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# Configuration | ||
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```{toctree} | ||
:hidden: | ||
configuration_basics | ||
credentials | ||
parameters | ||
advanced_configuration | ||
``` |
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# Contribute to Kedro | ||
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```{toctree} | ||
:hidden: | ||
contribute_to_kedro | ||
developer_contributor_guidelines | ||
backwards_compatibility | ||
documentation_contributor_guidelines | ||
technical_steering_committee | ||
development_for_databricks | ||
``` |
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# Data Catalog | ||
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```{toctree} | ||
:hidden: | ||
data_catalog | ||
kedro_io | ||
``` |
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# Deployment | ||
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```{toctree} | ||
:hidden: | ||
deployment_guide | ||
single_machine | ||
distributed | ||
argo | ||
prefect | ||
kubeflow | ||
aws_batch | ||
aws_sagemaker | ||
aws_step_functions | ||
airflow_astronomer | ||
dask | ||
``` |
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# Development | ||
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```{toctree} | ||
:hidden: | ||
set_up_vscode | ||
set_up_pycharm | ||
commands_reference | ||
debugging | ||
automated_testing | ||
linting | ||
``` |
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# Extend Kedro | ||
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```{toctree} | ||
:hidden: | ||
common_use_cases | ||
custom_datasets | ||
plugins | ||
``` |
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# First steps | ||
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```{toctree} | ||
:hidden: | ||
install | ||
new_project | ||
kedro_concepts | ||
``` |
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# Hooks | ||
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```{toctree} | ||
:hidden: | ||
introduction | ||
common_use_cases | ||
examples | ||
``` |
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# PySpark integration | ||
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```{toctree} | ||
:caption: PySpark | ||
:maxdepth: 2 | ||
:hidden: | ||
pyspark_integration.md | ||
``` |
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# Introduction to Kedro | ||
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```{toctree} | ||
:hidden: | ||
introduction | ||
``` | ||
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Kedro is an open-source Python framework to create reproducible, maintainable, and modular data science code. It uses software engineering best practices to help you build production-ready data science pipelines. | ||
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Kedro is hosted by the [LF AI & Data Foundation](https://lfaidata.foundation/), and you can find the [Kedro source code on GitHub](https://github.com/kedro-org/kedro). | ||
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# Kedro project setup | ||
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```{toctree} | ||
:hidden: | ||
starters | ||
dependencies | ||
session | ||
settings | ||
``` |
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# Logging | ||
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```{toctree} | ||
:hidden: | ||
logging | ||
``` | ||
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Kedro uses [Python's `logging` library](https://docs.python.org/3/library/logging.html). Configuration is provided as a dictionary according to the [Python logging configuration schema](https://docs.python.org/3/library/logging.config.html#logging-config-dictschema) in two places: | ||
1. [Default configuration built into the Kedro framework](https://github.com/kedro-org/kedro/blob/main/kedro/framework/project/default_logging.yml). This cannot be altered. | ||
2. Your project-side logging configuration. Every project generated using Kedro's CLI `kedro new` command includes a file `conf/base/logging.yml`. You can alter this configuration and provide different configurations for different run environment according to the [standard Kedro mechanism for handling configuration](../configuration/configuration_basics.md). | ||
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```{note} | ||
Providing project-side logging configuration is entirely optional. You can delete the `conf/base/logging.yml` file and Kedro will run using the framework's built in configuration. | ||
``` | ||
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Framework-side and project-side logging configuration are loaded through subsequent calls to [`logging.config.dictConfig`](https://docs.python.org/3/library/logging.config.html#logging.config.dictConfig). This means that, when it is provided, the project-side logging configuration typically _fully overwrites_ the framework-side logging configuration. [Incremental configuration](https://docs.python.org/3/library/logging.config.html#incremental-configuration) is also possible if the `incremental` key is explicitly set to `True` in your project-side logging configuration. |
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# Nodes and pipelines | ||
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```{toctree} | ||
:hidden: | ||
nodes | ||
pipeline_introduction | ||
modular_pipelines | ||
pipeline_registry | ||
micro_packaging | ||
run_a_pipeline | ||
slice_a_pipeline | ||
``` |
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# Notebooks & iPython users | ||
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```{toctree} | ||
:hidden: | ||
kedro_and_notebooks | ||
kedro_as_a_data_registry | ||
``` |
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