Change location of API docs to /api subdir (#3553)

* move api docs again Signed-off-by: Jo Stichbury <[email protected]> * Change links to API docs Signed-off-by: Jo Stichbury <[email protected]> * Update docs/source/data/advanced_data_catalog_usage.md Signed-off-by: Jo Stichbury <[email protected]> * Update docs/source/data/advanced_data_catalog_usage.md Signed-off-by: Jo Stichbury <[email protected]> * Update docs/source/data/advanced_data_catalog_usage.md Signed-off-by: Jo Stichbury <[email protected]> * Update docs/source/deployment/amazon_emr_serverless.md Signed-off-by: Jo Stichbury <[email protected]> --------- Signed-off-by: Jo Stichbury <[email protected]> Co-authored-by: Juan Luis Cano Rodríguez <[email protected]>
kedro-org · Jan 26, 2024 · 285cf1b · 285cf1b
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diff --git a/.gitignore b/.gitignore
@@ -137,8 +137,7 @@ venv.bak/
 # Sphinx documentation
 # Additional files created by sphinx.ext.autosummary
 # Some of them are actually tracked to control the output
-/docs/source/kedro.*
-/docs/source/kedro_datasets.*
+/docs/source/api/kedro.*
 
 # mypy
 .mypy_cache/

diff --git a/docs/source/kedro.config.rst → docs/source/api/kedro.config.rst b/docs/source/kedro.config.rst → docs/source/api/kedro.config.rst
diff --git a/...urce/kedro.framework.cli.cli.KedroCLI.rst → .../api/kedro.framework.cli.cli.KedroCLI.rst b/...urce/kedro.framework.cli.cli.KedroCLI.rst → .../api/kedro.framework.cli.cli.KedroCLI.rst
diff --git a/...ork.cli.hooks.manager.CLIHooksManager.rst → ...ork.cli.hooks.manager.CLIHooksManager.rst b/...ork.cli.hooks.manager.CLIHooksManager.rst → ...ork.cli.hooks.manager.CLIHooksManager.rst
diff --git a/...ework.cli.jupyter.JupyterCommandGroup.rst → ...ework.cli.jupyter.JupyterCommandGroup.rst b/...ework.cli.jupyter.JupyterCommandGroup.rst → ...ework.cli.jupyter.JupyterCommandGroup.rst
diff --git a/...framework.cli.utils.CommandCollection.rst → ...framework.cli.utils.CommandCollection.rst b/...framework.cli.utils.CommandCollection.rst → ...framework.cli.utils.CommandCollection.rst
diff --git a/docs/source/kedro.framework.cli.utils.rst → .../source/api/kedro.framework.cli.utils.rst b/docs/source/kedro.framework.cli.utils.rst → .../source/api/kedro.framework.cli.utils.rst
diff --git a/docs/source/kedro.framework.context.rst → docs/source/api/kedro.framework.context.rst b/docs/source/kedro.framework.context.rst → docs/source/api/kedro.framework.context.rst
diff --git a/...ework.session.shelvestore.ShelveStore.rst → ...ework.session.shelvestore.ShelveStore.rst b/...ework.session.shelvestore.ShelveStore.rst → ...ework.session.shelvestore.ShelveStore.rst
diff --git a/...mework.session.store.BaseSessionStore.rst → ...mework.session.store.BaseSessionStore.rst b/...mework.session.store.BaseSessionStore.rst → ...mework.session.store.BaseSessionStore.rst
diff --git a/docs/source/kedro.io.rst → docs/source/api/kedro.io.rst b/docs/source/kedro.io.rst → docs/source/api/kedro.io.rst
diff --git a/docs/source/kedro.logging.RichHandler.rst → .../source/api/kedro.logging.RichHandler.rst b/docs/source/kedro.logging.RichHandler.rst → .../source/api/kedro.logging.RichHandler.rst
diff --git a/docs/source/kedro.logging.rst → docs/source/api/kedro.logging.rst b/docs/source/kedro.logging.rst → docs/source/api/kedro.logging.rst
diff --git a/docs/source/kedro.pipeline.rst → docs/source/api/kedro.pipeline.rst b/docs/source/kedro.pipeline.rst → docs/source/api/kedro.pipeline.rst
diff --git a/docs/source/kedro.runner.rst → docs/source/api/kedro.runner.rst b/docs/source/kedro.runner.rst → docs/source/api/kedro.runner.rst
diff --git a/docs/source/configuration/advanced_configuration.md b/docs/source/configuration/advanced_configuration.md
@@ -1,7 +1,7 @@
 # Advanced configuration
 The documentation on [configuration](./configuration_basics.md) describes how to satisfy most common requirements of standard Kedro project configuration:
 
