prefetch: use a separate temporary cache for prefetching #730
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Deploying datachain-documentation with
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| Status: | ✅ Deploy successful! |
| Preview URL: | https://1557c442.datachain-documentation.pages.dev |
| Branch Preview URL: | https://prefetch-cache.datachain-documentation.pages.dev |
Deploying datachain-documentation with
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| Branch Preview URL: | https://prefetch-cache.datachain-documentation.pages.dev |
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #730 +/- ##
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Coverage 87.47% 87.48%
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Files 114 115 +1
Lines 10941 11033 +92
Branches 1504 1509 +5
=======================================
+ Hits 9571 9652 +81
- Misses 992 999 +7
- Partials 378 382 +4
Flags with carried forward coverage won't be shown. Click here to find out more. ☔ View full report in Codecov by Sentry. |
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Also did:
* minor refactor,
* removes `nrows` _hack_ and,
* disables prefetching when `nrows` is set, so that we don't download
the whole dataset.
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`dvc_objects.fs.utils.remove()` works for readonly files too.
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This PR will use a separate temporary cache for prefetching that resides in
.datachain/tmp/prefetch-<random>directory whenprefetch=is set butcacheis not.The temporary directory will be automatically deleted after the prefetching is done.
For
cache=True, the cache will be reused and won't be deleted.Please note that auto-cleanup does not work for PyTorch datasets because there is no way to invoke cleanup from the
Datasetside. TheDataLoadermay still have cached data or rows even after theDatasetinstance has finished iterating. As a result, values associated with acatalog/cacheinstance can outlive theDatasetinstance.One potential solution is to implement a custom dataloader or provide a user-facing API.
In this PR, I have implemented the latter. The
PytorchDatasetnow includes aclose()method, which can be used to clean up the temporary prefetch cache.Eg: