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Introduction

  • What is Kedro?
    • Learn how to use Kedro
    • Assumptions

Get started

  • Install Kedro
    • Installation prerequisites
      • Python
      • git
      • Virtual environment manager
        • Create a new Python virtual environment using conda
    • Install Kedro using pip or conda
      • Verify a successful installation
    • Install a development version
  • Kedro concepts
    • Node
    • Pipeline
    • Data Catalog
    • Kedro project directory structure
      • conf
        • conf/base
        • conf/local
      • data
      • src
  • Create a new Kedro project
    • Create a new empty project
      • Create a new project from a configuration file
    • Create a new project containing example code
      • Background information for the iris dataset example
      • Create the example project
      • Run the example project
      • Under the hood: Pipelines and nodes
      • Visualise the project
  • TL;DR
    • Logistics
    • Kedro concepts
    • Kedro project creation
    • What’s next?
    • Useful links

Tutorial

  • Kedro spaceflights tutorial
    • Scenario
    • Get help
    • Terminology
      • Project root directory
      • Dependencies
      • Standard development workflow
      • Optional: source control with git
        • Submit your changes to GitHub
  • Set up the spaceflights tutorial project
    • Create a new project
    • Project dependencies
      • Add dependencies to the project
      • Install the dependencies
    • Optional: configuration and logging
  • Set up the data
    • Download datasets
    • Register the datasets
      • Register csv data
      • Register xlsx data
      • Test that Kedro can load the csv data
      • Test that Kedro can load the xlsx data
    • Further information
      • Custom data
      • Supported data locations
  • Create a data processing pipeline
    • Data processing pipeline
      • Generate a new pipeline template
      • Add node functions
      • Assemble nodes into the data processing pipeline
      • Test the example
      • Optional: save the preprocessed data
      • Extend the data processing pipeline
      • Optional: save the model input table
      • Test the example
    • Checkpoint
  • Create a data science pipeline
    • Data science pipeline
      • Generate a new pipeline template
      • Configure the input parameters
      • Assemble the data science pipeline
      • Register the dataset
      • Test the pipelines
        • Slice a pipeline
    • Optional: modular pipelines
      • Extend the project with namespacing and a modular pipeline
        • How it works: the modular pipeline() wrapper
    • Optional: Kedro runners
  • Package an entire Kedro project
    • Add documentation to your project
    • Package your project
      • Package recipients
      • Docker, Airflow and other deployment targets
  • Spaceflights tutorial FAQs
    • How do I resolve these common errors?
      • DataSet errors
        • DataSetError: Failed while loading data from data set
        • DataSetNotFoundError: DataSet not found in the catalog
        • DataSetError: An exception occurred when parsing config for DataSet
      • Pipeline run

Visualisation with Kedro-Viz

  • Get started with Kedro-Viz
    • Visualise the spaceflights project
      • Exit an open visualisation
    • Automatic visualisation updates
    • Visualise layers
    • Share a pipeline visualisation
  • Visualise charts in Kedro-Viz
    • Visualisation with Plotly
      • Update the dependencies
      • Configure the Data Catalog
      • Create the template reporting pipeline
      • Add the Plotly reporting nodes
      • Update the reporting pipeline code
      • Run the pipeline
    • Visualisation with Matplotlib
      • Update the dependencies
      • Configure the Data Catalog
      • Add another node
      • Update the pipeline
      • Run the pipeline
  • Experiment tracking in Kedro-Viz
    • When should I use experiment tracking in Kedro?
    • Set up a project
      • Install the dependencies for the project
    • Set up the session store
    • Set up tracking datasets
    • Set up your nodes and pipelines to log metrics
    • Generate the run data
    • Access run data and compare runs
    • View and compare plots
      • Update the dependencies
      • Add a plotting pipeline
    • View and compare metrics data

Notebooks & IPython users

  • Kedro and Jupyter Notebooks
    • A custom Kedro kernel
    • Iris dataset example
      • catalog
      • context
      • pipelines
      • session
    • %reload_kedro line magic
    • %run_viz line magic
    • Convert functions from Jupyter Notebooks into Kedro nodes
    • Useful to know…
      • Managed services
      • IPython, JupyterLab and other Jupyter clients
    • Find out more
  • Kedro as a data registry
    • Usage

