Create a pipeline

This section covers the third part of the standard development workflow, and covers the following:

  • How to create each node required by the example

  • How to set up a pipeline

Data processing pipeline

You previously registered the raw datasets for your Kedro project, so you can now create nodes to pre-process two of the datasets, companies.csv and shuttles.xlsx, to prepare the data for modelling.

Generate a new pipeline template

In the terminal run the following command:

kedro pipeline create data_processing

This generates all the files you need to start writing a data_processing pipeline:

  • nodes.py and pipeline.py in the src/kedro_tutorial/pipelines/data_processing folder for the main node functions that form your pipeline

  • conf/base/parameters/data_processing.yml to define the parameters used when running the pipeline

  • src/tests/pipelines/data_processing for tests for your pipeline

  • __init__.py files in the required places to ensure that the pipeline can be imported by Python

├── README.md
├── conf
│   └── base
│       └── parameters
│           └── data_processing.yml
└── src
    ├── kedro_tutorial
    │   ├── __init__.py
    │   └── pipelines
    │       ├── __init__.py
    │       └── data_processing
    │           ├── README.md
    │           ├── __init__.py
    │           ├── nodes.py
    │           └── pipeline.py
    └── tests
        ├── __init__.py
        └── pipelines
            ├── __init__.py
            └── data_processing
                ├── __init__.py
                └── test_pipeline.py

Add node functions

Open src/kedro_tutorial/pipelines/data_processing/nodes.py and add the code below, which provides two functions (preprocess_companies and preprocess_shuttles) that each take a raw DataFrame and output a DataFrame containing pre-processed data:

Click to expand
import pandas as pd


def _is_true(x: pd.Series) -> pd.Series:
    return x == "t"


def _parse_percentage(x: pd.Series) -> pd.Series:
    x = x.str.replace("%", "")
    x = x.astype(float) / 100
    return x


def _parse_money(x: pd.Series) -> pd.Series:
    x = x.str.replace("$", "").str.replace(",", "")
    x = x.astype(float)
    return x


def preprocess_companies(companies: pd.DataFrame) -> pd.DataFrame:
    """Preprocesses the data for companies.

    Args:
        companies: Raw data.
    Returns:
        Preprocessed data, with `company_rating` converted to a float and
        `iata_approved` converted to boolean.
    """
    companies["iata_approved"] = _is_true(companies["iata_approved"])
    companies["company_rating"] = _parse_percentage(companies["company_rating"])
    return companies


def preprocess_shuttles(shuttles: pd.DataFrame) -> pd.DataFrame:
    """Preprocesses the data for shuttles.

    Args:
        shuttles: Raw data.
    Returns:
        Preprocessed data, with `price` converted to a float and `d_check_complete`,
        `moon_clearance_complete` converted to boolean.
    """
    shuttles["d_check_complete"] = _is_true(shuttles["d_check_complete"])
    shuttles["moon_clearance_complete"] = _is_true(shuttles["moon_clearance_complete"])
    shuttles["price"] = _parse_money(shuttles["price"])
    return shuttles

Assemble nodes into the data processing pipeline

The next steps are to create a node for each function, and to create a modular pipeline for data processing:

Add the following to src/kedro_tutorial/pipelines/data_processing/pipeline.py, so the create_pipeline() function looks as follows:

Click to expand
def create_pipeline(**kwargs) -> Pipeline:
    return pipeline(
        [
            node(
                func=preprocess_companies,
                inputs="companies",
                outputs="preprocessed_companies",
                name="preprocess_companies_node",
            ),
            node(
                func=preprocess_shuttles,
                inputs="shuttles",
                outputs="preprocessed_shuttles",
                name="preprocess_shuttles_node",
            ),
        ]
    )

Note

companies and shuttles refer to the datasets defined in conf/base/catalog.yml. These are inputs to the preprocess_companies and preprocess_shuttles functions. The named node inputs (and outputs) are used by the pipeline to determine interdependencies between the nodes, and hence, their execution order.

Be sure to import node, and your functions by adding them to the beginning of pipeline.py:

from kedro.pipeline import Pipeline, node, pipeline

from .nodes import preprocess_companies, preprocess_shuttles

Update the project pipeline

Now update the project’s pipeline in src/kedro_tutorial/pipeline_registry.py to add the pipeline for data processing:

Click to expand
from typing import Dict

from kedro.pipeline import Pipeline

from kedro_tutorial.pipelines import data_processing as dp


def register_pipelines() -> Dict[str, Pipeline]:
    """Register the project's pipeline.

