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Kedro Kedro-Viz Kedro-Datasets
kedro
0.19

Learn about Kedro

  • Introduction to Kedro
  • First steps
    • Set up Kedro
      • Installation prerequisites
      • Python version support policy
      • Create a virtual environment for your Kedro project
        • How to create a new virtual environment using venv
        • How to create a new virtual environment using conda
        • Optional: Integrate Kedro in VS Code with the official extension
      • How to install Kedro using pip
      • How to verify your Kedro installation
      • How to upgrade Kedro
      • Summary
    • Create a new Kedro project
      • Introducing kedro new
        • Project name
        • Project tools
        • Project examples
        • Quickstart examples
        • Telemetry consent
      • Run the new project
      • Visualise a Kedro project
      • Where next?
      • Flowchart of general choice of tools
    • Kedro concepts
      • Summary
      • Node
      • Pipeline
      • Data Catalog
      • Kedro project directory structure
        • Default Kedro project structure (all tools selected)
        • Example Kedro project structure (no tools selected)
        • Tool selection and resulting structure
        • conf
        • src
        • Customising your project structure
    • Create a Minimal Kedro Project
      • Essential Components of a Kedro Project
        • 1. Recommended Structure
        • 2. Mandatory Files
      • Creating a Minimal Kedro Project Step-by-Step
        • Step 1: Install Kedro
        • Step 2: Create a New Kedro Project
        • Step 3: Create pyproject.toml
        • Step 4: Create settings.py and pipeline_registry.py
        • Step 5: Create a Simple Pipeline
        • Step 6: Define the Project Settings
      • Conclusion
  • Learn Kedro with hands-on video
    • Who is this course for?
    • What you’ll learn
    • Index of videos
      • Part 0: Introduction
      • Part 1: Get started with Kedro
      • Part 2: Make complex Kedro pipelines
      • Part 3: Ship your Kedro project to production
      • Part 4: Where next?

Tutorial and basic Kedro usage

  • Next steps: Tutorial
    • Set up the spaceflights project
      • Create a new project
      • Install project dependencies
        • Install the dependencies
      • Optional: logging and configuration
        • Configuration best practice to avoid leaking confidential data
    • Set up the data
      • Project datasets
      • Dataset registration
        • Test that Kedro can load the data
      • Further information
        • Watch the video
        • Custom data
        • Supported data locations
    • Create a data processing pipeline
      • Introduction
        • Watch the video
      • Data preprocessing node functions
      • The data processing pipeline
      • Test the example
      • Preprocessed data registration
      • Create a table for model input
      • Model input table registration
      • Test the example again
      • Visualise the project
        • Watch the video
      • Checkpoint
    • Create a data science pipeline
      • Data science nodes
      • Input parameter configuration
      • Model registration
      • Data science pipeline
      • Test the pipelines
        • Slice a pipeline
      • Modular pipelines
        • Optional: Extend the project with namespacing and a modular pipeline
        • How it works: the modular pipeline() wrapper
      • Optional: Kedro runners
    • Test a Kedro project
      • Writing tests for Kedro nodes: Unit testing
      • Writing tests for Kedro pipelines: Integration testing
      • Testing best practices
        • Where to write your tests
        • Using fixtures
        • Pipeline slicing
      • Run your tests
    • Package an entire Kedro project
      • Add documentation to a Kedro project if you have not selected docs tool
        • Set up the Sphinx project files
        • Build HTML documentation
        • Documentation from docstrings
      • Package a Kedro project
        • Run a packaged project
        • Docker, Airflow and other deployment targets
    • Spaceflights tutorial FAQs
      • How do I resolve these common errors?
        • Dataset errors
        • Pipeline run
    • Watch the video
    • Get help
    • Terminology
      • Project root directory
      • Dependencies
      • Standard development workflow
  • Visualisation with Kedro-Viz
  • Kedro for notebook users
    • Add Kedro features to a notebook
      • Kedro spaceflights
        • The notebook example
      • Use Kedro for data processing
      • Use a YAML configuration file
        • Use a configuration file for “magic numbers”
        • Use a configuration file for all “magic values”
      • Use Kedro configuration
        • Use Kedro’s configuration loader to load “magic values”
        • Use Kedro’s configuration loader to load the Data Catalog
      • Where next?
      • Refactor your code into functions
    • Use a Jupyter notebook for Kedro project experiments
      • Example project
      • Loading the project with kedro jupyter notebook
        • What does kedro jupyter notebook do?
      • Loading the project with the kedro.ipython extension
      • Exploring the Kedro project in a notebook
        • catalog
        • context
        • pipelines
        • session
      • Kedro line magics
        • %reload_kedro line magic
        • %load_node line magic
        • %run_viz line magic
      • Debugging a Kedro project within a notebook
      • Useful to know (for advanced users)
        • IPython, JupyterLab and other Jupyter clients
  • FAQs and resources
    • FAQs
      • Installing Kedro
      • Kedro documentation
      • Working with Notebooks
      • Kedro project development
      • Configuration
        • Advanced topics
      • Nodes and pipelines
      • What is data engineering convention?
    • Kedro glossary
      • Data Catalog
      • Data engineering vs Data science
      • Kedro
      • KedroContext
      • KedroSession
      • Kedro-Viz
      • Layers (data engineering convention)
      • Modular pipeline
      • Node
      • Node execution order
      • Pipeline
      • Pipeline slicing
      • Runner
      • Starters
      • Tags
      • Workflow dependencies
    • Migration guide
      • Migrate an existing project that uses Kedro 0.18.* to use 0.19.*
        • Custom syntax for --params was removed
        • create_default_data_set() was removed from Runner
        • project_version was removed
        • Datasets changes in 0.19
        • Configuration changes in 0.19
        • Logging

