Lightning-AI / Lightning-AI/pytorch-lightning

Avoid network access during ordinary tests

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tests
Dominant language
Python
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Avg merge
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Merged PRs (30d)
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Description

## Proposed refactor

Some of our tests use the `MNIST` and `TrialMNIST` classes which download the MNIST data off the internet (or from a cache).

https://github.com/PyTorchLightning/pytorch-lightning/blob/948cfd24de4f64a2980395581f15544e5e37eab0/tests/helpers/datasets.py#L25

https://github.com/PyTorchLightning/pytorch-lightning/blob/948cfd24de4f64a2980395581f15544e5e37eab0/tests/helpers/datasets.py#L134

### Motivation

We should avoid this to reduce the inherent flakiness of network access.

### Pitch

If a test uses these classes, do one of 3 options:
- Remove the test, if we consider it's not useful (probably an old test)
- Update the test to use either `RandomDataset` or a `RandomMNIST` class which would "mock" the actual MNIST dataset but with random data.
- Move the test to `tests/benchmarks`

### Additional context

Master is currently blocked due to errors while reading the MNIST data zipfiles.

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cc @borda @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 in tests/helpers/datasets.py at the MNIST and TrialMNIST definitions, then find ordinary tests that use them. Review each usage and decide whether it should be removed, use RandomDataset or a random MNIST substitute, or move to tests/benchmarks. Done means ordinary tests no longer access the network or depend on MNIST downloads.

Written by the indexing model from the issue text.

Assessment

Tech stack
python, pytorch
Domain
machine-learning, testing-qa
Issue type
Refactor
Difficulty
4/5
Estimated time
3-5 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
25/100

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