    Returns:
    A mapping from a pipeline name to a ``Pipeline`` object.

    """
    data_processing_pipeline = dp.create_pipeline()

    return {
        "__default__": data_processing_pipeline,
        "dp": data_processing_pipeline,
    }

Test the example

Run the following command in your terminal window to test the node named preprocess_companies_node:

kedro run --node=preprocess_companies_node

You should see output similar to the below:

2019-08-19 10:44:33,112 - root - INFO - ** Kedro project kedro-tutorial
2019-08-19 10:44:33,123 - kedro.io.data_catalog - INFO - Loading data from `companies` (CSVDataSet)...
2019-08-19 10:44:33,161 - kedro.pipeline.node - INFO - Running node: preprocess_companies_node: preprocess_companies([companies]) -> [preprocessed_companies]
2019-08-19 10:44:33,206 - kedro.io.data_catalog - INFO - Saving data to `preprocess_companies_node` (MemoryDataSet)...
2019-08-19 10:44:33,471 - kedro.runner.sequential_runner - INFO - Completed 1 out of 1 tasks
2019-08-19 10:44:33,471 - kedro.runner.sequential_runner - INFO - Pipeline execution completed successfully.

To test the entire data processing pipeline:

kedro run

You should see output similar to the following:

2019-08-19 10:50:39,950 - root - INFO - ** Kedro project kedro-tutorial
2019-08-19 10:50:39,957 - kedro.io.data_catalog - INFO - Loading data from `shuttles` (ExcelDataSet)...
2019-08-19 10:50:48,521 - kedro.pipeline.node - INFO - Running node: preprocess_shuttles_node: preprocess_shuttles([shuttles]) -> [preprocessed_shuttles]
2019-08-19 10:50:48,587 - kedro.io.data_catalog - INFO - Saving data to `preprocessed_shuttles` (MemoryDataSet)...
2019-08-19 10:50:49,133 - kedro.runner.sequential_runner - INFO - Completed 1 out of 2 tasks
2019-08-19 10:50:49,133 - kedro.io.data_catalog - INFO - Loading data from `companies` (CSVDataSet)...
2019-08-19 10:50:49,168 - kedro.pipeline.node - INFO - Running node: preprocess_companies_node: preprocess_companies([companies]) -> [preprocessed_companies]
2019-08-19 10:50:49,212 - kedro.io.data_catalog - INFO - Saving data to `preprocessed_companies` (MemoryDataSet)...
2019-08-19 10:50:49,458 - kedro.runner.sequential_runner - INFO - Completed 2 out of 2 tasks
2019-08-19 10:50:49,459 - kedro.runner.sequential_runner - INFO - Pipeline execution completed successfully.

Visualise the pipeline

Kedro-Viz at this point will render a visualisation of a very simple, but valid, pipeline. To show the visualisation, run:

kedro viz

This command should open up a visualisation in your browser that looks like the following:

simple_pipeline

Persist pre-processed data

The nodes above each output a new dataset (preprocessed_companies and preprocessed_shuttles). When Kedro ran the pipeline, it determined that neither datasets had been registered in the data catalog (conf/base/catalog.yml). If a dataset is not registered, Kedro stores it in memory as a Python object using the MemoryDataSet class. Once all nodes depending on it have been executed, the MemoryDataSet is cleared and its memory released by the Python garbage collector.

You can persist the preprocessed data by adding the following to conf/base/catalog.yml.:

preprocessed_companies:
  type: pandas.ParquetDataSet
  filepath: data/02_intermediate/preprocessed_companies.pq

preprocessed_shuttles:
  type: pandas.ParquetDataSet
  filepath: data/02_intermediate/preprocessed_shuttles.pq

The code above declares explicitly that pandas.ParquetDataSet should be used instead of MemoryDataSet.

The Data Catalog will take care of saving the datasets automatically (in this case as Parquet) to the path specified next time the pipeline is run. There is no need to change any code in your preprocessing functions to accommodate this change.

We choose the Apache Parquet format for working with processed and typed data. We recommend getting your data out of CSV as soon as possible. Parquet supports things like compression, partitioning and types out of the box. Whilst you lose the ability to view the file as text, the benefits greatly outweigh the drawbacks.

