Use multiple processes when extracting ImageNet training archive
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Description
🚀 Feature
Use multiple processes when extracting ImageNet training archive.
Motivation
I recently extracting the ImageNet training archive with the code of torchvision and was suprised how long it took. I realised that after extracting the main archive, we only extract the subarchives one after another:
Pitch
I think we can speed that up significantly by using multiple processes to do this simultaneously. IMO doing this would have no drawbacks.
Additional context
If we want this feature, I could take it up, albeit with a low priority.
cc @pmeier
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start in torchvision/datasets/imagenet.py at the linked extraction logic around lines 183-184, where the ImageNet subarchives are processed sequentially. Investigate how the archive and its subarchives are currently extracted, then verify that the subarchives can be processed concurrently while producing the same extracted dataset.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- computer-vision, data
- Issue type
- Feature
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100