Kedro project setup

  • Kedro starters
    • How to use Kedro starters
    • Starter aliases
    • List of official starters
    • Starter versioning
    • Use a starter with a configuration file
    • How to create a Kedro starter
      • Configuration variables
      • Example Kedro starter
  • Dependencies
    • Project-specific dependencies
    • Install project-specific dependencies
    • Workflow dependencies
      • Install dependencies related to the Data Catalog
        • Install dependencies at a group-level
        • Install dependencies at a type-level
  • Configuration
    • Configuration root
    • Local and base configuration environments
    • Additional configuration environments
    • Template configuration
      • Globals
      • Jinja2 support
    • Configuration with OmegaConf
      • Templating for parameters
      • Environment variables for credentials
    • Parameters
      • Load parameters
      • Specify parameters at runtime
      • Use parameters
    • Credentials
      • AWS credentials
    • Configure kedro run arguments
  • Lifecycle management with KedroSession
    • Overview
    • Create a session
  • Project settings

Data Catalog

  • The Data Catalog
    • Use the Data Catalog within Kedro configuration
    • Specify the location of the dataset
    • Data Catalog *_args parameters
      • Example 1: Provide the project value to the underlying filesystem class (GCSFileSystem) to interact with Google Cloud Storage (GCS)
      • Example 2: Load data from a local binary file using utf-8 encoding
      • Example 3: Save data to a CSV file without row names (index) using utf-8 encoding
    • Use the Data Catalog with the YAML API
      • Example 1: Loads / saves a CSV file from / to a local file system
      • Example 2: Loads and saves a CSV on a local file system, using specified load and save arguments
      • Example 3: Loads and saves a compressed CSV on a local file system
      • Example 4: Loads a CSV file from a specific S3 bucket, using credentials and load arguments
      • Example 5: Loads / saves a pickle file from / to a local file system
      • Example 6: Loads an Excel file from Google Cloud Storage
      • Example 7: Loads a multi-sheet Excel file from a local file system
      • Example 8: Saves an image created with Matplotlib on Google Cloud Storage
      • Example 9: Loads / saves an HDF file on local file system storage, using specified load and save arguments
      • Example 10: Loads / saves a parquet file on local file system storage, using specified load and save arguments
      • Example 11: Loads / saves a Spark table on S3, using specified load and save arguments
      • Example 12: Loads / saves a SQL table using credentials, a database connection, using specified load and save arguments
      • Example 13: Loads an SQL table with credentials, a database connection, and applies a SQL query to the table
      • Example 14: Loads data from an API endpoint, example US corn yield data from USDA
      • Example 15: Loads data from Minio (S3 API Compatible Storage)
      • Example 16: Loads a model saved as a pickle from Azure Blob Storage
      • Example 17: Loads a CSV file stored in a remote location through SSH
    • Create a Data Catalog YAML configuration file via CLI
    • Adding parameters
    • Feeding in credentials
    • Load multiple datasets with similar configuration
    • Transcode datasets
      • A typical example of transcoding
      • How does transcoding work?
    • Version datasets and ML models
    • Use the Data Catalog with the Code API
      • Configure a Data Catalog
      • Load datasets
        • Behind the scenes
      • View the available data sources
      • Save data
        • Save data to memory
        • Save data to a SQL database for querying
        • Save data in Parquet
  • Kedro IO
    • Error handling
    • AbstractDataSet
    • Versioning
      • version namedtuple
      • Versioning using the YAML API
      • Versioning using the Code API
      • Supported datasets
    • Partitioned dataset
      • Partitioned dataset definition
        • Dataset definition
        • Partitioned dataset credentials
      • Partitioned dataset load
      • Partitioned dataset save
      • Partitioned dataset lazy saving
      • Incremental loads with IncrementalDataSet
        • Incremental dataset load
        • Incremental dataset save
        • Incremental dataset confirm
        • Checkpoint configuration
        • Special checkpoint config keys