Extend the data processing pipeline

The next step in the tutorial is to add another node for a function to join together the three DataFrames into a single model input table. First, add the create_model_input_table() function from the snippet below to src/kedro_tutorial/pipelines/data_processing/nodes.py.

Click to expand
def create_model_input_table(
    shuttles: pd.DataFrame, companies: pd.DataFrame, reviews: pd.DataFrame
) -> pd.DataFrame:
    """Combines all data to create a model input table.

    Args:
        shuttles: Preprocessed data for shuttles.
        companies: Preprocessed data for companies.
        reviews: Raw data for reviews.
    Returns:
        model input table.

    """
    rated_shuttles = shuttles.merge(reviews, left_on="id", right_on="shuttle_id")
    model_input_table = rated_shuttles.merge(
        companies, left_on="company_id", right_on="id"
    )
    model_input_table = model_input_table.dropna()
    return model_input_table

Add the function to the data processing pipeline in src/kedro_tutorial/pipelines/data_processing/pipeline.py as a node:

node(
    func=create_model_input_table,
    inputs=["preprocessed_shuttles", "preprocessed_companies", "reviews"],
    outputs="model_input_table",
    name="create_model_input_table_node",
),

The code above informs Kedro that the function create_model_input_table should be called with the data loaded from datasets preprocessed_shuttles, preprocessed_companies, and reviews and the output should be saved to dataset model_input_table.

Add an import statement for create_model_input_table at the top of the file:

from .nodes import create_model_input_table, preprocess_companies, preprocess_shuttles

Persist the model input table

If you want the model input table data to be saved to file rather than used in-memory, add an entry to conf/base/catalog.yml:

model_input_table:
  type: pandas.ParquetDataSet
  filepath: data/03_primary/model_input_table.pq

Test the example

To test the progress of the example:

kedro run

You should see output similar to the following:

2019-08-19 10:55:47,534 - root - INFO - ** Kedro project kedro-tutorial
2019-08-19 10:55:47,541 - kedro.io.data_catalog - INFO - Loading data from `shuttles` (ExcelDataSet)...
2019-08-19 10:55:55,670 - kedro.pipeline.node - INFO - Running node: preprocess_shuttles_node: preprocess_shuttles([shuttles]) -> [preprocessed_shuttles]
2019-08-19 10:55:55,736 - kedro.io.data_catalog - INFO - Saving data to `preprocessed_shuttles` (ParquetDataSet)...
2019-08-19 10:55:56,284 - kedro.runner.sequential_runner - INFO - Completed 1 out of 3 tasks
2019-08-19 10:55:56,284 - kedro.io.data_catalog - INFO - Loading data from `companies` (CSVDataSet)...
2019-08-19 10:55:56,318 - kedro.pipeline.node - INFO - Running node: preprocess_companies_node: preprocess_companies([companies]) -> [preprocessed_companies]
2019-08-19 10:55:56,361 - kedro.io.data_catalog - INFO - Saving data to `preprocessed_companies` (ParquetDataSet)...
2019-08-19 10:55:56,610 - kedro.runner.sequential_runner - INFO - Completed 2 out of 3 tasks
2019-08-19 10:55:56,610 - kedro.io.data_catalog - INFO - Loading data from `preprocessed_shuttles` (ParquetDataSet)...
2019-08-19 10:55:56,715 - kedro.io.data_catalog - INFO - Loading data from `preprocessed_companies` (ParquetDataSet)...
2019-08-19 10:55:56,750 - kedro.io.data_catalog - INFO - Loading data from `reviews` (CSVDataSet)...
2019-08-19 10:55:56,812 - kedro.pipeline.node - INFO - Running node: create_model_input_table_node: create_model_input_table([preprocessed_companies,preprocessed_shuttles,reviews]) -> [model_input_table]
2019-08-19 10:55:58,679 - kedro.io.data_catalog - INFO - Saving data to `model_input_table` (ParquetDataSet)...
2019-08-19 10:56:09,991 - kedro.runner.sequential_runner - INFO - Completed 3 out of 3 tasks
2019-08-19 10:56:09,991 - kedro.runner.sequential_runner - INFO - Pipeline execution completed successfully.