-By default, Kedro is set up to use the [OmegaConfigLoader](/kedro.config.OmegaConfigLoader) class.
+By default, Kedro is set up to use the [OmegaConfigLoader](/api/kedro.config.OmegaConfigLoader) class.
 
 ## Advanced configuration for Kedro projects
 This page also contains a set of guidance for advanced configuration requirements of standard Kedro projects:
@@ -20,7 +20,7 @@ This page also contains a set of guidance for advanced configuration requirement
 
 
 ### How to use a custom configuration loader
-You can implement a custom configuration loader by extending the [`AbstractConfigLoader`](/kedro.config.AbstractConfigLoader) class:
+You can implement a custom configuration loader by extending the [`AbstractConfigLoader`](/api/kedro.config.AbstractConfigLoader) class:
 
 ```python
 from kedro.config import AbstractConfigLoader
@@ -281,7 +281,7 @@ CONFIG_LOADER_ARGS = {
 }
 ```
 ### How to load credentials through environment variables
-The [`OmegaConfigLoader`](/kedro.config.OmegaConfigLoader) enables you to load credentials from environment variables. To achieve this you have to use the `OmegaConfigLoader` and the `omegaconf` [`oc.env` resolver](https://omegaconf.readthedocs.io/en/2.3_branch/custom_resolvers.html#oc-env).
+The [`OmegaConfigLoader`](/api/kedro.config.OmegaConfigLoader) enables you to load credentials from environment variables. To achieve this you have to use the `OmegaConfigLoader` and the `omegaconf` [`oc.env` resolver](https://omegaconf.readthedocs.io/en/2.3_branch/custom_resolvers.html#oc-env).
 You can use the `oc.env` resolver to access credentials from environment variables in your `credentials.yml`:
 
 ```yaml

diff --git a/docs/source/configuration/config_loader_migration.md b/docs/source/configuration/config_loader_migration.md
@@ -1,5 +1,5 @@
 # Migration guide for config loaders
-The `ConfigLoader` and `TemplatedConfigLoader` classes have been deprecated since Kedro `0.18.12` and were removed in Kedro `0.19.0`. To use that release or later, you must adopt the [`OmegaConfigLoader`](/kedro.config.OmegaConfigLoader).
+The `ConfigLoader` and `TemplatedConfigLoader` classes have been deprecated since Kedro `0.18.12` and were removed in Kedro `0.19.0`. To use that release or later, you must adopt the [`OmegaConfigLoader`](/api/kedro.config.OmegaConfigLoader).
 This migration guide outlines the primary distinctions between the old loaders and the `OmegaConfigLoader`, providing step-by-step instructions on updating your code base to utilise the new class effectively.
 
 ## `ConfigLoader` to `OmegaConfigLoader`

diff --git a/docs/source/configuration/configuration_basics.md b/docs/source/configuration/configuration_basics.md
@@ -2,7 +2,7 @@
 
 This section contains detailed information about Kedro project configuration, which you can use to store settings for your project such as [parameters](./parameters.md), [credentials](./credentials.md), the [data catalog](../data/data_catalog.md), and [logging information](../logging/index.md).
 
-Kedro makes use of a configuration loader to load any project configuration files, which is [`OmegaConfigLoader`](/kedro.config.OmegaConfigLoader) by default since Kedro 0.19.0.
+Kedro makes use of a configuration loader to load any project configuration files, which is [`OmegaConfigLoader`](/api/kedro.config.OmegaConfigLoader) by default since Kedro 0.19.0.
 