Nodes and pipelines

  • Nodes
    • How to create a node
      • Node definition syntax
      • Syntax for input variables
      • Syntax for output variables
    • **kwargs-only node functions
    • How to tag a node
    • How to run a node
  • Pipelines
    • How to build a pipeline
      • How to tag a pipeline
      • How to merge multiple pipelines
      • Information about the nodes in a pipeline
      • Information about pipeline inputs and outputs
    • Bad pipelines
      • Pipeline with bad nodes
      • Pipeline with circular dependencies
  • Modular pipelines
    • What are modular pipelines?
      • Key concepts
    • How do I create a modular pipeline?
      • What does kedro pipeline create do?
      • Ensuring portability
      • Providing modular pipeline specific dependencies
    • Using the modular pipeline() wrapper to provide overrides
    • Combining disconnected pipelines
    • Using a modular pipeline multiple times
    • How to use a modular pipeline with different parameters
  • 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
        • Multiprocessing
        • Multithreading
    • Custom runners
    • Load and save asynchronously
    • Run a pipeline by name
    • Run pipelines with IO
    • Output to a file
  • 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

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
  • Custom datasets
    • 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
    • Thread-safety
    • How to handle credentials and different filesystems
    • How to contribute a custom dataset implementation
  • Kedro plugins
    • Overview
    • Example of a simple plugin
    • Extend starter aliases
    • Working with click
    • Project context
    • Initialisation
    • global and project commands
    • Suggested command convention
    • Hooks
    • CLI Hooks
    • Contributing process
    • Supported Kedro plugins
    • Community-developed plugins

Hooks

  • Hooks
    • Introduction
    • Concepts
      • Hook specification
        • CLI hooks
      • Hook implementation
        • Registering your Hook implementations with Kedro
        • Disable auto-registered plugins’ Hooks
    • Under the hood
  • Common use cases
    • Use Hooks to extend a node’s behaviour
    • Use Hooks to customise the dataset load and save methods
  • 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

  • Logging
    • Default framework-side logging configuration
    • Project-side logging configuration
      • Disable file-based logging
      • Use plain console logging
      • Rich logging in a dumb terminal
      • Rich logging in Jupyter
    • Perform logging in your project

Development

  • Set up Visual Studio Code
    • Advanced: For those using venv / virtualenv
    • Setting up tasks
    • Debugging
      • Advanced: Remote Interpreter / Debugging
    • Configuring the Kedro catalog validation schema
  • Set up PyCharm
    • Set up Run configurations
    • Debugging
    • Advanced: Remote SSH interpreter
    • Advanced: Docker interpreter
    • Configure Python Console
    • Configuring the Kedro catalog validation schema
  • 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
      • Open the Kedro documentation in your browser
    • Customise or Override Project-specific Kedro commands
      • Project setup
        • Build the project’s dependency tree
        • Install all package dependencies
      • Run the project
        • Modifying a kedro run
      • Deploy the project
      • Pull a micro-package
      • Project quality
        • Build the project documentation
        • Lint your project
        • Test your project
      • Project development
        • Modular pipelines
        • Registered pipelines
        • Datasets
        • Data Catalog
        • Notebooks
  • Debugging
    • Introduction
    • Debugging Node
    • Debugging Pipeline
  • Automated Testing
    • Introduction
    • Set up automated testing with pytest
      • Install pytest
      • 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
  • Linting
    • Introduction
    • Set up linting tools
      • Install linting tools
      • Run linting tools
    • Automating linting with pre-commit hooks
      • Install pre-commit
      • Add pre-commit configuration file
      • Install git hook scripts

Deployment

  • Deployment guide
    • Deployment choices
  • 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’s primitives
    • 3. Parameterise the runs
    • 4. (Optional) Create starters
  • Deployment with Argo Workflows
    • 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
  • Deployment with Prefect
    • Prerequisites
    • How to run your Kedro pipeline using Prefect
      • Convert your Kedro pipeline to Prefect flow
      • Run Prefect flow
  • Deployment with Kubeflow Pipelines
    • Why would you use Kubeflow Pipelines?
    • Prerequisites
    • How to run your Kedro pipeline using Kubeflow Pipelines
      • Containerise your Kedro project
      • Create a workflow spec
      • Authenticate Kubeflow Pipelines
      • Upload workflow spec and execute runs
  • Deployment with AWS Batch
    • Why would you use AWS Batch?
    • Prerequisites
    • How to run a Kedro pipeline using AWS Batch
      • Containerise your Kedro project
      • Provision resources
        • Create IAM Role
        • Create AWS Batch job definition
        • Create AWS Batch compute environment
        • Create AWS Batch job queue
      • Configure the credentials
      • Submit AWS Batch jobs
        • Create a custom runner
        • Set up Batch-related configuration
        • Update CLI implementation
      • Deploy
  • Deployment to a Databricks cluster
    • Prerequisites
    • Running Kedro project from a Databricks notebook
      • 1. Project setup
      • 2. Install dependencies and run locally
      • 3. Create a Databricks cluster
      • 4. Create GitHub personal access token
      • 5. Create a GitHub repository
      • 6. Push Kedro project to the GitHub repository
      • 7. Configure the Databricks cluster
      • 8. Run your Kedro project from the Databricks notebook
      • 9. Using the Kedro IPython Extension
      • 10. Running Kedro-Viz on Databricks
  • How to integrate Amazon SageMaker into your Kedro pipeline
    • Why would you use Amazon SageMaker?
    • Prerequisites
    • Prepare the environment
      • Install SageMaker package dependencies
      • Create SageMaker execution role
      • Create S3 bucket
    • Update the Kedro project
      • Create the configuration environment
      • Update the project settings
      • Update the data science pipeline
        • Create node functions
        • Update the pipeline definition
      • Create the SageMaker entry point
    • Run the project
    • Cleanup
  • How to deploy your Kedro pipeline with 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
    • Final thought
  • How to deploy your Kedro pipeline on Apache Airflow with Astronomer
    • Strategy
    • Prerequisites
    • Project Setup
    • Deployment process
      • Step 1. Create new configuration environment to prepare a compatible DataCatalog
      • Step 2. Package the Kedro pipeline as an Astronomer-compliant Docker image
      • Step 3. Convert the Kedro pipeline into an Airflow DAG with kedro airflow
      • Step 4. Launch the local Airflow cluster with Astronomer
    • Final thought
  • Deployment to a Dask cluster
    • Why would you use Dask?
    • Prerequisites
    • How to distribute your Kedro pipeline using Dask
      • Create a custom runner
      • Update CLI implementation
      • Deploy
        • Set up Dask and related configuration

PySpark integration

  • Build a Kedro pipeline with PySpark
    • 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

FAQs

  • Frequently asked questions
    • What is Kedro?
    • What are the primary advantages of Kedro?
    • How does Kedro compare to other projects?
    • What is the typical Kedro project development workflow?
      • 1. Set up the project template
      • 2. Set up the data
      • 3. Create the pipeline
      • 4. Package the project
    • What is data engineering convention?
    • What best practice should I follow to avoid leaking confidential data?
    • How do I upgrade Kedro?
    • How can I use a development version of Kedro?
    • How can I find out more about Kedro?
    • Who maintains Kedro?
    • How can I cite Kedro?
    • Can I create a virtual environment without conda?
      • venv (instead of conda)
      • pipenv (instead of conda)
    • How can I get my question answered?
  • Kedro architecture overview
    • Kedro project
    • Kedro starter
    • Kedro library
    • Kedro framework
    • Kedro extension
  • Kedro Principles
    • 1. Modularity at the core ️📦
    • 2. Grow beginners into experts 🌱
    • 3. User empathy without unfounded assumptions 🤝
    • 4. Simplicity means bare necessities 🍞
    • 5. There should be one obvious way of doing things 🎯
    • 6. A sprinkle of magic is better than a spoonful of it ✨
    • 7. Lean process and lean product 👟

Resources

  • Images and icons
    • White background
      • Icon
      • Icon with text
    • Black background
      • Icon
      • Icon with text
  • 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