Use kedro viz --autoreload

Run the following command:

kedro viz --autoreload

The autoreload flag will ensure that changes to your pipeline are automatically reflected in Kedro-Viz. For example, commenting out create_model_input_table_node in pipeline.py will trigger a re-render of the pipeline:

autoreload

Note

This is also a great time to highlight how Kedro’s topological sorting works. The actual order of the node() calls in the pipeline is irrelevant; Kedro works out the execution graph via the inputs/outputs declared, not the order provided by the user. This means that you, as a developer, simply ask Kedro what data you want and it will derive the execution graph automatically.

Data science pipeline

We have created a modular pipeline for data processing, which merges three input datasets to create a model input table. Now we will create the data science pipeline for price prediction, which uses the LinearRegression implementation from the scikit-learn library.

Create the data science pipeline

Run the following command to create the data_science pipeline:

kedro pipeline create data_science

Add the following code to the src/kedro_tutorial/pipelines/data_science/nodes.py file:

Click to expand
import logging
from typing import Dict, Tuple

import pandas as pd
from sklearn.linear_model import LinearRegression
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split


def split_data(data: pd.DataFrame, parameters: Dict) -> Tuple:
    """Splits data into features and targets training and test sets.

    Args:
        data: Data containing features and target.
        parameters: Parameters defined in parameters/data_science.yml.
    Returns:
        Split data.
    """
    X = data[parameters["features"]]
    y = data["price"]
    X_train, X_test, y_train, y_test = train_test_split(
        X, y, test_size=parameters["test_size"], random_state=parameters["random_state"]
    )
    return X_train, X_test, y_train, y_test


def train_model(X_train: pd.DataFrame, y_train: pd.Series) -> LinearRegression:
    """Trains the linear regression model.

    Args:
        X_train: Training data of independent features.
        y_train: Training data for price.

    Returns:
        Trained model.
    """
    regressor = LinearRegression()
    regressor.fit(X_train, y_train)
    return regressor


def evaluate_model(
    regressor: LinearRegression, X_test: pd.DataFrame, y_test: pd.Series
):
    """Calculates and logs the coefficient of determination.

    Args:
        regressor: Trained model.
        X_test: Testing data of independent features.
        y_test: Testing data for price.
    """
    y_pred = regressor.predict(X_test)
    score = r2_score(y_test, y_pred)
    logger = logging.getLogger(__name__)
    logger.info("Model has a coefficient R^2 of %.3f on test data.", score)

Configure the input parameters

Add the following to conf/base/parameters/data_science.yml:

model_options:
  test_size: 0.2
  random_state: 3
  features:
    - engines
    - passenger_capacity
    - crew
    - d_check_complete
    - moon_clearance_complete
    - iata_approved
    - company_rating
    - review_scores_rating

These are the parameters fed into the DataCatalog when the pipeline is executed. More information about parameters is available in later documentation for advanced usage. Here, the parameters test_size and random_state are used as part of the train-test split, and features gives the names of columns in the model input table to use as features.

Register the dataset

The next step is to register the dataset that will save the trained model, by adding the following definition to conf/base/catalog.yml:

regressor:
  type: pickle.PickleDataSet
  filepath: data/06_models/regressor.pickle
  versioned: true

Versioning is enabled for regressor, which means that the pickled output of the regressor will be versioned and saved every time the pipeline is run. This allows us to keep the history of the models built using this pipeline. Further details can be found in the Versioning section.

Assemble the data science pipeline

To create a modular pipeline for the price prediction model, add the following to the top of src/kedro_tutorial/pipelines/data_science/pipeline.py:

from kedro.pipeline import Pipeline, node, pipeline

from .nodes import evaluate_model, split_data, train_model


def create_pipeline(**kwargs) -> Pipeline:
    return pipeline(
        [
            node(
                func=split_data,
                inputs=["model_input_table", "params:model_options"],
                outputs=["X_train", "X_test", "y_train", "y_test"],
                name="split_data_node",
            ),
            node(
                func=train_model,
                inputs=["X_train", "y_train"],
                outputs="regressor",
                name="train_model_node",
            ),
            node(
                func=evaluate_model,
                inputs=["regressor", "X_test", "y_test"],
                outputs=None,
                name="evaluate_model_node",
            ),
        ]
    )

Update the project pipeline

Add the data science pipeline to the project by replacing the code in register_pipelines in src/kedro_tutorial/pipeline_registry.py with the following:

def register_pipelines() -> Dict[str, Pipeline]:
    """Register the project's pipeline.

    Returns:
        A mapping from a pipeline name to a ``Pipeline`` object.