 ```{note}
 `ConfigLoader` and `TemplatedConfigLoader` have been removed in Kedro `0.19.0`. Refer to the [migration guide for config loaders](./config_loader_migration.md) for instructions on how to update your code base to use `OmegaConfigLoader`.
@@ -12,7 +12,7 @@ Kedro makes use of a configuration loader to load any project configuration file
 
 [OmegaConf](https://omegaconf.readthedocs.io/) is a Python library designed to handle and manage settings. It serves as a YAML-based hierarchical system to organise configurations, which can be structured to accommodate various sources, allowing you to merge settings from multiple locations.
 
-From Kedro 0.18.5 you can use the [`OmegaConfigLoader`](/kedro.config.OmegaConfigLoader) which uses `OmegaConf` to load data.
+From Kedro 0.18.5 you can use the [`OmegaConfigLoader`](/api/kedro.config.OmegaConfigLoader) which uses `OmegaConf` to load data.
 
 `OmegaConfigLoader` can load `YAML` and `JSON` files. Acceptable file extensions are `.yml`, `.yaml`, and `.json`. By default, any configuration files used by the config loaders in Kedro are `.yml` files.
 
@@ -63,7 +63,7 @@ Do not add any local configuration to version control.
 ```
 
 ## Configuration loading
-Kedro-specific configuration (e.g., `DataCatalog` configuration for I/O) is loaded using a configuration loader class, by default, this is [`OmegaConfigLoader`](/kedro.config.OmegaConfigLoader).
+Kedro-specific configuration (e.g., `DataCatalog` configuration for I/O) is loaded using a configuration loader class, by default, this is [`OmegaConfigLoader`](/api/kedro.config.OmegaConfigLoader).
 When you interact with Kedro through the command line, e.g. by running `kedro run`, Kedro loads all project configuration in the configuration source through this configuration loader.
 
 The loader recursively scans for configuration files inside the `conf` folder, firstly in `conf/base` (`base` being the default environment) and then in `conf/local` (`local` being the designated overriding environment).
@@ -128,7 +128,7 @@ kedro run --conf-source=<path-to-new-conf-folder>
 ```
 
 ### How to read configuration from a compressed file
-You can read configuration from a compressed file in `tar.gz` or `zip` format by using the [`OmegaConfigLoader`](/kedro.config.OmegaConfigLoader).
+You can read configuration from a compressed file in `tar.gz` or `zip` format by using the [`OmegaConfigLoader`](/api/kedro.config.OmegaConfigLoader).
 
 How to reference a `tar.gz` file:
 

diff --git a/docs/source/configuration/parameters.md b/docs/source/configuration/parameters.md
@@ -66,7 +66,7 @@ node(
 )
 ```
 
-In both cases, under the hood parameters are added to the Data Catalog through the method `add_feed_dict()` in [`DataCatalog`](/kedro.io.DataCatalog), where they live as `MemoryDataset`s. This method is also what the `KedroContext` class uses when instantiating the catalog.
+In both cases, under the hood parameters are added to the Data Catalog through the method `add_feed_dict()` in [`DataCatalog`](/api/kedro.io.DataCatalog), where they live as `MemoryDataset`s. This method is also what the `KedroContext` class uses when instantiating the catalog.
 
 ```{note}
 You can use `add_feed_dict()` to inject any other entries into your `DataCatalog` as per your use case.
@@ -110,7 +110,7 @@ The `kedro.framework.context.KedroContext` class uses the approach above to load
 
 ## How to specify parameters at runtime
 
-Kedro also allows you to specify runtime parameters for the `kedro run` CLI command. Use the `--params` command line option and specify a comma-separated list of key-value pairs that will be added to [KedroContext](/kedro.framework.context.KedroContext) parameters and made available to pipeline nodes.
+Kedro also allows you to specify runtime parameters for the `kedro run` CLI command. Use the `--params` command line option and specify a comma-separated list of key-value pairs that will be added to [KedroContext](/api/kedro.framework.context.KedroContext) parameters and made available to pipeline nodes.
 
 Each key-value pair is split on the first equals sign. The following example is a valid command:
 

diff --git a/docs/source/data/advanced_data_catalog_usage.md b/docs/source/data/advanced_data_catalog_usage.md
@@ -1,6 +1,6 @@
 # Advanced: Access the Data Catalog in code
 
-You can define a Data Catalog in two ways. Most use cases can be through a YAML configuration file as [illustrated previously](./data_catalog.md), but it is possible to access the Data Catalog programmatically through [`kedro.io.DataCatalog`](/kedro.io.DataCatalog) using an API that allows you to configure data sources in code and use the IO module within notebooks.
+You can define a Data Catalog in two ways. Most use cases can be through a YAML configuration file as [illustrated previously](./data_catalog.md), but it is possible to access the Data Catalog programmatically through [`kedro.io.DataCatalog`](/api/kedro.io.DataCatalog) using an API that allows you to configure data sources in code and use the IO module within notebooks.
 
 ```{warning}
 Datasets are not included in the core Kedro package from Kedro version **`0.19.0`**. Import them from the [`kedro-datasets`](https://github.com/kedro-org/kedro-plugins/tree/main/kedro-datasets) package instead.

diff --git a/docs/source/data/data_catalog.md b/docs/source/data/data_catalog.md
@@ -151,7 +151,7 @@ kedro run --load-versions=cars:YYYY-MM-DDThh.mm.ss.sssZ
 ```
 where `--load-versions` is dataset name and version timestamp separated by `:`.
 
-A dataset offers versioning support if it extends the [`AbstractVersionedDataset`](/kedro.io.AbstractVersionedDataset) class to accept a version keyword argument as part of the constructor and adapt the `_save` and `_load` method to use the versioned data path obtained from `_get_save_path` and `_get_load_path` respectively.
+A dataset offers versioning support if it extends the [`AbstractVersionedDataset`](/api/kedro.io.AbstractVersionedDataset) class to accept a version keyword argument as part of the constructor and adapt the `_save` and `_load` method to use the versioned data path obtained from `_get_save_path` and `_get_load_path` respectively.
 
 To verify whether a dataset can undergo versioning, you should examine the dataset class code to inspect its inheritance [(you can find contributed datasets within the `kedro-datasets` repository)](https://github.com/kedro-org/kedro-plugins/tree/main/kedro-datasets/kedro_datasets). Check if the dataset class inherits from the `AbstractVersionedDataset`. For instance, if you encounter a class like `CSVDataset(AbstractVersionedDataset[pd.DataFrame, pd.DataFrame])`, this indicates that the dataset is set up to support versioning.
 

diff --git a/docs/source/data/how_to_create_a_custom_dataset.md b/docs/source/data/how_to_create_a_custom_dataset.md
@@ -4,7 +4,7 @@
 
 ## AbstractDataset
 
-If you are a contributor and would like to submit a new dataset, you must extend the [`AbstractDataset` interface](/kedro.io.AbstractDataset) or [`AbstractVersionedDataset` interface](/kedro.io.AbstractVersionedDataset) if you plan to support versioning. It requires subclasses to override the `_load` and `_save` and provides `load` and `save` methods that enrich the corresponding private methods with uniform error handling. It also requires subclasses to override `_describe`, which is used in logging the internal information about the instances of your custom `AbstractDataset` implementation.
+If you are a contributor and would like to submit a new dataset, you must extend the [`AbstractDataset` interface](/api/kedro.io.AbstractDataset) or [`AbstractVersionedDataset` interface](/api/kedro.io.AbstractVersionedDataset) if you plan to support versioning. It requires subclasses to override the `_load` and `_save` and provides `load` and `save` methods that enrich the corresponding private methods with uniform error handling. It also requires subclasses to override `_describe`, which is used in logging the internal information about the instances of your custom `AbstractDataset` implementation.
 
