tensorflow / tensorflow/datasets
checksum updated
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Description
/!\ PLEASE INCLUDE THE FULL STACKTRACE AND CODE SNIPPET
Short description
Description of the bug.
tensorflow_datasets.core.download.download_manager.NonMatchingChecksumError: Artifact https://drive.google.com/uc?export=download&id=0B7EVK8r0v71pZjFTYXZWM3FlRnM, downloaded to C:\Users\Omar\tensorflow_datasets\downloads\ucexport_download_id_0B7EVK8r0v71pZjFTYXZWM3FlDDaXUAQO8EGH_a7VqGNLRtW52mva1LzDrb-V723OQN8.tmp.347438ae46e044e2ba3c37003ea58516\download, has wrong checksum:
- Expected: UrlInfo(size=1.34 GiB, checksum='46fb89443c578308acf364d7d379fe1b9efb793042c0af734b6112e4fd3a8c74', filename='img_align_celeba.zip')
- Got: UrlInfo(size=2.37 KiB, checksum='1af1d7f8e0e48dca7523b10852c4aa61fd0c30275f8963ccf191411a10de8324', filename='download')
To debug, see: https://www.tensorflow.org/datasets/overview#fixing_nonmatchingchecksumerror
Environment information
-
Operating System:
-
Python version:
-
tensorflow-datasets/tfds-nightlyversion: -
tensorflow/tf-nightlyversion: -
Does the issue still exists with the last
tfds-nightlypackage (pip install --upgrade tfds-nightly) ?
Reproduction instructions
<put a code snippet, a link to a gist or a colab here>
If you share a colab, make sure to update the permissions to share it.
Link to logs
If applicable, <link to gist with logs, stack trace>
Expected behavior
What you expected to happen.
Additional context
Add any other context about the problem here.
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 with the tensorflow_datasets.core.download.download_manager entry point and the linked documentation on fixing NonMatchingChecksumError. Reproduce the download using the complete stack trace, environment, and code snippet requested in the issue; done requires identifying the affected dataset and confirming the expected and actual artifact checksums are correctly handled.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python, tensorflow
- Domain
- data, machine-learning
- Issue type
- Bug
- Difficulty
- 3/5
- Estimated time
- 1-2 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 18/100