    """
    data_processing_pipeline = dp.create_pipeline()
    data_science_pipeline = ds.create_pipeline()

    return {
        "__default__": data_processing_pipeline + data_science_pipeline,
        "dp": data_processing_pipeline,
        "ds": data_science_pipeline,
    }

Include the import at the top of the file:

from kedro_tutorial.pipelines import data_science as ds
  • The two modular pipelines are merged together into a project __default__ pipeline using the + operator.

  • The data_processing_pipeline will preprocess the data, and data_science_pipeline will create features, train and evaluate the model.

Note

The order in which you add the pipelines together is not significant (data_science_pipeline + data_processing_pipeline would produce the same result), since Kedro automatically detects the data-centric execution order for all the nodes in the resulting pipeline.

Test the pipelines

Execute the default pipeline:

kedro run

You should see output similar to the following:

Click to expand
2019-08-19 10:51:46,501 - root - INFO - ** Kedro project kedro-tutorial
2019-08-19 10:51:46,510 - kedro.io.data_catalog - INFO - Loading data from `companies` (CSVDataSet)...
2019-08-19 10:51:46,547 - kedro.pipeline.node - INFO - Running node: preprocess_companies_node: preprocess_companies([companies]) -> [preprocessed_companies]
2019-08-19 10:51:46,597 - kedro.io.data_catalog - INFO - Saving data to `preprocessed_companies` (CSVDataSet)...
2019-08-19 10:51:46,906 - kedro.runner.sequential_runner - INFO - Completed 1 out of 6 tasks
2019-08-19 10:51:46,906 - kedro.io.data_catalog - INFO - Loading data from `shuttles` (ExcelDataSet)...
2019-08-19 10:51:55,324 - kedro.pipeline.node - INFO - Running node: preprocess_shuttles_node: preprocess_shuttles([shuttles]) -> [preprocessed_shuttles]
2019-08-19 10:51:55,389 - kedro.io.data_catalog - INFO - Saving data to `preprocessed_shuttles` (CSVDataSet)...
2019-08-19 10:51:55,932 - kedro.runner.sequential_runner - INFO - Completed 2 out of 6 tasks
2019-08-19 10:51:55,932 - kedro.io.data_catalog - INFO - Loading data from `preprocessed_shuttles` (CSVDataSet)...
2019-08-19 10:51:56,042 - kedro.io.data_catalog - INFO - Loading data from `preprocessed_companies` (CSVDataSet)...
2019-08-19 10:51:56,078 - kedro.io.data_catalog - INFO - Loading data from `reviews` (CSVDataSet)...
2019-08-19 10:51:56,139 - kedro.pipeline.node - INFO - Running node: create_model_input_table_node: create_model_input_table([preprocessed_companies,preprocessed_shuttles,reviews]) -> [model_input_table]
2019-08-19 10:51:58,037 - kedro.io.data_catalog - INFO - Saving data to `model_input_table` (CSVDataSet)...
2019-08-19 10:52:09,133 - kedro.runner.sequential_runner - INFO - Completed 3 out of 6 tasks
2019-08-19 10:52:09,133 - kedro.io.data_catalog - INFO - Loading data from `model_input_table` (CSVDataSet)...
2019-08-19 10:52:10,941 - kedro.io.data_catalog - INFO - Loading data from `params:model_options` (MemoryDataSet)...
2019-08-19 10:52:10,941 - kedro.pipeline.node - INFO - Running node: split_data_node: split_data([model_input_table,params:model_options]) -> [X_test,X_train,y_test,y_train]
2019-08-19 10:52:11,343 - kedro.io.data_catalog - INFO - Saving data to `X_train` (MemoryDataSet)...
2019-08-19 10:52:11,372 - kedro.io.data_catalog - INFO - Saving data to `X_test` (MemoryDataSet)...
2019-08-19 10:52:11,380 - kedro.io.data_catalog - INFO - Saving data to `y_train` (MemoryDataSet)...
2019-08-19 10:52:11,381 - kedro.io.data_catalog - INFO - Saving data to `y_test` (MemoryDataSet)...
2019-08-19 10:52:11,443 - kedro.runner.sequential_runner - INFO - Completed 4 out of 6 tasks
2019-08-19 10:52:11,443 - kedro.io.data_catalog - INFO - Loading data from `X_train` (MemoryDataSet)...
2019-08-19 10:52:11,472 - kedro.io.data_catalog - INFO - Loading data from `y_train` (MemoryDataSet)...
2019-08-19 10:52:11,474 - kedro.pipeline.node - INFO - Running node: train_model_node: train_model([X_train,y_train]) -> [regressor]
2019-08-19 10:52:11,704 - kedro.io.data_catalog - INFO - Saving data to `regressor` (PickleDataSet)...
2019-08-19 10:52:11,776 - kedro.runner.sequential_runner - INFO - Completed 5 out of 6 tasks
2019-08-19 10:52:11,776 - kedro.io.data_catalog - INFO - Loading data from `regressor` (PickleDataSet)...
2019-08-19 10:52:11,776 - kedro.io.data_catalog - INFO - Loading data from `X_test` (MemoryDataSet)...
2019-08-19 10:52:11,784 - kedro.io.data_catalog - INFO - Loading data from `y_test` (MemoryDataSet)...
2019-08-19 10:52:11,785 - kedro.pipeline.node - INFO - Running node: evaluate_model_node: evaluate_model([X_test,regressor,y_test]) -> None
2019-08-19 10:52:11,830 - kedro_tutorial.pipelines.data_science.nodes - INFO - Model has a coefficient R^2 of 0.462 on test data.
2019-08-19 10:52:11,869 - kedro.runner.sequential_runner - INFO - Completed 6 out of 6 tasks
2019-08-19 10:52:11,869 - kedro.runner.sequential_runner - INFO - Pipeline execution completed successfully.