Kedro projects

  • Customise a new project
    • Tools to customise a new Kedro project
      • Specify tools as inputs to kedro new
        • Project name
        • Tools
        • Shortcut
        • Example code
        • Shortcut
      • Specify tools using YAML configuration
      • Kedro tools
        • Linting
        • Testing
        • Custom logging
        • Documentation
        • Data structure
        • PySpark
        • Kedro Viz
      • Flowchart illustration
    • Kedro starters
      • How to use a starter
      • Starter aliases
      • Official Kedro starters
        • Archived starters
      • Starter versioning
      • Use a starter with a configuration file
      • Create a custom starter
  • Configuration
    • Configuration
      • OmegaConfigLoader
        • OmegaConf vs. Kedro’s OmegaConfigLoader
      • Configuration source
      • Configuration environments
        • Base
        • Local
      • Configuration loading
        • Configuration file names
        • Configuration patterns
      • How to use Kedro configuration
        • How to change the setting for a configuration source folder
        • How to change the configuration source folder at runtime
        • How to read configuration from a compressed file
        • How to read configuration from remote storage
        • How to access configuration in code
        • How to load a data catalog with credentials in code?
        • How to specify additional configuration environments
        • How to change the default overriding environment
        • How to use only one configuration environment
        • How to use Kedro without the rich library
    • Credentials
      • How to load credentials in code
      • How to work with AWS credentials
    • Parameters
      • How to use parameters
      • How to load parameters in code
      • How to specify parameters at runtime
    • Migration guide for config loaders
      • ConfigLoader to OmegaConfigLoader
        • 1. Install the required library
        • 2. Use the OmegaConfigLoader
        • 3. Import statements
        • 4. File format support
        • 5. Load configuration
        • 6. Exception handling
      • TemplatedConfigLoader to OmegaConfigLoader
        • 1. Install the required library
        • 2. Use the OmegaConfigLoader
        • 3. Import statements
        • 4. File format support
        • 5. Load configuration
        • 6. Templating of values
        • 7. Globals
        • 8. Deprecation of Jinja2
        • 9. Exception handling
    • Advanced configuration
      • Advanced configuration for Kedro projects
        • How to use a custom configuration loader
        • How to change which configuration files are loaded
        • How to ensure non default configuration files get loaded
        • How to bypass the configuration loading rules
        • How to do templating with the OmegaConfigLoader
        • How to load a data catalog with templating in code?
        • How to use global variables with the OmegaConfigLoader
        • How to override configuration with runtime parameters with the OmegaConfigLoader
        • How to use resolvers in the OmegaConfigLoader
        • How to load credentials through environment variables
        • How to change the merge strategy used by OmegaConfigLoader
      • Advanced configuration without a full Kedro project
        • Read configuration
        • How to use Custom Resolvers with OmegaConfigLoader
  • Data Catalog
    • Introduction to the Data Catalog
      • The basics of catalog.yml
        • Configuring dataset parameters in catalog.yml
        • Dataset type
        • Dataset filepath
      • Additional settings in catalog.yml
        • Load, save and filesystem arguments
        • Dataset access credentials
        • Dataset versioning
      • Use the Data Catalog within Kedro configuration
    • Data Catalog YAML examples
      • Load data from a local binary file using utf-8 encoding
      • Save data to a CSV file without row names (index) using utf-8 encoding
      • Load/save a CSV file from/to a local file system
      • Load/save a CSV on a local file system, using specified load/save arguments
      • Load/save a compressed CSV on a local file system
      • Load a CSV file from a specific S3 bucket, using credentials and load arguments
      • Load/save a pickle file from/to a local file system
      • Load an Excel file from Google Cloud Storage
      • Load a multi-sheet Excel file from a local file system
      • Save an image created with Matplotlib on Google Cloud Storage
      • Load/save an HDF file on local file system storage, using specified load/save arguments
      • Load/save a parquet file on local file system storage, using specified load/save arguments
      • Load/save a Spark table on S3, using specified load/save arguments
      • Load/save a SQL table using credentials, a database connection, and specified load/save arguments
      • Load a SQL table with credentials and a database connection, and apply a SQL query to the table
      • Load data from an API endpoint
      • Load data from MinIO (S3-compatible storage)
      • Load a model saved as a pickle from Azure Blob Storage
