Lightning-AI / Lightning-AI/pytorch-lightning

Label tracking meta-issue (edit me to get automatically CC'ed on issues!)

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Python
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Merged PRs (30d)
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Description

This issue is used by [lightning-probot](https://github.com/PyTorchLightning/probot) to manage subscriptions to labels. To subscribe yourself to a label, add a line `* label @yourusername`, or add your username to an existing line (space separated) in the body of this issue. **Do not try to subscribe in comments, the bot only parses the initial post**.

This is a copy of https://github.com/pytorch/pytorch/issues/24422.

As a courtesy to others, please do not edit the subscriptions of users who are not you.

Note: Some labels have sublabels (e.g. callback), but you won't be subscribing to them automatically if you choose the label at the top level.

---

### PR status
* ready @lantiga
* has conflicts
* won’t fix

### Issue/PR category
* design @lantiga @tchaton @justusschock
* breaking change @lantiga @justusschock
* bug @ethanwharris
* code quality
* deprecation @lantiga
* discussion @lantiga
* docs @lantiga
* feature @lantiga
* question @lantiga
* refactor @lantiga @justusschock
* release @lantiga

### Issue/PR severity
* priority: 0 @lantiga
* priority: 1 @lantiga
* priority: 2 @lantiga

### Issue/PR metadata
* duplicate @ethanwharris
* good first issue @ethanwharris
* help wanted
* admin @ethanwharris
* let’s do it! @tchaton
* waiting on author
* working as intended
* 3rd party
* experimental @ethanwharris
* needs triage
* community

### Generic labels
* pl

### Code section (pl)
* accelerator @justusschock @lantiga
* accelerator: cpu @justusschock @lantiga
* accelerator: cuda @justusschock @lantiga
* accelerator: hpu (external) @jerome-habana
* accelerator: mps @justusschock @lantiga
* accelerator: tpu @JackCaoG @Liyang90 @gkroiz
* callback @lantiga
* callback: device stats
* callback: early stopping
* callback: fine-tuning
* callback: gradient accumulation
* callback: lambda function
* callback: lr monitor
* callback: model checkpoint
* callback: model summary
* callback: prediction writer
* callback: pruning
* callback: swa
* callback: timer
* callback: throughput
* ci @ethanwharris
* environment @lantiga
* environment: kubeflow @lantiga
* environment: lightning @lantiga
* environment: lsf @lantiga
* environment: slurm @lantiga
* environment: torchelastic @lantiga
* environment: mpi @lantiga
* hooks @lantiga @justusschock
* io @justusschock
* lightningcli @mauvilsa
* lightningdatamodule @lantiga
* fabric @justusschock @lantiga
* lightningmodule @justusschock @lantiga
* logger @lantiga
* logger: comet
* logger: csv @ethanwharris
* logger: mlflow
* logger: neptune
* logger: tensorboard @lantiga
* logger: wandb @lantiga @morganmcg1 @borisdayma @scottire @parambharat
* logging
* loops @justusschock
* lr scheduler
* optimizer
* plugin @justusschock @lantiga
* precision: double @justusschock @lantiga
* precision: amp @justusschock @lantiga
* precision: bnb @lantiga
* precision: te @lantiga
* precision: half @lantiga
* profiler
* progress bar: rich
* progress bar: tqdm @lantiga
* progress tracking (internal) @lantiga
* strategy @justusschock @lantiga
* strategy: ddp @justusschock @lantiga
* strategy: deepspeed @lantiga
* strategy: dp (removed in pl) @justusschock @lantiga
* strategy: fsdp @lantiga
* strategy: hpu (external) @jerome-habana
* strategy: hivemind (external)
* strategy: xla @JackCaoG @Liyang90 @gkroiz
* tests @lantiga
* trainer @lantiga @justusschock
* trainer: connector @justusschock @lantiga
* trainer: fit
* trainer: argument @justusschock @lantiga
* trainer: predict
* trainer: test
* trainer: tune
* trainer: validate
* torch.compile
* tuner

### Feature (pl)
* fault tolerance @justusschock
* checkpointing @lantiga
* data handling @tchaton
* distributed @justusschock
* optimization @ethanwharris
* reproducibility @lantiga
* performance @lantiga

Contributor guide

Open the contributing guide

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

This is a lightning-probot subscription meta-issue rather than an implementation task. Read the issue body and the linked PyTorch issue to understand the label-subscription format; there are no files, tests, or code changes identified, and done means updating the subscription lines as instructed.

Written by the indexing model from the issue text.

Assessment

Tech stack
github
Domain
documentation, tooling
Issue type
Documentation
Difficulty
1/5
Estimated time
Under an hour
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
1/100

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