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‎.github/workflows/ci-pkg-install.yml

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@@ -71,7 +71,6 @@ jobs:
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pkg-folder: dist/${{ env.PKG_DIR }}
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pkg-name: ${{ matrix.pkg-name }}
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pkg-extra: ${{ matrix.pkg-extra }}
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pip-flags: "--pre" # todo: drop this when `lidata` is released (not as RC only)
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- name: Run CLI (via python)
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if: ${{ (matrix.pkg-name == 'lightning' || matrix.pkg-name == 'notset') && matrix.pkg-extra == 'app' }}

‎.pre-commit-config.yaml

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@@ -71,13 +71,18 @@ 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/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`
@@ -60,7 +60,7 @@ There are a couple of ways you can add a dynamic Work:
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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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@@ -50,10 +50,10 @@ And here's the output you get when running the App using **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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...

‎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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@@ -55,8 +55,8 @@ Usage
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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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@@ -32,8 +32,8 @@ As datasets grow in size and the number of nodes scales, loading training data c
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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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‎docs/source-pytorch/ecosystem/asr_nlp_tts.rst

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Using State-Of-The-Art Pre-trained TTS Model
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--------------------------------------------
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Generate speech using models trained on `LJSpeech <https://keithito.com/LJ-Speech-Dataset/>`,
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Generate speech using models trained on `LJSpeech <https://keithito.com/LJ-Speech-Dataset/>`_,
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around 24 hours of single speaker data.
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See this `TTS notebook <https://github.com/NVIDIA/NeMo/blob/v1.0.0b1/tutorials/tts/1_TTS_inference.ipynb>`_

‎docs/source-pytorch/tuning/profiler_basic.rst

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@@ -31,22 +31,22 @@ Once the **.fit()** function has completed, you'll see an output like this:
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FIT Profiler Report
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-----------------------------------------------------------------------------------------------
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| Action | Mean duration (s) | Total time (s) |
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-----------------------------------------------------------------------------------------------
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| [LightningModule]BoringModel.prepare_data | 10.0001 | 20.00 |
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| run_training_epoch | 6.1558 | 6.1558 |
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| run_training_batch | 0.0022506 | 0.015754 |
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| [LightningModule]BoringModel.optimizer_step | 0.0017477 | 0.012234 |
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| [LightningModule]BoringModel.val_dataloader | 0.00024388 | 0.00024388 |
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| on_train_batch_start | 0.00014637 | 0.0010246 |
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| [LightningModule]BoringModel.teardown | 2.15e-06 | 2.15e-06 |
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| [LightningModule]BoringModel.on_train_start | 1.644e-06 | 1.644e-06 |
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| [LightningModule]BoringModel.on_train_end | 1.516e-06 | 1.516e-06 |
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| [LightningModule]BoringModel.on_fit_end | 1.426e-06 | 1.426e-06 |
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| [LightningModule]BoringModel.setup | 1.403e-06 | 1.403e-06 |
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| [LightningModule]BoringModel.on_fit_start | 1.226e-06 | 1.226e-06 |
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-----------------------------------------------------------------------------------------------
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-------------------------------------------------------------------------------------------
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| Action | Mean duration (s) | Total time (s) |
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-------------------------------------------------------------------------------------------
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| [LightningModule]BoringModel.prepare_data | 10.0001 | 20.00 |
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| run_training_epoch | 6.1558 | 6.1558 |
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| run_training_batch | 0.0022506 | 0.015754 |
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| [LightningModule]BoringModel.optimizer_step | 0.0017477 | 0.012234 |
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| [LightningModule]BoringModel.val_dataloader | 0.00024388 | 0.00024388 |
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| on_train_batch_start | 0.00014637 | 0.0010246 |
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| [LightningModule]BoringModel.teardown | 2.15e-06 | 2.15e-06 |
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| [LightningModule]BoringModel.on_train_start | 1.644e-06 | 1.644e-06 |
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| [LightningModule]BoringModel.on_train_end | 1.516e-06 | 1.516e-06 |
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| [LightningModule]BoringModel.on_fit_end | 1.426e-06 | 1.426e-06 |
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| [LightningModule]BoringModel.setup | 1.403e-06 | 1.403e-06 |
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| [LightningModule]BoringModel.on_fit_start | 1.226e-06 | 1.226e-06 |
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-------------------------------------------------------------------------------------------
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In this report we can see that the slowest function is **prepare_data**. Now you can figure out why data preparation is slowing down your training.
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‎docs/source-pytorch/upgrade/sections/1_7_advanced.rst

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- `PR11871`_
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* - used ``Trainer.validated_ckpt_path`` attribute
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- rely on generic read-only property ``Trainer.ckpt_path`` which is set when checkpoints are loaded via ``Trainer.validate(````ckpt_path=...)``
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- rely on generic read-only property ``Trainer.ckpt_path`` which is set when checkpoints are loaded via ``Trainer.validate(ckpt_path=...)``
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- `PR11696`_
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* - used ``Trainer.tested_ckpt_path`` attribute
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- rely on generic read-only property ``Trainer.ckpt_path`` which is set when checkpoints are loaded via ``Trainer.test(````ckpt_path=...)``
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- rely on generic read-only property ``Trainer.ckpt_path`` which is set when checkpoints are loaded via ``Trainer.test(ckpt_path=...)``
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- `PR11696`_
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* - used ``Trainer.predicted_ckpt_path`` attribute
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- rely on generic read-only property ``Trainer.ckpt_path``, which is set when checkpoints are loaded via ``Trainer.predict(````ckpt_path=...)``
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- rely on generic read-only property ``Trainer.ckpt_path``, which is set when checkpoints are loaded via ``Trainer.predict(ckpt_path=...)``
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* - rely on the returned dictionary from ``Callback.on_save_checkpoint``

‎docs/source-pytorch/upgrade/sections/1_9_devel.rst

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- use DDP instead
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- `PR16386`_ :doc:`DDP <../../accelerators/gpu_expert>`
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* - used the pl.plugins.ApexMixedPrecisionPlugin`` plugin
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* - used the ``pl.plugins.ApexMixedPrecisionPlugin`` plugin
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- `PR16039`_
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‎docs/source-pytorch/upgrade/sections/1_9_regular.rst

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- `PR16184`_
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* - called the ``pl.tuner.auto_gpu_select.pick_single_gpu`` function
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- use Trainer’s flag``devices="auto"``
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- use Trainer’s flag ``devices="auto"``
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- `PR16184`_
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* - called the ``pl.tuner.auto_gpu_select.pick_multiple_gpus`` functions
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- use Trainer’s flag``devices="auto"``
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- use Trainer’s flag ``devices="auto"``
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- `PR16184`_
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* - used Trainer’s flag ``accumulate_grad_batches`` with a scheduling dictionary value

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