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Fix titles capitalization in docs
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docs/source/common_usecases.rst

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@@ -14,7 +14,7 @@ Common use cases
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.. customcalloutitem::
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:description: Learn to train Lightning models on the cloud
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:header: Cloud training
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:header: Cloud Training
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:button_link: clouds/cloud_training.html
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:card_style: text-container-small
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.. customcalloutitem::
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:description: Learn to train on your university or company's cluster
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:header: Cluster training
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:header: Cluster Training
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:button_link: clouds/cluster.html
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:card_style: text-container-small
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.. customcalloutitem::
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:description: Save time and money by training until key metrics stop improving or time has elapsed
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:header: Early stopping
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:header: Early Stopping
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:button_link: common/early_stopping.html
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:card_style: text-container-small
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.. customcalloutitem::
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:description: Here you'll find the latest SOTA training techniques such as SWA, accumulated gradients, etc...
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:header: Effective training techniques
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:header: Effective Training Techniques
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:button_link: advanced/training_tricks.html
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:card_style: text-container-small
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.. customcalloutitem::
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:description: Before coding a complex model, use lightning-flash to create a baseline in a few lines of code
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:header: Fast baselines
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:header: Fast Baselines
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:button_link: ecosystem/flash.html
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:card_style: text-container-small
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.. customcalloutitem::
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:description: Enable fault-tolerant training in clusters/clouds where machines might fail (ie: pre-emtible machines)
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:header: Fault-tolerant training
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:header: Fault-Tolerant Training
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:button_link: advanced/fault_tolerant_training.html
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:card_style: text-container-small
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.. customcalloutitem::
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:description: Use the model registry to mix and match your models and Datamodules
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:header: Model and Datamodule registry
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:header: Model and Datamodule Registry
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:button_link: common/lightning_cli.html#multiple-models-and-or-datasets
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:card_style: text-container-small
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.. customcalloutitem::
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:description: Train 1TB+ parameter models with these advanced built-in techniques
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:header: Model parallelism
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:header: Model Parallelism
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:button_link: advanced/model_parallel.html
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:card_style: text-container-small
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:description: Work with data on any local or cloud filesystem such as S3 on AWS, GCS on Google Cloud, or ADL on Azure
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:header: Remote filesystems
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:header: Remote Filesystems
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:button_link: common/remote_fs.html
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:card_style: text-container-small
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:description: Building the next Deepspeed, FSDP or fancy scaling technique? Add them to Lightning here
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:header: Strategy registry
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:header: Strategy Registry
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:button_link: advanced/strategy_registry.html
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:card_style: text-container-small
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:description: Use models training on large datasets to achieve better results when you don't have much data
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:header: Transfer learning (finetuning)
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:header: Transfer Learning (finetuning)
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:button_link: advanced/transfer_learning.html
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:card_style: text-container-small
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docs/source/index.rst

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@@ -102,7 +102,7 @@ Or read the `advanced install guide <starter/installation.html>`_
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:description: From hyperparameters sweeps to cloud training to Pruning and Quantization - Lightning covers the key use-cases.
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:header: Common usecases
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:header: Common Usecases
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:button_link: common_usecases.html
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