open("./data/07_model_output/variance.pickle", "rb") as f:
        return pickle.load(f)

pickler = LambdaDataSet(load=load, save=save)
io.add('v', pickler)

It is important to make sure that the data catalog variable name v matches the name v in the pipeline definition.

Next we can confirm that this LambdaDataSet works:

io.save('v', 5)
io.load('v')

Ouput:

Out[12]: 5

Finally, let’s run the pipeline again now serialising the output:

SequentialRunner().run(pipeline, catalog=io)

Ouput:

Out[13]: {}

Because the output has been persisted to a local file we don’t see it directly, but it can be retrieved from the catalog:

io.load('v')

Ouput:

Out[14]: 0.666666666666667
try:
    os.remove("./data/07_model_output/variance.pickle")
except FileNotFoundError:
    pass

Partial pipelines

Sometimes it is desirable to work with only a subset of a pipeline’s nodes. Let’s look at the example pipeline we created earlier:

print(pipeline.describe())

Ouput:

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

len([xs]) -> [n]
mean node
mean sos
variance node

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

Partial pipeline starting from inputs

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

print(pipeline.from_inputs('m2').describe())

Output:

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

variance node

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

Specifying that the partial pipeline from inputs m and xs is needed will result in the following pipeline:

print(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 it can been seen from the pipeline description, adding m in the from_inputs list does not guarantee that it will not be recomputed if another provided input like xs forces recomputing it.

Partial pipeline starting from nodes

Another way of selecting a partial pipeline is by specifying the nodes which should be used as a start of the new pipeline. For example you can do as follows:

print(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 create a partial pipeline starting from the specified node and continuing to all other nodes downstream.

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

kedro run --from-nodes="mean node"

Partial pipeline ending at nodes

Similarly, you can specify the nodes which should be used as an end of the new pipeline. For example, you can do as follows:

print(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 create a partial pipeline starting at the top and ending with the specified node.

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

kedro run --to-nodes="mean node"

Furthermore, you can combine these two flags to specify a range of nodes to be included in the new pipeline. This would look like:

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

or, when specifying multiple nodes:

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

Partial pipeline from nodes with tags

One can also create a partial pipeline from the nodes that have specific tags attached to them. In order to construct a partial pipeline out of nodes that have both tag t1 AND tag t2, you can run the following:

print(pipeline.only_nodes_with_tags('t1', 't2').describe())

Output:

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

Outputs: None
##################################

To construct a partial pipeline out of nodes that have tag t1 OR tag t2, please execute the following:

partial_pipeline = pipeline.only_nodes_with_tags('t1') + pipeline.only_nodes_with_tags('t2')
print(partial_pipeline.describe())

Output:

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

Outputs: None
##################################

Running only some nodes

Sometimes you might need to run only some of the nodes in a pipeline. To do that, you can do as follows:

print(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 partial pipeline, consisting solely of the nodes you specify as arguments in the method call.

You can check this out for yourself by updating the definition of the first node in the example code provided in pipeline.py as follows:

node(
    split_data,
    ["example_iris_data", "parameters"],
    dict(
        train_x="example_train_x",
        train_y="example_train_y",
        test_x="example_test_x",
        test_y="example_test_y",
    ),
    name="node1",
),

and then run the following command in your terminal window:

kedro run --node=node1

You may specify multiple names like so:

kedro run --node=node1 --node=node2
Note: The run will only succeed if all the inputs required by those nodes already exist, i.e. already produced or present in the data catalog.

Recreating Missing Outputs

Kedro supports the automatic generation of partial pipelines that take into account existing node outputs. This can be helpful to avoid re-running nodes that take a long time:

print(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 JSONLocalDataSet.

n_json = JSONLocalDataSet(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:

io.exists('n')

Output:

Out[15]: False

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

SequentialRunner().run(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 been removed:

SequentialRunner().run_only_missing(pipeline, io)

Ouput:

Out[18]: {'v': 0.666666666666667}
try:
    os.remove("./data/07_model_output/len.json")
except FileNotFoundError:
    pass