      • Load a CSV file stored in a remote location through SSH
      • Load multiple datasets with similar configuration using YAML anchors
      • Read the same file using different datasets with transcoding
        • How to use transcoding
        • How not to use transcoding
      • Create a Data Catalog YAML configuration file via the CLI
    • Kedro dataset factories
      • How to generalise datasets of the same type
      • How to generalise datasets using namespaces
      • How to generalise datasets of the same type in different layers
      • How to generalise datasets using multiple dataset factories
      • How to override the default dataset creation with dataset factories
      • CLI commands for dataset factories
        • How to use kedro catalog rank
        • How to use kedro catalog resolve
    • Advanced: Access the Data Catalog in code
      • How to configure the Data Catalog
      • How to view the available data sources
      • How to load datasets programmatically
      • How to save data programmatically
        • How to save data to memory
        • How to save data to a SQL database for querying
        • How to save data in Parquet
      • How to access a dataset with credentials
      • How to version a dataset using the Code API
    • Advanced: Partitioned and incremental datasets
      • Partitioned datasets
        • How to use PartitionedDataset
        • Dataset definition
        • Partitioned dataset credentials
        • Partitioned dataset load
        • Partitioned dataset save
        • Partitioned dataset lazy saving
      • Incremental datasets
        • Incremental dataset loads
        • Incremental dataset save
        • Incremental dataset confirm
        • Checkpoint configuration
        • Special checkpoint config keys
    • Advanced: Tutorial to create a custom dataset
      • AbstractDataset
      • Scenario
      • Project setup
      • The anatomy of a dataset
      • Implement the load method with fsspec
      • Implement the save method with fsspec
      • Implement the _describe method
      • The complete example
      • Integration with PartitionedDataset
      • Versioning
        • How to implement versioning in your dataset
      • Thread-safety
      • How to handle credentials and different filesystems
      • How to contribute a custom dataset implementation
    • KedroDataCatalog (experimental feature)
      • Kedro Data Catalog
        • How to make KedroDataCatalog the default catalog for Kedro run
        • How to access datasets in the catalog
        • How to add datasets to the catalog
        • How to iterate trough datasets in the catalog
        • How to get the number of datasets in the catalog
        • How to print the full catalog and individual datasets
        • How to access dataset patterns
  • Nodes and pipelines
    • Nodes
      • How to create a node
        • Node definition syntax
        • Syntax for input variables
        • Syntax for output variables
      • *args node functions
      • **kwargs-only node functions
      • How to tag a node
      • How to run a node
      • How to use generator functions in a node
        • Set up the project
        • Loading data with generators
        • Saving data with generators
    • Pipeline objects
      • How to build a pipeline
      • How to use describe to discover what nodes are part of the pipeline
      • How to merge multiple pipelines
      • How to receive information about the nodes in a pipeline
      • How to receive information about pipeline inputs and outputs
      • How to tag a pipeline
      • How to avoid creating bad pipelines
        • Pipeline with bad nodes
        • Pipeline with circular dependencies
        • Pipeline nodes named with the dot notation
      • How to store pipeline code in a kedro project
    • Modular pipelines
      • How to create a new blank pipeline using the kedro pipeline create command
      • How to structure your pipeline creation
      • How to use custom new pipeline templates
        • Creating custom pipeline templates
      • Providing pipeline specific dependencies
      • How to share your pipelines
    • Reuse pipelines and group nodes with namespaces
      • How to reuse your pipelines
      • What is a namespace
        • How to namespace all pipelines in a project
      • Group nodes with namespaces
    • The pipeline registry
      • Pipeline autodiscovery
    • Micro-packaging
      • Package a micro-package
      • Package multiple micro-packages
      • Pull a micro-package
        • Providing fsspec arguments
      • Pull multiple micro-packages
    • Run a pipeline
      • Runners
        • SequentialRunner
        • ParallelRunner
      • Custom runners
      • Load and save asynchronously
      • Run a pipeline by name
      • Run pipelines with IO
      • Configure kedro run arguments
    • Slice a pipeline
      • Slice a pipeline by providing inputs
      • Slice a pipeline by specifying nodes
      • Slice a pipeline by specifying final nodes
      • Slice a pipeline with tagged nodes
      • Slice a pipeline by running specified nodes
      • How to recreate missing outputs
  • Anonymous Telemetry
    • Collected data fields:
    • How do I withdraw consent?

