---
title: How to
description: >-
  - Avoid overfitting — Learn how to add validation and test loops - Build a
  model — Step by step guide to build your model - Configure hyperparameters
  from...
canonical: 'https://lightning.ai/docs/pytorch/stable/more/common'
---
# How to {#how-to}

- [Avoid overfitting](/docs/pytorch/2.6.6/reference/common/evaluation.md) — Learn how to add validation and test loops

- [Build a model](/docs/pytorch/2.6.6/reference/model/build_model.md) — Step by step guide to build your model

- [Configure hyperparameters from the CLI](/docs/pytorch/2.6.6/reference/cli/lightning_cli.md) — Make your experiments modular via command line interface

- [Customize the progress bar](/docs/pytorch/2.6.6/reference/common/progress_bar.md) — Change the progress bar monitoring and tracking

- [Deploy models into production](/docs/pytorch/2.6.6/reference/deploy/production.md) — Deploy models with different levels of scal

- [Optimize training](/docs/pytorch/2.6.6/reference/advanced/training_tricks.md) — Explore advanced training techniques

- [Find bottlenecks in your code](/docs/pytorch/2.6.6/reference/tuning/profiler.md) — Learn how to profile your experiments to find bottlenecks

- [Finetune a model](/docs/pytorch/2.6.6/reference/advanced/transfer_learning.md) — Learn how to use pretrained models

- [Manage data](/docs/pytorch/2.6.6/reference/data/data.md) — How to use basic to advanced data techniques

- [Manage experiments](/docs/pytorch/2.6.6/reference/visualize/logging_intermediate.md) — Learn to track and visualize with experiment managers

- [Organize existing PyTorch into Lightning](/docs/pytorch/2.6.6/reference/starter/converting.md) — Convert your vanila PyTorch to Lightning

- [Run on a multi-node cluster](/docs/pytorch/2.6.6/reference/clouds/cluster.md) — Learn to run on multi-node in the cloud or on your cluster

- [Save and load model progress](/docs/pytorch/2.6.6/reference/common/checkpointing.md) — Save and load progress with checkpoints

- [Save memory with half-precision](/docs/pytorch/2.6.6/reference/common/precision.md) — Use precision techniques to train faster and save memory

- [Set up large models efficiently](/docs/pytorch/2.6.6/reference/advanced/model_init.md) — Avoid memory peaks and speed up the initialization of large models

- [Train models with billions of parameters](/docs/pytorch/2.6.6/reference/advanced/model_parallel.md) — Scale GPU training for models with billions of parameters

- [Train in a notebook](/docs/pytorch/2.6.6/reference/common/notebooks.md) — Train models in interactive notebooks (Jupyter, Colab, Kaggle, etc.)

- [Train on single or multiple GPUs](/docs/pytorch/2.6.6/reference/accelerators/gpu.md) — Train models faster with GPU accelerators

- [Train on single or multiple TPUs](/docs/pytorch/2.6.6/reference/accelerators/tpu.md) — TTrain models faster with TPU accelerators

- [Train on MPS](/docs/pytorch/2.6.6/reference/accelerators/mps.md) — Train models faster with Apple Silicon GPUs

- [Track and Visualize Experiments](/docs/pytorch/2.6.6/reference/visualize/logging_intermediate.md) — Learn to track and visualize experiments

- [Use a pretrained model](/docs/pytorch/2.6.6/reference/advanced/pretrained.md) — Improve results with transfer learning on pretrained models

- [Use a pure PyTorch training loop](/docs/pytorch/2.6.6/reference/model/own_your_loop.md) — Run your pure PyTorch loop with Lightning

- [Truncated Back-Propagation Through Time](/docs/pytorch/2.6.6/reference/common/tbptt.md) — Efficiently step through time when training recurrent models

---

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