 
 ## Scenario
@@ -29,7 +29,7 @@ Consult the [Pillow documentation](https://pillow.readthedocs.io/en/stable/insta
 
 ## The anatomy of a dataset
 
-At the minimum, a valid Kedro dataset needs to subclass the base [AbstractDataset](/kedro.io.AbstractDataset) and provide an implementation for the following abstract methods:
+At the minimum, a valid Kedro dataset needs to subclass the base [AbstractDataset](/api/kedro.io.AbstractDataset) and provide an implementation for the following abstract methods:
 
 * `_load`
 * `_save`
@@ -307,7 +307,7 @@ Versioning doesn't work with `PartitionedDataset`. You can't use both of them at
 ```
 
 To add versioning support to the new dataset we need to extend the
- [AbstractVersionedDataset](/kedro.io.AbstractVersionedDataset) to:
+ [AbstractVersionedDataset](/api/kedro.io.AbstractVersionedDataset) to:
 
 * Accept a `version` keyword argument as part of the constructor
 * Adapt the `_save` and `_load` method to use the versioned data path obtained from `_get_save_path` and `_get_load_path` respectively
@@ -507,9 +507,9 @@ Inspect the content of the data directory to find a new version of the data, wri
 
 ## Thread-safety
 
-Kedro datasets should work with the [SequentialRunner](/kedro.runner.SequentialRunner) and the [ParallelRunner](/kedro.runner.ParallelRunner), so they must be fully serialisable by the [Python multiprocessing package](https://docs.python.org/3/library/multiprocessing.html). This means that your datasets should not make use of lambda functions, nested functions, closures etc. If you are using custom decorators, you need to ensure that they are using [`functools.wraps()`](https://docs.python.org/3/library/functools.html#functools.wraps).
+Kedro datasets should work with the [SequentialRunner](/api/kedro.runner.SequentialRunner) and the [ParallelRunner](/api/kedro.runner.ParallelRunner), so they must be fully serialisable by the [Python multiprocessing package](https://docs.python.org/3/library/multiprocessing.html). This means that your datasets should not make use of lambda functions, nested functions, closures etc. If you are using custom decorators, you need to ensure that they are using [`functools.wraps()`](https://docs.python.org/3/library/functools.html#functools.wraps).
 
-There is one dataset that is an exception: {class}`SparkDataset<kedro-datasets:kedro_datasets.spark.SparkDataset>`. The explanation for this exception is that [Apache Spark](https://spark.apache.org/) uses its own parallelism and therefore doesn't work with Kedro [ParallelRunner](/kedro.runner.ParallelRunner). For parallelism within a Kedro project that uses Spark, use [ThreadRunner](/kedro.runner.ThreadRunner) instead.
+There is one dataset that is an exception: {class}`SparkDataset<kedro-datasets:kedro_datasets.spark.SparkDataset>`. The explanation for this exception is that [Apache Spark](https://spark.apache.org/) uses its own parallelism and therefore doesn't work with Kedro [ParallelRunner](/api/kedro.runner.ParallelRunner). For parallelism within a Kedro project that uses Spark, use [ThreadRunner](/api/kedro.runner.ThreadRunner) instead.
 
 To verify whether your dataset is serialisable by `multiprocessing`, use the console or an IPython session to try dumping it using `multiprocessing.reduction.ForkingPickler`:
 

diff --git a/docs/source/data/kedro_dataset_factories.md b/docs/source/data/kedro_dataset_factories.md
@@ -220,7 +220,7 @@ The matches are ranked according to the following criteria:
 
 ## How to override the default dataset creation with dataset factories
 
-You can use dataset factories to define a catch-all pattern which will overwrite the default [`MemoryDataset`](/kedro.io.MemoryDataset) creation.
+You can use dataset factories to define a catch-all pattern which will overwrite the default [`MemoryDataset`](/api/kedro.io.MemoryDataset) creation.
 
