Lightning-AI / Lightning-AI/pytorch-lightning

Support relative paths in ModelCheckpoint state

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callback: model checkpoint feature pl
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Python
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Description

## 🚀 Feature

It would be great if `ModelCheckpoint` internal state supported relative paths.

### Motivation

Currently, if you specify a relative path for `dirpath`, `ModelCheckpoint` converts it to an absolute path under the hood. This makes it hard to resume training if the log directory is moved, or if resuming training from a different server with a different directory structure.

For example, I specify the relative path `a/b/c`, and `ModelCheckpoint` converts it to `cwd()/a/b/c`. The model trains correctly for a while. Then, HTCondor reschedules my job on a different server. Now, the `cwd()` is different, even though the relative path `a/b/c` is still the same. The job is unable to resume from checkpoint and I get:

> UserWarning: The dirpath has changed from to , therefore `best_model_score`, `kth_best_model_path`, `kth_value`, `last_model_path` and `best_k_models` won’t be reloaded.

### Pitch

Create an argument `relative_paths=True` that would allow `ModelCheckpoint` to use relative paths in its internal state.

### Alternatives

User can create their own checkpoint callback that supports relative paths. But, it would be much nicer if Lightning supported it :-)

### Additional context

______________________________________________________________________

#### If you enjoy Lightning, check out our other projects! ⚡

- [**Metrics**](https://github.com/Lightning-AI/metrics): Machine learning metrics for distributed, scalable PyTorch applications.

- [**Lite**](https://pytorch-lightning.readthedocs.io/en/latest/starter/lightning_lite.html): enables pure PyTorch users to scale their existing code on any kind of device while retaining full control over their own loops and optimization logic.

- [**Flash**](https://github.com/Lightning-AI/lightning-flash): The fastest way to get a Lightning baseline! A collection of tasks for fast prototyping, baselining, fine-tuning, and solving problems with deep learning.

- [**Bolts**](https://github.com/Lightning-AI/lightning-bolts): Pretrained SOTA Deep Learning models, callbacks, and more for research and production with PyTorch Lightning and PyTorch.

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cc @borda @carmocca @awaelchli @ninginthecloud @jjenniferdai @rohitgr7 @akihironitta

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

Start by locating the Python ModelCheckpoint callback and the tests covering checkpoint state restoration. Reproduce a resume with a relative dirpath after changing the working directory, then verify that the internal paths and saved state restore correctly without the directory-change warning.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
machine-learning
Issue type
Feature
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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