---
title: Common Workflows
description: >-
  Customize and extend Lightning for things like custom hardware or distributed
  strategies.
canonical: 'https://lightning.ai/docs/pytorch/stable/reference/common_usecases'
---
# Common Workflows {#common-workflows}

Customize and extend Lightning for things like custom hardware or distributed strategies.

- [Avoid overfitting](/docs/pytorch/2.6.6/reference/common/evaluation.md) — Add a training and test loop.

- [Build a model](/docs/pytorch/2.6.6/reference/model/build_model.md) — Steps to build a model.

- [Configure hyperparameters from the CLI](/docs/pytorch/2.6.6/reference/common/hyperparameters.md) — Enable basic CLI with Lightning.

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

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

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

- [Eliminate config boilerplate](/docs/pytorch/2.6.6/reference/cli/lightning_cli.md) — Control your training via CLI and YAML.

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

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

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

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

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

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

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

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

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

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

---

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