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Tensorstore's S3 retry implementation does not conform to S3 specs, resulting in checkpointing failing when it should not. #167
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See also: I was looking at that pull request more, and it appears that we need the following libraries imported
And then the C++ sdk has a bunch of sub-libraries.
In the meantime, if you suggest which codes are retriable then we can change the retry implementation. |
I'm not very knowledgable about AWS, but I took a look at the AWS SDK repo, and it looks like these two files have lists of retriable and non-retriable errors. Based on a blog post (https://guihao-liang.github.io/2020/04/12/aws-s3-retry#summary) it sounds like the first file takes precedence. |
Sure. I'm poking at this. The documentation is here:
Much of the error logic appears to be here: And the retryable errors are mainly listed here: |
Thanks a lot! |
Tensorstore's S3 implemenation appears to use the same
isRetriable()
implemenation as GCS. However, S3 and GCS have very different conditions for when a request should be retried. E.g., an S3 http 400RequestTimeout
error should be retried, but doesn't seem to currently be retried by tensorstore. However, not all 400 errors should be retried. It requires checking the response body for the error name.This results in checkpointing sometimes failing when it should not.
The ideal solution to this would probably be to switch to using the AWS SDK like
tf.io
does. This would also have other benefits like supporting multipart uploads, which also can help with reliability, and which is currently not supported in tensorstore.The text was updated successfully, but these errors were encountered: