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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
  • Ibis integration
    • Prerequisites
    • Setting up Ibis in your Kedro project
      • Configure database connection
    • Using the Ibis TableDataset with Kedro’s DataCatalog
    • Creating a database connection from credentials
    • Using Ibis in Kedro nodes
    • Best practices
      • Structuring SQL queries in your Kedro project
      • Performance optimization
      • Error handling and debugging
    • Example: Complete pipeline with Ibis and DuckDB
    • Conclusion

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 MWAA
      • How to run a Kedro pipeline on Apache Airflow using a Kubernetes cluster
        • Running multiple nodes in a single container
    • 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.pipeline
        • pipeline()
      • kedro.pipeline.Pipeline
        • Pipeline
      • kedro.pipeline.node.Node
        • Node
      • kedro.pipeline.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
  • Docs »
  • Python Module Index

Python Module Index

k
 
k
- kedro
    kedro.config
    kedro.framework
    kedro.framework.cli
    kedro.framework.cli.catalog
    kedro.framework.cli.cli
    kedro.framework.cli.hooks
    kedro.framework.cli.hooks.manager
    kedro.framework.cli.hooks.markers
    kedro.framework.cli.hooks.specs
    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.hooks
    kedro.framework.hooks.manager
    kedro.framework.hooks.markers
    kedro.framework.hooks.specs
    kedro.framework.project
    kedro.framework.session
    kedro.framework.session.session
    kedro.framework.session.store
    kedro.framework.startup
    kedro.io
    kedro.ipython
    kedro.logging
    kedro.pipeline
    kedro.runner
    kedro.utils

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