Integrations

  • PySpark integration
    • Centralise Spark configuration in conf/base/spark.yml
    • Initialise a SparkSession using a hook
    • Use Kedro’s built-in Spark datasets to load and save raw data
    • Spark and Delta Lake interaction
    • Use MemoryDataset for intermediary DataFrame
    • Use MemoryDataset with copy_mode="assign" for non-DataFrame Spark objects
    • Tips for maximising concurrency using ThreadRunner
  • How to add MLflow to your Kedro workflow
    • Prerequisites
    • Simple use cases
      • Easy tracking of Kedro runs in MLflow using kedro-mlflow
      • Artifact tracking in MLflow using kedro-mlflow
      • Model registry in MLflow using kedro-mlflow
    • Advanced use cases
      • Track additional metadata of Kedro runs in MLflow using Hooks
      • Tracking Kedro in MLflow using the Python API
  • Data and pipeline versioning with Kedro and DVC
    • Versioning data with .dvc files
      • Initialising the repository
      • Tracking your data with DVC
      • Going back to a previous version of the data
    • Advanced use cases
      • How to store data remotely
      • How to go back to a previous version of the data, stored remotely
      • How to version with DVC data pipelines
      • How to define Kedro pipelines as DVC stages
      • How to update a dataset
      • How to track code changes
      • How to track parameters
      • How to run experiments with different parameters
  • Data versioning with Delta Lake
    • Prerequisites
    • Using Delta tables in catalog
      • Save dataset as a Delta table
      • Load a specific dataset version
    • Inspect the dataset in interactive mode
    • Using Delta tables with Spark
  • Data versioning with Iceberg
    • Prerequisites
      • Set up the Iceberg catalog
    • Define a custom dataset to use Iceberg tables
    • Using Iceberg tables in the catalog
      • Save the dataset as an Iceberg table
      • Load a specific dataset version
    • Inspect the dataset in interactive mode

Development

  • Set up Visual Studio Code
    • Kedro VS Code Extension
    • Setting up venv / virtualenv in VS Code
    • Setting up tasks
    • Setting a custom Kedro project path
      • Set up a custom path using the command palette
      • Set up a custom path using the VSCode settings UI
      • Multi-root workspace integration
      • Example directory structure
      • Switching between multiple projects
      • Troubleshooting
    • Real time catalog validation with Kedro LSP
      • How it works
      • Viewing errors in the problems panel
    • Visualise the pipeline with Kedro-Viz
    • Debugging
      • Advanced: Remote interpreter debugging
  • Set up PyCharm
    • Set up Run configurations
    • Debugging
    • Advanced: Remote SSH interpreter
    • Advanced: Docker interpreter
    • Configure Python Console
    • Configuring the Kedro catalog validation schema
  • Debugging
  • Automated testing
    • Introduction
    • Set up automated testing with pytest
      • Install test requirements
      • Create a /tests directory
      • Test directory structure
      • Create an example test
      • Run your tests
    • Add test coverage reports with pytest-cov
      • Install pytest-cov
      • Configure pytest to use pytest-cov
      • Run pytest with pytest-cov
  • Code formatting and linting
    • Introduction
    • Set up Python tools
      • Install the tools
        • Configure ruff
      • Run the tools
    • Automated formatting and linting with pre-commit hooks
      • Install pre-commit
      • Add pre-commit configuration file
      • Install git hook scripts

Advanced usage

  • Project setup
    • Dependencies
      • Declare project-specific dependencies
      • Install project-specific dependencies
        • Install dependencies related to the Data Catalog
      • Reproducible environments
    • Lifecycle management with KedroSession
      • Overview
      • Create a session
      • bootstrap_project and configure_project
        • bootstrap_project
        • configure_project
    • Project settings
      • Application settings
      • Project metadata
        • Use Kedro without the src folder
  • Extend Kedro
    • Common use cases
      • Use Case 1: How to add extra behaviour to Kedro’s execution timeline
      • Use Case 2: How to integrate Kedro with additional data sources
      • Use Case 3: How to add or modify CLI commands
      • Use Case 4: How to customise the initial boilerplate of your project
    • Kedro plugins
      • Overview
      • Example of a simple plugin
      • Working with click
      • Project context
      • Initialisation
      • global and project commands
      • Suggested command convention
      • Advanced: Lazy loading of plugin commands
      • Hooks
      • CLI Hooks
      • Contributing process
      • Supported Kedro plugins
      • Community-developed plugins
    • Kedro architecture overview
      • Kedro project
      • Kedro framework
      • Kedro starter
      • Kedro library
      • Kedro extension
    • Create a Kedro starter
      • Install the cookiecutter package
      • Custom project creation variables
      • Example Kedro starter
        • project_name
        • repo_name
        • python_package
      • Extend starter aliases
  • Hooks
    • Introduction to Hooks
      • Concepts
      • Hook specifications
        • CLI Hooks
      • Hook implementation
        • Define the Hook implementation
        • Registering the Hook implementation with Kedro
      • Hook execution order
      • Under the hood
        • Plugin Hooks
    • Common use cases
      • Use Hooks to extend a node’s behaviour
      • Use Hooks to customise the dataset load and save methods
      • Use Hooks to load external credentials
      • Use Hooks to read metadata from DataCatalog
      • Use Hooks to debug your pipeline
        • Debugging a node
        • Debugging a pipeline
    • Hooks examples
      • Add memory consumption tracking
      • Add data validation
        • V2 API
        • V3 API
      • Add observability to your pipeline
      • Add metrics tracking to your model
      • Modify node inputs using before_node_run hook
  • Logging
    • Default logging configuration
    • How to perform logging in your Kedro project
    • How to customise Kedro logging
      • Change the verbosity of specific parts of Kedro
    • Custom CONF_SOURCE with logging
      • How to show DEBUG level messages
    • Advanced logging
    • How to customise the rich handler
    • How to enable file-based logging
    • How to use plain console logging
    • How to enable rich logging in a dumb terminal
    • How to enable rich logging in Jupyter
      • How to use logging without the rich library
  • Deployment
    • Single-machine deployment
      • Container-based
        • How to use container registry
      • Package-based
      • CLI-based
        • Use GitHub workflow to copy your project
        • Install and run the Kedro project
    • Distributed deployment
      • 1. Containerise the pipeline
      • 2. Convert your Kedro pipeline into targeted platform primitives
      • 3. Parameterise the runs
      • 4. (Optional) Create starters
    • Apache Airflow
      • Introduction and strategy
      • How to run a Kedro pipeline on Apache Airflow with Astronomer
        • Prerequisites
        • Create, prepare and package example Kedro project
        • Deployment process with Astro CLI
        • Deployment to Astro Cloud
      • How to run a Kedro pipeline on Amazon AWS Managed Workflows for Apache Airflow (MWAA)
        • Kedro project preparation
        • Deployment on AWAA
      • How to run a Kedro pipeline on Apache Airflow using a Kubernetes cluster
    • Amazon SageMaker
      • The kedro-sagemaker plugin
    • Amazon EMR Serverless
      • Context
      • Overview of approach
        • Resources
      • Setup
        • Prerequisites
        • Infrastructure
        • IAM
      • (Optional) Validate the custom image
      • Run a job
      • FAQ
        • How is the approach defined here different from the approach in “Seven steps to deploy Kedro pipelines on Amazon EMR” on the Kedro blog?
        • EMR Serverless already has Python installed. Why do we need a custom Python version?
        • Why do we need to create a custom image to provide the custom Python version?
        • Why do we need to package the Kedro project and invoke using an entrypoint script? Why can’t we use [CMD] or [ENTRYPOINT] with kedro run in the custom image?
        • How about using the method described in Lifecycle management with KedroSession to run Kedro programmatically?
    • AWS Step Functions
      • Why would you run a Kedro pipeline with AWS Step Functions?
      • Strategy
      • Prerequisites
      • Deployment process
        • Step 1. Create new configuration environment to prepare a compatible DataCatalog
        • Step 2. Package the Kedro pipeline as an AWS Lambda-compliant Docker image
        • Step 3. Write the deployment script
        • Step 4. Deploy the pipeline
      • Limitations
    • Azure ML pipelines
      • kedro-azureml plugin
    • Dask
      • Why would you use Dask?
      • Prerequisites
      • How to distribute your Kedro pipeline using Dask
        • Create a custom runner
        • Update CLI implementation
        • Deploy
    • Databricks
      • Use a Databricks workspace to develop a Kedro project
        • What this page covers
        • Prerequisites
        • Set up your project
        • Modify your project and test the changes
        • Summary
      • Use an IDE and Databricks Asset Bundles to deploy a Kedro project
        • Benefits of local development
        • What this page covers
        • Prerequisites
        • Set up your project
        • Create the Databricks Asset Bundles using kedro-databricks
        • Deploy Databricks Job using Databricks Asset Bundles
        • How to run the Deployed job?
      • Use a Databricks job to deploy a Kedro project
        • What are the advantages of packaging a Kedro project to run on Databricks?
        • What this page covers
        • Prerequisites
        • Set up your project for deployment to Databricks
        • Deploy and run your Kedro project using the workspace UI
        • Resources for automatically deploying to Databricks
        • Summary
      • Visualise a Kedro project in Databricks notebooks
      • Use an IDE, dbx and Databricks Repos to develop a Kedro project
        • What this page covers
        • Prerequisites
        • Set up your project
        • Modify your project and test the changes
        • Summary
    • Kubeflow Pipelines
      • Why would you use Kubeflow Pipelines?
      • The kedro-kubeflow plugin
    • Prefect
      • Prerequisites
      • Setup
      • How to run your Kedro pipeline using Prefect 2.0
        • Convert your Kedro pipeline to Prefect 2.0 flow
        • Run Prefect flow
    • VertexAI
      • The kedro-vertexai plugin
    • Argo Workflows (outdated documentation that needs review)
      • Why would you use Argo Workflows?
      • Prerequisites
      • How to run your Kedro pipeline using Argo Workflows
        • Containerise your Kedro project
        • Create Argo Workflows spec
        • Submit Argo Workflows spec to Kubernetes
        • Kedro-Argo plugin
    • AWS Batch (outdated documentation that needs review)
      • Why would you use AWS Batch?
      • Prerequisites
      • How to run a Kedro pipeline using AWS Batch
        • Containerise your Kedro project
        • Provision resources
        • Configure the credentials
        • Submit AWS Batch jobs
        • Deploy
    • Nodes grouping in Kedro: pipelines, tags, and namespaces
      • Grouping by pipelines
      • Grouping by tags
      • Grouping by namespaces

Contribute to Kedro

  • Contribute to Kedro
    • Kedro’s Technical Steering Committee
      • Responsibilities of a maintainer
        • Product development
        • Community management
      • Requirements to become a maintainer
      • Current maintainers
      • Past maintainers
      • Application process
      • Voting process
        • Other issues or proposals
        • Adding or removing maintainers

