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Common use cases
================

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   :description: Learn to train Lightning models on the cloud
   :header: Cloud training
   :button_link:  clouds/cloud_training.html
   :card_style: text-container-small

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   :description: Lightning checkpoints have everything you need to save and restore your models
   :header: Checkpointing
   :button_link: common/checkpointing.html
   :card_style: text-container-small

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   :description: Learn to train on your university or company's cluster
   :header: Cluster training
   :button_link: clouds/cluster.html
   :card_style: text-container-small

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   :description: Tricks for debugging your Lightning Models
   :header: Debugging
   :button_link: common/debugging.html
   :card_style: text-container-small

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   :description: Save time and money by training until key metrics stop improving or time has elapsed
   :header: Early stopping
   :button_link: common/early_stopping.html
   :card_style: text-container-small

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   :description: Here you'll find the latest SOTA training techniques such as SWA, accumulated gradients, etc...
   :header: Effective training techniques
   :button_link: advanced/training_tricks.html
   :card_style: text-container-small

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   :description: Avoid over-fitting (memorizing the dataset) with these techniques
   :header: Evaluation
   :button_link: common/evaluation.html
   :card_style: text-container-small

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   :description: Before coding a complex model, use lightning-flash to create a baseline in a few lines of code
   :header: Fast baselines
   :button_link: ecosystem/flash.html
   :card_style: text-container-small

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   :description: Enable fault-tolerant training in clusters/clouds where machines might fail (ie: pre-emtible machines)
   :header: Fault-tolerant training
   :button_link: advanced/fault_tolerant_training.html
   :card_style: text-container-small

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   :description: Make your models more flexible by enabling command-line arguments
   :header: Hyperparameters (via command-line)
   :button_link: common/hyperparameters.html
   :card_style: text-container-small

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   :description: Use the latest tricks to easily productionize your Lightning models
   :header: Inference in Production
   :button_link: common/production_inference.html
   :card_style: text-container-small

.. customcalloutitem::
   :description: Reduce configuration boilerplate with the Lightning CLI
   :header: Lightning CLI
   :button_link: common/lightning_cli.html
   :card_style: text-container-small

.. customcalloutitem::
   :description: Visualize your machine learning experiments with these experiment managers
   :header: Loggers (experiment managers)
   :button_link: common/loggers.html
   :card_style: text-container-small

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   :description: Use the model registry to mix and match your models and Datamodules
   :header: Model and Datamodule registry
   :button_link: common/lightning_cli.html#multiple-models-and-or-datasets
   :card_style: text-container-small

.. customcalloutitem::
   :description: Train 1TB+ parameter models with these advanced built-in techniques
   :header: Model parallelism
   :button_link: advanced/model_parallel.html
   :card_style: text-container-small

.. customcalloutitem::
   :description: Increase batch-sizes and improve speeds by training using 16-bit precision and more
   :header: N-Bit Precision
   :button_link: advanced/precision.html
   :card_style: text-container-small

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   :description: Enable manual optimization to fully control the optimization procedure for advanced research
   :header: Manual Optimization
   :button_link: common/optimization.html
   :card_style: text-container-small

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   :description: Use these profilers to find bottlenecks in your model
   :header: Profiling
   :button_link: advanced/profiler.html
   :card_style: text-container-small

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   :description: Use these built-in progress bars or learn how to make your own!
   :header: Progress Bar
   :button_link: common/progress_bar.html
   :card_style: text-container-small

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   :description: Compress model sizes to speed up model inference for deployment without loss of performance (accuracy)
   :header: Pruning and Quantization
   :button_link: advanced/pruning_quantization.html
   :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
   :header: Remote filesystems
   :button_link: common/remote_fs.html
   :card_style: text-container-small

.. customcalloutitem::
   :description: Building the next Deepspeed, FSDP or fancy scaling technique? Add them to Lightning here
   :header: Strategy registry
   :button_link: advanced/strategy_registry.html
   :card_style: text-container-small

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   :description: Simplify metrics calculations to scale-proof your models
   :header: Torchmetrics
   :button_link: ecosystem/metrics.html
   :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
   :header: Transfer learning (finetuning)
   :button_link: advanced/transfer_learning.html
   :card_style: text-container-small


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