Comments (2)
Posting with permission from @cperry-goog - we're collaborating with the Colab team to provide DagsHub Storage as an alternative to GDrive that is more scalable and built for use with large datasets. It's an S3-compatible bucket that has much simpler access controls, and can be mounted easily.
It might help avoid the issues above - here's a link to an example notebook to try it out
We're looking for community feedback, so I'd love to get your input if it helps with the issue at hand.
(If you're curious, DagsHub is a platform for ML teams which is why we think Colab should have a storage solution suitable for ML workloads)
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Can you provide a minimal repro notebook? Two quick notes:
- Files can take a while to sync to Drive if they are large
- you can call
google.colab.drive.flush_and_unmount()
to force drive to write all local files to Drive and then exit
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