CLI reference

  • Kedro’s command line interface
    • Autocompletion (optional)
    • Invoke Kedro CLI from Python (optional)
    • Kedro commands
    • Global Kedro commands
      • Get help on Kedro commands
      • Confirm the Kedro version
      • Confirm Kedro information
      • Create a new Kedro project
    • Customise or override project-specific Kedro commands
      • Project setup
        • Install all package dependencies
      • Run the project
        • Modifying a kedro run
      • Deploy the project
      • Pull a micro-package
      • Project quality
      • Project development
        • Modular pipelines
        • Registered pipelines
        • Datasets
        • Data Catalog
        • Notebooks

API documentation

  • kedro
    • kedro.load_ipython_extension
      • load_ipython_extension()
    • kedro.KedroDeprecationWarning
      • KedroDeprecationWarning
        • KedroDeprecationWarning.args
        • KedroDeprecationWarning.with_traceback()
    • kedro.KedroPythonVersionWarning
      • KedroPythonVersionWarning
        • KedroPythonVersionWarning.args
        • KedroPythonVersionWarning.with_traceback()
    • kedro.config
      • kedro.config.AbstractConfigLoader
        • AbstractConfigLoader
      • kedro.config.OmegaConfigLoader
        • OmegaConfigLoader
      • kedro.config.MissingConfigException
        • MissingConfigException
    • kedro.framework
      • kedro.framework.cli
        • kedro.framework.cli.catalog
        • kedro.framework.cli.cli
        • kedro.framework.cli.hooks
        • kedro.framework.cli.jupyter
        • kedro.framework.cli.micropkg
        • kedro.framework.cli.pipeline
        • kedro.framework.cli.project
        • kedro.framework.cli.registry
        • kedro.framework.cli.starters
        • kedro.framework.cli.utils
      • kedro.framework.context
        • kedro.framework.context.KedroContext
        • kedro.framework.context.KedroContextError
      • kedro.framework.hooks
        • kedro.framework.hooks.manager
        • kedro.framework.hooks.markers
        • kedro.framework.hooks.specs
      • kedro.framework.project
        • kedro.framework.project.configure_logging
        • kedro.framework.project.configure_project
        • kedro.framework.project.find_pipelines
        • kedro.framework.project.validate_settings
      • kedro.framework.session
        • kedro.framework.session.session
        • kedro.framework.session.store
      • kedro.framework.startup
        • kedro.framework.startup.bootstrap_project
        • kedro.framework.startup.ProjectMetadata
    • kedro.io
      • kedro.io.AbstractDataset
        • AbstractDataset
      • kedro.io.AbstractVersionedDataset
        • AbstractVersionedDataset
      • kedro.io.CachedDataset
        • CachedDataset
      • kedro.io.DataCatalog
        • DataCatalog
      • kedro.io.LambdaDataset
        • LambdaDataset
      • kedro.io.MemoryDataset
        • MemoryDataset
      • kedro.io.Version
        • Version
      • kedro.io.DatasetAlreadyExistsError
        • DatasetAlreadyExistsError
      • kedro.io.DatasetError
        • DatasetError
      • kedro.io.DatasetNotFoundError
        • DatasetNotFoundError
    • kedro.ipython
      • kedro.ipython.load_ipython_extension
        • load_ipython_extension()
      • kedro.ipython.magic_load_node
        • magic_load_node()
      • kedro.ipython.magic_reload_kedro
        • magic_reload_kedro()
      • kedro.ipython.reload_kedro
        • reload_kedro()
    • kedro.logging
      • kedro.logging.RichHandler
        • RichHandler
    • kedro.pipeline
      • kedro.pipeline.node
        • node()
      • kedro.pipeline.modular_pipeline.pipeline
        • pipeline()
      • kedro.pipeline.Pipeline
        • Pipeline
      • kedro.pipeline.node.Node
        • Node
      • kedro.pipeline.modular_pipeline.ModularPipelineError
        • ModularPipelineError
    • kedro.runner
      • kedro.runner.run_node
        • run_node()
      • kedro.runner.AbstractRunner
        • AbstractRunner
      • kedro.runner.ParallelRunner
        • ParallelRunner
      • kedro.runner.SequentialRunner
        • SequentialRunner
      • kedro.runner.ThreadRunner
        • ThreadRunner
    • kedro.utils
      • kedro.utils.load_obj
        • load_obj()
kedro
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Project setup¶

  • Dependencies
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Last updated on Nov 27, 2023.

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