 ```yaml
 "{a_default_dataset}":

diff --git a/docs/source/data/partitioned_and_incremental_datasets.md b/docs/source/data/partitioned_and_incremental_datasets.md
@@ -89,7 +89,7 @@ The dataset definition should be passed into the `dataset` argument of the `Part
 
 #### Shorthand notation
 
-Requires you only to specify a class of the underlying dataset either as a string (e.g. `pandas.CSVDataset` or a fully qualified class path like `kedro_datasets.pandas.CSVDataset`) or as a class object that is a subclass of the [AbstractDataset](/kedro.io.AbstractDataset).
+Requires you only to specify a class of the underlying dataset either as a string (e.g. `pandas.CSVDataset` or a fully qualified class path like `kedro_datasets.pandas.CSVDataset`) or as a class object that is a subclass of the [AbstractDataset](/api/kedro.io.AbstractDataset).
 
 #### Full notation
 

diff --git a/docs/source/deployment/argo.md b/docs/source/deployment/argo.md
@@ -23,7 +23,7 @@ To use Argo Workflows, ensure you have the following prerequisites in place:
 
 - [Argo Workflows is installed](https://github.com/argoproj/argo/blob/master/README.md#quickstart) on your Kubernetes cluster
 - [Argo CLI is installed](https://github.com/argoproj/argo/releases) on your machine
-- A `name` attribute is set for each [Kedro node](/kedro.pipeline.node) since it is used to build a DAG
+- A `name` attribute is set for each [Kedro node](/api/kedro.pipeline.node) since it is used to build a DAG
 - [All node input/output datasets must be configured in `catalog.yml`](../data/data_catalog_yaml_examples.md) and refer to an external location (e.g. AWS S3); you cannot use the `MemoryDataset` in your workflow
 
 ```{note}

diff --git a/docs/source/deployment/aws_batch.md b/docs/source/deployment/aws_batch.md
@@ -17,7 +17,7 @@ The following sections are a guide on how to deploy a Kedro project to AWS Batch
 To use AWS Batch, ensure you have the following prerequisites in place:
 
 - An [AWS account set up](https://aws.amazon.com/premiumsupport/knowledge-center/create-and-activate-aws-account/).
-- A `name` attribute is set for each [Kedro node](/kedro.pipeline.node). Each node will run in its own Batch job, so having sensible node names will make it easier to `kedro run --nodes=<node_name>`.
+- A `name` attribute is set for each [Kedro node](/api/kedro.pipeline.node). Each node will run in its own Batch job, so having sensible node names will make it easier to `kedro run --nodes=<node_name>`.
 - [All node input/output datasets must be configured in `catalog.yml`](../data/data_catalog_yaml_examples.md) and refer to an external location (e.g. AWS S3). A clean way to do this is to create a new configuration environment `conf/aws_batch` containing a `catalog.yml` file with the appropriate configuration, as illustrated below.
 
 <details>

diff --git a/docs/source/development/debugging.md b/docs/source/development/debugging.md
@@ -13,7 +13,7 @@ For guides on how to set up debugging with IDEs, please visit the [guide for deb
 
 ## Debugging a node
 
-To start a debugging session when an uncaught error is raised within your `node`, implement the `on_node_error` [Hook specification](/kedro.framework.hooks):
+To start a debugging session when an uncaught error is raised within your `node`, implement the `on_node_error` [Hook specification](/api/kedro.framework.hooks):
 
 ```python
 import pdb
@@ -48,7 +48,7 @@ HOOKS = (PDBNodeDebugHook(),)
 
 ## Debugging a pipeline
 
-To start a debugging session when an uncaught error is raised within your `pipeline`, implement the `on_pipeline_error` [Hook specification](/kedro.framework.hooks):
+To start a debugging session when an uncaught error is raised within your `pipeline`, implement the `on_pipeline_error` [Hook specification](/api/kedro.framework.hooks):
 
 ```python
 import pdb