Contribute to Kedro

  • Introduction
  • Guidelines for contributing developers
    • Introduction
    • Before you start: development set up
    • Get started: areas of contribution
      • core contribution process
      • extras contribution process
    • Create a pull request
      • Pull request title conventions
      • Hints on pre-commit usage
      • Developer Certificate of Origin
    • Need help?
  • Backwards compatibility & breaking changes
    • When should I make a breaking change?
    • The Kedro release model
  • Contribute to the Kedro documentation
    • How do I rebuild the documentation after I make changes to it?
      • Set up to build Kedro documentation
      • Build the documentation
    • Extend Kedro documentation
      • Add new pages
      • Move or remove pages
      • Create a pull request
      • Help!
    • Kedro documentation style guide
      • Language
      • Formatting
      • Links
      • Capitalisation
      • Bullets
      • Notes
      • Kedro lexicon
      • Style
  • Join the Technical Steering Committee
    • Responsibilities of a maintainer
      • Product development
      • Community management
    • Requirements to become a maintainer
    • Application process
    • Voting process
      • Other issues or proposals
      • Adding or removing maintainers

API documentation

  • kedro
    • kedro.config
      • kedro.config.ConfigLoader
      • kedro.config.TemplatedConfigLoader
      • kedro.config.OmegaConfLoader
      • kedro.config.MissingConfigException
    • kedro.datasets
      • kedro.datasets.api.APIDataSet
      • kedro.datasets.biosequence.BioSequenceDataSet
      • kedro.datasets.dask.ParquetDataSet
      • kedro.datasets.email.EmailMessageDataSet
      • kedro.datasets.geopandas.GeoJSONDataSet
      • kedro.datasets.holoviews.HoloviewsWriter
      • kedro.datasets.json.JSONDataSet
      • kedro.datasets.matplotlib.MatplotlibWriter
      • kedro.datasets.networkx.GMLDataSet
      • kedro.datasets.networkx.GraphMLDataSet
      • kedro.datasets.networkx.JSONDataSet
      • kedro.datasets.pandas.CSVDataSet
      • kedro.datasets.pandas.ExcelDataSet
      • kedro.datasets.pandas.FeatherDataSet
      • kedro.datasets.pandas.GBQQueryDataSet
      • kedro.datasets.pandas.GBQTableDataSet
      • kedro.datasets.pandas.GenericDataSet
      • kedro.datasets.pandas.HDFDataSet
      • kedro.datasets.pandas.JSONDataSet
      • kedro.datasets.pandas.ParquetDataSet
      • kedro.datasets.pandas.SQLQueryDataSet
      • kedro.datasets.pandas.SQLTableDataSet
      • kedro.datasets.pandas.XMLDataSet
      • kedro.datasets.pickle.PickleDataSet
      • kedro.datasets.pillow.ImageDataSet
      • kedro.datasets.plotly.JSONDataSet
      • kedro.datasets.plotly.PlotlyDataSet
      • kedro.datasets.redis.PickleDataSet
      • kedro.datasets.spark.DeltaTableDataSet
      • kedro.datasets.spark.SparkDataSet
      • kedro.datasets.spark.SparkHiveDataSet
      • kedro.datasets.spark.SparkJDBCDataSet
      • kedro.datasets.svmlight.SVMLightDataSet
      • kedro.datasets.tensorflow.TensorFlowModelDataset
      • kedro.datasets.text.TextDataSet
      • kedro.datasets.tracking.JSONDataSet
      • kedro.datasets.tracking.MetricsDataSet
      • kedro.datasets.video.VideoDataSet
      • kedro.datasets.yaml.YAMLDataSet
    • kedro.extras
      • kedro.extras.datasets
        • kedro.extras.datasets.api.APIDataSet
        • kedro.extras.datasets.biosequence.BioSequenceDataSet
        • kedro.extras.datasets.dask.ParquetDataSet
        • kedro.extras.datasets.email.EmailMessageDataSet
        • kedro.extras.datasets.geopandas.GeoJSONDataSet
        • kedro.extras.datasets.holoviews.HoloviewsWriter