Kedro runners

There are three different Kedro runners that can run the pipeline:

  • SequentialRunner - runs your nodes sequentially; once a node has completed its task then the next one starts.

  • ParallelRunner - runs your nodes in parallel; independent nodes are able to run at the same time, which is more efficient when there are independent branches in your pipeline and allows you to take advantage of multiple CPU cores.

  • ThreadRunner - runs your nodes in parallel, similarly to ParallelRunner, but uses multithreading instead of multiprocessing.

By default, Kedro uses a SequentialRunner, which is instantiated when you execute kedro run from the command line. If you decide to use ParallelRunner, ThreadRunner or a custom runner, you can do so through the --runner flag as follows:

kedro run --runner=ParallelRunner
kedro run --runner=ThreadRunner
kedro run --runner=module.path.to.my.runner

Note

ParallelRunner performs task parallelisation via multiprocessing. ThreadRunner is intended for use with remote execution engines such as Spark and Dask. You can find out more about the runners Kedro provides, and how to create your own, in the pipeline documentation about runners `.

You can find out more about the runners Kedro provides, and how to create your own, in the pipeline documentation about runners.

Slice a pipeline

In some cases you may want to run just part of a pipeline. For example, you may need to only run the data science pipeline to tune the hyperparameters of the price prediction model and skip data processing execution. You can ‘slice’ the pipeline and specify just the portion you want to run by using the --pipeline command line option. For example, to only run the pipeline named ds (as labelled in register_pipelines), execute the following command:

kedro run --pipeline=ds

See the pipeline slicing documentation and the kedro run CLI documentation for other ways to run sections of your pipeline.

Warning

To successfully run the pipeline, you need to make sure that all required input datasets already exist, otherwise you may get an error similar to this:

kedro run --pipeline=ds

2019-10-04 12:36:12,135 - root - INFO - ** Kedro project kedro-tutorial
2019-10-04 12:36:12,158 - kedro.io.data_catalog - INFO - Loading data from `model_input_table` (CSVDataSet)...
2019-10-04 12:36:12,158 - kedro.runner.sequential_runner - WARNING - There are 3 nodes that have not run.
You can resume the pipeline run with the following command:
kedro run
Traceback (most recent call last):
  ...
  File "pandas/_libs/parsers.pyx", line 382, in pandas._libs.parsers.TextReader.__cinit__
  File "pandas/_libs/parsers.pyx", line 689, in pandas._libs.parsers.TextReader._setup_parser_source
FileNotFoundError: [Errno 2] File b'data/03_primary/model_input_table.csv' does not exist: b'data/03_primary/model_input_table.csv'

The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  ...
    raise DataSetError(message) from exc
kedro.io.core.DataSetError: Failed while loading data from data set CSVDataSet(filepath=data/03_primary/model_input_table.csv, save_args={'index': False}).
[Errno 2] File b'data/03_primary/model_input_table.csv' does not exist: b'data/03_primary/model_input_table.csv'