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docs: enable Sphinx linter & fixing (#19515)
* docs: enable Sphinx linter * fixes
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‎.pre-commit-config.yaml

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@@ -71,6 +71,11 @@ repos:
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additional_dependencies: [tomli]
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args: ["--in-place"]
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- repo: https://github.com/sphinx-contrib/sphinx-lint
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rev: v0.9.1
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hooks:
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- id: sphinx-lint
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- repo: https://github.com/asottile/yesqa
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rev: v1.5.0
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hooks:
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- repo: https://github.com/astral-sh/ruff-pre-commit
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rev: "v0.2.0"
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hooks:
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- id: ruff
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args: ["--fix", "--preview"]
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- id: ruff-format
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args: ["--preview"]
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- id: ruff
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args: ["--fix", "--preview"]
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- repo: https://github.com/executablebooks/mdformat
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rev: 0.7.17

‎docs/source-app/core_api/lightning_app/communication_content.rst

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@@ -87,13 +87,13 @@ And here's the output you get when running the App using the **Lightning CLI**:
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.. code-block:: console
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INFO: Your app has started. View it in your browser: http://127.0.0.1:7501/view
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State: {'works': {'w': {'vars': {'counter': 1}}}}
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State: {'works': {'w': {'vars': {'counter': 2}}}}
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State: {'works': {'w': {'vars': {'counter': 3}}}}
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State: {'works': {'w': {'vars': {'counter': 3}}}}
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State: {'works': {'w': {'vars': {'counter': 4}}}}
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...
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INFO: Your app has started. View it in your browser: http://127.0.0.1:7501/view
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State: {'works': {'w': {'vars': {'counter': 1}}}}
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State: {'works': {'w': {'vars': {'counter': 2}}}}
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State: {'works': {'w': {'vars': {'counter': 3}}}}
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State: {'works': {'w': {'vars': {'counter': 3}}}}
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State: {'works': {'w': {'vars': {'counter': 4}}}}
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...
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----
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‎docs/source-app/core_api/lightning_app/dynamic_work_content.rst

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@@ -41,9 +41,9 @@ There are a couple of ways you can add a dynamic Work:
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def run(self):
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if not hasattr(self, "work"):
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# The `Work` component is created and attached here.
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# The `Work` component is created and attached here.
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setattr(self, "work", Work())
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# Run the `Work` component.
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# Run the `Work` component.
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getattr(self, "work").run()
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**OPTION 2:** Use the built-in Lightning classes :class:`~lightning.app.structures.Dict` or :class:`~lightning.app.structures.List`
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def run(self):
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if "work" not in self.dict:
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# The `Work` component is attached here.
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# The `Work` component is attached here.
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self.dict["work"] = Work()
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self.dict["work"].run()
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‎docs/source-app/glossary/environment_variables.rst

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@@ -24,4 +24,4 @@ Environment variables are available in all Flows and Works, and can be accessed
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print(os.environ["BAZ"]) # FAZ
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.. note::
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Environment variables are not encrypted. For sensitive values, we recommend using :ref:`Encrypted Secrets <secrets>`.
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Environment variables are not encrypted. For sensitive values, we recommend using :ref:`Encrypted Secrets <secrets>`.

‎docs/source-app/glossary/secrets.rst

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@@ -8,7 +8,7 @@ Encrypted Secrets allow you to pass private data to your apps, like API keys, ac
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Secrets provide you with a secure way to store this data in a way that is accessible to Apps so that they can authenticate third-party services/solutions.
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.. tip::
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For non-sensitive configuration values, we recommend using :ref:`plain-text Environment Variables <environment_variables>`.
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For non-sensitive configuration values, we recommend using :ref:`plain-text Environment Variables <environment_variables>`.
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************
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Add a secret

‎docs/source-app/glossary/sharing_components.rst

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@@ -34,7 +34,7 @@ Now, imagine you have implemented a **KerasScriptRunner** component for training
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Here are the best practices steps before sharing the component:
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* **Testing**: Ensure your component is well tested by following the ref:`../testing` guide.
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* **Testing**: Ensure your component is well tested by following the :doc:`../testing` guide.
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* **Documented**: Ensure your component has a docstring and comes with some usage explications.
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.. Note:: As a Lightning user, it helps to implement your components thinking someone else is going to use them.

‎docs/source-app/workflows/access_app_state.rst

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.. code-block:: console
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INFO: Your app has started. View it in your browser: http://127.0.0.1:7501/view
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State: {'works': {'w': {'vars': {'counter': 1}}}}
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State: {'works': {'w': {'vars': {'counter': 2}}}}
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State: {'works': {'w': {'vars': {'counter': 3}}}}
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State: {'works': {'w': {'vars': {'counter': 3}}}}
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State: {'works': {'w': {'vars': {'counter': 4}}}}
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...
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INFO: Your app has started. View it in your browser: http://127.0.0.1:7501/view
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State: {'works': {'w': {'vars': {'counter': 1}}}}
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State: {'works': {'w': {'vars': {'counter': 2}}}}
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State: {'works': {'w': {'vars': {'counter': 3}}}}
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State: {'works': {'w': {'vars': {'counter': 3}}}}
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State: {'works': {'w': {'vars': {'counter': 4}}}}
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...

‎docs/source-app/workflows/add_web_ui/react/communicate_between_react_and_lightning.rst

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@@ -47,7 +47,7 @@ Update React <-- Lightning app
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******************************
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To change the React app from the Lightning app, use the values from the `lightningState`.
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In this example, when the `react_ui.counter`` increaes in the Lightning app:
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In this example, when the ``react_ui.counter`` increaes in the Lightning app:
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.. literalinclude:: ../../../../../src/lightning/app/cli/react-ui-template/example_app.py
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:emphasize-lines: 18, 24

‎docs/source-pytorch/advanced/post_training_quantization.rst

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Minor code changes are required for the user to get started with Intel® Neural Compressor quantization API. To construct the quantization process, users can specify the below settings via the Python code:
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1. Calibration Dataloader (Needed for post-training static quantization)
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2. Evaluation Dataloader and Metric
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1. Calibration Dataloader (Needed for post-training static quantization)
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2. Evaluation Dataloader and Metric
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The code changes that are required for Intel® Neural Compressor are highlighted with comments in the line above.
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‎docs/source-pytorch/data/alternatives.rst

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The `StreamingDataset <https://github.com/mosaicml/streaming>`__ can make training on large datasets from cloud storage
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as fast, cheap, and scalable as possible.
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This library uses a custom built class:`~torch.utils.data.IterableDataset`. The library recommends iterating through it
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via a regular class:`~torch.utils.data.DataLoader`. This means that support in the ``Trainer`` is seamless:
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This library uses a custom built :class:`~torch.utils.data.IterableDataset`. The library recommends iterating through it
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via a regular :class:`~torch.utils.data.DataLoader`. This means that support in the ``Trainer`` is seamless:
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.. code-block:: python
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