        • kedro.extras.datasets.json.JSONDataSet
        • kedro.extras.datasets.matplotlib.MatplotlibWriter
        • kedro.extras.datasets.networkx.GMLDataSet
        • kedro.extras.datasets.networkx.GraphMLDataSet
        • kedro.extras.datasets.networkx.JSONDataSet
        • kedro.extras.datasets.pandas.CSVDataSet
        • kedro.extras.datasets.pandas.ExcelDataSet
        • kedro.extras.datasets.pandas.FeatherDataSet
        • kedro.extras.datasets.pandas.GBQQueryDataSet
        • kedro.extras.datasets.pandas.GBQTableDataSet
        • kedro.extras.datasets.pandas.GenericDataSet
        • kedro.extras.datasets.pandas.HDFDataSet
        • kedro.extras.datasets.pandas.JSONDataSet
        • kedro.extras.datasets.pandas.ParquetDataSet
        • kedro.extras.datasets.pandas.SQLQueryDataSet
        • kedro.extras.datasets.pandas.SQLTableDataSet
        • kedro.extras.datasets.pandas.XMLDataSet
        • kedro.extras.datasets.pickle.PickleDataSet
        • kedro.extras.datasets.pillow.ImageDataSet
        • kedro.extras.datasets.plotly.JSONDataSet
        • kedro.extras.datasets.plotly.PlotlyDataSet
        • kedro.extras.datasets.redis.PickleDataSet
        • kedro.extras.datasets.spark.DeltaTableDataSet
        • kedro.extras.datasets.spark.SparkDataSet
        • kedro.extras.datasets.spark.SparkHiveDataSet
        • kedro.extras.datasets.spark.SparkJDBCDataSet
        • kedro.extras.datasets.svmlight.SVMLightDataSet
        • kedro.extras.datasets.tensorflow.TensorFlowModelDataset
        • kedro.extras.datasets.text.TextDataSet
        • kedro.extras.datasets.tracking.JSONDataSet
        • kedro.extras.datasets.tracking.MetricsDataSet
        • kedro.extras.datasets.yaml.YAMLDataSet
      • kedro.extras.extensions
        • kedro.extras.extensions.ipython
      • kedro.extras.logging
        • kedro.extras.logging.color_logger
    • 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.shelvestore
        • kedro.framework.session.store
      • kedro.framework.startup
        • kedro.framework.startup.bootstrap_project
        • kedro.framework.startup.ProjectMetadata
    • kedro.io
      • kedro.io.AbstractDataSet
      • kedro.io.AbstractVersionedDataSet
      • kedro.io.DataCatalog
      • kedro.io.LambdaDataSet
      • kedro.io.MemoryDataSet
      • kedro.io.PartitionedDataSet
      • kedro.io.IncrementalDataSet
      • kedro.io.CachedDataSet
      • kedro.io.Version
      • kedro.io.DataSetAlreadyExistsError
      • kedro.io.DataSetError
      • kedro.io.DataSetNotFoundError
    • kedro.ipython
      • kedro.ipython.load_ipython_extension
      • kedro.ipython.magic_reload_kedro
      • kedro.ipython.reload_kedro
    • kedro.pipeline
      • kedro.pipeline.node
      • kedro.pipeline.modular_pipeline.pipeline
      • kedro.pipeline.Pipeline
      • kedro.pipeline.node.Node
      • kedro.pipeline.modular_pipeline.ModularPipelineError
    • kedro.runner
      • kedro.runner.run_node
      • kedro.runner.AbstractRunner
      • kedro.runner.ParallelRunner
      • kedro.runner.SequentialRunner
      • kedro.runner.ThreadRunner
    • kedro.utils
      • kedro.utils.load_obj
Kedro
  • Docs »
  • Python Module Index

Python Module Index

k
 
k
- kedro
    kedro.config
    kedro.datasets
    kedro.extras
    kedro.extras.datasets
    kedro.extras.extensions
    kedro.extras.extensions.ipython
    kedro.extras.logging
    kedro.extras.logging.color_logger
    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.shelvestore
    kedro.framework.session.store
    kedro.framework.startup
    kedro.io
    kedro.ipython
    kedro.pipeline
    kedro.runner
    kedro.utils

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